Search Results

Most of us have probably at some point written a list of the pros and cons, or advantages and disadvantages, of a particular decision in our professional lives. Perhaps you’ve even written one for a personal choice – like Charles Darwin, who in 1838 created a list of the pros and cons of marriage, concluding in the end that the pros outweighed such disadvantages as having less money for books or perhaps not being able to live in London. Sometimes however, we need to frame a more complicated set of choices or other data, and one dimension is not sufficient. That is where the consultant’s favourite decision-making tool comes in, the matrix, or four box grid. Years of conditioning as a strategy consultant mean that I get (too much?) satisfaction from untangling a problem onto the skeleton of a matrix or grid, but for many people it represents a simple and elegant approach to communicating something complex. The four-box grid (or in consultant shorthand, the 2x2 grid) can be applied in many different situations. For example, I’ve recently covered the Important vs Urgent matrix, or Eisenhower matrix, that I use to separate out those tasks which are important but not urgent in a business context. I also frequently use a 2x2 grid to compare strategic initiatives, perhaps in terms of impact and effort. You can also sometimes add a third dimension where the size of ‘bubble’ or shape on the grid is used to denote the size of an opportunity (e.g., profit potential) or another quantitative variable. Let’s talk about three of the most well-known four-box examples that you can use in your own decision making. What is an Ansoff Matrix? The Ansoff Matrix, also known as the Product/Market Expansion Grid, is a strategic management tool used to visualise and evaluate potential growth strategies for a business. Developed by management theorist H. Igor Ansoff in 1957, it presents four growth options based on the new dimensions of (i) products (new and existing) and (ii) markets, or customers (new and existing): Market Penetration: This focuses on selling existing products in existing markets. The aim is to increase market share, achieved through strategies like pricing, promotions, or increased distribution/marketing activity. Product Development: Here, companies introduce new products to existing markets. This involves innovation, research and development, and often requires understanding customer needs to introduce products they'll adopt. Market Development: This entails selling existing products in new markets. Strategies can include entering new geographic territories, targeting new customer segments, or using different sales channels. Diversification: normally the riskiest strategy, diversification involves selling new products in new markets. This can be related (a similar field or technology) or unrelated (in effect a completely new business venture) to your core business. I find this matrix particularly useful when a business is considering focusing on customer acquisition vs driving cross-sell and up-sell to existing customers. In general, I’ve found it to be a more straightforward route to growth for mid-sized companies to focus on acquiring more customers (in existing markets or new markets) rather than trying to diversify their product offering and cross-sell. What is a Boston matrix? The Boston Matrix, also known as the Boston Consulting Group (BCG) Matrix, is a strategic tool used by companies to evaluate their product portfolios. Developed in the 1970s by the Boston Consulting Group, it categorises products based on the two dimensions of (i) their market growth rate and (ii) their market share relative to competitors. This matrix divides products (think business units or brands, or even countries) into four categories: Stars: These are high-growth, high-market-share products, and are leaders in expanding markets. They often generate more cash than they consume in their routine operations but may also require substantial investment to maintain their position over time as the market evolves. Cash Cows: Products in mature markets with high market share but low growth. They generate more cash than is reinvested, providing funds for other parts of the business. But beware - these businesses are ripe for disruption as competitors will be tempted into the market Question Marks (or Problem Children): These have low market share in high-growth markets. They often consume more cash than they generate, and their future is often uncertain. Strategic decisions must be made about whether to invest in them or divest. Dogs: Low market share in low-growth markets. They may generate enough cash to be self-sustaining but are generally considered for divestment. The Boston Matrix aids companies in allocating resources among products and deciding where to invest, maintain, or divest. What about a SWOT analysis? Possibly the most used four-box grid, a SWOT analysis is a strategic planning tool used to evaluate an organisation's Strengths, Weaknesses, Opportunities, and Threats. Strengths and Weaknesses are internal factors, reflecting a company's resources, capabilities, and internal processes. Opportunities and Threats, on the other hand, are external factors, emerging from the environment, competitors, or market trends. This might be controversial, but I see very limited value in a SWOT analysis vs a simpler ‘Opportunities and Risks’ analysis, such as Darwin’s list. It is hard to create a SWOT analysis without some level of duplication between Strengths/Opportunities and Weaknesses/Threats – there is an inherent relationship between internal and external factors. For me, SWOT analyses are up there with pie charts as on balance hindering understanding and communication rather than helping. When to use a matrix or four-box grid? If you’ve not had occasion to use a matrix or four-box grid to date, then hopefully you’ve got a sense of how this simple tool can help you to frame a decision or clearly communicate a complex dataset, for example when you are: Writing a 100-day plan (axes: impact vs effort) Creating your business plan (axes: impact vs effort) Creating your ideal client profile (axes: likelihood, likeability) Comparing marketing channels (axes: ROI, headroom) Evaluating your customer base for white space (axes: share of wallet, growth potential) You don’t have to be a consultant to use it, and I’d recommend giving it a go! If you’d like to discuss how you can make strategic decisions in 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.

I’m fascinated by nominative determinism – the notion that our names can influence our professions or personalities. Usain Bolt, William Wordsworth, Tom Kitchin – to what extent was their path in life even slightly, subconsciously influenced by their name? Is it possible to invert the historical tradition of a a family surname originating from the profession of the bearer (as any Archer, Fletcher, or Mason could testify)? The ‘Feedback’ column in New Scientist magazine coined the phrase 'nominative determinism' in 1994 and ran a regular column on the subject for a number of years. An academic paper was published in 2002 in the Journal of Personality and Social Psychology entitled 'Why Susie Sells Seashells by the Seashore: Implicit Egotism and Major Life Decisions'. The authors found that 'people prefer things that are connected to the self (for example, the letters in one's name)', and are hence disproportionately likely to 'choose careers whose labels resemble their names (for example, people named Dennis or Denise are over-represented among dentists).' I think that this concept extends into how we choose names in business – for teams, roles, processes, even meetings. The name we chose can have some impact on the outcomes that can be achieved if it has even a modest framing effect on the participants. Those in the UK will be familiar with the ‘Ronseal effect’ – based on the advertising slogan that the best-selling wood stain ‘does exactly what it says on the tin’. What is customer acquisition? Customer acquisition is the combination of activities that a business uses to attract and convert new customers. It can include the work of a marketing function, a sales team, and perhaps even elements of product and operations. It is influenced by the strategic choices made by the board and management team. It also includes the brand and reputation of a business, and the extent to which current customers are wiling to act as advocates and recommend it. All these teams, individuals and other factors can influence the number of new customers that a business wins over a given period. I believe that using the term ‘customer acquisition’ is a powerful way to make this point and show how counter-productive an organisation’s structure can sometimes be, for example, the myriad ways I’ve seen marketing and sales teams fail to collaborate (including in one case barely being on speaking terms). I prefer to other terms such as 'Go-To-Market' as I think it more readily meets the Ronseal test. Given its multi-faceted nature, to accelerate customer acquisition, we have to consider both the strategic choices that a business can make, as well as how these translate into day-to-day operations – the people, processes, technology and data - which come together to attract and convert customers. Businesses that can successfully join the dots have the opportunity to create competitive advantage from their customer acquisition efforts. This is the reason that I describe the work that we do at Coppett Hill as ‘accelerating customer acquisition’. Most of our work involves helping our clients to improve their marketing and sales efforts, to support future growth and improve efficiency. We could describe our services as ‘strategic marketing & sales consulting’ – but we could provide ‘strategic marketing & sales consulting’ and not help our clients to win a single extra customer. So we use the name that best describes what Management teams are looking for and what we aim to achieve – it’s all in the name. How could you use the ‘power of a name’ in your business? Renaming your weekly meeting to encourage action-orientation? Or even – changing the name of your marketing function to ‘customer acquisition’. If you’d like to discuss how you can accelerate Customer Acquisition, please Contact Us. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

'Would you tell me, please, which way I ought to go from here?' If there was one lecture at university that impacted my professional path more than any other, it was Mark de Rond’s very first ‘Introduction to Strategy’ at the Judge Business School. He posed the question ‘What is Strategy?’ to the assembled students, and received a few volunteered answers, which were all in the right postcode but didn’t quite seem to nail it. He then put a cartoon on the screen, an excerpt from Lewis Carroll’s Alice in Wonderland. In the picture, Alice is attempting to find her way home when she is met by the magical Cheshire Cat, and asks him ‘Would you tell me, please, which way I ought to go from here?’, to which the Cheshire Cat answers, ‘That depends a good deal on where you want to get to’. Professor de Rond argued that at its essence, business strategy is the answer to these two questions: where are you trying to get to, and how are you going to get there. This definition has stayed with me, and has shaped every strategy consulting project, strategic review, and investment case I’ve worked on since. I am drawn to its simplicity and obvious common-sense qualities. In a sea of buzzwords and stilted AI-generated content, this short passage from Lewis Carroll could well be one of the best (unintentional) pieces of business writing. The question ‘what is strategy?’ is one that is worth revisiting if your business is about to start a business planning process, or if you are developing a value creation plan. A strategy is not the set of detailed initiatives you will no doubt come up with, nor is it a financial model with its underlying assumptions. It is a set of choices, revisited periodically, that should shape every aspect of how a business operates and how it is structured. Where to play and how to win? I think Alice’s question maps directly onto my favourite language for framing strategy development - ‘where to play and how to win’. This originates from the work of Michael Porter on the theory of Competitive Strategy – that to succeed a business must deliberately chose a different set of activities (to competitors) to deliver a unique mix of value to customers. Using this approach to setting business strategy entails making two types of choices: The proposition of a business – such as the markets and customers a business will serve, the products & services it will provide, how it will charge for these services, and how it will grow (for example undertaking M&A and/or focusing on organic growth); and The advantages a business has or can build vs its competitors that will allow it to ‘win’ – such as unique intellectual property, access to the best talent, sourcing advantages, a superior brand or reputation, and operational excellence. Of course - these choices are not made in isolation; you should logically choose to play where you have at least some chance of ‘winning’. To quote Michael Porter, ‘the essence of strategy is choosing what not to do’. This is a helpful way to ensure a ‘tight’ definition of the market you are trying to serve, which in turn can lead to real clarity on your sources of competitive advantage. Most of world’s largest companies today started out just trying to serve one market where they built a clear competitive advantage (Amazon > books, Google > search, Microsoft > PC operating system). There are some common traps to avoid with this approach to strategy as well. There are many examples where businesses have assumed that the advantages that allowed to win in their existing markets would be the same in a new market, most commonly when expanding into a new geography for example Tesco in US, BestBuy in the UK, or Starbucks in Australia. What does it mean to ‘win’ in strategy? The definition of ‘winning’ will be different for each business and is dependent on the timeframe we are considering. Some common factors that I think define ‘winning': Gaining market share / growing faster than market – the clearest sign that a business has some form of competitive advantage. Top quartile profitability among a set of relevant competitors – to prove that a business is not ‘buying’ market share growth and that growth is sustainable. Clear path for future growth – current success is not just a ‘flash in the pan’. Meeting the needs of different groups of stakeholders: shareholders, customers, employees, community. If you can demonstrate success in these areas, then it is safe to say you are probably ‘winning’ and that any investors will be happy! So if you are about to start a strategic planning process, develop a value creation plan or start a strategy consulting project – take a lead from Alice in Wonderland and ask 'would you tell me, please, which way I ought to go from here'. If you’d like to discuss how you can create a strategic plan for 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.

Perhaps your business has just received private equity investment, or you’ve recently joined a private equity backed business as the Chief Marketing Officer. Your first board meeting is approaching, and you’ve been asked to create a marketing update to include in the board pack. You want to make a good impression but have no idea what is expected. Based on my experience of both creating and reading many board packs, I’ve created this simple ten-point guide that might just save you time and a few long evenings, and hopefully help you to create a constructive conversation with your board. Demand indicators – think of these as your ‘inputs’: e.g., share of traffic in organic/paid search (ideally on more than just your Top 10 search terms); partner introductions / referrals; content views. Funnel conversion metrics and change over time: this could be vs last year in a seasonal business or last month / quarter otherwise. You might not own all the conversion steps, but you are best placed to show the end-to-end journey. If you have a longer sales cycle – show your pipeline by segment (e.g., customer or product type), stage and change from last month. I’m always wary about using a weighted pipeline unless the %s are based on robust historical data, so as a minimum show both weighted and unweighted values. Overlay your target customer segment(s) / Ideal Client Profile(s) on your pipeline / won customers – to show whether you are winning where we want to be. ROI analysis of your marketing spend based on proper attribution – potentially on single transaction/ contract value and/or estimated lifetime value. Include a breakdown by channel to avoid the Flaw of Averages! Depending on scope of your marketing team – include a focus on existing customer performance. This could potentially include lifecycle marketing, a separate pipeline and funnel for cross-sell and up-sell, together with gross and net retention metrics. Update on competitive positioning and relative performance – such as share of traffic, financial metrics where publicly disclosed, presence of direct competition in proposals for B2B, or any significant news from competitors. This is all about giving context on your performance and demonstrating that you are aware of what is going on in the wider market. Summarise customer feedback – including public reviews, and with a comparison to competitors where possible. Show the new/change rather than just the total or average which will move slowly. The gold standard is your own measurement of customer NPS (or a similar measure of customer advocacy) and a summary of the key issues for detractors with your plan to address them. In my experience, marketers are best placed to champion customer views, as opposed to leaving this for the operations section of your board back. Distil your main marketing activities into 3-5 initiatives and explain what you are doing, what outputs you expect, and how these translate into value creation (growth and/or improved margins) and reduce risk. For example – building a new suite of landing pages, thought leadership development, appointing a new agency, launching new marketing channel, developing an app. Provide a view on status and delivery timelines, with a clear summary of what the board should expect to see delivered ‘by next month’. This is last in my list, but could well be first in your presentation – a summary of key messages. You could write this as a list of things that are working vs need improvement, a summary by market, by channel or even just a summary of things that are on your mind. But think of this as your opportunity to shape the conversation you want to have with the board, as well as to demonstrate that you know what will be on their minds. Could these topics help to improve the quality your board conversations about marketing? Of course – please treat this list of topics as a starting point, you will clearly want to tailor this to your business model, the remit of your marketing function, and to evolve it over time. It might be that you don’t have all of the data available that I describe here – in particular if you are new to the business. It is okay to have a placeholder at first, or to describe that you are working on creating improved KPIs (and certainly better than just excluding a topic from your update). In terms of length, it is entirely credible to have just one page on each of the above topics, so 10 slides. You might have a couple of additional pages ad-hoc if you are sharing some deep-dive analysis, but the emphasis should be on clarity of communication. I thought it would also be helpful to offer some general tips to keep in mind when preparing your update: If you include an KPI, include a comparison and ideally a target. Numbers without context can be confusing and frustrating for those who aren’t in your business every day. Minimise jargon – talk about website visits rather than sessions, display ads rather than programmatic. Focus on the money – conversions, revenue, margins, spend and ROI. Use top of funnel metrics sparingly (e.g., ‘impressions’ or ‘eyeballs’). Avoid spin – you should give equal weight to what is ‘working’ and ‘needs improvement’. Your investors should understand that not everything will work first time and are instead looking to see that you test and learn at pace. If something ‘needs improvement’, be clear on your plan to get it back on track. Avoid historical description of activities and instead give a forward view of initiatives. Make sure your CFO is comfortable with how you are using financials – if an investor sees a discrepancy in the numbers this will be a distraction. Step back and think about your key messages. Run it through with a board member beforehand e.g., the Chairperson. Ask for feedback after your presentation. If asked for additional analysis, avoid them becoming regular slide unless necessary - otherwise your 10 slides will quickly become 20+ (I speak from experience!) Every company and board will be different, with established ways of working and preferences – so make sure to seek guidance from your CEO, chair and potentially investor ahead of preparing your board update. I hope that the above structure will help you to go into your first board meeting feeling prepared and demonstrating that you’ve tried to put yourselves in the shoes of your board colleagues and anticipate what they are seeking to understand. Have a try and let me know what you think, I’d love to hear your feedback. If you’d like to discuss how you can understand and improve marketing performance for a private equity-backed 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.

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.