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With 50% of consumers using AI-powered search today , AI and Large Language Models (LLMs) have become the central topic of many conversations within marketing teams. If you are a business owner, a marketing leader, or even an investor, you have no doubt been wrestling with the topics for the last year. This post intends to serve as a single, comprehensive guide to understanding both the “why” and the “how” of LLM marketing. Why should I care about the impact of AI on digital marketing?   Relying on leads only finding your website through traditional search is becoming increasingly unrealistic today. Over the last year, research behaviour has undergone a major shift, with LLMs starting to become the new front doors to your business.  While Google still wears the crown for the place people research, AI is clearly disrupting the mix. Queries that generate AI Overviews (AIOs) have increased by 16 percentage points since October 2024, and informational LLM queries, by 11 points.  This appears to have impacted user behaviour, with our clients receiving an increase in website searches, but a reduction in website traffic. Many users are starting to prefer the easy-to-access, synthesised information they receive from LLMs and AIOs, over manually researching and reviewing websites. We are becoming lazier!  It also seems that users are quicker to make a decision based on this AI-generated information. Across our clients, we have noticed that the conversion rate of AI-referred traffic is meaningfully higher than the conversion rate of organic traffic.  Conversion Rate: Organic Traffic vs AI Referral Traffic   I am not saying to completely neglect traditional Google Search as a channel, far from that. Recent evidence actually suggests that LLMs complement search engine use rather than displacing it .   However, it has never been more important to ensure that your business is appearing in AI-generated answers because if not, you seriously risk being left behind. How do I increase my AI visibility?   Firstly, there are three ways to be visible in AI-generated answers:  Three Ways to be Visible   Mentions – direct brand callouts in a response  Citations – links included in a response for user navigation  Sources – websites used to generate info in the response  To understand how to get your brand to appear as these, we must first touch on how LLMs work.  LLMs are machine learning models that generate direct, coherent and contextually relevant answers to user queries. They use pattern recognition to best match the user’s query with information obtained from two places:  Training data  And the web (the process of getting this information is called Retrieval Augmented Generation, or RAG)  In short, your brand’s visibility essentially comes from these two inputs. And naturally, the more you shape the information feeding into them, the more your brand will show up in AI responses.  The challenge with training data is that training only happens once per model. In other words, you have very limited influence over what gets baked into the internal knowledge base. On the other hand, web retrieval is different. It happens in real-time, and works through a process called query fan-out.   Query Fan-Out      The LLM segments a user’s prompt into shorter queries with varied intents  These fanned-out queries are run through search engines (like Google)  AI bots are distributed onto websites that appear in the results pages for these queries, and pick out relevant and credible information  The information is synthesised  Finally, a response is created  This is what you can optimise for, with many businesses seeing success from their efforts. For example, Ramp, an accounts payable business, applied various retrieval optimisation strategies, and within 1 month they increased their visibility by 19 percentage points.   The following article in this series will delve into the practical steps that can be taken to improve AIO and LLM visibility.  If you would like to see how your business benchmarks with our AI trackers or discuss your company’s approach to AI disruption in search marketing, please contact us.

The cat’s out of the bag; on January 16 th , OpenAI officially announced that ads are coming to ChatGPT.  However, don't get too excited because it is still in early stages of development and there is a lot that is still left unknown.  Here’s everything that we know about ChatGPT ads so far.  Where will users see ads?   Ad testing has begun for logged-in adults in the U.S. on the free and Go tiers (low-cost subscription).  ChatGPT Plus, Pro, Business, and Enterprise subscriptions will not show ads.  What shapes the ads users see? Current chat topic Historical ads users hide or engage with Past chats and memory OpenAI have made it possible for users to switch off ad personalisation. What will the ad format look like?   Ads will not influence the response that ChatGPT gives you.  OpenAI gives an example of what the first ad formats they plan to test might look like Instead, when there is a relevant, sponsored product or service based on your current conversation, ads will appear at the bottom of responses, and will be clearly separated and labelled.  New ad experiences are also in development that go beyond static messages and links. For example, an interactive ad where users can ask direct questions to the brand.  OpenAI gives an example of how interactive ads could let users engage directly with brands As an advertiser, what insights do you receive? You will only be able to see aggregated ad views and clicks , and will not be able to see more granular items like a user's personal details or conversation logs. As an advertiser, how do you advertise on ChatGPT?   At the moment, you can only share your interest in advertising . OpenAI have remained vague about when and how you will actually be able to implement ads – there is no ‘self service’ platform available yet.  There are rumours that the ads take a pay-per-impression (PPM) model .  However, whether this will remain true after the testing period, who knows?  As soon as we have concrete information about the topic, we will be first to let you know.  For now, instead of just waiting for ads to officially release, it is important to focus on organic visibility in both LLMs and search engine AI Overviews . If you would like to see how your business benchmarks with our AI trackers or discuss your company’s approach to AI disruption in search marketing, please contact us.

Congratulations to our client Baird Capital on completing their investment in Rapid Energy , a specialist provider of rapid-response and mission-critical temperature control hire solutions, supporting commercial and industrial customers across the UK. The business supports organisations during planned shutdowns, unexpected HVAC failures, and periods of peak demand, helping maintain operational continuity with reliable heating, cooling and climate-control systems.   Coppett Hill provided Go-to-Market Due Diligence (GTM DD) support to Baird Capital, prior to completion. We focused on the strength and scalability of the commercial model, including an assessment of the growth plan. This was supported by a detailed analysis of commercial performance, search marketing trends and close engagement with management. Our work highlighted opportunities for go-to-market value creation post-investment.    We wish Management, led by Julien Fougere, and the team at Baird Capital all the best for the next stage of growth.    If you would like to learn more about the work we are doing with investors on Go To Market due diligence, please get in touch.

Congratulations to our client Gresham House Ventures for completing their investment in Veremar k, a leading global workplace trust company, which recently ranked number 40 in The Times fastest growing technology companies.  Coppett Hill provided Go-To-Market due diligence (GTM DD) support to Gresham House Ventures, prior to completion. This included an appraisal of Veremark’s go-to-market maturity, a detailed review of the online search marketing opportunity and competitive environment, an assessment of the business plan, and recommendations for post-investment value creation.    We wish Management, led by Daniel Callaghan, and the team at Gresham House Ventures all the best for the next stage of growth.    If you would like to learn more about the work we are doing with investors on Go To Market due diligence, please get in touch.

We're pleased to share that Coppett Hill Growth Advisory has been named a finalist in this year's Real Deals Private Equity Awards Value Creation Adviser of the Year category. We're grateful to the clients who place their trust in us and proud of the team behind this recognition. Value creation is difficult, and this nomination makes us even more determined to deliver great work. The winners will be announced at the Real Deal Private Equity Awards ceremony on 15th April.

Congratulations to our clients J&J Fulfilment and LDC on the successful exit to QLS , a Netherlands-based eCommerce fulfilment specialist. J&J is Europe’s largest independent eCommerce fulfilment provider, serving more than 350 high growth mid-sized brands and online retailers.    This was Coppett Hill’s first project supporting investor-backed businesses preparing for exit . We worked with the Management team at J&J Fulfilment across a number of areas, including:  A review of the current approach to marketing, sales and account management  Recommended go-to-market improvements to be implemented prior to exit  Opportunities for go-to-market value creation post-exit, validated by data  Buyside questions to anticipate, including materials for management presentations    We wish Management, led by Emma Dempsey, all the best for the next stage of growth.  If you would like to learn more about the work we are doing with investors on Go To Market exit preparation , please get in touch.

Congratulations to our client LDC for backing the management buyout of Taking Care , a leading provider of technology-enabled care products and services, from AXA Health.  Taking Care provides personal alarms, smart home monitoring products and associated monitoring and emergency call response services for elderly people to enable them to live safely and independently in their own homes. The company supports over 150,000 end customers across the UK, has excellent customer ratings and is the exclusive telecare provider to Age.    Coppett Hill provided Go-To-Market due diligence (GTM DD) support to LDC, prior to completion. This included an appraisal of Taking Care’s go-to-market maturity, a review of the online search marketing opportunity, mystery shopping, assessment of the business plan, and recommendations for post-investment value creation.    We wish Management, led by Steve Gates, and the LDC team all the best for the next stage of growth.    If you would like to learn more about the work we are doing with investors on Go To Market due diligence , please get in touch.

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.

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 .