Customer Segmentation Approaches

I honestly can’t believe he shared this. But Tyler Calder (PartnerStack CMO) shared their whole GTM strategy with me. And it’s not influencer fluff, it works. Over the last year+ they’ve been able to: -- Increase pipeline value by 58%+ -- While DECREASING cost per dollar of pipe by 35%. -- And improving NRR, & ACV So… getting MORE efficient as they scaled, not less. As a marketer I’m always looking for real playbooks that I can actually use, because they come from another practitioner. Proven. No incentives. This is one. His playbook is simple but beautiful: (full breakdown here: https://lnkd.in/e6qJcsx7) 1️⃣ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: 𝗨𝘀𝗶𝗻𝗴 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 & 𝗜𝗖𝗣 𝗠𝗼𝗱𝗲𝗹 𝘁𝗼 𝘄𝗼𝗿𝗸 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝘀 (& 𝗻𝗼 𝗺𝗼𝗿𝗲 𝗻𝗼𝗻-𝗜𝗖𝗣 𝘀𝗽𝗲𝗻𝗱) -- They built an AI-powered ICP Model (with Keyplay) that lets them hyper-focus on accounts that are showing fit signals. Built on real modern fit signals like: 1. Are they using a PartnerStack competitor? 2. Are they actively hiring for partnerships? 3. Do they have multiple partner motions live (affiliate, referral, agency)? 4. Are they growing? Recently funded? Product-led? Employee count? 5. Are they investing into areas that partnerships could either compliment or displace because it’s more efficient? etc. 2️⃣ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗽𝗹𝗮𝘆𝘀 𝘁𝗼 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝘀 𝘄. 𝗺𝗼𝗱𝗲𝗿𝗻 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 -- Prioritize accounts by fit (Tier A, B, C, D) -- Tailor plays to accounts by segment & tier -- Use AI signals to segment deeper and hyper-personalize 3️⃣ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁: 𝗣𝗿𝗼𝘃𝗶𝗻𝗴 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴'𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯. Imagine what you could do if you knew every account in your market… You’d build a report that shows every account and their engagement. Then you’d report on how that changes weekly, monthly, quarterly… They do exactly that. This isn't a shiny tactic. But I guarantee if take this seriously you’ll get something out of it that will work. It's fundamentals done right. And a perfect reminder for any marketing leader. Read the in-depth breakdown here: https://lnkd.in/e6qJcsx7

CMOs want pipeline. CFOs want unit economics. Marketers tend to segment with metrics like customer count, ACV, or win rate. These are good at first. But they’re incomplete. The next level is to segment like a CFO Customer Lifetime Value (CLV) is a great bridge. CLV doesn’t just measure deal size or ease of closing. It captures *the full value* of a customer or segment over time: initial purchase, gross margin, retention, and expansion. It’s a great metric to tie marketing strategy to business outcomes. Here's an example... Which customer would you rather acquire? Customer A - $120K ACV. - Closed in 60 days - Costs $60K/yr to serve. - Churns in year 2. Customer B - $60K ACV. - Closed in 90 days - Costs $20K/yr to serve. - Expands in year 2 to $80K. - Expands in year 3 to $100K. Clearly B is more valuable in the long-term. The 5-year value (CLV) is ~6x higher. But a lot of times this dynamic gets missed when thinking about ICPs and segments because we stop with pipeline metrics. CLV helps divide your market by long-term value. This is especially key in an ABM motion where you are making big investments into relatively small segments of accounts. You want to spend resources on the accounts that your CFO will love. Want help measuring CLV by segment? DM me. I'm thinking I'd make a template for this during the holidays. #B2B #marketing #sales

Segmentation is a powerful tool in data science—by grouping entities with similar characteristics, companies can tailor experiences, drive growth, and better meet the needs of distinct customer or supply groups. In a recent blog post, Airbnb’s data science team shared how they built a structured framework to segment their global supply into distinct “supply personas.” Rather than using traditional approaches like RFM (Recency, Frequency, Monetary) analysis, they grounded the segmentation in the platform’s unique business dynamics—especially calendar-based behaviors that reflect how listings are used throughout the year. The team began with exploratory analysis and identified four key behavioral features: availability rate, streakiness, the number of quarters with availability, and the maximum consecutive months of availability. These signals were then fed into an unsupervised clustering model (k-means) to group similar listings. To make the results interpretable and usable at scale, the clusters were used to train a supervised model (i.e., a decision tree), allowing for consistent and scalable persona assignments. This framework enables Airbnb to apply a shared language around supply—supporting decisions in personalization, experimentation, and beyond. It’s a nice example of how thoughtful segmentation can bridge human intuition, modeling techniques, and operational needs. #DataScience #MachineLearning #Analytics #Airbnb #Segmentation #MLInterpretability #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gBu4gKpz

My Favorite Analyses: the Recency-Frequency matrix. This simple yet powerful framework goes beyond traditional segmentation to provide actionable insights into customer behavior. By focusing on how recently and how often customers engage with your brand, you can tailor your strategies to maximize lifetime value. Why it works: - Recency: Customers who have purchased recently are more likely to purchase again. It's a strong indicator of engagement and future behavior. - Frequency: Customers who purchase more often demonstrate loyalty and satisfaction, leading to a higher customer value. Recency and Frequency are the most important indicators of customer value, exhibiting more correlation to CLV than Monetary Value which is the third component in traditional RFM analyses. The Recency-Frequency matrix helps you categorize your customers into segments based on behaviors instead of factors like demographics or psychographics that imply actions. The analysis reveals distinct customer segments that require unique marketing strategies, including your Champions, the customers who Need Attention, and those who have Already Churned. Implementing the Matrix: Depending on the size of your customer dataset, the Recency-Frequency matrix can be built in a spreadsheet or a more hefty tool like SQL or R. - Excel/Google Sheets: Use `MAXIFS`, `COUNT`, `PERCENTRANK`, and a pivot table to build the Recency-Frequency matrix, but watch out for row limits. - SQL: Leverage functions like `DATEDIFF` and `COUNT` to calculate metrics, and segment with `NTILE`. - R: The `RFM` package handles large datasets with ease, offering advanced segmentation and visualization. This approach isn’t just theory — it’s a data-backed method for ensuring your marketing dollars are spent where they’ll make the most impact. DM me if you'd like to learn more, including the marketing strategies that I most commonly recommend for each Recency-Frequency matrix customer segment. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling #MyFavoriteAnalyses #ROI #MROI

Five years ago, Warburg Pincus LLC invested in BetterCloud and urged us to work on a project to narrow our ideal customer profile (ICP). It's the most impactful thing I've ever done to improve conversion rates, shorten sales cycles, increase deal size and ultimately transform the company. A big mistake many CEOs make is believing their product is for everyone. It’s tempting. More potential customers should mean more sales, right? But in reality, chasing too broad a market drains resources, distracts your team, muddles messaging, confuses your product roadmap, and kills go-to-market efficiency. Being laser-focused on your ICP drives alignment across product, messaging, and the go-to-market motion. When the right prospect engages, they’ll feel like you built it just for them. Anyone who has built a product or service knows that the things a small business needs are very different than what a huge enterprise needs. A company is different from a school. An IT buyer is different from a security buyer, a sales buyer is different from a marketing buyer, a director level decision maker is different than a C level decision maker… but we still believe we can sell to different segments and personas as the same time. The process to define and use your ICP is relatively straightforward but does take time. The larger your business, the more data you have, the more resources you have to crunch that data the more time you should spend to do it as scientifically as possible. The high level steps are: 1. Build a Customer Dataset: Gather all your customer data. Current and churned customers, won and lost opportunities. Enrich it with firmographic, business-specific, and buyer demographic data. 2. Engage Your Team: Your best sales and customer success people hold invaluable insights about your most successful (and worst) customers. 3. Analyze & Identify Pockets of Gold: Identify common attributes of high-performing accounts and avoid the traps of poor-fit customers. 4. Communicate the ICP to the entire company with the “why” behind the attributes that make up an ideal customer.  5. Rework your messaging to appeal to your newly defined ICP and narrow your growth initiatives to be focused only on the accounts that matter.  6. Assign the right ICP accounts to your reps and ensure they’re focused on the right buyer personas. 7. Product Development: Reassess your roadmap to align with the needs of your ICP. You should see impact fast. GTM funnel metrics will improve. Conversion rates should rise, with better leads turning into stronger opportunities. You may not get more leads, but their quality will increase. I’ve been discussing this with many Not Another CEO Podcast guests, so don’t just take my word for it. I wrote a deep dive on how to “Narrow Your ICP and Transform your Company”, with real examples from other companies. You can read the full article here https://lnkd.in/e5EN3XSR

Teams who take a “boil the ocean” approach to outbound will fail. Here’s how to fix it and build sequences that actually drive results: Step 1: Focus your team on accounts most likely to buy now, invest at a premium, and become long-term customers or referral sources. This means moving beyond “anyone who fits the ICP” and zeroing in on high-priority targets. Step 2: Create deeper, more meaningful segments from that refined group. Traditional segments are great for organizing territories but fall short for crafting sequences that resonate. Instead, you need segmentation that helps your team speak the language of specific sub-groups. Use multiple layers of data—firmographics, intent signals, and contact-level insights—to break your TAM into smaller, actionable groups. Step 3: Launch micro-campaigns that target those precise segments with messaging designed to feel tailor-made. When you take this approach, personalization becomes scalable because it’s rooted in segmentation. Your reps don’t waste time on one-off customization, and your messaging feels 99% relevant to the prospect. I've been teaching this process as #ValueBasedSegmentation for the better part of a decade. It’s the key to building sequences that drive higher CTRs, replies, and engagement without tedious manual effort. ➡️ With this approach, you’ll: - Improve email performance - Write copy that prospects actually care about - Give your team a clear roadmap for focused outbound 📌 How are you helping your team build relevance into their outbound sequences?

Remarketing is often the misunderstood middle child of performance marketing. Let’s break a couple of myths🔨 🎯 One size fits all fits probably no one:  I’ve seen many companies burn money on campaigns that don’t recognize that every section of their audience has their own motivations. Why, if I had a penny for every time I visited a site with no intent to purchase their product at all, only to spot a “Schedule a Demo Today” ad by them on whichever site I visit, I’d probably be the richest guy in SaaS! I read somewhere that 84% of users either ignore or are put off by retargeting ads! Shows how important it is to get it right. Start doing these things: - Segment visitors by page depth (1 page vs 3+ pages) - Track time-on-site thresholds (>2 min = higher intent) - Create separate campaigns for pricing page visitors vs. blog readers Tailor your content based on your audience’s behavior and stage in the buyer journey (URL path visitors, action completers, cart abandoners) 🎯 Retargeting works like a mosquito coil:  Retargeting is not plug and play, and it typically doesn’t stop with one level. Retarget for all customer stages. Not only demo and trial signups. This insulates your prospects from leaving the funnel midway. We’ve had cases where we spent thousands of dollars on a retargeting campaign only to make zero sales. But here’s what happened afterward ⭐ : When we triggered another retargeting campaign for the warmer folks from the previous campaign, giving them BOFU content, we made sales. A lot of it! What’s to learn here? You’re unlikely to be bet on with just the first touch point. You have to build that awareness consistently. Create a 3-tier remarketing structure: > Tier 1 (Cold): Educational content, industry reports > Tier 2 (Warm): Case studies, comparison guides > Tier 3 (Hot): Free trials, demos, limited-time offers Build custom audiences for each segment, assign specific content types to each, and implement frequency caps based on ‘bucket temperature’. Also, the focus should also be on increasing the credibility of your company rather than only pushing them towards the CTA. Here's one customized Google + LinkedIn campaign strategy we used for a client recently. What are some retargeting tactics that’s worked for you?

Some user groups have distinct usability needs, and to design experiences that truly meet those needs, we need to identify patterns in how different users interact with a product. Clustering helps group users based on shared behaviors rather than broad assumptions, allowing UX researchers to uncover deeper insights, optimize design decisions, and improve the overall experience. One of the most common clustering methods is k-means, which groups users around central points based on similarity. It is widely used for segmenting personas and analyzing behavioral trends but requires predefining the number of clusters, which can be a limitation. Hierarchical clustering offers an alternative by building a tree-like structure that reveals relationships between different user groups. This method is particularly useful for mapping engagement levels and understanding how different users interact with an interface. Density-based clustering, such as DBSCAN, identifies areas of high user activity while automatically separating outliers. This method works well for analyzing drop-offs, onboarding friction, and engagement patterns without assuming a fixed number of clusters. Gaussian Mixture Models take a probabilistic approach, allowing users to belong to multiple clusters at once. This is particularly useful for analyzing hybrid user behaviors, such as those who switch between casual and expert usage depending on the context. Fuzzy clustering is another approach that enables users to be part of multiple groups simultaneously. This is helpful when behavior is fluid and does not fit neatly into distinct categories. It is often used in personalization systems where engagement modes shift dynamically. Constraint-based clustering applies predefined business rules to the process, making it ideal for segmenting users based on factors like subscription tiers or access levels. Grid-based clustering, including the BIRCH algorithm, is particularly useful when working with large-scale datasets. Unlike other methods, BIRCH processes large amounts of data efficiently, making it a valuable tool for analyzing heatmaps, session recordings, and high-volume engagement metrics.

The fastest way to kill your healthtech startup is to sell to everyone. It feels like the smart move: You’ve built a powerful product, and it seems like everyone could use it. So you pitch hospitals, pharma companies, clinics, governments, even direct to consumers. More buyers = more chances to win… right? Wrong. Because each of those customers buys in a completely different way. - Different sales cycles. - Different value metrics. - Different decision-makers. - Different compliance barriers. When you try to be everything to everyone, you lose clarity, waste resources, and watch your momentum slip away. So, instead of burning out, focus your energy on picking one customer segment that can say “yes” fast and build exclusively for them first. Here’s how to find the right one for your startup: ▶︎ 1. Go where the pain is urgent Who feels the problem now - so intensely they’re searching for a solution and have budget to act? ▶︎ 2. Understand the full buying dynamic Your user is not your buyer, and your buyer is not your decision-maker. If you can’t map who influences, approves, and pays - you’ll get stuck in endless conversations with no real progress. ▶︎ 3. Go for speed — if your product is affordable If your product is affordable, prioritize speed. Go after buyers who move fast — like diagnostic labs, specialty clinics, or mid-sized provider networks. Fewer layers = faster pilots = faster feedback. ▶︎ 4. Go for budget — if your product is expensive If your product is very expensive, selling to independent practitioners or clinics may be tough. Chain hospitals or insurance companies may be more likely to invest if you can save them money in the long run. Early traction isn’t about pitching to everyone. It’s about choosing one segment that can say “yes” fast and succeed quickly with your product. That’s how you get faster pilots, sharper feedback, and investor confidence. Focus on your ICP, and everything else will follow. Who was your first paying customer and how did you pick them? #entrepreneurship #startup #funding

The Personalization-Privacy Paradox: AI in customer experience is most effective when it personalizes interactions based on vast amounts of data. It anticipates needs, tailors recommendations, and enhances satisfaction by learning individual preferences. The more data it has, the better it gets. But here’s the paradox: the same customers who crave personalized experiences can also be deeply concerned about their privacy. AI thrives on data, but customers resist sharing it. We want hyper-relevant interactions without feeling surveilled. As AI improves, this tension only increases. AI systems can offer deep personalization while simultaneously eroding the very trust needed for customers to willingly share their data. This paradox is particularly problematic because both extremes seem necessary: AI needs data for personalization, but excessive data collection can backfire, leading to customer distrust, dissatisfaction, or even churn. So how do we fix it? Be transparent. Tell people exactly what you’re using their data for—and why it benefits them. Let the customer choose. Give control over what’s personalized (and what’s not). Show the value. Make personalization a perk, not a tradeoff. Personalization shouldn’t feel like surveillance. It should feel like service. You can make this invisible too. Give the customer “nudges” to move them down the happy path through experience orchestration. Trust is the real unlock. Everything else is just prediction. #cx #ai #privacy #trust #personalization