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An increasingly common complaint across my network is the increasing volume (and clearly AI-generated nature) of cold outreach via email and LinkedIn – one recent example, shared anonymously, reads:  “Dear X, I wanted to share a quick thought. At XXX we automate key parts of your sales process to reduce human error and make data instantly accessible. This empowers your team to focus on decision-making, with AI handling the heavy lifting of data—pure, reliable, and ready to drive the best outcomes. Would it make sense to connect and explore how automating data flow can accelerate your sales cycles?”   However, while we have all been on the receiving end of this kind of message, such AI-driven sales enablement tools have received huge investment (from VC funds in particular), and are being increasingly deployed across businesses of varying sizes across all sectors – albeit with mixed success ( 70% of sales reps claim to be overwhelmed by the number of tools ).  So – what is going on? And what should businesses (and their investors) do about it?  We hope this article will help you to separate reality from hype, and provide a set of principles for how to think about and navigate this topic. This article does not  provide any specific recommendations or endorsements around specific software or vendors – in part because the market landscape is evolving so rapidly, and also because the right choice is so dependent on the specific business context.  Understanding the AI Sales Enablement Landscape   What are sales enablement tools, and why are they such a hot topic currently?  A sales enablement tool is software which is designed to help sales teams or sales people work more effectively (i.e. more productively, and/or increase conversation rates, deal flow, speed up the sales cycle, etc.). They are generally positioned to complement rather than replace core CRM systems (e.g. Salesforce, Hubspot), although in many cases CRMs are broadening out from their original core use cases, so are in effect competing against more focused tools. According to Salesforce, salespeople only spend about 28% of their time actually selling  – many sales enablement tools are designed to optimise or build efficiencies around non-selling tasks (e.g. research, documenting notes, working through follow-up actions, etc.)  A large number of solutions now exist in the market targeting different specific aspects of the sales ‘funnel’. These can broadly be categorised as follows:  Note: This is not intended to be an exhaustive list, and the examples above do not represent a recommendation or endorsement of the software. Given the nature of what these tools are designed to do, advances in Generative AI have significantly increased the speed of development cycles for new software and increased the scope for these types of solutions to be deployed effectively – for example, the quality of sales call transcripts we see from AI tools used by Coppett Hill clients has increased significantly over the past 12-24 months.   Cutting through the hype There are a large number of articles, datapoints, and LinkedIn posts which would lead you to believe that go-to-market (GTM) and sales leaders need to act urgently to deploy this type of software, otherwise they risk falling behind their competitors – for example this article  which states that 75% of sales leaders believe that organisations failing to incorporate AI in their GTM processes will fall behind competitors within the next 3 years. While there are certainly opportunities for AI-powered tools to deliver productivity benefits across the sales process for most businesses, leaders should approach with caution, as the risks (and costs) of poor selection, and/or poor implementation of these tools, can be significant. How to evaluate the opportunity and where to focus This is a complicated topic, so I spoke to a seasoned sales veteran (and good friend of Coppett Hill) who has experience of buying, selling and implementing these types of tools in global markets – Jimmy Simons , Head of Mid-Market Sales, EMEA, for Hightouch (recently valued at >$1bn). Jimmy’s advice is to start with the fundamental principles of understanding the customer (more on Coppett Hill’s guidance on ICP here ), and developing a deep understanding of the sales process. There will be no ‘one size fits all’ answer to where the opportunity for optimisation lies, and depending on the company culture, a top-down approach to vendor selection and deployment without consulting the sales team will be likely to backfire. “There are too many sales leaders that come into an organisation and start implementing new tools just because it worked well in their previous company – this approach usually doesn’t work”. Building a detailed understanding of the sales process, and getting detailed feedback from sales reps on where they are struggling, or see room for improvement, is likely to identify specific areas of focus. The other key consideration is to assess the impact on the existing sales process. Getting people to do things differently can be a challenge, so the better a solution fits into the existing sales process, the better – in some cases, tools can be implemented without sales teams needing to be aware that a tool has been deployed (e.g. richer data embedded into an existing CRM). If there is a high requirement on additional input data to execute on an implementation, this increases the risk for failure. Risk is another factor to consider – for example, software which provides specific prompts to sales reps on a live call is relatively high risk, whereas a solution which provides background research on a company prior to a prospecting call, is relatively low risk. Jimmy’s view is that optimised targeting represents a significant opportunity “taking data from a company’s CRM, and combining this with external data sources to enable a sales team to target the right buyer at the right company at the right time – this is a big opportunity which can be unlocked by AI and the input data is relatively straightforward”. How to execute No sales leader wants to adopt software which either doesn’t get adopted, or even worse, can’t be implemented properly. This is where RevOps comes in (more on our view of this role here ). Implementing any of these tools without a RevOps function will – in Jimmy’s language, guarantee a “world of pain” with implementation. Engagement with the users (mostly sales teams, but this could also extend to marketing and finance / data) through the process is key, and it goes without saying that any new tools needs proper onboarding and training to be implemented successfully. To encourage adoption, sales leaders (or Rev Ops) should monitor and track the impact of adoption, and report back with the data and case studies which evidence the impact. In many organisations, just getting sales people to use the core CRM properly can be a challenge, so leaders should not underestimate how difficult it can be to drive adoption, even where the use case is simple and the business benefit is obvious. Tools which help to “make the seller the hero” and help them to achieve their objectives (and ultimately make more money) are more likely to land. At Coppett Hill, we have seen execution issues in recent client engagements - we had a client which had implemented sales call transcript software - the quality of the transcripts was actually very good, however there were no actions or recommendations built into the workflow of the sales teams, so there was no meaningful impact on sales performance or productivity. Prior to implementing any tool, there needs to be careful consideration around how it is actually going to impact the workflow of the teams using it. As AI-driven enablement solutions become increasingly widely adopted across companies, their relative advantage for a business vs. its competitors diminishes – therefore, the quality of implementation (measured in part by the degree of adoption) is absolutely critical in driving return on investment from sales enablement tools. Another key measure of success should be a measurable, quantifiable impact on the business – what metric or KPI changed as a result of implementation of the tool? This question should be considered early in the process and also reviewed post-implementation. Conclusion AI developments have driven a proliferation of sales enablement tools in the market, with hype driving a sense of urgency that sales and GTM leaders need to act now to avoid falling behind competitors. While there is no doubt an opportunity across most businesses for these types of solution, there are many risks associated with tool selection and adoption. Our ‘top tips’ to avoid falling into common pitfalls are: Don’t implement a tool just because it works well in another organisation – there are no ‘one size fits all’ approaches to sales enablement solutions Ensure the customer remains central to any decision making process – what is the impact going to be on a customer or a prospect’s experience? Build a deep understanding of the sales process to identify where there is material opportunity for improvement Engage broadly across business stakeholders before committing to a change in process Monitor and report back on the impact of the new tool – identify up front what the expected quantified impact will be The landscape for sales enablement tools will continue to evolve quickly with new solutions being launched on a weekly basis – by sticking to the principles above you should be able to cut through the noise and make the right decisions for your business.

So you've defined your Ideal Customer Profile  (ICP) – now what? If your ICP is just a slide in your strategy deck, it’s useless. So how do you actually put it to use in your business? And how do you know it’s working?  Your Ideal Customer Profile(s) should run through the core of your organisation, guiding every choice and interaction you have with your customers. Done well, it creates consistency across all of your Go To Market activities - marketing, sales, account management and customer service – and even how you think about product development and service delivery. It ensures teams are all speaking the same language.   Here are a few practical ways to bring your ICP to life:  Adapt marketing activities   Prioritise channels where your ICPs are found e.g. targeting specific industry events, conferences and trade publications, or deciding which social channels to prioritise.    Tailor materials, messaging and imagery to address specific needs by ICP  e.g. dedicated pages on your website for that industry using relevant messaging, segmented email journeys.    Prioritisation of leads   Build target account lists for each ICP.   Categorise inbound leads into ICPs (or non-ICP fit) and prioritise ICP leads  e.g. triaging for faster response time, allocating ICP-fit leads to higher performing sellers.  This might even mean saying no to non-ICP leads (but not necessarily).  Lead scoring is one of the reasons it’s important that the attributes and behaviours defining your ICP are ‘prospectable’ i.e. visible from the outside and therefore possible to categorise before initial contact with them.   It should be possible to be quite specific even from the outside e.g. using AI to categorise sub-segments within an industry based on the website, or using tools like BuiltWith  to assess a company’s tech stack.  Align sales incentives to ICPs (which, by definition, should have higher lifetime value).  Tailor the sales funnel   Hire and train staff aligned to ICPs e.g. building industry understanding, network, and understanding of key pain points.   Adapt sales collateral e.g. messaging aligned to buyer rationale, relevant case studies and credentials.    Build sales playbooks aligned to specific ICPs e.g. addressing regulatory hurdles which might be specific to a certain industry.    Account management and customer service   Train staff to understand key needs and likely roadblocks and issues  e.g. data protection and cyber security requirements might be much higher for government or healthcare customers.    Tailor upsell and cross-sell journey e.g. SMEs might want you to bundle services and be a ‘one stop shop’, whereas Enterprise clients might be more focussed on APIs into their existing systems.    Focussing your customer services team on your ICPs could mean reducing support for non ICP-fit clients. This can be uncomfortable and mean losing some existing clients, but it can ultimately increase profitability in the longer term.  Product / proposition development   Align your proposition to the needs of your ICP. Clarity on your ICP(s) should make product roadmap prioritisation much clearer – if it doesn’t solve a key issue for your ICP, don’t build it.   This can be a real test of embedding ICPs beyond the ‘core’ of sales and marketing. Product teams may be more interested in building the ‘sexy new thing’ or have sunk cost in building something bespoke for non ICP-fit customers.    Pricing and packaging   Optimise and test different models by ICP e.g. a restaurant chain might have one ICP looking for a regular, consistent and efficient meal ( hello Dave and his family at Pizza Express ), for whom a loyalty scheme might work well. They might have another ICP looking for date nights, where they could test a Friday night set menu with wine to extract maximum value.   Embed the ICPs in your strategy, reporting and culture   Ensure the ICPs are known across the business – extend the reach from sales and marketing to product, operations, finance, etc – ensure everyone is speaking a consistent language  There is a strong culture and communication aspect to this, and it can take businesses a couple of years to really embed ICPs. You will get more buy-in as the approach starts to bear fruit, and institutional knowledge is built.  KPI reporting should reflect the ICPs e.g. focussing on ICP-fit new leads, splitting conversion stats into ICP-fit and non-ICP, and the ‘ICP lens’ should inform decisions and trade-offs for management and the Board  Splitting KPIs by segment can help identify ICPs, and then track trends Making hard trade offs   Here’s the real test: what are you willing to stop doing? This might be removing non-core markets from the website or taking things off the product roadmap. Are you willing to say no to non-ICP fit leads? Non-ICP customers aren’t the enemy — but they can  be a distraction. The ICP should be a critical lens on how resource is allocated. Non-ICP customers can quickly suck up time and end up low or negative profit!  How do you know it’s working?    When the ICP is clear and well embedded, there's a feeling of consistency. Everyone’s speaking the same language. Decisions are simpler. Things just run smoother.   Some of the ‘hard’ results should include  Reduced cost per lead   Reduced sales cycle length  Increased conversion rate  Increased customer satisfaction  Increased customer retention and lifetime value  More engaged and satisfied staff – feeling more knowledgeable and empowered   Stronger margins - increased efficiency across teams with everyone pulling in the same direction  Keep testing   Your ICP isn’t set in stone. It’s a living hypothesis. You will likely need to go through a few rounds of testing and refining. It is normal that your ICP might change over time. You might start with one then expand to a few more. You might also start to build out sub-segments within your original ICP as you refine it.   On an ongoing basis you should review the ‘likeability’ (lifetime value) and ‘likelihood’ (conversion) of your ICPs and sense check these are the right ones. It’s to be expected that conversion will be lower at first when you target a new ICP, as you build out knowledge and credibility. But if it doesn’t start to increase, and you’re continually facing roadblocks, whilst another ICP is flowing easily. Then recognise when it’s time to pivot.    If you’d like to discuss how you can create and operationalise Ideal Customer Profiles in your business, please Contact Us .

I’ve had the privilege (and, occasionally, the curse!) of sitting in on a lot of private equity board discussions about Go To Market. I’ve been the one pulling late nights writing slides for the board, as well as the one grilling the team on why sales missed plan (again). Done well, board meetings can cut through tough decisions to provide direction and clarity. Done badly? You find yourself down a rabbit hole trying to unpick last month’s performance line by line, and management feel like they undergo a monthly job interview.    This article aims to shed some light on how Go To Market (GTM) is discussed at a private equity Board. What questions are the Board trying to answer? What are some common pitfalls and how to avoid them? How to make the most of the Board?  Perhaps you have a new investor and want to understand how best to prepare for board discussions? Or you are part of the Marketing team preparing board updates and would like to understand what happens behind the scenes? Or perhaps you are a private equity investor who wants to shift to more strategic discussions? I hope this article provides some insight and practical suggestions.  Firstly, what is the Board actually trying to figure out?   What questions are the Board trying to answer? We have previously written about the key marketing information to include in a board update . Board discussions vary, but underneath most GTM updates, these are the core questions investors are trying to answer: Boards focus on short-term performance and long-term value creation — and what to do next. Did we hit the targets?  Sales, leads, marketing ROI, whatever the core metrics are Did we hit them predictably?  Are results consistent by channel, region, rep, or product? Are we on track to keep hitting them? Is pipeline strong enough? Is churn and CAC (cost of customer acquisition) in line with forecast? What else can we do to drive growth? Should we be investing more in things that are working? If not, do we understand why? And what are we doing about it? Are we on track to deliver the value creation plan?  And is that strategy still the right one? Are the big initiatives on track, or do we need to rethink? Are there strategic items to react to e.g. investment decisions, changes in the competitive landscape, evaluating the platform and team Therefore, what are our options?  For example: What if we ramped up spend on paid search? What if we hired more sales people? What if we entered a new market? What if we scrapped a whole segment? What are some common pitfalls and points of friction? “Show me the data” (but it’s in five different spreadsheets) Investors want a clear view of the numbers and drivers of performance. But many businesses don’t yet have the infrastructure to deliver clean, joined-up data. That’s often the point where we (Coppett Hill) get brought in — to stitch together CRM, marketing, finance, and ops data into something useful. Accountant vs Creatives Your Sales and Marketing leaders may be storytellers. Your investors may be ex-accountants or bankers who want everything MECE and modelled. That culture clash can be frustrating — but it can also be bridged. Good GTM data connects the day-to-day operational narrative to the boardroom story, without needing two separate worlds. The performance post-mortem Sometimes the meeting gets dragged into explaining every line of missed forecast. This usually happens when investors don’t feel confident that the commercial team understands (or is in control of) the GTM levers. That leads to an interrogation which nobody enjoys. The 100-page update:   Boards ask for more data each month. But unless something gets taken out , the pack just gets longer. Research   shows that 86% of PE professionals and 70% of NEDs feel there is far too much information in portfolio company board packs. Investment hesitancy (or is it?) When management brings forward a proposal — say, more budget for a new campaign — they might feel like the grilling from the Board is because their investors are risk averse or pushing back on cost. Often, though, investors want  to invest in growth. They just need to understand the ROI, the options and trade-offs before backing a decision. “What about that new AI thing I read about?”   Board conversations can get derailed by whatever trend the lead investor is excited about this week, or has seen in another portfolio company. Sometimes, these ideas spark great discussions. But thrown in without context, they can be overwhelming for an already stretched team. Disconnected data, culture clashes, and overloaded decks often derail GTM clarity in PE board meetings. What a great Board meeting feels like Board meetings are a big investment each month (just add up the prep time and “day rates” in the room). Too often, they can feel like a reporting chore. Done well, they should feel like an opportunity for collaboration, leveraging experience, problem solving and building trust. For Go-To-Market leaders, a good meeting can unlock a ton of value: The chance to move fast when something’s working When there’s clear evidence that a lever is delivering — say, PPC with a strong ROI and a manageable cash profile — the Board can give the green light to go bigger, even beyond the original budget. These discussions can also help resolve internal gridlock: getting CFO buy-in on spend, for example, becomes a lot easier when the full Board sees the link to additional value. An outside lens on tough trade-offs Non-execs aren’t in the day-to-day weeds. That means they can provide independent challenge, call time on slow-moving initiatives, and bring sharper focus to what really matters. The hardest and often most valuable thing a Board can agree is, “Let’s stop doing this.” Lessons from elsewhere (including the messy stuff) Your investors have seen things. Maybe it’s how to structure GTM for a US launch, or what not to do when hiring internationally. Their scars — and the scars from other portfolio companies — can save you months of pain. Ask them what’s worked, what’s failed, and what they’d do differently. Introductions that actually help This is often underrated. Good investors (and engaged NEDs) should be able to connect you to peers, specialists, and third parties who’ve delivered great work. Be specific in your ask — and be shameless in asking. Even if they don’t know someone directly, they can often source a recommendation from across the portfolio Practical tips for GTM leaders Here’s how to make your GTM section of the Board meeting actually useful (and maybe even enjoyable): Anchor in value creation and budget impact : Frame your update around 3–5 strategic levers tied to the value creation plan and their impact on the budget — including revenue targets, budgeted spend, and efficiency metrics Focus your metrics : Pick a handful that really drive performance — think search visibility, ICP-fit leads , ROI, churn. Kill the vanity stats : Avoid stats like website visits and social media followers. Only include metrics that link to value e.g. if referral is a key channel, then yes — share rate might matter. Tell a story with your numbers : Use visuals (e.g. charts showing trends over time). Include historical context and targets. Be clear what question the data is answering — one chart per question. Provide a summary upfront : Use the first page to say what matters. Flag where you want feedback or decisions. Context is everything : Remind your audience of how things fit into the overall strategy. Reference wider market dynamics when sharing trends. Don’t bury bad news.  Be upfront. The most productive conversations often start with a concern honestly raised. Cut the jargon : Remember that sales and marketing is not an area most investors will have first-hand experience of. You wouldn’t expect the CTO to present the board with details about the javascript code, so keep the narrative accessible, without losing the quantitative aspects. The classic, “Imagine explaining this to your mum”, is a good litmus test for simplicity and clarity. Practical tips for Investors Set expectations upfront: Help management understand what the Board wants to see — especially if it’s their first time in a PE-backed environment. Balance steering with supervision : Let the team run the day-to-day, but use Board time to challenge assumptions and shape direction. If you need more comfort around performance, it might be better to have a focussed conversation outside the Board. If you ask for data, be clear why: What decision will it inform? Is it needed ongoing or is it one-off? Help management to focus and prioritise : Don’t just keep adding requests and ideas to the list. Share the gold : Your network, your failures, your portfolio learnings, practical examples — that’s where you can add massive value. Be careful with curveballs : That AI podcast idea you mentioned might be a throwaway comment… but someone’s going to spend days building a deck on it. Bring in subject matter experts when needed:  At the surface level, GTM topics can seem common sense, but they get technical fast. External parties can help navigate this outside the boardroom. We are often asked to support companies with understanding things like unit economics  and marketing attribution  – seemingly simple topics, which are often misleadingly calculated and interpreted. For investors and operators alike, sharpening how Go To Market is discussed at the Board isn't just about reporting — it’s about driving growth. The right conversation, backed by the right data, can shift priorities, surface blockers, and accelerate momentum. How do your GTM board discussions stack up?   If you’d like to discuss how to enable more effective Go To Market discussions at your board, please  Contact Us.

An Ideal Customer Profile (ICP) is a description of the customers that you would most like to acquire for your business. In other words. If one more customer walked in the front door, brackets metaphorically). What do you want them to look like? The ICP is a more specific version of the long-used “target market” concept in marketing. The ICP, however, goes beyond demographic/firmographic information and can include customer problems, the triggers that have caused the customers to start considering a purchase, organizational structure, decision-making processes, and more. This level of specificity has become increasingly common for two reasons in my view: (i) digital marketing offering more precise targeting and segmentation options; and (ii) Customer Relationship Management (CRM) software, like Salesforce, allowing companies to gather more nuanced customer data, making it possible to build out detailed profiles. Both marketing and sales teams can use an ICP when setting up marketing activities and selecting target audiences, creating messaging and designing the customer journey. They can also help product and operations team to maximise the relevancy of your proposition and customer experience to your target customers. Your ICP should embody your collective understanding of your target customers and maximise your organisational alignment around meeting their specific needs. As organisations grow, many will develop multiple ICPs, perhaps for different product/service offerings or just for different use cases – but without an understanding of your ICP you risk low conversion, dissatisfied customers and poor retention. To read more about the problems of not having a specific enough ICP, I’d recommend reading this classic HBS case study . How to define an Ideal Customer Profile? There are three inputs to consider when defining an Ideal Customer Profile for your business. Think of these inputs as overlapping circles in a Venn Diagram: They are: “Likeability” – which customers are going to be worth the most to your business over time, because they spend the most and/or stay the longest? This is equivalent to their Customer Lifetime Value , which I’ve talked about previously. You need to dig into your headline analysis of customer lifetime value to understand which profiles lead to the highest LTV, to avoid falling into the Flaw of Averages . “Available targets” – this is the number of potential customers of any type that are available for your to target, which should be an output of a market & customer segmentation exercise . If possible, I like to think about this as the number of prospective customers who are likely to be ‘in-market’ at any given time, say within a year. For example, if the typical model for your product/service is a three year contract, than at most 1/3 of your prospective customers will be in-market in a single year. You also need to adjust for customers you have already won in any given segment. “Likelihood” – which prospective customers are most likely to convert, based on how well your product/service meets their needs, or in other words – solves their problem(s). Your ICP(s) should by identified by combining these three elements – think about it like an (illustrative) formula that you can use to estimate the potential value of an ICP: If you are starting out an exercise of thinking about defining your ICP, one approach is to create three options, each one maximising these three variables – the profile with the best LTV, the profile with the highest number of available customers, and the profile with the best conversion rate. Then think about the commonalities and differences between these profiles. This process is very iterative in my experience - sometimes you need to ‘pilot’ an ICP and go through a few rounds of ‘test and learn’. Signs that you’ve got the right ICP include improved new business performance, shortened sales cycles and improvements in customer satisfaction. One watch-out is that your ICP needs to be specific enough that it will exclude a reasonable proportion of the wider market. Without this level of specificity, your organisation won’t be able to make the trade-offs needed to truly meet the needs of the customers you are targeting. For example, if you are selling B2B, an ICP that describes a target company size of ‘50+ employees’ without an upper limit, is probably not specific enough. I’ve seen that reticence to be too specific can make the whole exercise a bit pointless. I’ve talked previously about both understanding customer lifetime value (LTV) and how you can create a market & customer segmentation to understand the number of potential customers that match a potential ICP – so let’s dig in a bit more on ‘likelihood’. The importance in understanding customer problems Let me share a personal example to illustrate the importance of understanding customer problems. I hit a very significant personal milestone recently, one that I’ve been working towards for nearly a year – Gold status with the Pizza Express loyalty programme. A culmination of many weekend trips with the children for pizza and pasta all over the UK. For me to be such a loyal customer, Pizza Express is clearly solving some problems for me. However, it isn’t the quality of the food alone that has driven my loyalty – sure the food is good, but living in London there are certainly some better options for Italian food close by. So what have they got right? For me, there are three problems they solve better than any other option: Speed – my children are impatient in the extreme (I don’t know where they get that from…). Pizza Express averages food and drinks for the kids on the table within ten minutes of being seated, which saves a lot of stress. I’m also able to pay on the app so there is no hanging around for the bill at the end of a meal. Consistency – my son in particular is an anxious eater. Knowing his food is going to look and taste the same at any Pizza Express location means he is relaxed throughout the experience. Availability – there are so many Pizza Express sites, that almost wherever we go we can find one nearby. This means that often it will be the first thing I look for when thinking about where to go for a family weekend lunch – I know it makes my life easier! Now these problems may be entirely different to that of other prospective customers, making Pizza Express less of a likely choice for them. But the proposition of Pizza Express is so well suited to solving my problems as a customer, they’ve won my loyalty. To stop thinking about food briefly, understanding the different problems that your prospective customers face is an essential part of selecting your ICP. Working out the profiles of customers for whom your current proposition is a ‘perfect fit’ can lead to higher conversion, shorter sales cycles, lower Cost Per Acquisition , higher customer retention and more customer advocacy. You should think through each aspect of what you offer your customers and how you deliver value to them. In the case of Pizza Express, it isn’t really the food that has differentiated them for me, but the service and scale. The same can be true in many commoditised markets – you might in theory offer the same product as your competitors, but you can still create meaningful differentiation through your service – for example by using technology to make your organisation faster or easier to work with for customers, or giving the customer more flexibility, choice, and control. Just think about Amazon as an example of this. Understanding customer problems is best achieved through talking to your customers – usually a combination of both qualitative and quantitative research. I always find it insightful to ask what is happening before a customer decided to start searching for businesses like yours e.g. a major life or business event. It is also important to understand what has made stay a customers. You can look at other internal data for example which profiles of customers convert the highest today, or move through your pipeline with the best velocity. I’ve created a checklist for customer research and to help you avoid the common mistakes I’ve seen people make when collecting and analysing customer research data. In summary Your Ideal Customer Profile is something that should run through the core of your organisation, shaping the choices you make at every single customer touchpoint. It is data-led, based on understanding your market & customer segmentation, drivers of customer lifetime value, and the customer problems you are setting out to solve. It is specific and prospectable – not just a pen portrait. You might have to iterate a few times to get it right. Leverage the power of an Ideal Customer Profile to align your organization, streamline your customer journey, and drive sustainable growth. If you’d like to discuss how you can create and use Ideal Customer Profiles 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.

You might be bracing for a dystopian tale of Large Language Models (LLMs) overthrowing the mighty Google and its dominant position in search marketing. We aren’t quite at this stage yet, but we’ve decided to shed light on what’s happening and launch the new Coppett Hill AI Index, which will track the intersection of generative AI and search marketing every month. AI's interpretation of what will matter for the future of search marketing Why does this matter? It seems like every other day there’s a new headline proclaiming the death of search engines such as Google and Bing, and the rise of ChatGPT, Perplexity and other LLMs. Add in a judge ruling against Google in a major case over its search monopoly, and the DOJ reportedly planning to demand the sale of its Chrome browser, and it’s no wonder CMOs are questioning (and being questioned about) their search strategies. At Coppett Hill, we decided to sift through the rhetoric and flashy headlines to see where the data actually leads us. It’s important to acknowledge that there’s no single comprehensive data source available; most sources provide only fragments of the story. By piecing together various information sources available, as well as our own data, we’ve worked to present a clearer and more complete picture. There are two primary ways generative AI is currently reshaping search and search marketing: LLMs taking traffic from search engines as users change their online research behaviour. Search engines’ AI overviews reducing clicks to advertiser websites through both paid and organic rankings. The Case for LLMs Disrupting Search There’s no denying the appeal of using LLMs for web searches. Why wrestle with robotic searches like “best restaurant London cheap” when you can ask an LLM chatbot, “Where can I find affordable but good restaurants for a casual meal with friends in central London?” LLM’s tools offer conversational, intuitive ways to search—and that’s shaking things up. A recent survey by  Evercore  asked over 1,300 U.S. respondents about their search habits. They found that 8% of people now use ChatGPT as their primary search engine, up from just 1% in June. That is a significant rise. Meanwhile, amongst those surveyed, Google’s share of users fell from 80% to 74% during the same period. In fact, Gartner has predicted that by 2026, traditional search engine volume will drop by 25% due to chatbot-like LLM applications. While that might sound dramatic, let’s examine the data. One of the most direct ways we can measure this shift is by looking at LLM referrals - instances where users engage with AI-generated responses by clicking through to websites. To be clear, LLM referrals occur when a user asks a language model a question and clicks on a link provided in its response. Is this the future of search? Should CMOs be racing to maximize their visibility in LLM-generated results? Introducing the Coppett Hill AI Index To help answer this question for our clients, at Coppett Hill we’ve developed a new monthly AI Index: a structured framework designed to measure AI-driven traffic and give marketing teams a better understanding of how AI search impacts website visibility and engagement. Our AI Index covers a mix of B2B and B2C companies across multiple sectors, and covers two trends: AI referral traffic from large language models (LLMs) to advertiser websites. The presence of Google’s AI Overviews on the search results page for keywords which drive organic search traffic to advertiser websites. What’s happening with LLM traffic to websites? Since October 2024, our AI Index highlights a sharp increase in LLM-attributed traffic, particularly as a proportion of overall organic search. While LLM-driven referrals are still a small proportion of overall traffic, their rapid growth suggests that marketing teams will need to rethink their organic strategies in the coming years. Coppett Hill LLM Referral Traffic Tracker Are Search Engines Really Losing Ground? While LLM-driven search is showing signs of growth, does this mean search engines are losing their grip? Let’s consider Google, as the dominant search engine in Europe and the US. So far, the numbers don’t point to a company in trouble. In Q4 2024, Google’s ad revenue remained strong, reaching $72 billion - a 10.6% year-over-year increase from $65 billion in Q4 2023. This growth pushed total ad revenue for FY24 to approximately $256 billion, up from $237 billion in 2023, reinforcing Google's dominance in digital advertising. However, if the trends we’ve seen in the last three months continue, Google and other search engines should be worried – fewer users starting their research journeys via search engines means fewer organic search visits for advertiser websites, but also fewer paid search clicks earning revenue for the search engine. It is also reasonable to think that search engines will be more focused on protecting paid search clicks than organic search visits, so revenue data isn’t going to tell us the full story. The Big Picture The story isn’t as simple as LLMs vs. search engines. Yes, LLMs are gaining traction, and some users are migrating to these platforms as a starting point for online research. But the overall impact on search engine volume and revenue is not yet dramatic – albeit this depends heavily on the industry. For instance, few users currently turn to LLMs to book flights or shop for products, but many rely on it for tasks like researching and learning (where some advertisers such as education providers and advice sites are reporting a more significant impact). Search Engines’ Double-Edged Sword If LLMs represents external competition, search engine AI Overviews (AIOs) pose a more complex challenge from within. By integrating generative AI into their own search results, search engines have created a double-edged sword. On the one hand, these summaries improve the search experience by providing concise answers directly on the results page, saving users from sifting through multiple websites. On the other, they diminish the need for users to click through to brand websites, cutting into organic traffic and pushing those hard-earned organic rankings further down the page. While still speculative, concerns about these effects are gaining traction among media executives and SEO experts. Forecasts  suggest that organic search traffic to publishers’ websites could decline by as much as 20% to 60% due to AIOs. But, in true Coppett Hill fashion, let’s look at the data again to see how much weight these concerns actually hold. "Search in the Gemini era" Examining The Data Firstly, a Statista  report found that for news-related queries, the first organic search result is pushed down by an average of 980 pixels—equivalent to a full-page scroll. This makes it significantly harder for users to engage with organic links. BrightEdge data from June 2024 highlights notable shifts in the deployment of AIOs, reflecting Google’s ongoing changes to this feature: The prevalence of AIOs declined from 11% to just 7% of total queries, suggesting a more selective application, perhaps a result of well-publicised accuracy issues. The impact varies significantly across industries: Education queries saw a reduction in AIO appearances from 26% to 13%. This is one of the sectors where AIOs have been most frequent ( sparking a recent lawsuit from US education provider Chegg , which claimed a 50% YoY reduction in website traffic in January 2025) Meanwhile, AIOs almost disappeared for entertainment-related queries These findings suggest that AIO impacts are industry dependent. It’s also clear that AIOs are still in a testing phase and their use is likely to evolve rapidly. How do AIOs influence user behaviour? SEER  Interactive analysed 7,800 Google queries from June to September 2024 to address this question. Their findings include: Presence:  AI Overviews appeared for only 7% of queries that feature paid ads, accounting for just 2.2% of total impressions, reaffirming their minimal impact on paid search performance overall. Click through rate (CTR): where AIOs are present, they appear to have a significant impact on click through rates: Paid CTR dropped by 12 percentage points (from 21.3% to 9.9%) when an AIO was present. Organic CTR declined dramatically, by ~70%, from 2.94% to 0.84%, despite organic rankings remaining stable (average position 5.9 vs. 5.6). Overall, all roads point to AIOs reducing CTR for both paid and organic media. However, there is a silver lining: being included as a source in an AI Overview significantly boosts both visibility and credibility. For example, SEER reports that websites cited as a source within an AIO saw their organic CTR nearly double, rising from 0.6% to 1.08%. At Coppett Hill, we wanted to go beyond click-through rates and measure how frequently AIOs actually appear in search results for Google (by far the dominant search engine among our clients). Using the same mix of companies as in our AI referral analysis, we tracked the share of advertiser organic traffic exposed to these overviews. Coppett Hill AIO Tracker Our tracker shows that the presence of AIOs when weighted by advertiser traffic (i.e. on the search terms that actually matter to advertisers) has been stable in recent months. AIOs are more likely to be found at true ‘top of funnel’ / research keywords. This highlights their growing role in driving ‘no-click’ user journeys, where searchers find answers directly in AI-generated responses without visiting a website. Does This Mean the End of Organic Search Traffic? Not yet. The impact of AIOs is currently limited in scope, appearing for just 12% of keywords on Google as of February 2025 (when weighted by traffic).  However, AI Overviews are driving an increasing number of no-click journeys, where users find answers directly within search results without visiting a website. As search behaviour shifts, featuring within an AIO is one of the few ways to mitigate the impact. Being featured as a source in an AI Overview can increase visibility and organic CTR. This sentiment was echoed by Liz Reid, head of Google Search, who said: “The links included in AI Overviews get more clicks than if the page had appeared in a traditional web listing for that query.” This creates an opportunity for brands to adapt and optimise their content for AIOs rather than viewing them solely as a threat. So, What Should You Do? For most advertisers, there’s no immediate need to overhaul your search marketing strategy—yet. However, AI’s growing role in search means businesses should be proactive in understanding and adapting to these changes. What Should Businesses Be Doing? Stay ahead by testing and adapting – Google remains the largest AI-powered marketing channel, and businesses need to treat it as an evolving space. Testing how AI-generated results affect search intent, rankings, and click-through rates is essential to understanding what drives visibility in this new landscape. Monitor LLMs’ role in your audience’s journey – Businesses need to track how their audience engages with LLMs and ensure their content remains visible in AI training data to maintain future discoverability. At the same time, it’s crucial to continue monitoring the situation. Stay informed about the evolution of AIOs within your industry and be ready to adjust your strategy as necessary. The Final Word AI Overviews and LLMs are reshaping the search marketing landscape, but they don’t signal the end of search engines. Google’s dominance in search remains strong, and for most industries, traditional search marketing continues to yield strong results. However, given the rate of change, we expect 2025 to be a year of significant transformation. We’ll be publishing our AI Index monthly to help you keep on top of the latest trends. If you’d like to see how your business benchmarks within our AI trackers or discuss your company’s approach to AI disruption in search marketing, please contact us.    All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

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.