Advanced Segmentation Tactics

If you’re segmenting based on engagement, you’re already behind. Everyone does 30/60/90 day engagement windows. It’s not advanced. It’s basic hygiene. Here’s the real segmentation play most marketers miss: Segment by intent signals, not just opens/clicks. Examples: • Viewed shipping/returns policy? ➝ Hit with reassurance focused CTA • Time on product page > 30 seconds? ➝ Trigger a cart based reminder • Opened 5+ product emails but never clicked? ➝ Try plain text emails with a customer story • AOV based segments - low priced vs high priced ➝ show them the right products • FAQ viewers ➝ Give them more trust • Recent abandon carts/checkouts ➝ Leverage their interests • Time since they opted in for a coupon ➝ Remind them about it • Time since last purchase ➝ Show them complimentary products The list goes on and on... THEN add your engagement for best deliverability Engagement ≠ intent. Intent = actual buying behavior. Stop treating every click the same. Treat the reason behind the click differently.

Years doing cold outreach taught me this: Bad segmentation will break your campaigns Look, I get it—spray and pray is easy. It’s low maintenance, and sometimes it even works. But here’s the problem ❌ Low reply rates ❌ Risk of burning your dream clients ❌ Wasted email volume on unqualified prospects The result? Fewer meetings booked per week. Here’s what to do instead: 𝟭. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝘀𝗽𝗲𝗰𝘁 𝗹𝗶𝘀𝘁 Scrape your Total Addressable Market (TAM) using Apollo.io (or similar). Then, upload the data into Clay for deeper segmentation. 𝟮. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗯𝘆 𝗳𝗶𝗿𝗺𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝘀 Break your list down by: ✅ Industry ✅ Seniority ✅ Revenue* ✅ Company size ✅ Role/Department To get precise revenue data, use waterfall enrichment: 🔹 Clearbit 🔹 HG Insights 🔹 RocketReach 🔹 People Data Labs 🔹 Owler - A Meltwater Offering This helps you focus on high-probability prospects who are more likely to convert. 𝟯. 𝗚𝗼 𝗱𝗲𝗲𝗽𝗲𝗿 𝘄𝗶𝘁𝗵 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Leverage Claygent to segment based on unique attributes: 🔍 Does the company offer Buy Now, Pay Later? 🔍 Are they SOC II, GDPR, or ISO 9001 compliant? 🔍 Do they have a podcast? Use yes/no questions or multiple-choice (max 3 options) to improve accuracy. The goal? Gather enough intelligence to anticipate their pain points, and solutions before even reaching out. 𝟰. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝘆-𝗯𝗮𝘀𝗲𝗱 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Ocean.io helps you find hundreds of companies similar to your highest-paying clients, while a simpler (but still effective) approach is to segment by industry and refine it over time. 𝟱. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗯𝘆 𝘃𝗲𝗻𝗱𝗼𝗿𝘀 & 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸 Another powerful way to qualify leads is by the vendors they use: ⚡ BuiltWith – See what technologies are installed on a website. ⚡ ScrapeLi – Check if they follow a certain company on LinkedIn. ⚡ PredictLeads – Scrape employee certifications & job postings to understand what software they’re using. At the end of the day, better segmentation = better results. 𝗤𝘂𝗶𝗰𝗸 𝗿𝗲𝗰𝗮𝗽: Scrape a lead list Segment by firmographics Use Claygent for advanced segmentation Use case study-based segmentation Use vendor-based segmentation P.S. Are you implementing these methods?

You’re not scaling because your targeting is stuck in 2020. Most Meta ad accounts I audit? They're wasting 50%+ of their budget on outdated strategies. After 6 years managing ads for 50+ DTC brands, here are 10 advanced Meta ad targeting strategies I wish I knew sooner: 1. Segment warm traffic by intent, not just action. All website visitors aren’t equal. Someone who hit “Add to Cart” is 4x more valuable than someone who bounced on homepage. 👉 Create buckets like: ATC no checkout, checkout no purchase, and optimize separately. 2. Use broad + narrow stacking smartly. Want high spend and high control? Stack broad interests with niche behaviors. It boosts quality without killing scale. 3. Exploit ViewContent lookalikes with time decay. A 7-day VC audience LLA performs wildly better than a 180-day one. Recency = Relevancy. 4. Localized LLA stacking beats generic any day. Instead of 1 big 1% US LLA, try 2% segmented by top geo clusters. - Higher ROAS. - Lower CPM. - More relevance. 5. Combine pixel + email data for hybrid lookalikes. Upload your most engaged buyers (repeat purchases, high LTV) from Klaviyo or Shopify, then blend that with pixel ATC data. This hybrid LLA often outperforms standard purchase LLAs by 20–30%. 6. Use Engagement Retargeting by Content Type. Not all engagers are equal. Segment viewers by Reels vs. Stories vs. Posts, then retarget based on where they spent attention. Reels viewers? Hit them with dynamic UGC. Post engagers? Use long-form, education-heavy creatives. The most underrated tactic? Running Advantage+ catalog retargeting on 1-day view windows. Almost no brand does it, yet it drives insane ROAS for abandoned viewers. What’s working right now: • Skewing audiences female 25-44 if AOV < $50 • Excluding 180-day buyers from all TOF • Manual placement splits on IG-only warm campaigns REPOST this if you're: • A DTC founder spending over $2K/mo • A media buyer tired of stale results • Scaling a product under $100 What’s your most overlooked targeting trick right now? Let’s share what’s actually working👇

If you work in distribution, are you still guessing which customers need attention, which ones might churn, and how to prioritize your outreach? Guessing and corporate lore are no longer necessary when proactively managing B2B churn and driving up CLVs. Advanced analytics and predictive algorithms are democratized, and LLMs are here to help us build optimal predictive churn models tailored to our industry and business. Transactional, behavioral, and firmographic customer segmentation gives distributors a clear roadmap. By analyzing historical purchasing behavior, engagement patterns, and profitability metrics, you can identify which customers deserve proactive communication, tailored promotions, personalized discounts, or more generous credit terms. Moving beyond one-size-fits-all approaches lets you deploy your marketing budgets and sales efforts where they matter, driving sustainable customer lifetime value and organic growth. What if you could anticipate churn 90 days in advance and take action today? Modern machine learning techniques—now widely accessible—integrate seamlessly with your CRM. Or, if it works better for your sales teams, serve up the actions you need to take via daily/weekly emails, Excel tools, or Power BI / Tableau. Whatever fits better with your sales ops rhythm and commercial team analytics maturity. Sales teams receive daily or weekly alerts on their phones or tablets, pinpointing customers at the highest risk of leaving and explaining the reasons behind the risk. Armed with these insights, your sales team can proactively engage customers with relevant offers, from upselling new product lines to extending credit terms or introducing value-added services that strengthen loyalty. **** Consider a consumer durables distributor who recently deployed predictive churn capabilities. By layering advanced algorithms on top of their CRM, their sales reps saw a prioritized list of customers at risk, in descending order of revenue-at-risk. They leveraged targeted promotions and services—sometimes as simple as a timely check-in via email or in person—to re-engage customers before revenue evaporated. The result? Higher retention, increased cross-sell and upsell conversions, and a more efficient allocation of sales resources. **** This isn’t about adding complexity to your sales team’s day—it’s about giving them the tools and foresight to be proactive. When your reps know who’s likely to churn and why, they can deliver timely, personalized outreach that protects revenue and boosts lifetime value. These capabilities are no longer relegated to B2C or enterprise-grade B2B companies. Mid-market distributors of all sizes must build these capabilities to drive insights-based sales ops at scale. 

“If you’re not thinking segments, you’re not thinking.” - Theodore Levitt Here’s a brief history of market segmentation: 1950s: Segmentation started with basic demographics—age, location, gender—because that was the easiest data to collect and analyze. 1960s: Marketers began adding psychographics, gathering insights into customer attitudes and traits to create more specific profiles. 1970s: The rise of large transaction databases enabled real-time point-of-purchase data collection, leading to segments based on purchase behavior. 1980s: Needs-based segmentation emerged, driven by powerful computers and advanced clustering techniques. This allowed researchers to group customers based on desired product features and benefits. While needs-based segmentation was a step forward, it often missed the mark because customers aren’t product engineers. They struggle to articulate what specific products or features they need. But here’s the thing: Customers excel at describing the outcomes they want to achieve when using a product to get a "job" done. When discussing their desired outcomes, they can identify 100 to 150 different metrics to describe success at a granular level. Today's most effective market segmentation? It focuses on understanding how customers rate the importance and satisfaction of each outcome. This insight allows marketers to craft targeted messages and develop products that resonate deeply with each segment. Here’s 3 examples of Outcome-Based Segmentation in action: 1. J.R. Simplot Company identified a segment of restauranteurs who needed a French fry that stays appealing longer in holding, leading to a tailored product solution. 2. Dentsply found a segment of dentists who believed that the quality of a tooth restoration depended on consistently achieving solid bonds, allowing them to tailor their products to this need. 3. Bosch discovered a segment of drill–driver users who primarily wanted a tool optimized for driving, rarely using it as a drill. This insight helped Bosch create targeted and effective marketing strategies. Outcome-based segmentation represents a significant leap forward. It focuses on real opportunities... ...and measurable activities that are underserved by the competition. Outcome-based segments provide a clear path to innovation and market success.

🫨 Segmentations are like Tinder dates: Nice picture, fancy text, zero value Over the past few years, I’ve reviewed countless segmentations. Sad truth: nearly all of them are worthless. Nice pics, fancy texts but zero value. Why? Because they're often done wrong. The problems 1. No strategic “so what” Most segmentations randomly cluster consumers without link to the deeper strategic questions of the business. They fail to inform value proposition, portfolio choices, pricing, or channel priorities and don’t answer: - Who do we win with today? - Who do we want to win with tomorrow? - What does it take? 2. Not actionable Agencies hand over pretty slides, but teams ask: What the heck do we do with this? Most segmentations can’t guide concrete decisions because they’re built on the wrong dimensions. They aren’t tied closely enough to business levers (product, channels, pricing, communication) and therefore lack actionability regarding your value propsosition and 4P choices. 3. Not value-focused Many segmentations are colorful “personas” with needs and lifestyles but NO mention of size of the prize. However, without sizing the opportunity, you can’t prioritize segments based on value at stake. No sizing = no way to make trade-offs or plan a growth roadmap that goes from the top to the bottom in terms of value at stake and right to win. 4. No P2P metrics included You have your segments, but it's unlcear what's the awareness, cosideration and purchase within those segments? Big mistake because it leaves key questions open. The solutions 1. Strategic “so what.” A good segmentation informs your strategy. It should answer: - What’s the value of our targets? - Who should we prioritize? - What’s our right to win? - How does this shape our 4P choices? 2. Actionable Your segmentation is a decision-making tool, not a poster. It must enable “where to play / how to win” choices i.e. tailoring your portfolio, adjusting pricing strategy, aligning channels, and refining price-point architecture. 3. Value-focused Every segment should have a clear economic value attached. This allows you to quantify the size of the prize and prioritize resources accordingly. 4. Clear on status-quo of P2P That's for the G.O.A.T.S: your segmentation allows you to understand what's the awarenss, consideration and conversion in the segments which forms a solid starting point for budget allocation. Bottom line: Bad segmentations are like Tinder dates: nice on paper, disappointing in reality. Great segmentations are more like finding “the one” and change the game. If you’re ready for that upgrade, let’s connect. A few client spots remain for 2026. --- JK & Associates AG focuses on growth strategy and execution for consumer brands.

Funnel analysis is essential for understanding where and why users drop off in structured workflows like onboarding, checkout, or sign-up flows. Unlike clickstream analysis, which maps the broader user journey, or session analysis, which focuses on individual interactions, funnel analysis zeroes in on goal-driven processes, tracking user progression and highlighting abandonment points. What’s evolving today is how we approach funnel analysis. With more natural behavioral data and machine learning enhancements, we’re moving beyond static drop-off reporting. AI-driven insights now allow teams to predict drop-offs before they occur, identifying early warning signs like hesitation patterns or inefficient navigation loops. This proactive approach enables UX researchers to refine workflows dynamically, improving user retention before friction escalates. Advanced segmentation is also revolutionizing funnel tracking. Instead of analyzing drop-offs solely through broad demographic data, researchers can now segment users based on behavioral clusters - how they interact with key touchpoints, their engagement duration, or even their likelihood of return. This behavioral-first approach allows for personalized interventions that cater to different user types, ensuring a more seamless experience for all. Beyond traditional conversion tracking, we’re incorporating statistical methods like survival analysis to estimate how long users remain engaged in a funnel and Markov modeling to understand the probability of transitioning between different steps. Instead of treating drop-offs as simple yes/no outcomes, these approaches quantify the likelihood of users completing a process based on their prior actions, leading to more precise and actionable insights. Funnel analysis is no longer just about counting conversions, it’s about deeply understanding user intent, predicting disengagement, and designing experiences that encourage progression. The shift from static reporting to predictive UX optimization is already underway.

I once opened a client’s GA4 account and saw 42 custom segments. Forty-two. Every channel, every micro-behavior, every theoretical funnel stage, broken into separate views. It looked impressive until we realized no one was using them to make a decision. Here’s the danger: over-segmentation creates the illusion of insight. But in reality? It often paralyzes decision-making. ➞ Here’s what I help clients do instead: 1. Build meaningful segments based on shared behaviors: ↳ Combine user traits with action patterns: Engaged Pricing Viewers (users who viewed pricing + triggered a CTA). ↳ Avoid segmenting by single actions - those lead to noise, not trends. 2. Tie each segment to a business decision: ↳ Ask: What would we do differently if this group grew or shrank? ↳ If the answer is unclear, the segment isn’t useful - it’s decoration. 3. Limit active segments to a small, high-value list: ↳ In most GA4 setups, 5–7 audience segments are enough to drive 80% of reporting clarity and campaign targeting. ↳ The rest? Archive or delete. → You don’t need more segments. → You need more strategic ones. → Because when everything is segmented, nothing is prioritized. As a solo consultant, I’ve seen over-segmentation create more confusion than clarity. The best teams I’ve worked with don’t track everything - they track what matters most. How many GA4 segments do you actually use in your decisions? A) 0–5 B) 6–15 C) More than 15 (and I’m scared to delete them)

The best gift your RevOps team could give you this Christmas is a clear view of account segmentation within your business. It’s a somewhat confusing topic, so let me break it down a bit. Most B2B companies can rattle off their Total Addressable Market (TAM). But far fewer have a clear definition of their Ideal Customer Profile (ICP)—and that’s where the real opportunity lies. TAM is everyone you could sell to. ICP is who you should sell to. Getting specific about your ICP means focusing your time, energy, and resources on the right customers—the ones who bring the most value to your business and who benefit the most from your product. Once you know your ICP, the next step is account segmentation: breaking down your customer base into tiers or personas to refine your approach further. Think of it like sorting through a pile of gifts to find the perfect ones for the people who matter most. This is where metrics come into play. You need to analyze each segment to understand how they behave, how much value they bring, and how costly they are to serve. Here are a few to focus on: • Customer Acquisition Cost (CAC): Which segments are the most expensive to acquire? Are they worth it? • Lifetime Value (LTV): Which segments bring the highest long-term value? • Deal size: Do certain segments tend to bring larger deals that justify more investment? • Sales velocity (sales cycle length): How quickly do segments move through the pipeline? Faster cycles mean faster revenue. • Churn rate: Which segments are the most likely to stay, and which ones churn out too quickly? Based off these metrics, you can prioritize the segments that deliver the best return on your time, energy, and dollars. You’ll be able to craft sharper messaging, target more effectively, and focus your resources where they’ll have the biggest impact. Yes, account segmentation takes time. It requires clean, reliable CRM data and a clear strategy. But the payoff? Massive. You’ll target better, lower churn, and build stronger relationships with the customers who matter most. As you wrap up the year, consider making account segmentation a priority for next year. It’s not the easiest gift to give your business, but it’s one that keeps paying off, year after year. Merry Christmas—and here’s to a successful 2025! 🎄🎁

In 2025, marketers that aren’t accelerating their segmentation will fail. Harsh? Maybe. True? I certainly think so. Here's what I'm seeing at large tech companies: — They buy expensive ABM platforms — Load them with 3rd party data — Press the magic 'auto-segment' button — Take the outputs directly — Target the outputs with millions in budget and OpEx — Fail miserably — Blame the tech (truth is, you’re lazy) This isn't segmentation. There is no such thing as out of the box segments (that work). Guess what? All your competitors can just buy the same exact thing. You need to get creative. Real segmentation is an art: — Deep customer interviews — Sales call analysis — Win/loss patterns — Product usage data — Support ticket mining — Customer success insights The intersection of available data and segmentation is where the marketing magic happens: — Behavioral patterns emerge — Pain points crystallize — Buying triggers surface — Customer DNA forms You start to truly understand your ICP. Once you have this foundation, AI becomes powerful: — Pattern recognition across segments — Predictive segment modeling — Micro-campaign recommendations — Content resonance scoring — Next-best-action insights But AI without proper segmentation is like giving a Ferrari to an 8 year old. 2025 winners will: 1. Develop a segmentation strategy 2. Align with sales, customer success, finance 3. Build proprietary data models 4. Use AI to accelerate insights 5. Run micro-campaigns at scale 6. Continuously refine and adapt 2025 losers will: Still be marketing generic content to non-ICP leads and hoping for the best. Which are you? ********** 👋🏼 I'm Steve. I left 16 years in corporate marketing including brands like HP, IBM, Google, and DigitalOcean to found a boutique marketing agency. We specialize in the future of demand gen and ABM and I would love to talk with you.