Search Results

Around two thirds of trackable web traffic comes from search engines, whether from paid listings (paid search or PPC) or organic/free listings (organic search or search engine optimisation - SEO). The chances are that search represents a very meaningful source of online traffic, leads and customers for your business – even if it is at the start of a long B2B purchase journey. It follows that when you are setting your digital marketing strategy, you should be seeking to understand your potential opportunities for growth within paid and organic search, and then tracking changes on an ongoing basis. Search engines helpfully provide a lot of information of advertisers on the performance of their paid search activities, but this is not the case in organic search meaning that marketers must rely on third party tools to track their SEO rankings. In our experience, organic search is overlooked in terms of its commercial importance to most organisations, receiving much less Management time, fewer metrics in the board pack and insufficient investment than other marketing channels. Your SEO rankings can often account for 50% of new customer acquisition once you have clear picture of marketing attribution , at a very attractive Cost Per Acquisition CPA compared to other channels. This lack of attention results from a combination of the difficulty of measuring/tracking the value of your SEO rankings and the (misplaced) idea that organic search traffic is both hard to influence and in terminal decline, so why spend time focusing on it. A Search Headroom analysis that is based on your SEO rankings can help change some of these perceptions and help to bridge the gap between senior managers and technical SEO practitioners. What is a Search Headroom analysis? A search headroom analysis highlights a business’s share of its potentially addressable organic search traffic at a specific point in time. The higher up your business appears in the organic search rankings for any given keyword, the greater your share of traffic will be. You can think of this like a digital version of a traditional ‘market share’ analysis. The difference is that traditional market share is based on the ‘stock’ of customers in a market (e.g. a car manufacturer’s share of all the cars on the road today), where as a search headroom analysis considers the ‘flow’, the customers who are actively searching for a given product or service (e.g. a car manufacturers share of the new cars sold this year). Businesses that are growing will often have a higher share of search traffic than their overall market share – hence this can be a valuable leading indicator of growth. In principle there are a handful of steps to follow when creating a Search Headroom analysis: Build a list of your own web domains and those of your competitors; Use a third-party tool to produce a list of all the keywords where each domain appears in the search results, and their respective SEO rankings; Aggregate the results together and remove duplicated keywords, irrelevant keywords, and those where the ranking is so far down the results, they are unlikely to generate any traffic; Combine with data on the overall monthly searches for each keyword; Translate the rankings into estimated traffic for each domain on each keyword (considering both the ranking and other search results page (SERP) features which could impact click-through rate (CTR)); Group the keywords into common sense segments for your product/service; Explore the results at a segment, keyword and even landing page level; and Review the search results for your most important keywords to check for any additional competitor domains to include the next time you update the analysis. Figure 1 - example summary from a Search Headroom analysis showing 'market share' by domain and keyword segment. A Search Headroom analysis is a very powerful tool as it can be built both ‘outside in’ using 3rd party providers of search results tracking, as well as being enhanced with more accurate internal data, for example when estimated CTRs. We have used this approach both as operators and investors as a result, to understand a market overall, dig into competitor strategy or track who is gaining/losing share. You can also apply this methodology in other channels e.g., Amazon. A Search Headroom analysis is also very useful when you are planning big changes in your digital marketing strategy – launching a new website, re-platforming an existing website, or planning a domain consolidation. All these changes can cause significant and immediate change in your SEO ranking that you need to carefully monitor and mitigate. What digital marketing insights can a Search Headroom analysis generate? Understanding your business’s search headroom can yield many interesting insights about your business, your competitors, and your market. For example: Your ‘market share’ of organic search traffic, and how this varies by segment and keyword Trends in your ‘market share’ over time Level of fragmentation/concentration of traffic in your market Seeing where your competitors are winning traffic, but you are not Growth YoY in terms of search volume (10 years ago it seemed like every keyword was growing in volume terms, but overall search volumes are now relatively stable, so growth in searches tends to correlate with overall market growth)) The level of volatility in your market i.e., how often SEO ranking changes How commercial/sophisticated the digital marketing strategies are in your market i.e., understanding the mix of organic vs paid traffic (search ads and shipping ads in some categories) Where you are doing well in organic search but not paid search and vice versa, by comparing the Search Headroom analysis to your paid search data How overlapped your market is with other markets that may have similar search terms – think about a market like cyber security where you will find many different overlapping niches as well as job seekers and students searching very similar keywords Seeing your SEO rankings at keyword level (where do you rank vs. where you ‘should’ rank based on the product/service offering of your business) Where you may have recently lost high volume SEO rankings Whether your SEO/ content team are spending time in the right areas to both protect your most valuable SEO rankings and grow your visibility in the areas of biggest opportunity Comparing the performance of your different landing pages (and those of your competitors) Figure 2- example keyword level market share from a Search Headroom. What makes this difficult? Whilst the benefits of a Search Headroom analysis are hopefully clear, this is something that many businesses have never attempted. One of the challenges in the complex, technical nature of search marketing, and in particular SEO. In our experience, many talented, technical SEO professionals don’t think about top-down opportunity enough and for most management teams SEO is the ultimate ‘black box’ where cause and effect are very hard to understand. The closest teams often get to quantitative reporting of their search marketing is a page in the board pack listing their top 10 keyword rankings. There are a few other factors that make producing a Search Headroom analysis hard: The (very) long tail matters – previously businesses I’ve worked with have generated as much as 90% of their organic search conversions from keywords with fewer than 100 monthly searches. You will likely need a dataset with thousands or tens of thousands of keywords, potentially more if you are an established business in a large market e.g., online travel. This means that completing the Search Headroom analysis need more advanced analytical skills which your team may not have. Most SEO tools – and there are lots – track a selection of SERPS and produce various metrics like ranking, perhaps even estimated traffic, but this is rarely out in the context of overall category and certainly doesn’t highlight opportunities and risks – so you need to do your own analysis to produce an overall view of your ‘market share’ as well as being able to drill down. To get a complete view of your SEO rankings you may need to combine data from multiple tools. There are many features that can appear on the search results page and influence the click-through rate for any given ranking. The number of paid search ads, shopping ads, maps, and featured answers all play a role – you ended to look carefully at your own data to make sensible estimates for each potential scenario, again adding to the analytical complexity. The recent emergence of generative AI is going to lead to a lot of change in the SERPs over the next couple of years which will only add to this complexity. Deciding how to segment your keywords can be subjective and requires some test & error – how many groupings to create and how to define them. In our experience, we find it helps to remember that not all traffic is equal in terms of its likelihood of converting, so we will create segments that differentiate by level of intent e.g., whether a search includes a high purchase intent word like ‘buy’, ‘compare’ or ‘reviews’. We will also always create a segment for branded terms as these behave very differently with very high click-through rates on your own brand terms. The changing search engine landscape – this will vary by business and geography, but Google has c.85% share globally , and Bing has been gaining share and has reached 8%. For now, if you build your Search Headroom analysis based on Google you will get an accurate enough answer, unless you are focused on one of the handful of markets where Google is not the outright market leader (e.g. China, South Korea). Novel products / services with limited directly relevant search traffic - I’ve worked with businesses at the vanguard of a new category where consumers are not yet searching explicitly for their product or service in high volumes. This means a Search Headroom analysis will typically show the business as having a very low share of some large, adjacent keyword categories – which isn’t especially actionable in the short term. In these cases, we narrow down the keyword focus to the handful of directly relevant keywords but monitor closely for new keywords which will be likely to appear every month. How can I create my own Search Headroom analysis? None of these difficulties should prevent you from undertaking a Search Headroom analysis – the insights you can generate will be truly insightful, helping you to both spot opportunities and manage risks. At Coppett Hill, we've created our own tool to create Search Headroom analyses for our clients, "Searchscope". We've combined our experience of SEO across many different industries and geographies with proprietary AI to rapidly produce actionable insights that can be updated every month. This saves our clients considerable time and effort in understanding this critical area on an ongoing basis. If you’d like to discuss how you can use your SEO rankings to use our Searchscope tool to create a Search Headroom analysis for your business, please Contact Us . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

Many marketing reports I have reviewed over the years have focused on ‘vanity’ metrics like impressions, likes and views - which look impressive on the surface - but made no mention of profit or return on investment (ROI).   In the spirit of challenging assumptions, this is the first in a series looking at whether any of these ‘vanity’ metrics are correlated with or predictive of profitable growth, based on the work we do with our clients to create a single customer view across their various marketing data sources.   I have always been fascinated with alternative indicators – for example measuring light pollution from space as a proxy for African economic growth or using satellite imagery of car park utilisation to predict retail like for like growth .   First up in this series is email open rate. We have looked at this in two contexts: in a long sales cycle business (think B2B enterprise software or high value consumer goods) vs conversion to purchase, and in a transactional B2C business vs lifetime value .   Example 1 – long sales cycle conversion   This example represents a very high value B2C purchase, with a sales cycle of c. 1 year – so I think of it more like a B2B sales process. We see that there is a linear relationship between marketing email open rate (i.e. excluding 1-1 contact with salespeople) and ultimate conversion. Example 2 – transactional B2C lifetime value   In this typical B2C ecommerce example, in a category with a high purchase frequency, the relationship is slightly different – getting email open rate above c.20% correlates with a meaningful increase in customer lifetime value (measured in terms of profit of course). Above this, there is a still a positive relationship but less strong than in our first example.   What we have also looked at here is whether there might be covariance with the number of emails received – but when you isolate to just those customers who have received >50 emails, the trend is almost identical. How can we use this insight to drive growth?   Of course, you may be wondering- is this just correlation or more fundamental causation? In the first example, common sense says that a prospect who is more engaged is more likely to open emails. In the second example, a customer who feels a connection to a brand and its content may well be more likely to open emails.   I would make the case that in some ways this does not really matter, because the most valuable way to use this insight is as a correlation, in other words a predictor of prospect conversion or customer lifetime value. This could allow you to prioritise sales resources, direct churn prevention activities or indeed simply to produce a more accurate forecast of business performance.   As to whether working on your email strategy can increase the correlated business metric – that is something you should test. With both client examples described above, the next layer of detail suggests the opportunity to make gains – looking at the specific email campaigns, automated journeys, and scope for more A/B testing.   One of the best characteristics of email marketing as a Chief Marketing Office (CMO) is that compared to your website, app, or other digital products, you are likely to have full control of A/B testing without the need for your tech team to get involved. Send days, times, subject lines, and email content can all be optimised.   If you have the data available, check this comparison to conversion rate and lifetime value for your business. If you do not have this at your fingertips, it is worth the effort to start to tie your different marketing data sources together to create a single view of the customer journey to uncover insights such as this. Next up in this series will be enquiry response time – please do suggest any other indicators you would like to see tested. If you’d like to discuss how you can join together your marketing data sources to understand these relationships for your business, please Contact Us . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

CEOs and PE firms alike have mastered the art of value creation through improving financial, talent, and operational efficiencies, but sometimes go-to-market market performance improvements seem to cause more trouble, and nowhere is this more prevalent than in pricing. Though we’ve covered pricing in a previous article , it can feel very theoretical, so I wanted to hear from someone who’s been on the front lines for some more practical advice, and who better to provide that than a seasoned pricing leader like Chris Pople, Head of Pricing at Antalis, and previously of Adecco, SIG, Cromwell and RS Components. Chris has been working in pricing for 15 years, and with that brings a wealth of experience on the science, and art, of pricing. I wanted to get an idea of the do’s and don’ts of pricing; the quick-wins, common pitfalls, and best practices that Chris has picked up on throughout his career, and as he was introducing his work, I learned my first lesson. “It’s not just about the numbers”, he begins. “In most of my pricing roles, I spend less than 10% of my time on pricing. Most of it is focused on change management, and getting businesses focused on value based-selling.”   He emphasises that most businesses lose focus on what they do to solve the customer’s problem, and according to Chris, realigning the whole organisation to those values is an important early step in any pricing strategy. Drawing from his experience with growth consultancies, and his love for Leicester City FC, Pople offers a new perspective through a football analogy: “Most consultancies see the pricing team as the manager or coach of the team. I see us more as the grounds staff. We’re here to make the pitch as best as it can be, setting the boundaries on which the sales team play their game.” As our conversation delves into the complexities of pricing strategies, it becomes clear that Pople advocates for a more holistic approach. “Pricing,” he asserts, “Is one of 5 or 6 functions that bring value to the customer. Very rarely have I seen a pricing project executed by a pricing team on its own.” He lists the teams he most often collaborates on these projects with: “Essentially the whole business entity”. He says, describing how these functions work together to generate value, articulate it to customers, and represent that value with a price, then concludes:  “I would say a more accurate representation of pricing is as a part the customer value management portfolio of functions.” As he sits back, I take a moment to dig deeper on the teams involved, inquiring about the lack of pricing teams in most organisations, and to whom the burden usually falls. “The clever organisations are creating their own pricing functions,” He replies, “but in the vast majority of cases, pricing has been a growing function from within, not standalone.” He talks about pricing being integrated into sales, finance, product development, and the various associated drawbacks, then argues that the best place for pricing is within a transformation team. “All companies are on a transformation journey, just at different stages.” He then addresses some typical points of resistance to such changes, often hearing ‘we haven’t got a pricing problem’ or ‘that’s the best we can do in the market’, and the plight of the sales team, who typically receive mixed messages about the strategy of the organisation. Then Pople shares a trick he commonly employs to rectify this: “I like to get them into a conversation: what are we famous for, what are we known for, what do we also do”. By segmenting the product range or service offering in this way, he can start focus pricing competitiveness in the ‘famous for’ areas, whilst margin enhancing at the other end of the spectrum, simultaneously helping the sales team to understand the organisation’s core values. During his tenure as a pricing specialist, Chris often meets resistance to some of the changes he suggests. To mitigate this, he likes to find a set of advocates in the sales community: “I’ll take someone through the pricing logic, get them bought in, launch it, and when they start seeing results those advocates are all in. Then you’ll get others who see what’s happening and say ‘well I can do that, can you help me?’, and slowly you’ll bring people around.” He adds “You’ll always get some people that are never going to buy in, so I take the 80-20 rule. If I can take 80% of the people on the journey with me that’s good enough, I’ll let the management team deal with the other 20%.” Reflecting on mistakes and lessons learned, he stresses the importance of incremental change. “The biggest mistake,” he shares, “is trying to do things quickly for impact when actually the business really isn’t ready for it.” As we approach the end of our allotted time, both with less interesting meetings looming, I take the opportunity to enquire about some of both the surprisingly simple, and deceptively complex, changes that he’s implemented in the past. “Some of the easiest things to do are reviewing terms, whether its discounts, contracts, etcetera; Harmonising price distribution, and categorising your potential tactics by risk so you know where to start.” One of the hardest things to get right, he explains, is truly understanding competitive pricing. Due to a lack of price transparency in the market, people often get uncomfortable extrapolating what limited data they have to make more informed decisions. He recites the common steps he takes: doing a contractual terms review, clearing out loss making products/clients, looking at sales behaviour, challenging them, and setting a strategic direction, then finishes with one final analogy: “Often pricing seems like a chasm that you’re trying to leap, but you don’ have to cross in a standing jump, you can build momentum with a series of small changes and by the end, you might not even realise you’re on the other side.” Chris’s Tople Tips: Focus on value-based selling Get the whole business involved “What are we famous for, what are we known for, what do we also do” Find your advocates in the sales function (80/20) Start at low risk changes, build momentum Make small, incremental steps If you’d like to discuss how you can understand the drivers of customer value to inform pricing strategy for your business, please Contact Us .

Last year, Director of Revenue Operations was one of the fastest growing jobs in the US . Yet here at Coppett Hill we find that many businesses still lack clarity what the role can mean and its potential impact. The true value of Revenue Operations (RevOps) lies in building an efficient, scalable revenue engine by aligning processes, systems, and data across teams, helping address revenue leakage  and deliver seamless customer experiences.     Recently, I spoke with Nik Kumar, Head of Revenue Operations at Mention Me , to explore how RevOps works in practice, the challenges it addresses, and practical advice for building a successful function that gains internal buy-in.   Mention Me is a referral platform that helps marketers identify, acquire, and nurture their best customers through referral programs. To give a sense of the scale of their operations, approximately 2,000 companies engage with their marketing and sales funnel each year, with an additional 15,000-20,000 prospects in their CRM who meet their  ideal client profile (ICP).    Revenue Operations in Practice: engineering efficiency and growth   As Mention Me’s Head of RevOps, Nik works behind the scenes to connect marketing, sales, client services, and finance to streamline the entire client lifecycle, from lead generation to ongoing client management. While marketing and sales can be seen as the production line driving the business forward, Nik likens RevOps to factory engineers: connecting parts, identifying bottlenecks, and flagging issues for resolution. For instance, a drop in lead volume can have significant downstream implications for the output of this ‘factory’. Nik’s role is to anticipate and address these challenges before they disrupt operations: “If our inbound lead numbers are low, we’re going to face an expansion problem nine months down the line. My role is to keep the whole production line moving and predict where it might break before it actually does.”   This proactive approach requires constantly evaluating key questions, such as: How are leads acquired, nurtured, and handed off to sales? How are they handed over to the onboarding team once they become a client? Reflecting on this, Nik emphasises the difficulty of connecting these dots without a unified function overseeing the entire journey: “It’s impossible to provide insight across the entire process unless it’s handled by one function.”   To bring this to life, Nik highlights a couple of recent examples where RevOps has delivered tangible value at Mention Me. “I’ve been working with our Chief Growth Officer (CGO) to improve forecast accuracy,” Nik shares. “We’ve implemented automated visualisations that compare, as a percentage, our committed revenue on any given day of the month with the actual revenue closed by the end of the same month. This insight allows us to adjust our tactical in-month plays if the forecasted figure early in the month isn’t where we want it to be, ensuring we approach deals differently where needed. While forecasting hasn’t been our strong suit in the past, this visual has shown a stable line over the past six months, with our forecasts consistently within ±10% of the end of month value.”  Continuing on the value of predictive insight, Nik describes how his team have also helped address year-one churn, a primary concern for their Head of Client. “We realised we weren’t focussing on the right factors - we were over-forecasting client performance when they joined and not servicing them to the appropriate level. By identifying metrics that signal lagging performance within the first six months, we can now predict churn risks and highlight where actionable improvements are needed.”  These examples highlight the importance of understanding each component of the revenue-generating process while maintaining an end-to-end perspective. By establishing systems to flag potential issues before they arise, an effective RevOps function creates more proactive processes for identifying bottlenecks in customer acquisition and retention. “At which point,” Nik notes, “it’s about getting the right people in a room, presenting the issue, and explaining how they can solve it.”  Example analysis: Comparing the timeline of the sales process, from marketing enquiry to sales handover, between competitors  RevOps Challenges: Aligning Teams and Processes for Success   One of the biggest challenges in RevOps is managing competing goals across teams. “Misaligned targets – such as marketing focusing only on MQLs and sales only on closed-won deals – often lead to disagreements about source ownership and a lack of responsibility during deals,” Nik explains. This misalignment not only hinders collaboration but also makes it difficult to trace what went wrong in pivotal deals and identify areas for improvement. At Coppett Hill, we recommend that marketing and sales plans are aligned with companywide business development goals, such as new logos, retention and expansion, or ensuring the success of M&A integration. Clearly defining roles and responsibilities across teams, while uniting them around shared outcomes, is fundamental to successfully executing a go-to-market strategy .  RevOps can play a key role in achieving this alignment by providing teams with the tools and insights they need to collaborate effectively. Reflecting on one of his first RevOps successes, Nik shares: “Our sales team can now open an opportunity and see everything that’s happened with a prospect they’re working with. Having all the inbound activity, event participation, and interactions in one place provides invaluable talking points for building relationships and nurturing the deal. Having worked in account management for five years, I know how important that whole-picture view is – not just for closing deals but for the entire sales cycle.”   Insights like these do more than streamline processes – they also foster buy-in from teams by demonstrating tangible improvements to workflows and outcomes. When teams see how RevOps enhances efficiency and supports their goals, alignment becomes more achievable.   Building a RevOps Function: Where to Start and What to Focus On   Having discussed the challenges of misaligned goals in RevOps, Nik emphasises that the first step in building a successful function is addressing inefficiencies in core operations. “Things will often be more misaligned than they appear, even if you believe they are functioning well,” he explains. For example, when Nik joined Mention Me, opportunity generation was managed on spreadsheets, with a time-to-action of three days, and little accountability for lead follow-up. This left plenty of time for competitors to engage with leads first, making it an immediate priority. “By implementing the relevant processes, everything became more seamless and efficient, and we saw almost instant results, with more opportunities being generated.”   A key takeaway from our discussion was the focus on processes over tools. Nik notes, “You don’t need to be dogmatic about the tools you use. Instead, focus on the cohesion of the underlying processes in which the tools are utilised, understand where each process occurs, and what happens downstream. Once you’ve identified these and the corresponding gaps, you need to look at which opportunities will bring the most ROI when fixed.”   In deciding which areas are best to focus on, Nik recommends asking key questions at every stage of the customer journey:  How do we acquire leads?  What happens to a lead once it’s in the system?  How do we turn it into a potential deal?  What happens across the deal cycle?  How do we close deals and manage onboarding?  How do we manage clients during their lifecycle?  How do we identify and address issues before they impact customers?  Understanding the interdependencies between these stages is crucial. For instance, onboarding clients faster enables you to deliver value more quickly, which in turn simplifies renewal conversations. While implementing an end-to-end perspective may require significant shifts in your business, Nik highlights that “when executed effectively, you will notice improvements after a few months. Reporting, identifying areas for improvement, and targeting efforts become part of the daily routine, rather than sporadic realisations.”  In turn, when hiring for a Head of RevOps, technical expertise is secondary to curiosity and a persistent focus on improvement. “The technical part isn’t the focus; anyone can learn it”, notes Nik. “Instead, the ideal candidate is a problem solver – someone who isn’t afraid to question why things are done a certain way and to challenge the status quo. They need to be comfortable asking difficult questions.” This role requires someone who is constantly looking to fix inefficiencies, uncover opportunities, and make things better. Importantly, they should have the independence to remain impartial across functions, ensuring that their recommendations are rooted in what’s best for the business as a whole. This combination of strategic thinking and operational execution is what sets apart effective RevOps leaders.     Conclusion In providing a unified view of the customer journey – from marketing to sales to customer success – RevOps aligns teams, standardises processes, and integrates data-driven decision-making into daily operations. In today’s economic climate, where attitudes have shifted away from “growth at all costs”, investing in a RevOps function can provide you with a critical value creation lever, delivering insights that enhance both operational execution and strategic performance.   If you’d like to discuss how Re vOps could improve y our go-to-market performance, please contact us .

When I sat down to think about the very first piece to write for CoppettHill.com, Customer Lifetime Value was an obvious choice, as it sits at the centre of so many topics that I want to cover. In fact, most conversations about growth and marketing investment come back to the value of an individual customer or different types of customer – whether that is defining your Ideal Customer Profile (ICP), choosing how much to spend on marketing, or considering how to develop your proposition for the benefit of customers. In simple terms though, the best use of Customer Lifetime Value in my experience is to determine what a business should rationally be prepared to ‘pay’ to acquire a customer. In today’s environment of pressure on marketing & sales budgets and an emphasis on customer retention, this feels like an even more important question to tackle, so let’s jump in. What is Customer Lifetime Value? Customer Lifetime Value is the profit contributed by a unique customer over their lifetime transacting with a business. Or to put it the other way round, the profit a business would lose if a unique customer had never existed. We’ll come back to what ‘lifetime’ means in practice later. This concept has its history in database marketing - think catalogue retailers and credit card providers, those businesses where it was easiest to build a single view of customer transactional behaviour over time in days before the internet. The term ‘Customer Lifetime Value’ was used at least as early as 1988 in ‘Database Marketing’ by Merlin Stone, and first featured in the Havard Business Review in 1989. What I really appreciate about the concept of Customer Lifetime Value is that it has stood the test of time – I recently re-read this article from 1998 (the year Google was founded) it still rings true today. This makes it one of the very few marketing or growth concepts to have made the shift from analogue to digital marketing largely unscathed. I’d argue that it has become even more relevant as marketers have become more data-driven over the past 20 years. How to use Customer Lifetime Value? There are four main uses of Customer Lifetime Value that I see, starting with the most frequent: To calculate ROI on marketing spend, when combined with Cost Per Acquisition (CPA) data; To compare between different customer segments (which can tell you either attributes of customers that make them more attractive to your business and/or groups for whom your proposition is a better fit); To measure the impact of historical business changes over time – seeing how Customer Lifetime Value changed; and To model the potential impact of future business changes – to combine different assumptions and forecast customer profitability scenarios. Whilst LTV is a great concept to embed in both daily decision making and big strategic decisions, I don’t think it is well suited to routine monthly Management/Board reporting. As a lagging, historical measure it is unlikely to move by much month to month, so it is better suited for an annual strategy day, or to be operationalised into marketing decisions e.g. Paid Search bidding for different customer segments or partnership commercial models. How to calculate Customer Lifetime Value? To calculate Customer Lifetime Value, you need to consider all revenues associated with a unique customer, then remove all direct costs, a fair share of variable operating costs, and any reacquisition costs for subsequent transactions. The specifics here will vary by business model and for each customer, but to give some more examples: Revenue – this should include both the main transactional revenue from a customer, but also any ancilliaries or one-time income, for example cancellation insurance added to a holiday booking, or one-off implementation fees associated with a SaaS subscription. Don’t forget to also allow for discounts offered to customers – only count the true revenue received. Direct costs – the best way to think about this is your gross margin – either the costs of physical goods or services, as well as staff costs allocated to a specific transaction. Don’t forget to also include the costs associated with ancillaries or one-time income, as well as things like bank fees/payment processing, logistics, insurance, returns etc. Fair share of variable operating costs – these are costs that you might not allocate to specific customers on a day-to-day basis, but which broadly correlate to the number of customers you are serving – for example Customer Service or Support teams. This is the one where there is normally the most debate about what to include in an LTV analysis. Reacquisition costs – some repeat transactions will have additional marketing or sales costs, for example the staff cost to secure a renewal in a SaaS business, or a price comparison website commission fee for an insurer. Getting hold of the data put this analysis together takes time, in my experience it is normally easier in B2C than B2B businesses as you will typically already have access to customer-level revenue data. You may have to be creative - I’ve had to use invoice level data from finance systems or stitch customer data together from multiple sources - but I've always managed to find the right information in the end. Some of the inputs into a Customer Lifetime Value calculation will be at unique customer level (normally revenue data), for others you will need to make assumptions for segments of customers or for everyone (normally cost data). There are many tools available that claim to have some version of Customer Lifetime Value analysis available ‘out of the box’, but I prefer to start by calculating it directly. There will always be limits to analytical capabilities with a set of pre-configured reports/dashboards, and most will make at least one of the common mistakes I talk about later on. What does ‘Lifetime’ really mean? Every customer’s lifetime with your business will be different – and just because they may have stopped transacting with you for now, doesn’t mean they will never come back. To get round this dilemma, I use the concept of a ‘lifetime window’ in my Customer Lifetime Value analysis. This is a standard period of time, often 3 or 5 years, from the first transaction with a customer. It allows for standardised analysis and comparison between unique customers or customer segments. Determining which time period to use for the ‘lifetime window’ isn’t an exact science, but is a trade-off between the length of the window and how many customers will be eligible for the analysis. If we set a 5 year ‘lifetime window’, our historical analysis won’t include customers acquired less than 5 years ago. You should only decide this once you’ve assembled your historical data – and is why should always build the longest time-series of data as possible, within reason. This makes historical Customer Lifetime Value analysis particularly challenging for new businesses. In these situations I’ve used a much shorter window, sometimes 12 months or less. You may have seen examples of Customer Lifetime Value analysis which use a method of dividing annual revenue by an expected annual churn rate, sometimes also with a discount factor. Whilst this often produces very high estimates of Customer Lifetime Value (great when talking to potential investors), I’d always stick to using actual, historical behaviour if you are trying to make strategic choices. The pattern of revenue will vary based on your business model, or potentially within your business – is your product/service an annual purchase, a frequent purchase or a subscription? Some businesses may even only transact once with the vast majority of customers (think divorce lawyers or funeral directors!). Using a ‘lifetime window’ will help to standardise any analysis. What is a good Customer Lifetime Value? The answer is clearly ‘it depends’. This will entirely depend on your business, and I wouldn’t advocate using Customer Lifetime Value as a benchmark metric in isolation. There are some obvious rules of thumb however – within a niche of comparable propositions, you will see higher lifetime value for those businesses with (i) better margins, (ii) better repeat rates, and (iii) better ability to up-sell/cross-sell to customers. What segments should I consider when analysing Customer Lifetime Value? One of the most powerful questions you can answer with Customer Lifetime Value analysis is “Who are our most valuable customers?”. To answer this, you can analyse the relative LTV of different segments based on different customer-level dimensions. These could be ‘attributes’ such as age, location or industry vertical; or ‘behavioural’ such as what the customer purchased first or which marketing channel they came from. This is a process of elimination, test many different dimensions and narrow down to the ones that make a difference. When you find the combination of dimensions that allows you to create a segmentation that balances the best spread of LTV vs equal distribution of customers, you can start to operationalise this. This could be with just one characteristic, for example risk type in an insurance business, or a combination of 2 or more dimensions. It is best to not over-complicate your segmentation at first as it will be harder for your stakeholders to understand and then hard to operationalise. Make sure that you always pay attention to any outliers in your analysis - very low or loss making customers, or super profitable customers. These can lead you either to great insights or bugs in your analysis that need fixing, and sometimes both! What are the common mistakes with using Customer Lifetime Value? This isn’t an exhaustive list, but there here are five of the most common mistakes I’ve seen when reviewing Customer Lifetime Value metrics: Only considering revenue – the most common mistake, where LTV is stated at revenue rather than profit level. This can lead to poor decision making and ultimately value erosion. Ignoring reacquisition costs – repeat purchases from customers will often carry additional costs, sometimes very significant ones, for example in businesses which spend significantly on advertising. Did the customer repeat purchase because they were loyal or because they saw your advert again when searching generically online? Not factoring in customer service costs – in some business models, there is a significant amount of staff cost required to service existing customers, which can be ignored in LTV analysis. There is some subjectivity on where to draw the line, but a share of the cost of large teams such as Customer Service or Support should be factored into your analysis Ignoring changes in a business over the historical period e.g. the introduction of new revenue streams or a major change in pricing. This can complicate analysis, but if you want to use this metric to make choices today about the future, you should make adjustments to historical data to best reflect the future value of customers you have acquired today. In practice that might mean re-stating historical revenue for some customers. Using Customer Lifetime Value to make decisions in isolation . You aren’t seeing the whole picture if you do this – for example you could have a great picture on “Who are our most valuable customers”, but they could represent only a tiny proportion of your potentially addressable market, or have a very low conversion rate vs other customer segments. Make sure to combine LTV analysis with market size and conversion rate data. How to increase Customer Lifetime Value? I’ve written a separate piece about this which you can find here . If you’d like to discuss how you can better understand and use Customer Lifetime Value in your business, please Contact Me . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

It’s the obvious follow-on from any analysis of Customer Lifetime Value (CLV or LTV) – ‘that’s great, but how can we increase LTV’? It’s actually a great question, as it will force you to think about growth from a customer perspective – and in my experience has led to some of the highest quality discussions around the board table. It is a key part of establishing your own marketing flywheel . There are of course many different ways to increase Customer Lifetime Value, but I wanted to offer my top five. These are inevitably quite generic, but think of these as conversation starters for you to adapt to your own business. As with most choices about strategic growth, you won’t be able to tackle many of them at once, so be sure to prioritise based on potential impact on LTV and expected effort. Five ways to increase Customer Lifetime Value 1. Change the customer mix: if you’ve analysed LTV for different segments of your customer base, you will have some insights about which types of customers are worth more to your business. This segmentation might be based on ‘attributes’ such as age, location or industry vertical; or ‘behaviours’ such as what the customer purchased first or which marketing channel they came from. You can then start to adapt your go-to-market efforts to attract more of the higher LTV efforts – by changing your marketing mix, messaging or perhaps offering discounts. For example, I’ve worked with a travel business which saw that customers who booked larger properties for their first booking had a higher LTV, as they would typically continue to book larger properties on subsequent trips. They started to spend more on search terms which attracted extended family/group bookings as a result of this insight. 2. Develop up-sell and cross-sell opportunities: consider how to develop additional revenue opportunities with your customers – could be ancillary add-ons like premium delivery and insurance/cancellation products in the B2C world, or enhanced service levels and additional features in SaaS models. Often these are also higher margin than the core product or service offering so have disproportionate impact on Customer Lifetime Value. 3. Pricing optimisation: as the saying goes; ‘some of your customers would have paid more, the challenge is working out which ones’. Although it is getting more attention in the current macro-economic environment, in my experience pricing is one of the most under-used value creation levers. When it comes to increasing Customer Lifetime Value, pricing analysis can be used to design different packages for customer use cases, or to incentivise repeat purchasing behaviour through discounting. You should also consider the role of regular price increases in your business. It is also worth examining the highest value customers that your LTV analysis identifies, as this can often lead to opportunities for different proposition/pricing models – for example business customers using a B2C platform. 4. Reduce cost to serve: the process of allocating both direct costs and a fair share of variable costs to unique customers as part of LTV analysis can offer valuable insights about the efficiency of how you deliver your proposition. For example, I’ve worked with a SaaS business that saw a disproportionate number of support cases (and resultant costs) from one part of their product suite. By changing how customers are onboarded, and improving the quality of support documentation, they were able to reduce support calls and increase LTV. 5. Improve customer retention or repeat purchasing behaviour: whilst some of the ideas above might also improve customer satisfaction and retention, I’ve always found it incredibly helpful to focus directly on the reasons why customers churn or fail to repeat. This exercise requires a lot of primary research with customers, analysing reviews and listening back to support calls. One shortcut is that in my experience, Net Promoter Score is (unsurprisingly) well correlated with propensity to repeat. For example in a travel business I worked with, customers rating their likelihood to recommend a business as 9 or 10 were 3x as likely to repeat book than those rating 6 or below. This insight provided both motivation to focus on the drivers of dissatisfaction but also allowed the Management team to quantify the impact of customer service improvements on Customer Lifetime Value. And finally… Don’t forget that increasing Customer Lifetime Value might not be the best place to spend your time and money right now. The most obvious growth lever to pull next might just be more customers through better conversion. There can also be negative impacts on conversion of changes to LTV, for example imagine what would happen if you doubled prices. If you’d like to discuss how you can increase Customer Lifetime Value in your business, please Contact Me . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

The concept of Cost Per Acquisition (CPA) or Customer Acquisition Cost (CAC) seems incredibly simple – but there is often more to it than meets the eye. What is the difference between CPA and CAC? Nothing! These terms are used interchangeably. For simplicity I’m going to just talk about Cost Per Acquisition (CPA). What is CPA? We can define Cost Per Acquisition as the total costs associated with customer acquisition divided by the number of new customers in any given time period. One important consideration is whether you are calculating for all transactions or customers, or differentiating between costs associated with first time customer acquisition and reacquisition/retention marketing. This will be more important in some business models (eg occasionally repeated purchases like travel or clothing) than others (like subscription products). Splitting costs in this way can be tricky but is important if you want to compare your (new customer) CPA with Customer Lifetime Value . What to include when calculating CPA Each business I’ve worked with has calculated CPA in a different way – and it is key to understand what is included/excluded before drawing any conclusions from trends or comparisons. In general, you should include all costs associated with customer acquisition, which will vary based on your business model but might include: Media costs – both digital (like Paid Search or Paid Social spend) and traditional media (like TV or events). Agency costs – all of the various agencies you may work with from digital agencies, creative agencies through to PR and translation. Technology – you should be factoring in the costs of technology that plays a role in your customer journey – your website, ecommerce platform, email or marketing automation tool, CRM, conversion rate optimisation tool, ad serving, bidding and analytics platforms. Partners / affiliates – this could range from channel partners, marketplaces, intermediaries through to influencers and ambassadors. Content creation – the cost of producing content – copywriters, imagery, video production Personnel – the fully loaded costs of your marketing and sales teams, including the value of any commission based incentives. An important aspect of CPA is that it is an aggregate measure, typically analysed at a segment level which corresponds with how you make decisions on marketing costs, for example for a particular product/service or single marketing channel. Revenue allocation between channels will typically come from your attribution model. You will likely have to make some assumptions about how to allocate costs between these segments, but using common sense and following a simple volume or revenue based allocation will normally suffice. It isn’t particularly helpful to calculate CPA for a specific customer as this look artificially low as it ignores the ‘wasted’ spend on customers who didn’t convert, but which is an unavoidable consequence marketing activity. Some costs make more sense to factor into Customer Lifetime Value than CPA as they are more related to the transaction or product/service than the acquisition itself, for example bank fees. As a rule of thumb, only include costs associated with getting to the point of transaction in your CPA – anything directly associated with the transaction should be captured in Customer Lifetime Value. One of the most powerful uses of your CPA metrics is the LTV : CPA ratio, which I’ll cover in a separate article soon. How to reduce Cost Per Acquisition (CPA) / Customer Acquisition Cost (CAC) There are three levers to consider which can help you to reduce CPA: Change the mix: any analysis of marketing channel performance will show you where you have the highest CPA, potentially unprofitable at a customer level. Reducing your spend in this area is the simplest way to reduce overall CPA. Lower the cost per lead (or click): examine where your leads / website traffic is coming from and how you might be able to increase cost efficiency at a channel level. Common tactics I’ve used are increasing your Quality Score in Google Adwords, or renegotiating partner/affiliate agreements. Increase your conversion rate: often the most effective lever to reduce CPA, use a test and learn approach to improve each stage of your customer journey, both online and offline. This process is often best informed by conducting primary research with your customers (and ideally lost prospects) to understand where they found points of friction in your customer journey. One of the most common tactics is to increase the speed with which you respond to inbound enquiries, which I’ve always found to be highly correlated with conversion rate. Lower your cost to convert: this is particularly relevant if you have sales teams, for example shortening your sales cycle, reducing the number of interactions or using automation to encourage more self-service. For example, I’ve worked with an insurance business which progressively built out their online journeys to reduce the number of telephone calls and consequently reduced cost to convert. Although most businesses will be able to use these levers to reduce CPA, most Management teams will care just as much (or even more) about driving growth. It is very difficult to pursue both growth and marketing efficiency, even though I’ve seen many business plans promising both. The most successful businesses I’ve worked with have been able to balance out efforts to reduce CPA with driving growth – consider carefully what assumptions you use in your business plan. The reason for this is that as you grow your marketing budget, you will typically see diminishing returns – in other words, the more leads you try to drive, the higher the cost per lead. There are two drivers of this: Many paid channels such as Google Adwords operate a bidding model, to secure more traffic you need to place a higher bid. If you’ve fully optimised your CPA, to grow leads you will need to start looking at more expensive ways of generating traffic or leads, e.g. starting to run paid digital marketing if you’re not already doing so, launching in countries with lower conversion rates. The most successful businesses I’ve worked with have been able to balance out efforts to reduce CPA with driving growth – consider carefully what assumptions you use in your business plan. Cost Per Acquisition may seem like a simple metric, but spending time analysing how it is calculated and how it can be optimised is a key part of growth acceleration. It is a key part of creating your own marketing flywheel . If you’d like to discuss how you can better understand and use Cost Per Acquistion in your business, please Contact Me . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

The ratio of Customer Lifetime Value (LTV) to Cost Per Acquisition (CPA or CAC) is one of the most important commercial metrics for any business. It represents the fundamental unit economics of customer acquisition and how efficiently a business is able to grow. In simple terms it is the return on investment (ROI) of marketing spend. In spite of this, in my experience most Management teams of small and mid-market businesses have never calculated it. You can understand why, as there is often a lot of setup analysis involved. As a task, this almost always falls into the 'important but not urgent' category and struggles to get to the top of your to-do list. Instead, marketing efficiency is calculated using CPA on its own, or perhaps by looking at marketing spend as a % of revenue. However, once you step in the investment world, the LTV:CPA ratio is favoured by bankers and private equity investors, often having a prominent position in Management presentations and sales documents. This is because it is both easy to understand for non-marketers and highly comparable both within and between market segments. For investors looking to assess future growth potential, a lot is inferred from the LTV:CPA ratio, making it important for all Management teams to understand well ahead of any investment process. Needless to say, the first step is to ensure that both input metrics are calculated comprehensively, I’ve written previously about how to do this for Customer Lifetime Value and Cost Per Acquisition . For any investor, I would also recommend probing the basis of each metric rather than taking a quoted LTV:CPA ratio at face value. In my experience, when this ratio is calculated as part of an investment process, there are normally some shortcuts taken which unsurprisingly can result in an overstated ratio (for example, using considering customer revenue rather than customer profitability for LTV). I've compared some real-world LTV to CPA ratio examples in another post to help you do this. The LTV:CPA ratio will tell you the ROI of marketing spend in the time period over which Customer Lifetime Value is calculated, normally 3 or 5 years. One alternative metric which uses the same inputs is the Payback Period, normally quoted as the number of months it takes for a customer to generate profit equal to the initial CPA. It is helpful to consider both metrics so that you understand the ‘J-Curve’ of customer acquisition - how long a business will be ‘out of pocket’ at both profit and cashflow levels after spending to acquire a customer. Whilst most Management teams are happy to invest to accelerate growth, there is often a constraint on cashflow or a minimal level of in-year profitability required. What is a good LTV:CPA ratio? As described in my introduction to Cost Per Acquisition , CPA is a metric which will typically increase as a business seeks to drive more demand (i.e. you will see diminishing returns) - you could think of it like a supply curve. This means that the LTV:CPA ratio will narrow as a business grows faster, all things being equal. We therefore need to consider the LTV:CPA ratio in combination with growth rate. For businesses experiencing good double digit annual growth, say 20-50%, I’ve seen 5-year LTV:CPA ratios mostly in the 3:1 to 5:1 range. If the ratio is above this level, there is normally potential to accelerate growth by investing more in marketing. If the ratio is at the bottom end of this range or even narrower, this is often an indication of a very competitive market (e.g. travel or personal lines insurance), and/or an early stage business with lots of scope to optimise conversion and Customer Lifetime Value. This could also indicate some inefficiency in marketing spend which could be addressed to improve the LTV:CPA ratio. How to use the LTV:CPA ratio The LTV:CPA ratio can be set as a target by Management teams, and used to optimise marketing spend both between and within channels. It allows boards and Management teams to trade off short-term vs long-term profitability by adjusting the level of marketing investment and consequent growth rate (I’m going to talk about this in more depth in an upcoming piece). Using the ratio in this way is a key indicator that your marketing function is operating as a profit centre rather than a cost centre . For example, if a business is working to a target LTV:CPA ratio of 4:1, the marketing and sales teams can optimise their activities to this level of ROI, with the growth rate will change as a consequence of these changes. One very important watch-out is that this ROI ratio should be implemented as a minimum ratio rather than an average. Using an average can mask a lot of inefficient marketing spend. Over time you can also apply your ROI target with increasing granularity. For example, if you are running Paid Search (PPC) activity, you might start using the target at a campaign level, then move on to ad group and ultimately at keyword level. It is also important to make sure the input Customer Lifetime Value is frequently updated for any changes in business model or customer behaviour – for example if additional marketing investment starts to generate customers with a lower LTV this would be an important consideration. If you’d like to discuss how you can better understand and use the LTV:CPA ratio in your business, please Contact Me . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

It goes without saying that being able to efficiently acquire new customers is integral to long-term profitable growth. But what if it your capability to acquire new customers became a source of sustainable competitive advantage in the same way that superior scale, proprietary data, long term customer relationships or hard to get accreditations are? Sustainable competitive advantage is typically defined as the ability to outperform competitors in a way that is very hard for competitors to replicate. In my experience, businesses in a variety of markets have been able to create sustainable competitive advantage through leveraging the marketing flywheel effect. Building such an advantage is key part of your marketing function operating as a profit centre rather than a cost centre . What is the marketing flywheel To turn your customer acquisition efforts into a source of sustainable competitive advantage, you need to a virtuous cycle of customer acquisition, also known as a marketing flywheel because of its potential multiplier effect on growth. There are four components to this flywheel, which follow the customer lifecycle: 1. Generate demand from wide variety of sources 2. Optimise cost and quality of demand 3. Maximise conversion / yield of demand 4. Understand and maximise customer lifetime value To create the virtuous cycle, or flywheel effect, you will need to drive a process of continuous improvement in each of these four areas. The result is that you will be able to be able to spend more per unit of demand that your competitors because you have confidence in making a higher return on this marketing investment. This investment might be in the form of media spend, partner funding or potential even discounts. For example, a foodservice concession operator could be prepared to offer the best terms to a landlord as you have confidence in making a higher yield than any of your competitors. As a business starts to develop this flywheel effect, they benefit from seeing even more data to optimise against and reinforce the initial advantage. Think of this as like an experience curve – the more you do something, the easier and better you do it. When I started leading marketing efforts in the car rental sector in 2014, my big competitor was part of Priceline Group, and they were consistently appearing in the top position in paid search. To outbid them, I realised I would have to increase my bids by at least 3x – which would have been deeply unprofitable at that point. As I started to learn more, I realised that my competitor was applying learnings from their Priceline stable-mate, Booking.com – one of the most well known examples of the marketing flywheel, using constant experimentation (sometimes more than 1,000 experiments at once) to drive improvements to conversion and yield. Their approach has been documented in a great HBS case I’d recommend reading, or a helpful summary here . I spent the next three years creating our own marketing flywheel to close the gap and allowing us to compete effectively. How to create a marketing flywheel in your business There are many ways you can start to drive optimisations in each of the four stages of the marketing flywheel, and I’ve suggested a few ideas to get you started. As the Booking.com example highlights, what matters is that you test many different ideas, take the learnings and evolve continuously. Make sure that when designing a test, you will come up with a definitive answer – ‘this maybe works’ is an unhelpful outcome. 1. Generate demand from wide variety of sources Finding the most effective channels to reach your target audience – in your particular market there will be many different options for how to reach your prospective customers – digital marketing, partnerships, events, above the line, outbound lead generation. Understanding your headroom for growth in different channels is an important input here, for example completing a Search Headroom analysis in organic search. Adopt a systematic approach to testing each channel – at enough scale that you will understand the incremental impact on your marketing outcomes. 2. Optimise cost and quality of demand I’ve talked about this subject in the context of Cost Per Acquisition , which I would suggest reading. Within each marketing channel, test all of the variables you can control – the targeting, creatives, copy, and landing pages for digital marketing; the content and format of events, or the commercial model in partnerships. Improve the accuracy of your measurement and attribution – for example I’ve worked with an online travel business that generated significant competitive advantage from having the best mobile device attribution model. 3. Maximise conversion / yield of demand Examine every aspect of your marketing journey from the customer perspective to remove frictions and reinforce your value proposition. Do this for both the online and offline parts of your journey, e.g. consider for a SaaS business consider whether a self-serve or assisted sales motion is most effective. I’ve always found mystery shopping your own product or service produces a long list of potential improvements. Pricing optimisation – review both your approach to packaging and headline pricing. I’m going to cover this in more depth in a future article. 4. Understand and maximise customer lifetime value I’ve talked previously about how to calculate and improve Customer Lifetime Value. The key is that you need to have enough confidence to use your calculation of LTV as the basis for your decisions about marketing investment i.e. using LTV:CPA ROI . If you miss this critical step, you are unlikely to be able to create that competitive advantage as someone else in your market will probably be thinking this way. You will likely uncover which customer segments offer the right balance of both superior lifetime value and ability to target in large numbers through your marketing efforts. If you are wondering where to start developing the marketing flywheel in your business, you could ask yourself: · If you cleared your diary for tomorrow, what would you spend your time on? · What part of the flywheel do you know the least about in your own business? · Where have you spent the least time to date? · Where is the biggest bottleneck in your customer acquisition efforts? · How do you benchmark vs your competitors in each of the four stages? It is important to remember that even if you create competitive advantage through superior customer acquisition, you should never get complacent. Your know-how will leave each time a member of your marketing team moves onto a new role in a different, competing organisation – as evidenced by the number of travel start-ups now led by Booking.com alumni. ‘Sustainable’ advantage does not mean ‘permanent’. If you’d like to discuss how you can create a marketing flywheel in your business, please Contact Me . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

What is Marketing Attribution? Marketing attribution is the process of determining how different marketing activities contribute to customer acquisition and retention. It plays a critical role in measuring the effectiveness of marketing efforts and helps businesses to optimise their budgets and strategies for maximum return on investment (ROI) and minimum waste. Purchase journeys starting today are more likely than not to start online, whether they are B2C or B2B (the stats are from 2018 and I think it safe to assume that the %s would be even higher today). They are also incredibly complex, with Google claiming that the average purchase journey has between 20 and 500 touchpoints. Regardless of the specific number of touchpoints, it is easy to understand how they can quickly accrue - customers interact with brands through a multitude of channels, such as social media, search engines, review sites, email campaigns and in person at stores or events. These interactions can occur across multiple devices and may span several days or even weeks. Furthermore, in complicated B2B purchases, multiple individuals within a purchaser’s organisation might be involved in the decision-making process over many months. As a result, a Chief Marketing Officer must understand the cause-and-effect relationship between their marketing initiatives and customer behaviour. Without a consistent approach to attribution, you can find every channel coming up with their own ‘measures of success’, often provided by media owners – which understandably tend to overstate performance. This is an important step in creating sustainable competitive advantage through adopting a ‘test and learn’ approach to customer acquisition, something I’ve talked about previously . It is also a key step in shifting from marketing operating as a cost centre to a profit centre, with the ability to measure and improve Return on Investment (ROI). How to create a Marketing Attribution model? The topic of attribution ‘modelling’ can provoke a host of reactions from marketers, often based on their own flavour of marketing – some think that attribution modelling systematically undervalues top-of-funnel brand marketing, others will obsess on the precise allocation of credit between different touchpoints to maximise the accuracy of their models. My take is that the clue is in the name, an attribution model is just a model – a tool that should help you make better commercial decisions. It will almost certainly be wrong in some respects – you are trying to get as close as you can to replicating real-world customer behaviour, but it is unrealistic to expect to achieve 100% accuracy. My main suggestion to anyone spending time on attribution is to not let perfect be the enemy of good enough – this is something you should iterate over time. One important qualifier before you invest meaningful time in analysing attribution – ask yourself whether you have a high enough volume of customers that you couldn’t just interview each customer to understand why they purchased (for example, if you only have 10 enterprise clients, this could be a better approach). Regardless of whether you go down the attribution modelling route, it is always interesting to ask your customers how they heard about you, but this isn’t a substitute for a robust attribution model. I follow four steps to build an attribution model, and I’ll talk through each in turn: Building a base dataset for each unique prospect journey; Applying a model to ‘attribute’ credit between each touchpoint in a unique prospect journey; Start making decisions based on your model and test if it increases ROI & growth; and Supplement your model with additional measurement techniques if needed e.g. incrementality testing and/or marketing mix (econometric) modelling. 1. How to build an attribution dataset? The cornerstone of your attribution model is to create a proprietary dataset of unique prospect journeys. Think of this as a data table where each row represents a touchpoint between a potential customer and your business, sorted in order from first to last. In the columns are a variety of details about each touchpoint for example a date-time stamp, the type of interaction, details of the device and browser for website visits, a unique identifier such as GCLID from Google Analytics, traffic source details and any flags related to your customer journey e.g., whether the individual logged into your website. The types of touchpoints you can include at an individual user / prospect level will vary based on your current marketing activities and your business model, but might include: Online interactions: Website visits Formfills and content downloads on platforms such as LinkedIn/Meta Email opens Mobile app downloads / sign-ins Display ad views Offline interactions: Outbound calls attempted/completed Inbound calls (for example using specialist call tracking software) Booked sales calls & demos Event attendance Partner referrals In-store purchases Some types of touchpoints are very difficult or impossible to track at an individual user/prospect level – and you may be better to assess their impact through a different approach such as incrementality testing, which I’ll talk about later in this post. I think it is incredibly valuable to build this dataset for yourself. Relying on third party tools can limit your ability to inspect the underlying data. Being able to explore this dataset for yourself is powerful – to prompt questions and to check that the results make sense. Relying on channel-level attribution exposes you to the challenge that media owners often overstate their own impact. Just try adding up the ‘conversions’ claimed by Google Ads, LinkedIn, and Meta for example – you will get a number that is often greater than your total actual conversions. I’m not going to get too technical in how you go about doing this as the specifics will depend on your business model and existing data stack – I’m happy to discuss if you want to contact me. However, the default I follow is to start with exporting data from Google Analytics for ecommerce / B2C businesses and combining marketing automation and CRM data for B2B business. The tools you need to do this e.g., csv downloads vs APIs, excel vs SQL/Python, will depend on the size of your business and nature of your customer journey. An important step for any advertiser is to be able to join marketing and customer journey data to sales data / conversions at a customer level. You will need to use a common key or unique identifier to do this (for example collecting Google Analytics Client ID – GCLID – at point of conversion into your own systems such as a CRM). This join allows you to understand the value of a specific conversion more precisely at a profit level (or even better customer lifetime value prediction). Now some of your touchpoints will contain personal identifiers such as an email address or perhaps your own unique identifier that allow you to join them together. Others will effectively be ‘anonymous’ so you will need to look for other ways to combine them. The main approach I use is to use GCLID which for most online visits will be persistent for any given device over time. This is placed in a cookie by GA so has been somewhat impacted by changes to cookie consent, but provided your Google Analytics is configured server-side will survive the upcoming deprecation of third-party cookies. This approach doesn’t help to join different devices that belong to a single user however – for this I will look for any combinations of GCLID with your own unique identifiers or personal information to ‘join’ two or more GCLIDs together. For example, I supported an ecommerce business to track GCLID from their email click-throughs and ‘my account’ visits, which had a high mix of mobile visits. By combining with their own unique identifiers, they could then join mobile and desktop sessions in the purchase journey. For B2B journeys where there are potentially many individuals involved, I will often use email domain to group individuals from the same prospect. You might also be able allocate individuals to accounts within your marketing automation platform and/or CRM system. When joining together touchpoints into journeys you will need to think about some rules such as – ‘how long should I allow between touchpoints before we are really looking at a new journey?’. The answer to this should be common sense based on your product/service and typical sales cycle – 28 days is too long for grocery delivery, too short for enterprise software and probably just right for high-value holidays. 2. How to define an attribution model? Now you’ve got your list of interactions for each journey, you need to decide how to allocate the ‘value’ of each conversion against the contributing touchpoints. There are a few standard models that you will probably have heard of: First touch/click – all the credit is allocated to very first touchpoint in a journey, a proxy for how the customer heard about you in the first place. To me this normally offers the right balance of common sense and simplicity. Last touch/click – all the credit is allocated to the very last touchpoint in a journey. The default for several tracking tools such as Google Analytics. Tends to overstate the value of both navigational channels such as branded Paid Search and affiliate channels such as discount sites. Decay & divide evenly – less common but basically credit is divided across multiple touchpoints, with decay rewarding the most recent touchpoints and divide evenly doing exactly what the name suggests. I think these are arbitrary and have never used them. ‘Data driven’ - a term you will hear a lot, as the new GA4 will default to ‘data-driven’ attribution. This will mean something different in every context but beware any black box where the rules or model principles are not explained. I would personally steer clear of this unless you are able to get a very clear understanding of what any ‘data-driven’ model is trying to do. Which to choose? Well, the real answer is that you should test all of them to understand which most closely matches the true incremental impact of each aspect of your marketing activity. But that isn’t really a very practical approach! My advice is to start by keeping it as simple as possible. I’ve seen lots of people agonise over this decision and often 80% right is good enough. My default is first touch/click – it makes the most sense to me and will often skew attribution in a logical way towards upper funnel and generic marketing activity. In a recent example of a model that I built in the travel sector, non-brand Paid Search saw 60% more profit allocated to it with a first touch/click model compared to a last touch/click model. I should say that adopting a first touch/click model does not mean that the channels, content and creative that sit in the middle and the latter stages of the customer journey are not contributing – in fact they are normally critical to successful conversion. However – allocating significantly more of your budget to these activities is unlikely to attract brand new prospects to your business, notwithstanding the point I’ve made before about the role of continuous improvement throughout your customer journey. The right answer will also differ for each business – when I worked in the super competitive travel industry, I adopted a custom data-driven model where I was able to understand the underlying principles – but for many of the SMEs I worked with as an investor, this would have been massive overkill. 3. How to use an attribution model? So, you now have a model that gives you a view of marketing spend and ROI at a channel, campaign, and potentially keyword/creative level. The first step in using your new attribution model is introducing it anywhere you are making decisions about marketing spend – whether big picture strategic discussions around the board table, or granular bidding decisions in performance marketing channels such as Paid Search or LinkedIn. You should start to see whether it leads to (a) better quality discussions and (b) higher ROI and profitable growth. A good place to start is to focus on anywhere that your attribution model suggests is losing you money: whether that is whole channels, campaigns, or individual keywords with very low ROI. One thing I always look at is search terms which haven’t generated any revenue for a while e.g., the last 3/6/12 months. You can initially be more confident when using the results of your model in respect of digital, click-based media – you may need to supplement with some of the additional analysis I explain below for offline and impression-based media. You should adopt the same ‘test and learn’ approach with your attribution model as you would do with other aspects of your marketing and customer journey. Make some changes and monitor the results carefully. Did the results match what your attribution model suggested? Don’t be afraid to make changes if not. Finally, it is imperative to communicate to your stakeholders. Many times, I’ve seen a marketing team start to measure performance in a new way and a finance team continue to use the old approach, as they don’t understand the reasons for the change and are not confident in its rigour. This is another one of the many reasons to keep things simple and ‘auditable’. 4. How to supplement your attribution model? Depending on the nature of your marketing activity and your customer journey, you may be able to refine your attribution model with some additional data sources and analytical techniques, for example: Incrementality testing : for marketing activities which you cannot measure directly, but which you would expect to generate an immediate response from your target audience (e.g., Paid Social, Direct Mail, TV/Radio in some cases). The approach involves defining target segments who will receive the advertising and comparing the outcomes of the following days/weeks against a control group who were not. These groups are often defined geographically (e.g., only in the South West, or only in certain postcodes), but you can really use any criteria where you are confident in achieving a robust control group and where your tracking will allow you to analyse the results (e.g., don’t segment on postcodes if your customer journey doesn’t collect postcodes). You need to make sure your target audience is exposed enough to the advertising so be sure to allocate sufficient budget and narrow your target audience if necessary. Marketing mix modelling: an econometric modelling technique for larger advertisers and those who don’t ‘own’ the customer journey such as FMCG brands. This approach looks for correlations between marketing spend and growth in traffic/leads/conversions. This approach does not rely on understanding individual customer journeys so in my experience tends to produce an aggregated view of channel ROI rather than super granular results e.g., at keyword or creative level. One of the limitations to keep in mind with any attribution model is that it will skew towards short-term marketing activities which drive an immediate response – marketing mix modelling can offer a longer-term perspective. I’m planning to cover this in more depth in a future post, as it is something I’ve had less personal exposure to given the businesses I’ve worked with. Hold-out area: this may sound like a simple one, but if possible, I always try to keep a geographic area exposed to as little paid advertising as possible – perhaps a city/county/state. Like the incrementality test, it is a helpful way to understand the impact of organic channels such as SEO and word of mouth referrals. Primary research: asking your customers ‘how did you start the process of researching your purchase’ and ‘where did you first hear about us’ is a very worthwhile exercise. An attribution dataset can never capture the importance of word-of-mouth referrals and the reputation of your business. Don’t assume customers will have perfect recall, but qualitative interviews can be insightful alongside an attribution model. Across the many times I’ve run this type of research, for both B2B and B2C advertisers, word-of-mouth is invariably the single most common source of leads, accounting for up to 30% of purchases. Some conclusions In case you haven’t realised by now, building a robust attribution model is a never-ending process. There will always be ways you bring in additional datapoints, your marketing activities will be constantly changing, as will your customer journey. There are two principles that I try to keep in mind: 1. Attribution can never be perfect – it just must be good enough to make decisions each that will help you improve marketing efficiency. Given this, getting a first version of your model up and running then quickly iterating will create more value than spending months obsessing about a vastly complex data-driven attribution model, or joining every single touchpoint in a long enterprise sales journey together. 2. You are trying to understand human behaviour and your ability to influence it through data – so always ask yourself whether what you are seeing makes sense. All those journeys that start with a ‘Direct’ traffic source weren’t just people waking up one morning and spontaneously typing your website address into their browser. Make sure you can explain the human behaviour behind what your attribution data is telling you. There are several tech providers out there who can help with some aspects of building an attribution model – I appreciate that the approach set out here may seem quite technical or require access to development resource that you don’t have. However, understanding the analytical process end-to-end is still important so that you can see both the strengths and limitations of any tech solution you may consider. In a future post I’ll share some views on the providers that I’m aware of. Finally, if you need motivation to tackle your own attribution challenges, when we put in place an attribution model at CarTrawler we were able to reduce the Cost Per Acquisition in non-brand Paid Search by 60% at the same time as increasing volumes by 50% - as we could see which search terms and devices were driving the most valuable customers and use ROI to guide decision making for the first time. If you’d like to discuss how you can build a view of marketing attribution in your business, please Contact Me . All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.