Customer Segmentation Approaches

Most marketers are wasting their Meta budget targeting the same tired audiences. But what if you could bring AI-powered segmentation synced directly into Meta Ads Manager? Here's what's changing in paid social right now 👇 The old way is hurting your ROAS: • Upload static lists to Meta (already outdated) • Manual segmentation taking hours • Same audiences as everyone else The Wunderkind approach changes the game: → AI segments refresh automatically through direct Meta integration  Wunderkind’s identity resolution on your site turns high-intent visitor activity into actionable, synced audiences → Fresh audiences based on actual site behavior → Option to exclude already-engaged email/SMS users → Direct API integration — set it and watch it work Think about these scenarios: Scenario 1: Cart abandoners with specific product affinities Traditional: Limited match rates from pixel-only data With AI segmentation: Higher-quality, identity-resolved inputs can lead to stronger match quality and targeting efficiency Scenario 2: High-intent browsers excluding recent purchasers Traditional: Manual uploads every few days With automation: Daily updates, automatic exclusions Scenario 3: Cross-channel suppression (email openers) Traditional: Complex workflows, constant maintenance With integration: One-click setup, major waste reduction Here's the reality: Your competitors are still uploading CSVs like it's 2019. Smart marketers are running AI-powered audiences that refresh continuously. 🎯 This isn't just about better targeting. It's about turning your entire Meta strategy into a precision instrument. ✅ Higher-quality audience inputs than pixel-only methods ✅ Real-time audience updates (not weekly uploads) ✅ Unified strategy across Meta + email + SMS ✅ Actually respects privacy while improving performance Stop feeding Meta stale data. Start feeding it intelligence. Get the complete Meta Audiences guide → https://lnkd.in/gMhcWgZN Your ROAS will thank you. 🚀 #WunderkindPartner

Segmentation beats personalization. Personalization is terribly inefficient... (and oftentimes unnecessary outside of highly strategic enterprise selling). Think about the ads that really grab your attention. None of them have your name in them. Or mention podcasts you were interviewed in or posts that you wrote. These ads work because they're segmented based on patterns amongst small-ish groups of people. Outbound should be treated similarly. Pro tip: this approach works WAY better over the phone than via email. The expectation for personalization and quality is much higher in emails than over the phone. Here are a few ideas for segmenting your lists so you don't have to personalize so much: ✅ By region/location If you sell anything brick & mortar, SLED, etc—segment your accounts by geographic region. You really don't have to personalize much when you can: - Name-drop local businesses/organizations - Drop the location This sounds like: "Hi David, we work with Fit & Fashion right down the road in SLU. It's Jason with ________. Ring a bell?" ✅ By tech stack Let's say you sell a tool that enhances Salesforce. Or Jira. Or some other specific tool. Segment your accounts by tech stack. This sounds like: "Hi Katie, we're partnering with engineering teams who wish sandboxes were way easier to set up and use in Zendesk. It's Jason with ________. Got a min?" ✅ By persona Let's say you sell to ecomm solutions to SMB retail business owners. This sounds like: "Hi Tom, we're working with several retailers in the Seattle area. It's Jason with ________. Heard our name tossed around?" (H/T Armand Farrokh) ✅ By trigger This list gets pretty extensive. Hiring, job changes, customer/champion change, M&A, expansion/contraction, promotion, etc This sounds like: "Hi Dave, congrats on the promotion. It's Jason from __________. Was just talking to a new HR leader yesterday who's running into all kinds of complications scaling international hiring. That by chance something you're running into?" ✅ By niche One of my favorites. Take a well-recognized logo like Rippling. You could go after direct competitors, but it's even better to focus on non-competitive products selling to the same personas. This sounds like: "Hi Cierra, we're working with Rippling to help scale their product suite for HR leaders. It's Jason with ________. Thought you might want to hear how they've doubled ACV in the last 6 months. Have a min?" ~~~ Before you think of personalization, start with segmentation. Do the work upfront to avoid having to customize too much. Agree or disagree? We're training entire sales orgs at companies like Shopify, Rippling, Zoom, and many more on how to land more meetings with outbound. Interested in custom training for your team? DM or email me jason [at] outboundsquad.com for more info.

📊 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗔𝗕𝗖 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 An ABC analysis helps identify which 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀, 𝗼𝗿 𝗼𝘁𝗵𝗲𝗿 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 drive most of your revenue or margin and which contribute the least, providing insight into their value and relative importance. This dynamic version shows one way to make the analysis interactive and flexible, but it can easily be adapted to different business contexts and needs. In this setup, users can: ✅ Choose the classification period (𝗟𝗮𝘀𝘁 𝟲, 𝟭𝟮, 𝗼𝗿 𝟮𝟰 𝗺𝗼𝗻𝘁𝗵𝘀) calculated backward from the selected month ✅ Adjust thresholds for 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗔 and 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗕 using sliders (you can also choose 80/20, which aligns with the Pareto principle) ✅ Choose whether to classify by 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 or 𝗚𝗿𝗼𝘀𝘀 𝗣𝗿𝗼𝗳𝗶𝘁 ✅ Filter by 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗨𝗻𝗶𝘁 💡 Quickly answer questions like: – “Is a small group of Category A customers responsible for the majority of total revenue (the 20/80 principle)?” – “How dependent is our revenue on our largest (Category A) customers?” – “What share of all customers falls into Category C, the ones contributing the least to total revenue?” ⚙️ Setup highlights ▪️Disconnected table for 𝗔𝗕𝗖 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 ▪️Parameters for 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱𝘀 (𝗔 %, 𝗕 %) ▪️Disconnected table for 𝗽𝗿𝗲𝘀𝗲𝘁 𝗽𝗲𝗿𝗶𝗼𝗱𝘀 ▪️𝗦𝘂𝗽𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿 used for Revenue, Gross Profit, and GM% calculations based on preset periods ▪️𝗗𝗔𝗫 𝗹𝗼𝗴𝗶𝗰 for ABC classification using cumulative revenue or gross profit % (and closest thresholds), combined with a derived visual-level filter This version was inspired by techniques from the 𝗗𝗮𝘁𝗮 𝗩𝗶𝘇 𝗙𝗼𝗿𝗴𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 and discussions with Achmad Farizky and Gustaw Dudek 🙌 💬 How do you segment your customers? What approach works best?

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

Something remarkable happened when we started bringing Customer Success leaders into our sales conversations. The traditional sales process transformed into a strategic partnership discussion that benefited everyone involved. After implementing this approach across hundreds of deals, we discovered benefits that went far beyond our initial expectations. Sales teams gained a deeper understanding of post-implementation challenges, which helped them qualify opportunities more effectively. Instead of focusing solely on closing deals, they began asking questions about operational readiness, internal champions, and resource allocation. Prospects received authentic insights into what successful implementation truly requires. Our CS leaders shared real examples of customers who thrived and openly discussed common obstacles they might face. This transparency built trust and helped prospects make informed decisions. Better aligned customer expectations from day one. When CS leaders joined these conversations, they highlighted potential roadblocks and success metrics based on similar customer profiles. This practical guidance helped prospects understand the work required to achieve their desired outcomes. This early involvement proved invaluable for our CS team. They gained visibility into the customer's vision before contracts were signed, allowing them to proactively plan resources and create tailored onboarding strategies. A surprising result was the reduction in "rescue" situations during implementation. We eliminated many issues that typically surfaced months into the relationship by addressing potential challenges during sales discussions. The data supported our approach. Deals that included CS leaders showed 40% higher implementation success rates and 25% faster time-to-value. More importantly, these customers renewed at significantly higher rates. For those considering this approach, start small. Choose strategic opportunities where CS insights could substantially impact the prospect's decision-making process. Document the outcomes and refine your strategy based on that feedback. Great customer relationships begin with the very first conversation.

Personalisation is talked a lot about in commerce, yet I am seeing very few SMB's walk the talk. Personalisation with purpose is key to investing time and resources which are finite in any SMB retailer and I suspect why it is rarely implemented. Segmeting your customers into four key groups is a crucial first step: Loyal customers - repeat, full price shoppers Discount customers - repeat, discount shoppers First time customers - single purchase shoppers At risk customers - haven't purchased in 6 months (adjust time frame to your average repurchase rate time period) Now build a strategy accordingly: Loyal customers - exclusive first to know when new product drops (don't scream sale to them) Discount customers - first to know when you go on sale First time customers - serve them the products second time customers purchase At risk customers - send them a really hot offer to see if you can entice them back, maybe even ask them why they haven't shopped again with you? How does a retailer "one up this", look at patterns in what your customer groups buy. A great example of this is a strategy we rolled out with a footwear brand which I shared with the Klaviyo team in Sydney this week... Before going on sale, segment your slow moving or end of season products into sizes. Build custom landing pages (this can be down using search filters as well) showing the styles and products in that customer groups size. You may just be blown away too that you end up selling through all your slow moving or end of season products at full price. A simple strategy targeting niche customer size groups with styles in their size will not just drive revenue and profit, but loyalty too. These customers likely struggle to find their size more often than not! How often have you gone to a store and found what you wanted was not available in your size... 🙄 What's your top tip for tackling personalisation?

After sending over a billion emails for 600+ brands… here are my 7 top tips for selecting the right audience. You can have the best email creative in the world — but if it lands in the wrong inbox, it won’t convert. Audience is everything. Here’s what we’ve learned at esbconnect after years of powering customer acquisition for brands like Tails.com | B Corp , AA Insurance and ASOS.com : 1. Target by behaviour, not just demographics Look for people who open, click, and act. Intent beats age and gender every time. 2. But… don’t always go for the obvious behaviour When Tails.com wanted to reach new pet owners, you'd assume targeting people engaging with pet brands would outperform, right? Wrong. They were being over-targeted. Instead, we found higher conversion by targeting segments engaging with health, home and subscription offers — less crowded and more curious. 3. Test broad, then narrow Start wide to understand what actually performs — then double down. Too niche too soon and you lose scale and surprise wins. 4. Layer in recency Someone who interacted with an email yesterday is more likely to convert than someone who did 3 weeks ago. Recency = relevance. 5. Don’t ignore ‘non-buyers’ Sometimes your best audience is one that’s never bought from the category — yet. Think curious, not converted. 6. Think beyond income — target by contextual wealth We’ve seen clients waste budget by targeting £100k+ earners assuming they’re affluent. But some of the wealthiest people are those on modest incomes with low outgoings — think high equity, long-term property owners with few financial ties. 7. Make it locally relevant A £1m house in London doesn’t signal the same wealth as it does in Scotland or Wales. Tailor your audience selection to geography and cost of living — precision wins. Audience strategy isn’t guesswork. It’s data, nuance, and constant testing. Want help finding your best segments? We’ve got 17 million opted-in UK profiles and years of experience to test with.

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

Personalisation has become the defining battleground in retail, but an execution gap exists in customer segmentation according to Retail Economics' latest research with beBettor. Precision targeting has become critical as product discovery is increasingly fragmented and price competition is being rewritten by disruptors such as SHEIN, Temu and TikTok Shop. Yet, a significant execution gap exists when it comes to customer segmentation. Four in five retailers acknowledge the importance of customer segmentation, yet only 60% rate their current capabilities as sophisticated. It marks the largest disparity between perceived importance and real-world execution across all major data applications in retail. A paradox is emerging where retailers are collecting more customer data than ever before, but many are falling short in making it meaningful. Our interviews with retailers reveal too many still rely heavily on internal datasets, such as purchasing history, without layering in the external insights that could transform segmentation and targeting to cut through the market. This isn't a technology problem. Around half of retailers report no major barriers to incorporating segmentation tools. The issue instead lies in strategic alignment, a lack of investment in external data to enrich existing segmentation models, and the ability to activate segmentation insights effectively. We believe external data is a missing link, representing one of retail’s most undervalued assets: ·      It offers a window into the financial and emotional reality of your customers beyond your shopfront – to anticipate trends, spot demand shifts and offer more relevant engagement. ·      Retailers who understand this shift will be strong players in the new era of hyper-personalisation. When retailers layer insights such as affluence data onto behavioural signals, they can personalise to offer the right messaging, to the right customer, at the right time.   How retailers use it matters. The difference between interesting data and actionable insights is the ability to embed data into workflows and culture. This means linking it to specific commercial outcomes, such as greater personalisation, sharper pricing or improved loyalty, and ensuring it’s easily accessible to decision-makers across the business. The ability to pull this off effectively is often limited by in-house resource and knowhow. This is where partnerships and investment in AI-powered segmentation tools matter. Retailers must open the door to third parties to draw in new insights, capabilities and accelerate impact. If they don’t, their competitors will. Great to speak with Retail Week’s George Arnett for their analysis piece about how retailers can better leverage data. 🚨 🚨 🚨 How to fix customer segmentation is explored in our latest research – full report available to read below: https://lnkd.in/eC7Ucrwy