A lot happened in search during 2023 and frankly, it has been hard to keep up with the pace of developments surrounding AI. In the video below, I've picked out some of the biggest stories of the year and more importantly, shared some view and what they may mean for the future.

If you're an in-house marketer and currently planning your performance marketing activities for 2024, take a look because there are a bunch of trends and ideas to be at least thinking about. If you're interested in agency support, go grab a copy of our credentials deck here and see why we were recently awarded Large SEO Agency of the Year at the UK Search Awards.

And here are the links mentioned in the video if you'd like to read more:

1. Google introduced Search Generative Experience
2. Performance Max added generative AI tools
3. Google tested mixing paid and organic results
4. Google now requires election Ads to disclose synthetic/AI content
5. The VP of Google Ads stepped down
6. The U.S. Department of Justice gave us a peek at how rankings work
7. Google changed how they treat ranking for pages with video
8. We saw a Helpful Content Update
9. Google changed their stance on the production of content generated with AI
10. And four Google Core Updates
11. Google Analytics 4 was released
12. And finally, SEOs ruined the internet. Apparently.

Either you’ve decided to bring a new marketing automation platform into your tech stack, or you have a fully implemented solution and are looking to optimise its usage. But should you hire a full-time employee to manage your CRM system, or partner with a specialist agency?

Having worked both in-house and for marketing automation agencies, I’ve come across the in house marketing vs. agency debate many times, and have seen the effects of both decisions first hand. While there are positives and negatives on both sides, I’m firmly convinced that working with an agency partner to implement and maintain your CRM is the right call in the vast majority of cases.

In this post, I’ll walk you through the key considerations when choosing whether to hire or outsource your marketing automation, so you can make the right decision for you and your business.

Factors to consider before choosing in-house or agency

When a skills gap is identified, the default position for most businesses is to reach out to recruiters and bring someone into the team on a permanent basis. Before scouring LinkedIn, it’s worth taking a step back to think about the size and shape of the project at hand. Consider the following:

• How long is the project likely to last?
• How will workload vary throughout that time?
• What does good look like in 6, 12 and 24 months?
• Who else in the business has similar or complementary skills?

By getting a better understanding of the amount and type of work required at each stage of the implementation, you’ll be able to make a more informed decision about how to resource it.

Pros and cons of agency vs in-house: Which is better?

It’s a common misconception that hiring in-house is always the most cost-effective way to fill a skills gap. CRM management has historically fit this mould. For example, it typically takes 6-12 months to realise the full value of Salesforce, and close management is required on an ongoing basis. In this case, it usually makes sense to have someone on hand full-time.

More modern marketing automation platforms like HubSpot, however, need little to no admin and can be effectively managed by existing Marketing and Sales teams. HubSpot also has a much shorter time to value of just 57 days. This means that past the initial implementation, your new CRM admin is likely to be twiddling their thumbs.

The one exception to this is if you’re hiring a CRM professional with strong marketing automation skills. If your long term plan is to have them build out inbound marketing campaigns, there may be enough work to sustain a permanent position.

Ultimately, both options can work in different circumstances and it’s up to you to make the decision that best suits your needs.

Digital marketing agency vs in-house

Hire in-house	Partner with an agency
Implementation period > 6 months	Implementation period < 6 months
Regular CRM management required	Minimal CRM management required
Role includes long-term marketing automation activity	Marketing automation activity covered elsewhere

Common misconceptions of in-house CRM managers

Below, we’ll explore some of the common misconceptions of in-house CRM managers to help you decide if agency or in-house is the best route for your business.

It’s always cheaper to hire in-house

CRM consultants don’t come cheap and it’s not uncommon to pay up to £70,000 per year. If you can’t find enough ongoing work to sustain them for 40 hours per week, a specialist agency may actually work out cheaper.

Hire in-house	Partner with an agency
3 months @ £70,000 PA = £17,500	HubSpot Marketing Professional Onboarding, 90 days = £2,600

You need someone in-house to accommodate ad-hoc requests

While it’s true your employees don’t have other clients to contend with, marketing automation agencies will usually work with you to agree to SLAs. It’s actually in their best interest to deliver a high standard of work in order to retain clients.

But who will run the reports!?

In the early days of CRM, it was common to have someone manually run weekly and monthly reports, and distribute them to stakeholders ahead of key meetings. With HubSpot’s self-service dashboards, this is now easy to automate.

Other benefits of selecting a marketing agency

What other benefits can you expect from partnering with a marketing automation agency?

Many agencies have multi-channel expertise, allowing you to dip in and out of services such as PPC and SEO to support your wider marketing activity. Working with multiple teams under one roof also promotes closer collaboration, leading to efficiencies of scale and better results as insights from one channel can more easily be applied to another.

Agencies that specialise in a particular marketing automation platform, such as HubSpot, also have the benefit of experience across a wide variety of clients. They can tell you what’s working for other businesses like yours, to help you get ahead. Many agencies also have a close partnership with their chosen CRM provider, giving them access to additional knowledge and early access to new features via private beta.

Conclusion

The main consideration when it comes to resourcing your CRM implementation and management should be what happens after the initial project. If the workload is likely to be highly involved for a prolonged period, it may be worth investing in in-house skills

If the long-term roadmap is irregular or not yet defined, however, it may be worth employing an agency to cut costs. If expected work doesn’t arrive later down the line, it’s much easier to reduce agency spend than to renegotiate or terminate a full-time contract.

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How much faster would you grow if you were capitalising on every feature, insight and automation of your sales, marketing and CRM software? Personalise, optimise, and scale your growth with a proven inbound marketing agency.

Recently we’ve spoken a lot about forecasting (see The Good, the Bad and the Unpredictability of SEO Forecasting, and Forecasting & the Importance of Uncertainty). If forecasting is your thing, then you’re in luck, because this post continues with that theme.

Have you ever been in a situation where you put together a forecast for the coming year, but after you agree to it with your boss you realise you’ve just missed an important peak period and now you can never meet your KPIs?

Or have you ever tried to put together a forecast, but realised you’re spending ages figuring out what “average” performance actually is?

Or have you tried using a forecasting tool, and found that no matter how hard you try - the numbers it returns are wildly optimistic or pessimistic, and you can’t convince it to pay attention to the right trends?

If so - this post is for you!

To set the scene for this blog - there are a couple of main approaches to forecasting:

1. Pattern-Based Forecasting - This involves looking at past performance (seasonal patterns, year-on-year growth etc.) and assuming the future will follow the same patterns. An example of this is Facebook Prophet.
2. Plan-Based Forecasting - This involves assuming something will change in the future and estimating future performance based on that. An example of this is making a keyword list, and estimating the extra conversions you could get from targeting those keywords.

Pattern-based forecasting isn’t great at factoring in changes (i.e. you implementing a new marketing strategy) and plan-based forecasting isn’t great at factoring in things like the seasonal variations we know come up every year.

In order to get the best of both worlds, we need to combine pattern-based mathematical insights about ‘seasonality’ with our plan-based knowledge of what we’re going to change, and what YoY trends are reasonable. We’ll explain below in more detail why you need that combination.

In the past, that would have been a real pain, but we’re going to show you how to do just that. We’re also sharing this Free Google Sheet to help you.

Pattern-based forecasting

The importance of pattern-based forecasting in understanding seasonality

Businesses need seasonality in forecasts because:

1. It helps reduce time spent wondering why the actual figure is much higher/lower than the forecast.
2. If we don’t factor in seasonality - we can miss warning signs that the high period actually wasn’t as high as it needed to be to meet our targets, or we could accidentally miss the high period entirely.

If we are selling Halloween costumes on our ecommerce website, our forecast should be able to predict the spikes in traffic in the build-up to Halloween. Not only because we want to prepare for that period, but also because, if Halloween is quieter than we expect, we need to look at the rest of the year to figure out how we’re going to make up the shortfall.

In the case of a Halloween shop this is pretty simple but, for many websites, there are numerous peaks and troughs throughout the year which would be really challenging to factor in manually.

Alongside this type of seasonality, there may be numerous other external factors which may be impacting traffic such as new competitors coming onto the market, changes in pricing and strategy, or external events (such as COVID).

Pattern-based forecasting tools such as Prophet help us identify seasonal patterns without having to spend hours figuring out if April is always a low month, or whether March and May are high months, and whether we should expect all three of them to be even higher or even lower next year.

These pattern-based forecasts, though, make some unwise assumptions.

Pattern-based forecasts rely on using historical data to find seasonal effects, and then apply those seasonal effects to the future. The other key component in these forecasts, though, is the year-on-year broader trend. This is a key way it can go wrong. 

Essentially, these tools figure out patterns like monthly or weekly seasonality and simultaneously try to figure out whether your numbers are growing or shrinking overall (i.e. should we expect Christmas next year to be bigger or smaller than Christmas last year).

What can happen is that these forecasts assume that the year-on-year broader trend, whether up, down or flat will continue indefinitely. For example, they can assume revenue will double every year even past your total market size.

See the example below with seasonality removed, so we can just see the trend.

Similarly, these forecasts can make unrealistic downward predictions (even dipping below zero) if the broader trend is in that direction.

Clearly, however strong the seasonality forecasting is from pattern-based forecasts, if the trend is painting a really unrealistic story, then the forecast is not particularly useful.

The funny thing is, we can look at those charts and see, at a glance, what’s wrong. We can probably even guess how to fix it, but it can be hard to communicate that to Prophet and other similar tools.

We need a way to be able to combine mathematical insights about ‘seasonality’ with the ability to set the trend based on our understanding of the business/ industry.

The ideal solution here is that you can combine: 

• All the power of pattern-based forecasts and the machine learning that is used to understand the seasonality and wider patterns.
• A strong dose of common sense so you can set the appropriate broader trend based on your knowledge of the business, market potential etc. in order to avoid unrealistic trend projections.

A baseline forecast

We can produce a baseline forecast by taking the outputs of a pattern-based tool like Prophet, stripping out the year-on-year (YoY) trend and just applying seasonality to our current performance. Essentially, it helps us create a picture of what the next year or two could look like if our performance stays just the way it is now.

If we think that stripping out YoY trends entirely is too far then we can just dampen it (maybe we say that the YoY trend for the next year will be half half as strong as predicted).

Because we can adjust the YoY trend without overwriting seasonality, it’s much easier to decide what the future realistically looks like if we change nothing about our current marketing.

Once you have this baseline forecast you then apply any additional increases you expect throughout the forecasting period (i.e. from building new pages in SEO or running a paid media campaign).

From there you have your “final forecast” which is basically: 

Final Forecasted Figures = Baseline Forecast Figures + Additional Figures

• Blue Line - Baseline Conversions
• Red Line - Baseline Conversions + Additional Projected Conversions.

Baseline forecasts can help you to avoid missing periods where there are likely to be peaks and troughs. For example, if you’re planning out your marketing activity, using a baseline forecast will help you to understand seasonality so you plan activities around the high points, as opposed to the low points (or vice versa).

Using our solution

If you don’t need any more information and you just want to start using the sheet we put together, here it is.
There are a bunch of instructions in there for you to follow but broadly:

1. Run a forecast in Prophet (Facebook has given a how-to here. Robin also covers this in his on-demand Python course)
2. Take the output forecast (every column) and paste it into the tab named Step 1 - Copy & Paste Prophet Output
3. Adjust the % in Output 1 - Graphs to see your forecast line move up and down
4. Take the figures from Output 2 - Raw Data to use as a baseline to combine with your plan-based forecast

 What is our solution for building a more reliable baseline forecast?

Knowing the limitations of pattern-based forecasting, it’s important to build a baseline forecast that accounts for seasonality that isn’t so heavily impacted by the overall trend.

This prevents either:

• Creating a too-optimistic baseline forecast with growth expected to continue at unrealistic levels.
• Creating a too-pessimistic baseline forecast where the predicted values go below levels that we’d expect.

It’s tough to strike a balance between a forecast that takes into account the broader trend, but doesn’t create unrealistic predictions. It takes knowledge of the business and the market in which the business is operating, as well as an understanding of the website and the strategy that’s come before.

Let’s dive into how we’ve sought to address this using Facebook Prophet.

What goes into a Prophet forecast?

Prophet takes into account several components when making a forecast which are actually summarised in the outputs (see below). 

Prophet takes the historical data, breaks it down into these components and then combines those components together to get the output forecast:

Trend

It’s the overall long-term movement of the number you’re predicting, either upward, downward or flat. This is basically “How does Christmas next year compare to Christmas last year”.

Seasonality

This breaks down patterns that repeat over known, fixed periods of time, such as daily, weekly, or yearly cycles.

You’ll often see websites have more traffic at certain periods throughout the year and relative slumps throughout other parts of the year, and it’s important this is reflected in the forecast.

Holidays and Events

This considers known special events or holidays that might cause unusual spikes or drops in the data such as Christmas, Easter, or more relevant business events that have a more significant impact on traffic, such as Black Friday.

Prophet takes the historical data, breaks it down into these components and then combines those components together to get the output forecast.

Finding a more realistic overall trend

When Prophet builds the final forecast it adds & multiplies together all of the numbers (trend, seasonality, etc.). The broader trend plays a significant role in the calculation. 

Fortunately for us, the difficult part is really the first step - splitting historic data into the individual patterns. Once we have those numbers combining them together to get the actual forecast is pretty simple maths. 

So, in order to create a more reasonable trend we can:

1. Run prophet and get it to give us all of the numbers for each of those breakdowns (seasonal, trend etc.)
2. Adjust the trend number however we like
3. Use formulas in Google Sheets to replicate what Prophet does in the final step, except we combine our new adjusted trend with the other data

As a sense-check we can compare our adjusted forecast with:

1. The “Pure Trend” Forecast - the standard Prophet output with no adjustments. The trend is not capped in any way, and the weekly, monthly, and yearly seasonality is applied across the forecast. This is pretty much exactly the output you’d normally get from Prophet.
2. The “Zero Trend” Forecast - We basically flatten the “Trend” and then apply that to the rest of the forecasting period. This essentially means we are assuming no year-on-year (YoY) change, but the weekly and monthly seasonality is still applied across the forecast. 

Being able to adjust our forecast to fall between the “Zero Trend” and “Pure Trend” options lets us model a more balanced view of the future, handling any of the more unrealistic expectations.

When will this Google Sheet come in handy?

This approach is really useful if you are creating a forecast using Prophet, and you’re getting far too optimistic or pessimistic forecasts based on historical trends. 

This will allow you to use more of your own judgement in order to set a reasonable level between the “pure” forecast (with no adjustment to the trend) and a version of the forecast with trend capped.

In conclusion

As lots of people have said before, forecasting is more art than science. It requires a lot of understanding from a business. Pattern-based tools like Prophet are a fantastic way to break down patterns in numbers, but it’s important for us to have an easy way to handle any weird assumptions those tools might make. At the end of the day - Prophet doesn’t know your business, you do, and so we’re really glad to share a way for you to have more control over how your forecasts come together.

We’d love to hear your thoughts, feel free to reach out on Twitter to @airadigital and @da_westby

If there’s a single ‘universal’ business truth from the last three years, it’s that the Covid-19 pandemic has significantly impacted growth. In many sectors, that impact has been negative.

But that’s not the case for ecommerce.

Nationwide lockdowns had a big effect on the wider economy, with many businesses putting the brakes on planned investment, but with consumers stuck at home our online purchases skyrocketed:

Global ecommerce sales grew by an average of 20.33% year-on-year from 2014 to 2019, and by just 12.37% from 2018 to 2019. However, in 2020 online sales increased by 25.72% to $4.2 trillion.

Ecommerce sales increased by a further 22.66% in 2021, but the pace of growth slowed in 2022, rising by just 9.71%.

The sector is projected to grow by 10.37% this year, another modest increase compared to the boom of 2019-21, but ecommerce remains bullish despite the current economic landscape.

Of course, just because the sector is continuing to grow during tough times, that doesn’t mean your ecommerce business is seeing the same results.

With consumer spending power reduced by inflation and businesses being more cautious with where they invest their money, there’s a good chance that you’re seeing sales volume fall—despite what the macro statistics might be saying.

These five strategies can drive increased ecommerce revenue in 2024

The economy is in bad shape and the giants of ecommerce are piling on the pressure, but that doesn’t mean you can’t succeed in 2024 and beyond.

Last year, Brightpearl’s annual Lightning 50 table identified the UK and US’ fastest growing ecommerce retailers. In the UK, the top five were Pinter, Progress JJ, Passenger, Kick Game and Wild Cosmetics. These brands aren’t yet household names, but they’re making waves in a competitive ecommerce marketing landscape:

In order to emulate these ecommerce brands and grow your revenue next year, you need to be across the following five strategies—and challenge your marketing and customer service teams to build them into their planning.

1. Adopt a multi-channel approach

Research by the Harvard Business Review found that a massive 73% of shoppers will browse several channels before buying a product, which means you need to be maximising your visibility with an omnichannel approach.

Another interesting statistic to come out of the HBR study was that these multi-touch point customers were more valuable compared to single channel customers, spending on average 4% more on every shop in-store and 10% more online. The study also revealed the impact of pre-purchase research, with ‘deliberate searching’ leading to greater value purchases. Without an omnichannel presence, the only touch point you control during this research phase is your own website.

Further evidence of the benefits of an omnichannel strategy can be found in an SAP study in which companies reported increased sales, increased customer loyalty, a better competitive advantage and a better customer experience.

We’ll look at the threat the likes of Amazon pose to smaller and independent retailers later in this article, but sometimes if you can’t beat them, join them.

Amazon is the world’s largest marketplace, so like it or not, if you don’t have a presence there you’ll be missing out on a lot of product searches. In fact, 66% of consumers from the EU5 countries (UK, Germany, France, Italy and Spain) and the US start their search for products on Amazon rather than search engines. Selling into the US market? Almost nine out of ten (89%) of US consumers say they’re more likely to buy products on Amazon than any other ecommerce website.

Ultimately, you’ve got two options: treat Amazon as a competitor or embrace the platform as an additional revenue generator.

Who’s doing this well?

Passenger Clothing (#3 on the fastest growing ecommerce brand list) fully embraces the multi-channel approach.

Passenger’s range of outdoor apparel and footwear can be found on numerous online stores, including Amazon, John Lewis, Cotswold Outdoor and OPUMO.

Their full range is available directly from Passenger-Clothing.com, but if they were restricted to just selling to their estimated 27,000 monthly website visitors, they wouldn’t be among the fastest growing ecommerce retailers in the UK.

2. Acquire zero and first party data

In order for you to grow and hit your revenue targets you need to expand your customer base, and this requires data.

However, this is getting harder to come by.

With web browsers dropping the use of third-party cookies, regulations around privacy—such as GDPR in the EU and the UK’s 2018 Data Protection Act—as well as increased consumer awareness about how their data is being used, you need to get better at acquiring first and zero-party data in 2024.

Zero-party data	First-party date	Second-party data
Data the consumer intentionally and proactively shares with you, which can be collected via website forms, polls and surveys.	Data collected directly from your audience, including from previous purchases, website analytics and social media platforms.	Data you didn’t collect yourself, for example via a trusted partner.

While second-party data can still be useful, privacy laws have made it more complicated to leverage, particularly in a B2C market. What’s more, the sheer volume of marketing messaging consumers receive into their email inboxes and on social media has made it less effective.

This is why it’s so important to leverage the data your customers and potential customers have actually provided to you directly—and be better at acquiring it.

As Fatemeh Khatibloo, VP principal analyst at Forrester, said during an interview with Wayin, “zero-party data is gold.”

“When a customer trusts a brand enough to provide this really meaningful data it means the brand doesn’t have to go off and infer what the customer wants or what their intentions are.”

Of course, the challenge is in acquiring this data in the first place…

Who’s doing this well?

Fans of your brand might not need much encouragement to sign-up for your newsletter to receive new product updates, but new customers need an incentive.

Both Progress Jiu Jitsu and Naturewall offer just that, offering discounts in exchange for potential customer data:

3. A social commerce strategy is vital to capture the next generation of consumers

It’s important for ecommerce retailers to adopt an omnichannel approach in 2024, but some channels are more important than others, and social should be a top priority for you next year.

First and foremost, your social media strategy should be informed by your target market, so if you typically sell products to the over-50s you probably don’t have to worry about your TikTok presence right now.

However, if you’re selling to a millennial or Gen-Z audience, these channels are no longer something that can be neglected. In fact, a 2021 study found that social media was the preferred shopping channel for these demographics in the US market.

“Younger generations find authenticity and trust in different places than older generations,” said William Margaritis, senior VP of digital and ecommerce at Reprise Digital, during an interview with The Drum.

“Predominantly, they trust their peers and they trust influencers. As brands reduce spend and are less present in the shopping conversation, peers and influencers will rise.

“Social commerce and live commerce have already become regular means of shopping in Asia and are rising rapidly in Latin America. The US and Europe won’t be far behind.”

‘36% of shoppers would be more likely to buy on social media if they had the ability to purchase in-app.’

Executed correctly and your social channels will build trust, drive brand awareness, customer engagement and sales.

The reason social commerce can be so effective is in its convenience, eliminating steps from the purchase process and allowing a consumer to go from product discovery to purchase within seconds.

Instagram Shopping is a great example of social commerce, with your customers able to browse and purchase items directly within the platform, without having to be redirected to your website—and this is powerful. A study by Curalate found that more than one third (36%) of shoppers would be more likely to buy something they discovered on social media if they had the ability to directly purchase in-app.

Pinterest is another platform that offers ecommerce brands a revenue opportunity. Nine out of ten Pinterest users have used the platform for purchase inspiration, while almost all of them (98%) have tried a brand they discovered there.

Pinterest’s Product Pins have been optimised to make purchasing through the platform as easy as possible, with a little price tag in the corner indicating that the item is available to buy. When clicked (or tapped), they show info only available on Product Pins, including product description and brand name, as well as boasting a larger product tile.

4. Customer lifetime value will be one of the most important success metrics in 2024 (and beyond)

Ecommerce competition is increasing and it’s getting harder to attract new customers—especially in the current economic climate—and this is why it’s so important to put an emphasis on increasing customer lifetime value in 2024.

When your customers return more consistently you have more flexibility in terms of what you can spend to acquire them, but maximising LTV requires first-class customer service.

However, customer service ‘skimpflation’ is on the rise according to a Ujet.cx report.

The study found that two thirds of consumers had experienced brands cutting back on customer service in the past six months, specifically in the areas of quality of service, wait times, and expertise and helpfulness.

Consumers take this seriously, with 87% reporting that they’d spend less or stop shopping altogether with an ecommerce retailer if they cut corners with their customer service.

Maximising LTV isn’t just about customer service though, and there are marketing strategies you can adopt that will stimulate more frequent orders, and increase average order values.

What’s the state of your post-purchase follow-up with customers? Are you making sure you stay top of mind and using their purchase history to share relevant discounts and product offers?

Are you doing analysis to see which products are frequently bought together and cross-promoting them at the checkout?

Growing sales volume and bringing in new customers is great, but increasing customer LTV and AOV is going to be crucial to growing your revenue in 2024, while these are the metrics that deliver stronger revenue results over time. Make sure you’re challenging your marketing team to put work into growing these metrics and report on them frequently.

Who’s doing this well?

One of the main drivers of Wild Cosmetics’ (aka Wild) success (#5 on the fastest growing ecommerce brand list) is their exceptional reputation for product quality and customer service—and this helps to maximise LTV.

Wild boasts a Trustpilot score of 4.7 from 28,600 reviews, with many of those reviews calling out the brand’s excellent customer service.

Wild offers customers the choice between one-off purchases and a subscription model, while their cancellation/refund policy is also highly customer-centric. Subscriptions can be cancelled at any time (customers aren’t locked in for any set period) and new customers get a 30-day satisfaction guarantee, ensuring a complete refund if they’re unhappy with the product.

5. If you’re not mobile-first, you’ll never be ahead

Retail mcommerce (ecommerce sales on a mobile device) is expected to account for almost half (43.4%) of all retail ecommerce sales in 2023, so if your online store’s mobile experience hasn’t been optimised you will be losing out on a huge proportion of your potential revenue.

Of course, the need for a strong mobile presence isn’t anything new. The difference now is that ecommerce businesses need to be mobile-first, and if you neglect this you’re going to see a significant impact on mobile sales. One survey of UK consumers found that two thirds would abandon purchases on a mobile device if the process was too difficult. If retail mcommerce is indeed responsible for almost half of all ecommerce sales, you can’t afford to risk 66% of those.

A mobile-first approach doesn’t just mean the website homepage fits nicely on a smaller screen. The experience has to be fast, have a streamlined layout, be easy to navigate and have a simple and straightforward checkout process. You also need to design the website with touch screens in mind, with buttons and links large enough to tap easily without misclicking.

Who’s doing this well?

Footwear and apparel retailer Kick Game (#4 on the fastest growing ecommerce brand list) embodies the mobile-first approach.

The homepage is sleek and uncluttered, their category pages are clearly laid out with lots of breathing space afforded to different sections, while the specific product pages are easy to navigate with large images of the products.

The checkout process is also very efficient, with payment options displayed immediately within two taps of adding a product to the cart. Cumbersome checkout processes are one of the biggest drivers of lost revenue, with studies over the last decade putting cart abandonment rates anywhere between 56.82% and 81.4%.

The key challenges restricting your growth

There are a myriad of unique factors that have an impact on every business, but if you’re an ecommerce retailer it’s likely one of these five key challenges are restricting your growth in 2023.

1. The competition from Amazon

A key challenge for brands across all sectors is how to beat the competition, but in the ecommerce market there’s one business that reigns supreme: Amazon.

As of June 2022, Amazon dominated the US ecommerce market by such a large margin that it exceeded the combined market share of Walmart, Apple, eBay, Target, The Home Depot, Best Buy, Costco, Kroger, Wayfair and Macy’s.

Amazon dominates the UK ecommerce market too, although not by quite as large a margin:

The impact of Amazon on the wider ecommerce market has been so great it’s got its own name: the ‘Amazon Effect’.

There are three primary side effects of this.

1. It’s harder for smaller ecommerce retailers to stand out

The dominance of Amazon isn’t restricted to the economic landscape but the search one too.

Google a product and an Amazon listing won’t be far away, regardless of whether that’s electronics, DIY equipment, children’s toys or beauty products. The ecommerce giant appears in Google Ads listings, organic search results and is often the first Google Shopping result, and this was demonstrated in a 2021 Searchmetrics study of the search landscape of seven ecommerce verticals.

The graph below, comparing the organic traffic of Amazon versus the UK’s largest retailers, illustrates this organic stranglehold:

Their market share might not be as high in comparison to the US market, but this domination over the SERPs shows how hard it is for the big players, let alone smaller ecommerce retailers.

Amazon came out on top in four of them (Apparel, Beauty, Health and Sporting Goods), while it was second in the other three (DIY, Furniture and Electronics).

This means that, unless someone is already aware of your brand, it’s going to be hard to get discovered online in the first place.

2. The expectation of cheap / free delivery

Although not available for all products, Amazon Prime has changed the game when it comes to delivery. Free next day delivery (and in some cases same day delivery) is no longer seen as exceptional, but it’s simply not economically viable for many standalone ecommerce retailers.

If consumers can buy the same product from Amazon and receive it 24 hours later free of charge, why would they pay for slower delivery?

3. It’s hard to compete with Amazon on price

Amazon is an aggregated marketplace and this means it can connect consumers with the products they want at the best possible price. What’s more, the scalability of a corporation as big as Amazon means it can work to much smaller margins compared to standalone ecommerce retailers.

The cost of living crisis will be adding to this pressure, with consumers more likely to prioritise price over other factors such as brand loyalty. Consumers might prefer to support local and/or independent retailers, but with household finances tight they’re more likely than ever before to choose the cheapest option.

2. The cost of living crisis

The majority of Britons are feeling the pinch at the moment, with the cost of living crisis and soaring interest rates impacting households across the income spectrum. A report looking at how the crisis has affected UK, US and German consumers published by Digital River found that less than half of UK adults (47%) reported being ‘currently financially comfortable’, compared to more than a third (35%) ‘getting by’ and 17% who are ‘struggling’.

As mentioned above, the growth in ecommerce sales slowed in 2022, with 41% of consumers saying that online purchases would be one of the first expenditures they’d cut if they needed to reduce their spending.

However, soaring interest rates and inflation will have hit your ecommerce business in other ways too, with increasing overhead costs and high interest rates making new hires and investment more of a risk.

3. Customer expectations around sustainability

Given the economic climate, price is one of the primary decision factors for consumers right now, but it’s not the only decision factor.

We’re more aware of how our actions and choices affect the environment than ever before, and this is driving one of the most critical trends in global ecommerce: sustainability.

Ensuring sustainable business practices can mean additional overhead costs to retailers, specifically around manufacturing and distribution, but it’s also an opportunity to de-risk your business.

For decades, globalisation and the low cost of production overseas has seen local manufacturing decline, but the pressure on global supply chains caused by Covid-19 (and Brexit in the UK specifically) and increasing transportation costs mean more and more companies are returning to local production.

What’s your background in the ecommerce space?

Aira has worked with ecommerce brands just like you before—and we’ve got receipts…

Discover how we helped ecommerce retailers just like you to increase their revenue, including average order value and customer lifetime value.

Don’t worry, this isn’t another post about how “everything is changing”. In fact it’s about a problem that digital marketers have been facing for a long time. Forecasts.

It’s also about how ‘fan’ charts (like the one below) can help. You can’t normally make them in Google Sheets but we’ve put together a no-code-needed solution for you to do just that!

Forecasts - the impossibility of predicting the future

Forecasting is a big source of tension in businesses.

On the one hand, in most cases it’s a bad idea for a company to make decisions totally at random. Not many people would suggest changing your marketing spend every month based on a dice roll.

On the other hand, it can take months or years to roll out big business changes. So, to know what a reasonable decision is, we have to know what’s going to happen a long way in the future—we need to forecast.

I know that some marketing specialists will tense up reading that sentence, because it’s impossible to know what will happen in the future, right?

Ignoring global pandemics, recessions, and machine learning revolutions, there's all kinds of changes that can mean the next year for our business will completely change.

Even if we know the next 12 months will be exactly the same as the last, it’s pretty difficult to know exactly what worked last year, and by exactly how much. We end up having to get into that messy realm of attribution. We'll cover that in a later post.

So, business leaders need to make a decision based on something, whether that be a forecast or evidence to prove their decision is justified. Meanwhile, most specialists tend to be uncomfortable with giving a forecast the whole company will work towards because the expertise they use to produce the figure tells them just how flawed that figure can be.

What’s the solution? Refuse to make a prediction? Go back to rolling the dice? Pick a number out of the air?

Not quite.

Reminding ourselves that no one needs it to be exact

If you sit down with a business leader and ask them whether they are expecting a perfect prediction to base their decisions on (down to the dollar/pound), they would probably look at you as if you’re mad. They know these things can’t be exact, but they also know that an informed guess is still probably better than that dice roll.

Of course, it can be easy to forget that we don’t need an exact number when we’re talking about forecasts. If an agency is talking to you about £100K growth, that’s the number you’re going to discuss. You won’t keep saying “plus or minus 10%”.

The real fear that specialists have is that everyone agrees “this forecast isn’t exact” at the time, but at some point (maybe when the CEO looks at your projections, maybe when it’s time to review results) someone forgets that it’s a best guess and starts holding people to account over one number.

We’re not going to talk about avoiding blame culture here (perhaps a good future topic for Paddy Moogan’s ‘The New Leader’). Instead, we’re going to talk about a visual way to remind everyone, every time they look at a forecast, that our prediction falls within a range. Introducing fan charts.

I’m a big fan of fan charts

This is a fan chart which my colleague, Dave, made in Google Sheets. Don't worry about the specific data it’s showing, let’s just think about what it communicates.

When I read this chart, one of the things I think is “we’ve got a pretty clear idea of revenue until about half way through the year, and then it could fall in a range”.

Of course the other thing I think is “Wahey 📈📈📈”, but fan charts work just as well if it’s 📉📉📉.

You might recognise fan charts from tools like Causal Impact or Facebook Prophet, they are both great forecasting and statistical analysis tools we use at Aira.

Example from Casual Impact

Example from Facebook Prophet

To use those predictive models the way we do at Aira, you usually need to be able to code (or you need access to a team like Aira’s Innovations team). But you don’t need code for the visualisation, which I think is a really overlooked part of their value.

Just having the quick, visual cue that the real answer probably falls somewhere within a range can speed up communication massively.

How do I make a fan chart in Google Sheets?

Luckily enough, Dave Westby and I put together a template sheet which you can use right here.

Just paste your data into this table. You don’t have to work in months, it can be in days, weeks, years, whatever. You can add as many rows as you want below the table and they will appear in the chart.

The ‘Revenue (Low)’ column is what you think the worst-case scenario is, ‘Revenue (high)’ is best case. You can rename the columns of course.

If you’re using the fan chart to show a combination of past data and a future prediction, you can use the ‘Past data’ column to mark a row as historical, and that’ll create the orange line in the chart.

If you don’t know what your upper/lower bounds are but you think it could be plus or minus 20%, there’s also a tab where you can just put the number you want to chart, and then write your % margin of error and it will automatically calculate a fan chart for you.

Go forth and use fan charts!

As I mentioned before - there are some really powerful tools like Causal Impact and Facebook Prophet which produce fan charts as a kind of side effect of statistical analysis. The mathematical work that those libraries do is very valuable, but I think the fan chart visualisations are very much overlooked.

Thanks to tools like those, fan charts are an increasingly common way to express uncertainty so that business leaders can understand it and still make decisions.

Even if we only use them to reduce that friction, to get to the important conversations about how we’ll try to achieve the forecast, that’s a huge benefit.

So in summary:

• Forecasting is hard, and it’s basically impossible to get exact numbers
• We still need to base decisions on something, so our best options are to be as accurate as possible, and communicate our limitations
• Fan charts are a huge help in communicating limitations of a forecast
• Use our sample sheet to make your own!

A lot of things are going in the wrong direction—but your revenue doesn’t have to.

Soaring inflation, rising interest rates, low growth, the constant spectre of recession—suffice to say, it’s arguably the worst time for consumers and businesses since the 2008 financial crisis.

The double whammy of Covid-19 followed by the war in Ukraine has led to unprecedented pressure on global supply chains and commodity prices, and this is significantly impacting the prices consumers and businesses are paying for goods and services. The unpredictability of these macro issues are leading to delays in investments, with greater scrutiny of purchasing decisions.

The SaaS sector is feeling significant pressure, and across the industry this is manifesting in reduced sales, pressure to reduce cost and a need to maintain existing client relationships. Meanwhile, some of the biggest players in the industry have had to make significant layoffs.

According to Layoffs.fyi, at the time of writing almost 800 separate companies have laid off over 210,000 employees. This includes approximately 12,000 at Google, 10,000 at Meta and 10,000 at Microsoft.

Many of our clients in this sector are feeling their own versions of this pressure—no doubt your organisation is feeling the strain too.

In this article we’re going to take a look at what SaaS leaders are saying about the current situation, the big issues you’re probably facing and, most importantly, how you can deal with them.

Want the highlights? Download the report's executive summary.

The view from the top - what SaaS leaders are saying

The threats in the current economy can’t be denied, but when Salesforce President & CMO Sarah Franklin, Box CMO Chris Koehler, and Attentive CMO (and ex-Twilio CMO) Sara Varni came together for Mutiny’s The Second Lever conference, the message was clear: this is an opportunity for SaaS vendors.

Inevitably, your potential customers are anxious about inflation, supply chain disruption and labour shortages, but SaaS can actually help businesses meet these challenges.

"The three CMOs were also unanimous in the response to whether now was the time to cut marketing budgets—100% no."

“The good thing about SaaS businesses is that technology is the solution our customers are seeing as the way that they’re going to automate themselves, get more productivity, more efficiency, and just help businesses run better,” said Franklin.

Chris Koehler agreed that difficult economic conditions don’t mean that the pain points your software can solve are going away: “It doesn’t matter if you’re an SMB…or a large enterprise: You’re trying to figure out, ‘How do I create a better customer experience? How do I engage my customers? And quite frankly, how do I make my employees collaborate in a world that is very hybrid?’”

The three CMOs were also unanimous in the response to whether now was the time to cut marketing budgets—100% no.

“Let’s not talk about how we cut; let’s talk about how we can be more efficient…Cutting marketing is very short-term thinking, and that’s what’s going to cut your future growth,” said Franklin.

SaaStr founder and host Jason Lemkin agreed: “When you cut marketing you cut the medium term. You’re cutting off your pipeline in one, or maybe even two years.”

This isn’t to say you shouldn’t be making sure you’re spending your marketing budget more wisely.

At SaaStr 2022, 6sense CMO Latané Conant explained her approach to ensuring marketing campaigns are performing: follow the red.

“Whenever I see an underperforming metric in my marketing dashboard, I proactively investigate to see what needs to be done to turn it green.”

These SaaS marketing leaders might be preparing to double down on their marketing in the wake of the current economic crisis, but are they in the minority?

Many SaaS organisations will take the opposite view to the CMOs we’ve looked at here and, while exercising prudence during tough times is always wise, there are some common mistakes businesses make that will only make things worse…

6 issues SaaS leaders are facing… and how you can tackle them

Issue #1: Marketing budgets under pressure

During challenging times, marketing is often one of the first departments to be scaled back. Many organisations view marketing as a luxury and instead prioritise core operational expenses they deem essential to keeping the business running.

There’s also the argument that, if consumers and businesses are investing less money anyway, marketing to your prospects at the same level as during good times is a waste of money.

It’s a common reaction, but research suggests that doing so hurts organisations in the medium term. 

A study conducted by McGraw-Hill Research (now part of S&P Global Market Intelligence) in 1986 found that ‘firms that maintained or increased advertising during the 1980-81 recession averaged significantly higher sales growth during the recession and for the following three years than those which eliminated or decreased advertising.’

Note the reference there to ‘the following three years’. Increasing marketing spend during an economic downturn might not lead to immediate results, but you’re laying the foundations for success when the recovery begins.

In the wake of the 2008 financial crisis, Peter Steidl highlighted data which supports this from the Profit Impact of Marketing Strategies (PIMS) in his book ‘Survive, Exploit, Disrupt: Action Guidelines for Marketing in a Recession’.

The PIMS database showed that return on capital employed (ROCE) increased by 8% for companies that increased marketing spend, by 9% for companies that maintained spending, and 10% for companies that cut spending. Fine margins, but that would suggest you should cut your marketing spend, right?

However, when you look at the difference in performance once the recovery begins, it’s clear who comes out on top. In the first two years following a recession, ROCE increased by 4.3% for organisations that increased spend, compared to just 0.6% for companies that maintained spending, and a drop of 0.8% for companies that cut spending.

There’s a great example from recent history that demonstrates this dynamic.

In the wake of Covid, the travel industry was among the biggest losers. National lockdowns and travel restrictions were in place throughout 2020 and 2021, but Airbnb and VRBO responded very differently.

Airbnb slashed their ad spend during this time, but VRBO stepped it up. Between January and February 2021, VRBO spent $90.8million in advertising, while Airbnb spent just $8.9million.

The result?

VRBO’s bookings recovered by 61%, compared to a 15% drop for Airbnb in the same period.

“Be fearful when others are greedy, and greedy when others are fearful.”

— Warren Buffet

How to tackle it: Don’t talk about how you can cut, talk about how you can be more efficient

You don’t necessarily have to take VRBO’s approach and increase marketing spend, but the mistake is thinking in terms of cuts rather than efficiency.

You need to get forensic in looking at your current marketing and identifying what’s working, what’s underperforming and what’s flat-out failed. Shift budget towards ad campaigns that deliver the highest ROI, be more discerning in the events you sponsor and scrutinise every penny spent—but don’t just implement a blanket cut in your marketing budget. Pulling money from marketing and taking decisions that aren’t backed by data is just as likely to cost the organisation money as save it.

Issue #2: Neglecting the decision makers

If you’re selling to a B2B market, who’s the person you need to convince to invest? Is it the employee(s) who will be using your software every day, or the CXO controlling the purse strings?

Of course, there will be multiple stakeholders in a B2B sales process, but the final decision maker is typically at board level. That’s not a profound statement, but then why do so many SaaS vendors prioritise the user buyer in their marketing messaging?

You talk about specific features and why your software is easier to use than your competitors’ products.

Valuable points to make, but the economic buyer doesn’t care about that fancy widget that no other software offers. They care about how your product is going to impact the top or bottom line.

How to tackle it: create messaging for both user and economic buyers

In B2B SaaS sales cycles the user buyer is often the person conducting the initial research. They’ll also be the first person to make an enquiry, so it’s essential you have compelling marketing messages ready for them.

However, the further you get into the sales process, the more economic buyers will get involved. Once in the room with CXOs, you can’t just deliver the same messaging as with user buyers.

These two groups have unique challenges and you need to be able to show your solution delivers for everyone’s needs.

How to sell to user buyers

In many ways, user buyers are your reason for being. Making their day-to-day job easier is what your product is designed to do, and this is how they’re going to judge your solution.

This means they want to evaluate user experience and operational impact. Testimonials and reviews from satisfied users are great here, but the best way for them to do so is through hands-on experience of the software via demonstrations and free trials.

How to sell to economic buyers

If you’ve got a list of target accounts you can either start by trying to get in front of user buyers, or go straight to the decision makers. This is harder, but can be a faster route to sale.

Lots of organisations are trying to secure meetings with the same economic buyers you are, and they’re getting reached out to a lot. This means you need to give them a very good reason to take a call or book a meeting with you.

Generic elevator pitches won’t cut it. Make sure you’ve got some quantifiable benefits you can offer straight out of the gate to grab their attention.

For the sales pitch itself, remember that economic buyers aren’t solutions experts like the user buyers you’re trying to sell to, and they don’t have the time to be. In the majority of cases, they simply want to understand the potential return on investment.

During sales pitches, economic buyers love to see case studies and examples of where you’ve helped businesses like theirs increase revenue and reduce costs.

It helps to get straight to the point with economic buyers too, so avoid padding your presentations with information that’s not going to interest them, such as company history and culture. The specifics of your SaaS solution are important for user buyers, but economic buyers don’t need to understand how the platform can be used day-to-day, so communicate this with user buyers when the economic buyers aren’t in the room.

Put together an org chart

Every stakeholder needs to be considered and engaged with for a B2B SaaS sales pitch to go smoothly. We’ve talked about both economic and user buyers, but there are key differences within these groups you need to have visibility of.

A detailed org chart will map out everyone within the business and what you need to do to get them onboard: what pains are they experiencing, what are they trying to achieve, what objections might they have to your solution?

This will take some time to research, and you might need to get the support of your Champion within the organisation to make sure it’s accurate, but it’ll be worth the time investment.

Issue #3: Pressure to prioritise the bottom of the funnel

When generating new revenue becomes more of a concern, SaaS vendors often focus more of their efforts on the bottom of the funnel. This isn’t a mistake. In fact, we recommend to the SaaS companies we work with to start at the bottom and work backwards, prioritising tactics that will deliver the fastest ROI.

The issue is when you only focus on the bottom of the funnel. Afterall, 96% of visitors that come to your website are not ready to buy.

Sales teams abandon value-led comms in favour of outbound efforts with cold calls and emails. Marketing stops creating awareness-level content and instead focuses on pushing features and benefits of their software.

It's not a mistake to focus more of your efforts on the bottom of the funnel. The issue is when you only focus on the bottom of the funnel.

There are two problems with this strategy.

Firstly, outbound sales can work for you, but it’s hard. Developments in tech and automation have simplified the process in recent years, but response rates have reduced dramatically. Tools, people and economic state make this an expensive—and not always predictable—channel to facilitate growth.

Secondly, neglecting the top and middle of the funnel will see your pipeline dry up in the medium to long term. Event sponsorships, gated content and brand awareness efforts won’t necessarily drive sales today, but they do drive the leads that will become new customers tomorrow.

Further to the above statistic highlighted by the Adobe eBook on lead generation, a 2022 study published by LinkedIn’s B2B Institute and the University of South Australia’s Ehrenberg-Bass Institute for Marketing Science found that just 5% of a business’ total addressable market has intent to buy. Further to this, research by Gartner revealed that 83% of a lead’s journey is not spent with a sales rep. The final 17% is where a lead is closed and becomes a customer, but they’ll never get there without being nurtured throughout the funnel.

83% of a lead’s journey is not spent with a sales rep:

That 5% might be where the money can be found today, but neglecting the remaining 95% is going to cost you opportunities in the future.

How to tackle it: implement a multi-channel marketing strategy

If growing revenue quickly is a priority, putting more resources into the bottom of the funnel isn’t a mistake, but it could be if it’s at the expense of all your other channels.

Make sure you continue with a multi-channel marketing strategy. That means keep posting on your social media channels. Continue creating educational content that helps your prospects understand the solutions to their challenges.

Sales’ time will be much better spent reaching out to warmer leads, and it will also help to ensure your pipeline is healthier in 6-12 months. Additionally, utilising both brand building and performance elements in a campaign often increases overall return on ad spend compared with spending on performance channels alone.

Two great examples that demonstrate the impact of brand building - specifically content marketing - are Lumen5 and Moosend.

Lumen5, a dedicated social media marketing tool, went from 25,000 website visits to over 500,000 in less than a year thanks to content marketing—but they also created multiple content offers that captured leads who were at different stages of their buyer’s journey.

For Moosend, a marketing platform specialising in email marketing, a full funnel marketing strategy increased their organic traffic by 10X, while they’ve also created a number of gated resources for converting that traffic into leads, including webinars and infographics.

Issue #4: It’s hard to stand out in a crowded SaaS market

You’ve identified your user’s pain points and solved them with an effective SaaS solution. Your pricing is competitive and the UI is slick.

But guess what? You’re not necessarily as special as you think you are.

Back in 2012, the average number of competitors for SaaS firms was three. By 2017 this increased to nine, and the SaaS market is still growing rapidly. The global SaaS market is expected to hit $720.44billion by 2028.

Your solution might be the best around, but don’t overestimate your USPs and expect them to speak for themselves.

This isn’t just about the specific features of the software. For example:

• Your solution might have the most comprehensive functionality, but if your onboarding and knowledge base is poor people won’t be able to use it.
• You might be the lowest cost solution on the market, but you don’t offer a free trial.

How to tackle it: effectively differentiate yourselves from the competition

In order to ensure your SaaS product stands out from the crowd, you need to understand what the crowd is offering.

Conduct thorough competitor analysis to identify their strengths and weaknesses, and user research to discover what your prospective customers actually want from a SaaS product.

From this research you should develop (or refine) your ideal customer profiles (ICP) and buyer personas.

Note that these aren’t the same thing.

ICPs are only relevant to B2B SaaS vendors, and describe what your perfect customer looks like—a fictitious company that boasts all of the qualities you’d want from a customer that’s the best possible fit for your products or services.

Help me define my ideal customer profiles

By contrast, buyer personas are a semi-fictional representation of the people that buy from you. For B2B SaaS sales, they refer to the decision makers involved in the sales cycle, including both user and economic buyers.

Help me define my buyer personas

Having a clear understanding of what motivates your customers will help define your marketing efforts, as well as how your product and processes can be optimised.

If you’re best-in-class in one aspect of your solution make sure you’re emphasising that during sales meetings. If you’re lagging behind in another, put more effort into improving that aspect of your offering.

In a crowded SaaS market differentiation is essential, and this requires an organisation-wide effort to understand and communicate where the value in your solution lies.

Also remember that businesses and consumers make decisions based on subjective criteria too, so it’s not enough to simply push software features and neglect to consider how your marketing and sales strategy makes your target audience feel.

There’s also a connection here as to why cutting marketing budgets is a mistake. Reducing your presence in the market will give your competitors an advantage in the fight for your target market’s attention, and it might be hard to recover when the economy improves.

Once you’ve clearly identified the needs and challenges of your target market, and how you stack up against the competition, you need to nail your value proposition to ensure you’re standing out in your market and speaking directly to your personas. Again, this requires input from multiple teams—not just marketing. Sales, account managers and customer success reps will all have valuable input here in helping marketing to craft messaging that answers the target market’s pain points, having engaged with them throughout different stages of the user journey.

Issue #5: Pressure to focus on new leads/sales at the expense of existing users

We get it.

Growth is slowing down, the economic forecasts aren’t looking good and you’re worried about hitting your revenue targets. It’s time to focus all our efforts on generating new leads and closing new customers, right?

This isn’t just a common mistake that SaaS organisations make—it’s a mistake businesses across sectors make.

Yes, generating new business is crucial to growth, but it’s your current users who are the foundation of your annual recurring revenue (ARR).

If the organisation neglects existing users you’re going to see an increase in churn and find it even harder to meet those growth goals.

Current users who are the foundation of your annual recurring revenue (ARR).

Kevin Stoll, Vice President of Capgemini told SaaStock: “You still see large organisations overspending in areas that aren’t related to retention of customers.

“They still have too many focuses: they’re trying to break into multiple geos and launch new products.”

But what might this ‘neglect’ look like?

• Cutting the Customer Success and Service teams and trying to manage existing users with less resources.
• Focusing all of your marketing efforts on new business and not sharing product updates, special offers and educational content with existing customers.
• Focusing all of your sales efforts on new business and missing out on upsell and cross-sell opportunities from existing customers.

How to tackle it: Work closely with Customer Success teams to ensure you’re driving retention and long term usage

New business will (almost) always generate greater net new revenue, but you’re not going to grow MRR and ARR without a satisfied existing customer base, and there are three key reasons for this:

Increased customer churn

A 2022 survey of 110 private SaaS companies found that the median gross dollar churn for the sector is 14%.

The more you shift your focus from existing users to new business, that 14% is only going to increase, but you’ll also see an increase in involuntary churn: the result of expired credit cards, technical issues etc. It’s estimated that as much as 40% of churn could be caused by losing customers who don’t even want to leave.

Fewer positive reviews and referrals

One of your most powerful sales tools are existing users.

G2’s 2021 Software Buyer Behavior Report found that ‘86% of software buyers, across segments, use peer review sites when buying software’, which means your potential new customers will be deterred by negative reviews—or a lack of positive ones.

This is the rationale behind HubSpot’s Flywheel model.

It explains the momentum gained when the entire organisation is aligned around delivering an exceptional customer experience.

Existing customers are also a new revenue opportunity

As mentioned above, a new customer will probably always offer a larger net new revenue opportunity—but upsells and cross-sells from existing (and, most importantly, satisfied) customers might be easier to close.

“The cost of acquisition is quite a bit lower. You already know their preferences and what they do, and they know how they use your product,” said Kevin Stoll.

They already know your product, hopefully understand its value, and will be more open to investing more money than someone who’s never used the software before.

Issue #6: It’s hard to find the right marketing partner

The vast majority of organisations—from B2B to B2C, from SaaS to ecommerce—use similar strategies and market themselves across the same platforms. So, as long as you’re working with marketing experts, you’re in safe hands, right?

Not really.

A marketer or marketing agency who doesn't specialise in a particular niche (such as SaaS) might be an expert in Google Ads, SEO, copywriting or social media, but they don’t necessarily know your market, understand a SaaS sales cycle or the KPIs that matter most to you.

How to tackle it: work with SaaS marketing experts

If you’re looking to make an internal hire or employ the services of an agency or freelancer, make sure they have experience in the SaaS market. If it’s the latter, ask for case studies and testimonials from SaaS organisations they’ve worked with.

Not every agency will be able to back up their claims with examples that demonstrate genuine results. Some can...

The latest edition of Aira’s innovation update is here! Fortunately, it’s really not hard to come up with things to talk about.

Again, we’ve got a quick summary of some of the stuff we’ve been reading recently, some updates of what we’ve been doing at Aira, and this time some details on some of the ways our Consultants have been using new tools.

What we’ve been reading

To be honest, we’re finding it hard to cut down our reading list for these updates, but here’s a selection of interesting pieces on some different topics:

• Recast did a great quick-read on how companies of different sizes/stages of growth should think about attribution. Honestly, for a Mixed Media Modelling tool company, it’s pretty impressive that Recast dedicates so much time to telling people when they might not need MMM.
• YouGov released a whitepaper about how consumers are responding to the cost-of-living crisis. They cover 13 countries but with a particular focus on the UK and US. Some interesting stats in there but, as with anything survey-based, it’s worth bearing in mind the data can be skewed by who happened to respond.
• Google have created a Machine Learning model that can take an item of clothing and show how it would look on different body types. While this is cool, we imagine that most clothing companies will want more control over the way customers see their clothes than simply relying on Google correctly guessing what designers changed between sizes.
• McKinsey think that AI is going to add 4.4 trillion to the global economy, but only as long as we make sure people are supported as they adapt roles/change jobs (this is not a short read)

What we’ve been doing

• We shared an internal tool we’ve been using to reformat tables to be presentable (this kind of unexciting work is exactly the kind of thing we want to take off consultants’ plates)
• We hosted our first HubSpot User Group in June, with a range of talks on AI. External speaker Claudia Higgins from Conductor spoke about the state of AI in SEO and was followed up by Aira’s very own David Westby who looked at AI, Google and Accountability with a keen focus on the changes being implemented by the search engine and the potential ramifications for businesses. Matthew Purchase, also from Aira, delivered a talk on a brief history of AI and HubSpot, looking at the existing AI features within the platform, the recent acquisitions ,and speculated on where this could go.
• We announced the return of MKGO for this September, with free tickets currently available here. The event will be held at Milton Keynes Gallery, and we've got some great speakers lined up, including Azeem Ahmad, Mehul Garg and our very own Katherine Monkcom, so what are you waiting for? Grab your free ticket before they run out!

What we’ve been thinking - Everyone is innovating

At Aira we believe pretty strongly that innovation shouldn’t just come from the ‘Innovation’ team - they should come from the people working directly on client problems. People who are getting daily exposure to what our clients need. It’s the same reason our Consultants are the ones who wrote up their perspective on how Google’s adoption of AI is likely to change the search landscape.

In this update we wanted to share some stories about how our Consultants are using tools like ChatGPT to supercharge their day-to-day work.

Building two image compression tools using ChatGPT - Mathew Stuckey

I recently did some work for a client which required compressing a large amount of their image catalogue in a bid to improve load times.

After becoming frustrated with the constant back and forth due to the file limits imposed by web-based tools, I decided to look into creating my own image compression tool via ChatGPT which I could run through codepen.io (check it out here!).

Initially, I’d simply asked if it could “code me an image compression tool”, which obviously it told me it could do easily, and then gave me a code script that didn’t function in the slightest. 

I spent a lot of time chatting with GPT, simply telling it that the tool didn’t work listening to its apologies and then retrying with the next line of code it gave me. Eventually I gave up. 

But the next day, after hitting another file limit on a free tool, I revisited the idea. After some reading up on image compression and GPT prompting, I eventually came up with the following prompt;

“You are a high-end developer with substantial knowledge of coding and image compression techniques.

I would like you to give me the individual HTML, CSS and Javascript code for an image compression tool. The tool should be able to handle both JPEG and WEBP files.

It should allow for multiple files to be uploaded at once up to a limit of 20.

The tool will then compress those files using lossy compression for the webp and jpeg at a rate of 0.3.

The tool should have a progress bar so that the user can see the status of the compression job. 

The output should be a list, so that the user can redownload the individual files as they need.

The tool should be styled in a simple, modern way and be easy to navigate for users.”

This gave me a MUCH better looking output which, after copying into Codepen, still didn’t work at all.

But it was far closer.

One of the benefits of using a tool like Codepen is it allowed me to have a back and forth with GPT by just presenting it with any errors that were shown in the console box, it was then able to assess the error and adjust the code accordingly. 

In fact, after copy and pasting the single error that was thrown up this time around, GPT was able to debug its own code and promptly provided me with an amended version of the Javascript.

Once this code was swapped over, success! I now had a fully functional compression tool that would allow me to compress client images without any intrusive ads, usage limits or requirements to upgrade to a pro version.

The success of this made me look into other tools I could also create using the power of GPT. So, I also put together a similar WEBP conversion tool which can take multiple image files and convert them all into WEBP format, with the output being an automatically downloaded .zip file of the new images. 

Where tools like Convertio only allow for two conversions at a time, I can now transfer an entire image catalogue in one go and convert a client's images to a faster format in one click. This saves a huge amount of time and effort, meaning I have more time to focus on work elsewhere.

Categorising content using ChatGPT - Kathryn Monkcom

Like my colleagues, I’ve been spending some time investigating ways in which ChatGPT can help us do better work, faster. Of course, we don’t always just assume these tools are the best ones for the job and ,in this case, GPT isn’t ready to fly solo, but there’s a couple of things we can do now.

When it comes to inbound strategy, planning concepts for gated content can be one of the more time-consuming tasks. It often comes down to:

1. Research what’s already out there
2. Come up with some new ideas and choose one
3. Test how it performs
4. Repeat

A good way to narrow down your options is to try to find examples of high traffic content that not many people have covered. And the best way to do that is to scrape as many websites as possible in your niche, categorise the content and see what’s getting a lot of traffic, but doesn’t seem to be covered in much detail.

I wanted to see if GPT could help us speed up the categorisation step. I found that I had to do a lot of manual cleanup on the output, so I don’t think it’s quite ready to do everything yet. I ended up using GPT as a first pass, followed by some trusty RegEx and then my own manual checking. 

Here’s my process to give you a head start so you can come to your own conclusions - or come up with something better!

1. I used Ahrefs to scrape my client’s site, and their competitors, getting monthly traffic and a title for each page
2. I used GPT for Sheets™ and Docs™ to group the pages into one of: Checklist, Guide, Resource, Ebook, Template, Other. You could also use it to group by topic or theme, i.e. ‘Marketing Automation’ or ‘Strategy’.
3. I then used sheet regex formulas over the same page titles to put together flexible categories based on the same terms. This was a great way to sense-check/overrule GPT categories.
4. For each group I counted up the traffic, and the total number of pages, to find categories where there was lots of traffic but not that many pages.

By looking at traffic numbers and page count I could quickly see that Checklists were one of the best performing but least used formats. My client had been focusing all their time on guides, which performed well but had a lot of competition. This was great insight that helped us plan our next few months of highly effective content.

As I mentioned, GPT didn’t do a great job of categorising the pages alone. It seemed to rely a lot on exact word match. For now - RegEx is more reliable but I did get some help from our GPT-based “make me a formula” tool to speed that up, so there was still some value to be had there!

The next thing we’re going to try is scraping the sites further to get page content and check them for related words or phrases. With that extra information, GPT may well take the lead!

Prompt engineering to help with SEO tasks - Matthew Kohli

I am a well-known ChatGPT-enthusiast at Aira and I’ve spent quite a lot of time looking for ways to use the tool to improve our work. For example, our copy briefing process.

Aira’s copy briefing process goes something like this;

1. SEO Strategist does keyword research, competitor analysis, and client interviews to understand the industry
2. SEO Strategist turns that research into a detailed brief (with recommended structure and titles)
3. An expert copywriter (normally expert in both copywriting and the client industry) writes the content.

In my experience, GPT can’t do steps 1 or 3. It gets facts wrong, doesn’t have the depth of a good copywriter, and doesn’t care about business impact (not to mention all the industry context it is missing). However, it is pretty great at rewriting or restructuring information, so it can speed up step 2 by a lot.

Here’s the process I use:

First, complete your research (kind of goes without saying) then download “LinkReader” and “Webflow” ChatGPT plugins. Finally, use this prompt structure:

Here’s the output:

Meta Description: "Boost your SEO with our comprehensive guide on internal linking. Learn the basics, best practices, and strategies to improve your site's performance."

Heading level and title	Brief explainer for the writer
H1: Internal Linking For SEO: Comprehensive Guide	This is the main title of the article. It should introduce the topic and grab the reader's attention.
H2: Understanding the Basics of Internal Linking	Explain what internal linking is, why it's important, and how it impacts SEO.
H3: What is Internal Linking?	Define internal linking in a simple, easy-to-understand way.
H3: The Importance of Internal Linking for SEO	Discuss the benefits of internal linking for SEO, including site navigation, spreading link equity, and improving page authority.
H2: Internal Linking Best Practices	Provide a detailed guide on how to effectively implement internal linking strategies.
H3: Using Anchor Text Effectively	Discuss the importance of anchor text in internal linking and provide tips on how to choose the right anchor text.
H3: Balancing Link Value	Explain how to balance the distribution of link value across the website.
H3: Creating a User-Friendly Link Structure	Discuss how to create a link structure that enhances user experience and boosts SEO.
H2: Developing an Effective Internal Link Strategy	Provide a step-by-step guide on how to develop an effective internal link strategy.
H3: Conducting an Internal Link Audit	Discuss how to conduct an internal link audit and why it's important.
H3: Identifying Opportunities for Internal Links	Provide tips on how to identify opportunities for internal links within the website.
H3: Implementing the Internal Link Strategy	Discuss how to implement the internal link strategy, including adding new links and updating existing ones.
H2: Case Study: Successful Internal Linking in Action	Provide a case study or example of a successful internal linking strategy. This could be a client success story or a well-known example in the industry.
H2: Common Mistakes to Avoid in Internal Linking	Discuss common mistakes in internal linking and provide tips on how to avoid them.
H2: Conclusion: The Power of Internal Linking for SEO	Wrap up the article by reinforcing the importance of internal linking for SEO and encouraging the reader to implement the strategies discussed.

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If you have any questions about the topics we've covered in this month's Pulse, don't hesitate to get in touch.

Welcome to the second Marketing Innovation Pulse newsletter. I'm Dave, Senior Data & Insights Consultant at Aira, and part of the innovations team here at Aira.

The main focus of this newsletter is on forecasting. More specifically, SEO forecasting.

With marketing budgets increasingly scrutinised, it’s never been more important to be able to make predictions about what value you’re able to bring (especially as an agency). We’ve spent a lot of time thinking about this… and working on a solution.

This newsletter will give you a bit of an insight into that thinking, as well as what else we’ve been up to.

What we’ve been reading

Unsurprisingly, there’s still a fairly heavy 'Machine Learning' lean this month, but thankfully many of the big tech companies are making moves in one way or another, which means a bit more variety in our reading list!

• We've been reading articles about Perplexity.ai, an interesting ChatGPT alternative, which is meant to be more concerned with factual accuracy and includes embedded links to sources as standard.
• A short thread from Barry Adams about newspapers and zero search volume keywords.
• The announcement about Google Product Studio which could, among other things, help advertisers grab your products out of existing images and put them on a totally different background.
• Canada is preparing to pass a law that makes Facebook pay news sites to distribute their content. In response, Facebook is preparing to stop showing news sites in Canada (starting with a test on 5 June). We’re not sure quite how this will pan out but it’s interesting to watch all the same!
• A fascinating, in-depth article looking at accountability in artificial intelligence. This is likely to be a topic we’ll be hearing and discussing a lot about moving forward. 

What we've been doing

• Aira was welcomed as part of the HubSpot Partner Advisory Council.
• We announced an internal tool that generates Google Sheets formulas based on a description of what you want to do.
• After Google I/O, Paddy shared his thoughts on the latest developments in search, and our search teams shared their thoughts about how AI is likely to change search.
• We tried out 'Baby AGI', which claims to take complicated instructions, build a plan and complete the plan based on making multiple OpenAI API calls. Honestly? We found the results underwhelming.
• We also tried out 'Dalai' a Large Language Model (like GPT) that you can run on your laptop. Interesting, but so incredibly slow that it’s not really useful for much other than research, particularly given how cheap GPT is.

What we've been thinking - the key challenges in building forecasts, and how we approach SEO forecasting

With marketing budgets receiving greater scrutiny within a tougher economic landscape (certainly in the UK), businesses are increasingly focused on how efficient they can be with their marketing spending.

When speaking to different organisations, we’ve noticed an increased emphasis on this question:

“If I were to invest x, what would my return be?”

Generally, SEO professionals can be reluctant to forecast precise numbers (and consequently be accountable for hitting those numbers), as there are many external factors that can impact traffic.

These factors include core algorithm updates from Google, the introduction of new SERP features (especially with the AI changes that Google is planning) and other external factors that can all impact the traffic, conversions and ultimately, revenue.

At Aira, though, we understand that forecasts are crucial to making business decisions, so we’ve put a lot of thought into how best to estimate sessions, conversions and revenue.
In this post, we’ll explain some of the main challenges of forecasting and the strategies we use to handle them.

There are three crucial elements of a forecast

• Business relevance
• Adaptability
• Acceptable degree of accuracy (and inaccuracy communicated clearly)

Business relevance

Each business is unique, and as a result, each website is going to be distinct as well, with varying seasonal demand, conversion rates, average order values and timelags between people visiting the site and the business making money.

If we were to compare an ecommerce website selling shoes with a B2B SaaS company specialising in bespoke ERP software, the levels of traffic, conversions and seasonality will differ wildly.

For the ecommerce website, 90%+ of the website pages are likely to be transactionally focussed (i.e category pages or product pages) and the time between someone visiting the website and then converting is likely to be relatively small, with a substantially lower average order value than the SaaS company.

On the other hand, for a B2B SaaS company, the site is likely to have far more of a mix of informational content, alongside some more commercially focused content. Both of these types of content will have different conversion rates and time lags from an initial visit to the site to the business making money.

As a result, the forecast needs to be able to cater to these different businesses and the way in which they drive traffic and make money.

Adaptability

Forecasts need to be adaptable to the different scenarios.

In an ideal world, a forecast would just require a click and away we go, but in reality, there needs to be a lot more thought that goes on in the background.

Let’s say, for example, that you’re forecasting conversions for an ecommerce site and you’re initially going to focus on improving CRO. You’d want to run the forecast with conversion rates set at the current level, but also run the same tests with an improved conversion rate and compare the two.

The key is asking the right questions and being able to cater to the forecast accordingly.

When it comes to forecasting organic sessions, the key levers we can pull are predominantly centred on improving existing content, producing new content and improving the technical state of the site, and it’s useful to be able to map out different scenarios.

Some of the questions we may ask are:

• If we were to prioritise technical health first, what impact is that going to have on existing content?
• Would we be better focused initially on building new content or optimising existing content (i.e where is there greater opportunity)?
• How will different budgets allow us to do different levels of work - and what consequent impact will that have on the bottom line?

Acceptable degree of accuracy (and inaccuracy communicated clearly)

Forecasts are seldom going to be bang on the money, but a forecast should at least be in the right ballpark.

This greater accuracy comes from:

a) Using historical website data and pattern-based forecasting.

b) Making the forecast bespoke for the specific business and the type of website they have, their conversion rates, lags, average order values, and the types of customers they are looking to engage and take action on their site.

An important element of the forecast result is also communicating the acceptable degree of accuracy and inaccuracy, and transparently displaying a margin of error based on upper and lower limits.

Challenges of forecasting

A forecast is not a guarantee. It’s a prediction based on combining business intelligence and historical data in order to make estimations about the future.

Using historical data is central in order to understand seasonal trends and the overall trend of traffic, and be able to map these into the future. Historical data is also crucial in terms of understanding conversion rates, average order values, time to go from first session to conversion, etc (otherwise we’d just be picking them out of thin air).

There are two main types of forecasting methods that can be used which have their own relative benefits and drawbacks:

• Pattern-based forecasting
• Opportunity-based forecasting

Pure pattern-based approach

Pattern-based forecasting involves analysing past trends, patterns and relationships in the data to project future results. Examples of this include Facebook’s Prophet and Richard Fergie’s Forecast Forge.

Example output using Facebook’s Prophet. 

Benefits:

• The forecasts take in the context of the current site and as a result, understand seasonal variations throughout the year and more effectively map them into the future. 
• The forecasts are grounded in the context of what has been achieved and are therefore able to understand and build forecasts that follow the prior trends - whether that be up or down (or stable).
• If the current projections are down, then the forecast will continue to forecast down. The benefit of this is that the forecast is grounded in reality and won’t always assume an improvement, which is more likely with an opportunity-based forecast. This means that the forecasts are likely to be more representative of the reality of site traffic. 

Challenges:

• Pattern-based forecasts rely on historical data, so if historical data doesn’t exist the model is limited. The general advice is that you’d want at least two years in order for the model to really understand seasonality.
• The model can’t predict what it hasn’t seen before. If we’re forecasting organic sessions based on significant technical improvements and an avalanche of new content, the model, in itself, would not be able to factor that in.
• The model can be overly optimistic. If a new site has launched and has seen a continuous uplift in traffic in the first year, for example, the model will assume that this will continue regardless of the Total Overall Market.
• The model assumes that you’re not going to make any drastic changes which, if you’re an SEO agency, doesn’t really fit in with the type of work you’d like or in most cases need to do.
• The model doesn’t involve looking at what customers actually want and doesn’t lend itself to any action - instead, it is more focused on predictions based purely on if the status quo continued.

Pure ‘Opportunity’-based approach

Opportunity-based forecasting focuses on making future predictions based on how much of a total potential market we'd be able to own. At Aira, we have the Keyword Navigator which allows us to see the maximum number of additional sessions we could acquire from new or existing pages, and how many pages it would take to build/optimise.

Example output from looking purely at incremental traffic.

Benefits:

• In contrast to the pattern-based approach, opportunity-based forecasts don’t assume that we’ll keep growing forever. There is an understanding built in that there is a total addressable market, and it’s more a question of how much of that market we could own.
• It’s grounded in the reality of the market we’re operating in, whether there’s a huge opportunity for growth or whether there are more marginal gains.
• The model is centred on the customers and the specific metrics they are looking for. That means that forecasts can be more impacted by specific actions (as opposed to overall trends, which is more common with pattern-based forecasts).

Challenges:

• Unlike pattern-based forecasts, this approach doesn’t take into account historical trends. For example, if website traffic has been falling year on year, this model will not factor that in and will assume incremental growth year on year.
• Seasonality is often ignored, which again, means that many opportunity forecasts just show incremental growth instead of being able to understand that in ‘low months’ when your products are less popular you might actually expect a drop in traffic.
• Opportunity-based forecasts have a tendency to be too conservative compared to pattern-based forecasts where there is no upper ceiling. This is due to the fact there is a defined upper limit that your forecast couldn’t surpass.
• Using opportunity forecasts is useful for saying ‘this is what is in reach if you do everything possible this year,’ but it takes more time to look at forecasting monthly projections based on a limited amount of activity per month.
• Another issue with this approach is that lags are not often factored in and as a result, the assumption is that as soon as you start working, you instantly see an uptick in sessions and conversions. In reality, there is a time delay in doing the initial work, getting it implemented on-site and then seeing the benefits.

Aira’s solution to forecasting organic performance

At Aira, we’ve developed a solution that allows us to develop forecasts that rely on combining the overall potential traffic from new and existing pages with historical projections from previous years.

This solution can be broken down into:

• Stage 1 - Calculating the total organic traffic opportunity, for us using the Keyword Navigator. You can also see an explanation of some of the reasoning we use in my blog post here.
• Stage 2 - Breaking down the Total Opportunity into tasks and calculating how many of those tasks we could get done over time based on a specific budget.
• Stage 3 - Adding lags in order to cater for time for work to be delivered, implemented on-site and start ranking.
• Stage 4 - Using seasonality to upweight/downweight impact over time.
• Stage 5 - Making everything formulas and variables in order to allow flexibility and map different scenarios.
• Stage 6 - Presenting the forecasts using confidence intervals.

We’ll cover the most important stages in the sections below.

Taking overall opportunity and breaking down to individual tasks

Total potential traffic numbers are a brilliant feature of our Keyword Navigator Tool, as we’re able to see:

• The amount of traffic we could attain from building new pages and optimising existing pages.
• How many pages it would require to build/optimise in order to hit that overall traffic number.

Essentially, we estimate where we could rank based on current search results and we calculate how much more traffic (and revenue) opportunity we have, per keyword, using Search Console clickthrough curve, current site rankings and on-site conversion rate.

However, it would be a pretty wild assumption to assume this is the total addressable market and we’ll get everything in a year unless you do the maths.

Breaking down the potential opportunity into specific tasks allows us to ground these traffic estimations into actual work, which further allows us to estimate how much additional traffic we could look to drive month on month.

This also allows consultants to keep accountability and make considerations for other tasks such as quarterly reviews, technical audits, and weekly meetings where the team is not going to work on tasks that will directly increase traffic (though are still important).

This also allows us to communicate with clients about the rate that we’ll be moving and consequently what we would be able to do with a greater budget, or if we were to prioritise some tasks over others.

To break the traffic estimate down into tasks, we look at things like:

• Are we already ranking for this search? Are we already ranking pretty well? If so, is it a matter of simple optimisations?
• Are we ranking for highly related keywords? Check out this post for how we use SERP similarity to cluster keywords.
• What’s the technical health of the website?

Factoring in lags

We’d all love to publish a new page, and ‘BAM!’ immediately get traffic and conversions coming to our site straight off the bat. In reality, though, there are a number of time lags that are going to impact how long it takes to start benefiting from new content.

The most obvious of these is the time it takes to actually start doing the content-related work, such as finalising the keyword research, writing the briefs, writing the copy, reviewing the copy and finally uploading the copy to the site.

Another consideration is the time it takes for content to be crawled, indexed and rank which can vary significantly between different websites depending on the technical state of their site and the type of site they have (i.e news publisher or jobs site is generally going to have a quicker indexation time compared to a small B2B site).

So, in all of our calculations, we figure out when we’d expect the work to be done and then work out when we’d expect that work to have an impact based on what we understand about lags for that business.

We also factor in the average time between initial sessions becoming conversions which again significantly differ between different types of websites (i.e. ecommerce compared to B2B SaaS). These all help us to allow more realistic predictions of how additional traffic and conversions are likely to occur.

Seasonality

One method for understanding seasonality is simply using last year's traffic, but by using machine learning we create a basic trend line for the coming year, handling seasonality and growth, and smoothing out outliers.

If we just took that as our forecast for the year ahead, we’d run into the issues we mentioned above about pure pattern-based approaches. So instead, we figure out the day-by-day trends and use them to upweight/downweight our impact estimates. If weekly and monthly seasonality shows that a day is typically 50% lower than average, we take our impact estimates for that day (based on Step 2) and drop it by 50%. The same approach works for a steady upward/downward trend over time. This offers all the benefits of pattern-based forecasting but is grounded in the reality of the actual opportunity a business has!

One reason this is important is that prioritising work earlier may well pay dividends if the site is in good shape by the time a high month rolls around. This may be through prioritising content-focused or technical work earlier, or even pulling budget forward in order to capitalise on busier periods.

In the example below we can see that Month 8 is when this search starts to drive a lot of the traffic to the site, therefore prioritising work earlier on will mean that once the busy season rolls around, our new content has already been indexed and is more likely to drive sessions and, ultimately, conversions and revenue.

Making everything formulas and variables

As discussed earlier, having a forecasting model which is flexible and adaptable is really important in order to map out different scenarios. Our forecasting model is based on formulas that allow us to do exactly this.

This allows us to answer questions such as:

• What if we improved the conversion rate?
• What if we frontloaded our budget?
• How would our forecast change if the client’s in-house team took control of the content copy production? Or how would it look if we split this 50/50?
• What is the difference in sessions/conversions with different levels of budget?
• What if we focused on technical SEO initially at the cost of content production?
• What would the model look like if we adjusted the confidence intervals?

What we mean here is, there are probably 10 different things a consultant needs to update manually when changing our forecast. Everything else is totally formula based. There’s no manual copy-pasting of data from one tab to another. It was pretty hard to wrap our heads around some of the array formulas we needed to use, though we were able to call upon Aira’s AI Google Sheet formula helper, ‘make me a formula’, at times for inspiration.

Presenting the forecast with confidence intervals

When presenting the results of the forecast, it’s important to clearly communicate:

1. The number we’re sharing isn’t the only possible outcome
2. What a best case scenario looks like
3. What a worst case scenario looks like

Making the upper and lower bounds on the line graph below was a challenge in itself, but suffice to say it’s all created entirely in Google Sheets. We’ll share how we did it in another post!

To wrap it up

The way that we approach SEO forecasts is a blend of historical data and estimates of how much incremental traffic we can bring based on how much of a total addressable market we are able to claim.

There are plenty of elements that play a role in forecasts and are a combination of using the existing data, adding in business intelligence and making decisions, and mapping out different scenarios about the schemes of work.

Google’s recent generative AI announcement has spun the heads of search enthusiasts over the past week, particularly businesses owners and the marketing community. Paddy, our CEO and co-founder, has recently commented on the changes and summarised the Google I/O announcement.

At Aira, we recognise the gravity of these changes and the need to change with it. SEO is being centred back to its basic principles: marketing your product and your ability to get your message across to the right audience at the right time.

We have already begun embracing this change by gravitating towards performance marketing and away from the traditional marketing approach by removing our digital PR services as they currently stand (you can read more about this change here).

As always, we are very focused on client experience, and as such we’re actively thinking about the impact that these changes could have on their businesses, how we can work better as a strategic partner, as well as serving them and growing their business.

The shift away from traditional SEO as we know it to a broader, more user-centric approach has been an expected shift Google has been leaning towards for some time. We’re excited to see where these new changes take us.

In this post, our experts at Aira have come together to discuss:

• How AI is going to change the face of performance marketing
• The changes we’re already making at Aira in light of AI

It’s become abundantly clear that change is afoot, and in all likelihood these changes will be fundamental to how businesses operate and market via search on a day-to-day basis. Not only this, but the way we operate as an agency will have to change.

Discover below how our experts are incorporating these changes and where we think the industry is headed next.

How is AI going to change the way people search?

Jasmine Lee, SEO Consultant

There’s no question that this is going to lead to huge changes in how SEOs operate. Whilst many marketers are afraid that SEO will become redundant, in my opinion as long as people are still searching for information on the web, SEO will always be around.

There are lots of ways I think AI is going to change search but here are my fundamentals:

• The domination of zero click results: Whilst a basic observation, there’s no avoiding the impact that these changes will have on click-through rates. The basic principles of targeting featured snippets will still apply since Google will be sourcing its information.  However, the importance of wider context, brand authority to speak on particular subjects and content quality will remain pivotal aspects of search strategy. Clicks will be much harder to obtain, but the information Google presents from your website has to be exciting enough to capture the attention of users enough to explore the source.
• Ultra-personalised results pages: I’m of the opinion that we’ll get to a point fairly soon where users will type in a query, and every results page presented to each user will look different. We’ve already seen location-based personalisation for some time - there’s no reason why this won’t be the case when Google will have so much information on individual user preferences and  search trends.
• Rich result importance: Finally, presenting information to Google in the most understandable and accessible way possible will be another vital consideration. I hope you like schema, because I don’t see it going away any time soon based on these changes. We’re already seeing product results using schema, and presenting your information in a prime way for Google search will only offer more opportunities.

How are SEOs at Aira using AI already?

AI is fast becoming a huge advantage for the team at Aira to automate manual tasks and free up time for strategic thinking. From an SEO perspective, we’ve already been working on AI tools that allow grouping keywords by user intent, mass image compression and auto-generation of meta titles and descriptions. These innovations save us much needed time to consult on clients and free us up to work on much higher-value tasks

I want to be clear that AI acts as an assistant and does not replace the value that SEOs can offer businesses. The human, critical thinking element of curating SEO strategy and providing proactive recommendations is something that cannot be replicated by a machine. Our role will change, but not be replaced.

How is AI going to change content marketing and distribution?

Chloe Hutchinson, Head of Content Marketing

The conversational nature of generative AI (i.e. asking follow-up questions without losing the context of initial queries) means that businesses are going to need to better understand customer personas, intent, and the moments and emotions that trigger action at a deeper level than ever before. 

To better market content effectively, understanding who target users are at this deeper level will be vital to being able to predict the questions they’ll ask Google, and where else they might sit online to acquire the answers they’re looking for. This has always been important, but moving forward it’ll be essential that this is at the heart of all content marketing.

Generative AI also means that with follow-up questions, Google will have direct access to an insight they’ve never had before: whether they’ve answered the user’s question or not. This will give them a better understanding of user engagement with the information presented. 

Therefore, the future could offer us insights that simply aren’t available now, such as how many searchers had their queries satisfied by our content - and if not, what common follow-up queries were. If this is the case, we will be able to learn more about our users so we can ‘join the conversation’ (as a brand, or on behalf of clients) - using new insights to personalise, improve, reformat and re-distribute content, to continually optimise performance.

Finally, there is a possibility that AI will change the buyer’s journey. Users in the awareness, consideration and decision stages will utilise AI to find answers and recommendations, meaning they may not even need to visit a brand website. 

This does two things. First it makes attracting target users to a website ever harder (and could decrease organic traffic), which highlights the increasing importance of content distribution, attracting users to your website from various platforms where your target users spend time. Secondly, it increases the importance of the relevance of content on a website, serving user intent and answering user queries to the fullest extent, whilst ensuring all content is optimised to Google quality rater guidelines, to be in with a higher chance of being featured at the top of the SERPs.

We are already using AI in our process to help inspire and provide starting insight for a number of steps in our content marketing process

AI can help to provide some insight into target personas. It can help by identifying most common searches, highlighting problems or pain points, providing insights into a target persona’s potential buyer journey online, and displaying spaces where target personas could sit online via existing featured content. This is taken into a target persona workshop and built upon by the content marketing and consulting team. 

When given enough context, AI can help with ideation thought starters, including types of content that have been well received in the space, types of content which might appeal to a particular target audience, and content ideas which could be used for distribution. The team can then build upon these, looking at them through a target persona lens, adding additional research, brand context and historical knowledge.  

AI can also help with inspiration for press releases, outreach emails, quotes from brands and what else might be of interest to journalists - the team don’t take the outputs directly, but use these to inspire and build upon to create the strongest approach and best quality outputs. 

How is AI going to change copywriting?

Lucy Hancock, SEO Copywriter

I’d say it’s more important than ever to have quality content on your site. Content that reflects your brand, your product or service and helps the user solve their problem. While more people will use AI to write copy, and AI can *technically* write copy, it can’t replace the skill and quality that copywriters have. I’d say tone of voice and context are going to be more vital than ever - as these are things AI struggles to master.

I think we’ll also see less mass content production, with a focus on reviewing/repurposing existing content to maintain its value. Gone are the days of publishing as many blogs as possible! Now, I think the focus will be on refreshing existing content to make it the best it can be.

How are we using AI already in our processes?

While we won’t use AI to write copy at Aira, we have been using it to support copy brief creation and also with our research. AI is great at condensing large quantities of information in a way that’s easy to digest - which is super helpful for copywriters needing to write about tricky topics! 

In the copy team, we’ve found AI works as a great research tool to help us get to grips with complicated topics (asking it to explain a topic in simple terms, for example), generate headings for copy briefs and supporting content ideation. And while it won’t replace the creative minds of our team, it can support us, and in turn, save us time that we can use to do what we love most - write.

How is AI going to change paid media?

Holly Pierce, Paid Media Strategist

AI is already a prominent part of paid media, and we can expect that as time goes on, the adoption and intelligence of AI and machine learning in paid media will continue to improve our activity in efficiency, effectiveness and personalisation. With an industry that is constantly changing with new technologies and updates, being adaptable and experimental is key to staying ahead of competition and performance, and AI is no exception to that.

I can see the continuous development of AI and machine learning changing core specific areas:

1. Ad placement and ad types: With the rise of performance max, I believe that we can expect to see new solutions incorporated into this campaign type as it grows in intelligence. I predict that eventually, this will also lead to sunsetting campaign formats such as Discovery. It will also be interesting how (and if) chatbots such as Chat GPT will be incorporated into the search engine environment, and what effect this will have on the paid advertising space.
2. Continued improvement to AI & machine learning led products: DSAs, performance max, smart shopping, broad match, bidding strategies.
3. Voice and Visual search optimisation.
4. Measurement and predictive analytics: We understand the use of predictive analytics with customer behaviour and identifying trends, but it would be great to see improvements in measurement and business impact reporting predictions.

I believe that the role of a paid media specialist will also continue to change with the rise of intelligence in AI tools, moving daily priorities into three core areas:

1. Data strategy: Ensuring a robust conversion strategy so that the data we feed into the algorithm is most accurate to optimise towards business goals.
2. Creative strategy: Generating and testing tailored creative to construct the most relevant experience to users.
3. CRO: Understanding how we can better websites and landing pages for Google to crawl for automated creative, and fabricate the most seamless user experiences to improve conversion rate.

How are we using AI already in our processes?

AI already plays a fundamental role within paid media strategy. Below are some examples of how we already utilise AI in Paid Media:

1. Ad targeting and audience layering: Google has a vast amount of audience segments generated through machine learning for attributes such as consumer behaviour, interests. We utilise these within our targeting to ensure efficiency that we are reaching the right users, but also layering over observation audiences to discover new insights about our clients ideal customer profiles.
2. Ad creation and optimisation: Using responsive ad formats and providing multiple options for Google’s algorithm to then decide which combination of creative is going to be the most relevant to each individual user.
3. Using and experimenting with Google’s solutions that utilise AI as stated above to create the most relevant solution to each individual.
4. Smart bidding and broad match keywords: Using smart bidding strategies alongside broad keywords to increase opportunity and optimise toward business objectives in real time.
5. Data management: Ensuring we are utilising 1st party data and inputting information related to business objectives back into the algorithm to give Google the ammunition it needs to optimise towards users that are most likely to become customers. 
6. Ideation and experimentation: Using AI-powered tools to discover new audiences, brainstorm new creative ideas. Tools such as Chat GPT can also be great to help with initial stages of research for new clients. 

We are continuously exploring ways to integrate AI into our work at Aira, as a tool to support our human expertise and save time on manual tasks. This leaves more headspace for critical thinking, creative strategy and improving the client experience.

It's been a couple of days since Google I/O and I've reflected a little on the key themes and points. Yesterday, I shared a bunch of thoughts, ideas and notes with the team at Aira. Below is an edited version of what I shared with them.

If you're an Aira client reading this (hi!) and would like to chat more about this, drop your account lead an email and they'd be happy to put you in touch with me. If you're a brand who are looking to stay ahead of what comes next and don't work with us yet, you can email me here to arrange a chat.

Some thoughts on the Search section of Google I/O and the potential impact on clients

Everything in this section relates to this 13 minute segment from Cathy Edwards. If you work in search and haven't watched this yet, I'd highly recommend that you do. Here are a few themes and ideas that I took from Cathy's keynote.

A clear expectation that people will change how they search

The clear highlight here was the introduction of generative AI at the top of search results. The interesting part for me was that there were more than a few references to changing how people search. Here are some examples:

• "Things that you may never have thought to ask search for before."
• "Turn to Google for things that you never thought you could."
• "Unlock new ways to search."

Google are recognising that conversational AI is different to search as we know it right now - they're trying to bring the two products together into one interface. Personally, I'm not convinced that they are compatible in the short to medium term. Is it too much of a behavioural switch? I can see behaviour changing, but very slowly.

The current design also looks a bit, clunky... particularly with the links to sources in the top right corner:

I'd expect this to change over time and be refined more.

Trusting generative AI content

One quote that stuck out to me was "People will always value the opinions of other people" - context was that no matter what AI can do, people will eventually want to know that recommendations/information etc comes from a person. I'm going to agree to a certain extent, but there are also a bunch of queries where people just want facts that aren't debatable or about opinion. So a bunch of queries are at risk of never sending traffic to websites here imo - more on this below.

There is a legitimate question on whether users will even notice that content is from AI as opposed to a human. Assuming that Google keeps marking this content as such, which they strongly implied that they would across multiple formats, I think this is less of a concern.

Thinking beyond the new feature above for a second, where it gets really interesting for me is a scenario where a website publishes AI generated content (not human at all) and that content actually does answer the query of a user who arrives from search. Should Google care that the content wasn't written by a human if the user is happy? I mean, they probably shouldn't if the user is happy, but I suspect that they will care - publicly at least. I don't know what I would do if I were Google here, thankfully, that's not my job! But it's an interesting one to think about because I think that they'll need to balance their desire to satisfy users against the fact that they can't appear to let the floodgates to AI generated content on the web open.

Google also talked about identifying "synthetic" content (specifically, images) via watermarking and metadata. Whilst focused on images, you have to figure that they are looking at all content here eventually as a way to identify AI generated content.

Interesting way to describe it too - I'm predicting that we'll see "synthetic content" added to Google Webmaster/Spam guidelines within 6-12 months.

The impact on search as a channel

Organic search is a risk for sure to some industries (a few noted below). But I still think it's too early to know what the actual impact will be - we all need to play with Bard/Generative AI search to see how it may change things. For me, I still think that AI has a huge trust issue around informational/comparative content. Facts and statistics are easy to get right, but so much of search is people going on a journey and at some point, they want to be told what to think or do by a human. I'm not sure how AI bridges that gap in organic search.

I'd be scared and thinking about how to adapt if I'm any of the following:

• A template website of any kind e.g. legal documents, HR templates, health and safety docs etc.
• StackOverflow/tech support forums.
• Pinterest or any stock image website (more on why later)
• Educational sites that don't have a USP or brand that users buy into.

In regards to paid search, it's not going anywhere and needs to grow to keep shareholders happy. I'd expect it to get more expensive at least in the medium term if generative AI is integrated more into SERPs before Google monetises it - I wonder if they will take a short term hit and show fewer ads, but CPCs will increase to offset things. I wouldn't be surprised if Google reduces the number of Ads at the top of SERPs and drives the price up.

Speaking of revenue for Google, AI as a revenue stream for Google is interesting to think about - there were lots of partners being talked about in the Cloud section of I/O in particular. I suspect that the revenue potential from API access is a drop in the ocean for Google. So I think that partners will be allowed to use AI models by Google in exchange for sending data back in the other direction to refine and help Google learn. PaLM 2 API access are examples of this I think.

I also can't help but think about the cost of integrating AI into each search. Chat GPT costs are high - Google may be able to benefit from their scale here, but obviously has a lot more searches than Microsoft. Does this scale? You'd have to assume that it does because that's what tech does, but if they also want to get more complex with what AI can do, that requires more processing power, so scale savings may get cancelled out. 

Robin also pointed out to me that this is actually part of Satya Nadella's game plan - Google taking a hit on margin on searches hurts them more than Microsoft taking the same hit. Whether this actually happens is another matter, but I think that it's definitely a challenge that Google will be addressing.

What brands should be thinking about

Firstly, don't panic. I've seen a lot of "hot takes" on LinkedIn and Twitter over the last two days and frankly, I think far too many marketers try to be the first to react to these kinds of events, rather than taking a step back and actually thinking about things. So my first piece of advice is not to be one of them.

Secondly, there is clear disruption happening and with disruption comes chaos. Fortunately, chaos and disruption lead to creativity and innovation. So once any initial panic has subsided, I'd be looking at this as an opportunity. SEO in particular has been dead for about 15 years now, we're used to it.

To make things tangible, I do think that brands need to adapt in a few ways. I'm thinking more about this at the moment, but a few initial thoughts come to mind:

• If you've lost organic impressions or traffic already because of featured snippets/answer boxes pushing you further down the SERPs, I'd expect this to get worse and plan for it. Look at the queries that are most at risk and forecast what the downside will be if this gets worse. I suspect that the queries that are already featured snippet heavy are the ones that generative AI will chip away at even more.
• If you provide any kind of content that can be seen as single answer or factual, I'd expect to lose traffic. Again, we've seen this already from Google where they're able to provide a concrete, clear answer to a question within a SERP - meaning that the user doesn't need to click through to a website. If any of your content provides answers to a question that aren't opinion, controversial and literally a fact, then I'd plan for losing traffic.
• Content at the consideration and decision stages of the buyer's journey is about to get a whole lot more important. I think that companies who invest in brand, product and content at these stages of the buyer's journey are more likely to win, even if generative AI within SERPs takes off.

The impact on ecommerce brands

The keynote had some clear examples of commercial queries, one being buying a bike. Links to more products were very prominent within the generative AI box and is built upon their existing Shopping Graph. You have to bet that they have already talked about monetising this.

I did notice on these results that key attributes of a product or retailer stood out massively. Things like discounts, deals, delivery times and return policies all stood out as a point of differentiation. One example search result was "Here are some dresses with two day delivery" - retailers need to enable Google to understand these types of USPs/attributes that they have if they want to stand out.

I think that ecommerce brands need to truly think about what makes their service or product different to everyone else and what value they add to the buyer's journey and the problems that a buyer experiences at the start and middle of that journey.

For me, this isn't new - ecommerce brands who aren't thinking about this are already behind. The fundamentals are still the same, but those who don't address those fundamentals will be left behind with these types of changes from Google.

Changes to local results

There was a brief glimpse into how local results may change with an example comparing different restaurants that a user may want to visit. The result shows three different restaurants side by side and each one had different attributes being displayed.

Like retailers, I think local businesses are going to need to give Google this kind of info to stand a chance of being here in these results.

The impact on image search

Google shared that they have partnered with Adobe Firefly which allows you to use Bard to create images on the fly using Adobe images and technology. I feel like this may have an impact on image search and copying images. You'll basically be able to create unique images for whatever you want, so going to copy them from elsewhere isn't needed as much. I'd worry if I was Pinterest or any stock photo website.

They also talked about improving search results by allowing someone to click a button that shows them more information about the image.

Highlights and further reading

Before going into the highlights and cool new products/tech that was announced. An important piece of context should be kept in mind - the vast majority of Google's revenue still comes from Ads. Sure, their stock price jumped by 5% after Google I/O, but ultimately, their long term roadmap still leads towards selling more and more Ads. So I'd advise keeping that in mind when you think about these updates and where they may lead to.

Interesting product changes and innovations related to AI

• Gmail
  • Help me write - expands on smart compose and will write an email reply for you, using details from the incoming email. You can then refine by making it longer/more formal/elaborate. Pretty nifty little feature and I think it will get used a lot. I'd be interested in how we could/should use it for prompting good replies to client emails.
• Maps
  • Immersive maps are being introduced for 15 major cities by the end of the year which allow you to preview a route before you take it. Pretty cool for walking or biking through cities. The visual demonstration was very impressive and it integrates the weather and air pollution, so you can see if it's likely to rain later when you start your walk.
• Photos
  • Magic editing is being introduced. Advanced photo editing - made me a bit sad tbh, it almost feels like too much editing e.g. changing poses and adding extensions of objects that aren't in shot or taking away clouds to make it look like a sunnier day than it was. Super cool, but I'm not sure that's what photos are about.
• Workspace new features:
  • Help me write - within Docs, you can use it to help you write whatever content you want. I think this may have a big impact on templated document websites such as legal / HR templates / employment contracts etc. The legal industry generally is at risk here I think with the less customised stuff that they do.
  • Help me organise - within Sheets, you can ask it to create a table for you with an abstract description and it will do it. Super cool.
  • Help me visualise - within Slides, you can create images from text in slides to make things more visual. You can also ask it to add speaker notes that are based on your slides. This could have a useful side feature which is to get a "summary" of your slides and see what the takeaways may be.
  • Getting prompts within Google Docs (called Sidekick)
    • They've identified that sometimes, people need help with prompts, so they have developed "Sidekick" which sits at the side of a Doc or email. It helps people continue when they get stuck with writing. For example, you get a few paragraphs into an article, need some help, but don't know how. Sidekick reads the text and gives you some prompts which you can click on and get ideas from. It occurs to me that the same tech could also be used to help someone continue their journey in search too.
  • Sidekick in Gmail - same kind of thing as above but the use case was to summarise email threads and even reference Docs/Sheets/Slides that are included in the thread. Could be mega useful for summarising long email threads with clients.
• Bard new features/integrations - it's clear that they are trying to connect Bard with more Google products. A few examples:
  • Export responses directly into Gmail, Docs and Sheets.
  • Export code to Collab and Replit.
  • Upload an image to Bard and get captions generated to describe that image - my first thought was ALT text generation en masse would be a potential use case here.
  • The presentation really focused on Bard as a standalone tool outside of search i.e. not part of Search itself (although that came later) and then a "Google it" button which allows you to go to Search if you'd like. But it feels like they aren't doubling down on Bard within Search alone - it's still going to be a standalone tool.
  • From within Bard, you can bring in other Google Products e.g. Maps. So the question is - what else can they bring into Bard?
  • They are already connecting with 3rd party apps e.g. Adobe / Redfin / Spotify / Indeed.
• Project Tailwind - scans Drive and uses the content as expertise. So you can essentially get content recommendations from AI based on your own tone/expertise/style etc.
• They are developing more tools for evaluating information and trust.
  • About this image tool - will show when similar images first appeared and were indexed. All Google generated images will have metadata to help identify it. Publishers and creators can do the same and mark them in search as AI generated. You have to bet that they are trying stuff here with other content forms (video and copy) too.
• Universal audio translator is a really cool new tool that translates audio in realtime.
  • Could open up education and learning to new territories without needing to have human translators. Videos/Podcasts etc. Only accessible to authorised partners (for now) to help prevent misuse and deep fake enablement.
• Adversarial testing is something they are developing which is designed to help find fake content/misinformation and highlight it. For example, helping dispel fake moon landings photos - the tech here is surely being used for general information/credibility too on the web too.