Predictive Modeling for Conversion Improvement

What if you could predict which users are actually valuable before they convert? Most performance marketing strategies focus on what’s already happened - who clicked, who converted, and how much they spent. But what if you could optimise campaigns based on what will happen? Well that’s exactly what propensity models enable. By analysing user behaviour and intent signals, we can predict the likelihood of a conversion - allowing brands to make smarter, faster decisions across paid search and social. Understanding what a Propensity Model is A propensity model is a machine learning approach that predicts how likely a user is to take a specific action - whether it’s making a purchase, signing up, or returning to your site. Instead of treating all users the same, it helps advertisers: ✅ Identify high-value users before they convert ✅ Adjust bids dynamically based on predicted value ✅ Prioritise ad spend toward users who are more likely to convert Why Does This Matter? Ad platforms like Google and Meta rely on past conversion data. But for brands with long purchase cycles, waiting weeks or months for that actual revenue to come in isn’t practical. With propensity modelling, we estimate conversion value earlier and feed that data directly into bidding algorithms—enabling real-time optimisation. How It Works: 1️⃣ Data Collection – Analyse behavioural signals (session length, page views, interactions, historical purchases, etc). 2️⃣ Model Training – Machine learning identifies patterns that indicate conversion likelihood. 3️⃣ Real-Time Scoring – Every user gets a propensity score, predicting their likelihood to convert. 4️⃣ Activation in Paid Media – These scores are pushed to ad platforms, dynamically adjusting bids based on predicted value. Some results: Over the past 12 months, some brands using propensity models that we have built have seen ROI increase by 40% and conversion volume grow by 150% - driving significantly higher revenue at improved efficiency. But propensity modelling isn’t just for performance marketing. Its insights can help predict total future customer value and inform CRM, communication strategies, financial modelling, and beyond. Behavioural Insights The screenshot below is an example of a behavioural importance analysis, showing which user actions influence future value most. How to interpret the plots: - Each point represents a user record. - X-axis (SHAP Value): Left = lower probability of conversion, Right = higher probability. - Colour Scale: Blue = lower impact, Red = higher impact. Key takeaways - Propensity models provide a critical data point for understanding future customer value. - Integrating these signals into ad platforms can give brands a major advantage in bidding. - Their applications extend beyond performance marketing—impacting CRM, financial modelling, and overall business strategy.

Predict, Personalize & Perform : From Leads to Loyalty Let’s be honest—customer lifecycle marketing (CLM) in B2B used to be a fancy word for “email nurture” and “CRM segmentation. But today, with AI, machine learning, and predictive data models, CLM is becoming something much more powerful: ➡️ A living, learning ecosystem that adapts to each buyer journey in real time. Here’s how we’re seeing AI and ML revolutionize CLM in B2B: 🔍 1. Predictive Journey Mapping Machine learning algorithms are helping identify where an account or contact actually is in the funnel—not just where your CRM says they are. ✅ No more generic MQL > SQL flows ✅ Dynamic scoring based on behavior, content engagement, and intent signals ✅ Real-time stage shifts based on predictive fit and readiness — 📈 2. Hyper-Personalized Nurturing (at Scale) AI models now create content clusters matched to personas, industries, and even buying committee behavior. 🎯 Email sequences, LinkedIn ads, and landing pages are personalized based on: Buyer role Past touchpoints Predicted product interest ICP match + firmographic data It’s not just segmentation—it’s micro-personalization powered by behavioral AI. — 🔁 3. Intelligent Retargeting & Re-Engagement Using ML-powered intent data and anomaly detection, you can now: Spot churn risks before they happen Trigger re-engagement sequences based on drop-off patterns Retarget accounts that show subtle buying signals across web, search, and social Retention is no longer reactive. It's predictive. — 📊 4. Revenue Forecasting + Attribution Modeling Thanks to data science, we can model: Which touchpoints actually move pipeline Which leads are likely to convert within a time window How to attribute revenue across full-funnel programs—not just the last touch This gives marketing the credibility and confidence we’ve needed for years. — 💡 The CLM Stack of a Modern B2B Org Should Include: ✔️ Customer Data Platform (CDP) ✔️ AI-powered segmentation + scoring ✔️ Predictive content engines (LLMs + RAG) ✔️ Lifecycle orchestration tools (e.g. Ortto, HubSpot, Marketo w/ ML layers) ✔️ Analytics + BI layer for optimization 🧠 Final Thought: In 2025, CLM isn’t just “marketing automation” with better templates. It’s about building an AI-powered engine that understands, anticipates, and activates each step of the buyer journey. You don’t need more content. You need smarter orchestration. 💬 Curious to hear from other B2B leaders: How are you bringing AI into your lifecycle marketing stack?

𝐅𝐨𝐫 𝐲𝐞𝐚𝐫𝐬, 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐫𝐚𝐧 𝐨𝐧 𝐡𝐢𝐧𝐝𝐬𝐢𝐠𝐡𝐭. Dashboards told us what already happened—open rates, MQLs, churn numbers. By the time we saw the problem, it was too late. 𝐋𝐞𝐚𝐝𝐬? 𝐃𝐞𝐚𝐝. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬? 𝐆𝐨𝐧𝐞. 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐁𝐮𝐫𝐧𝐞𝐝. But AI and predictive analytics are flipping the game. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. 𝐈𝐭’𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞. 🔹 𝐋𝐞𝐚𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Traditional lead scoring is broken. A whitepaper download? That’s not intent—it’s noise. When we actually analyzed behavioral data using platforms like HubSpot, we found that multiple pricing page visits and engagement with onboarding content predicted conversions 3x better than generic lead scores. 𝐖𝐢𝐭𝐡 𝐦𝐮𝐥𝐭𝐢-𝐭𝐨𝐮𝐜𝐡 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 and 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐜𝐨𝐡𝐨𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ✔ Leads with 𝐫𝐞𝐩𝐞𝐚𝐭 𝐯𝐢𝐬𝐢𝐭𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐩𝐚𝐠𝐞 had a 𝟑𝐱 𝐡𝐢𝐠𝐡𝐞𝐫 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 ✔ Prospects engaging with 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐝𝐞𝐦𝐨𝐬 moved through the funnel 𝟒𝟐% 𝐟𝐚𝐬𝐭𝐞𝐫 ✔ Combining 𝐢𝐧𝐭𝐞𝐧𝐭 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐰𝐢𝐭𝐡 𝐟𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬 increased lead quality 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐧𝐠 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐜𝐨𝐬𝐭𝐬 We stopped chasing the wrong leads. And our pipeline? Tighter than ever. 🔹 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 A churn report tells you what you lost. But by then, it’s a post-mortem. Advanced platforms flag disengagement before it happens. A simple tweak—triggering check-ins for inactive accounts—cut churn by 15% in six months. A simple intervention—𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐢𝐧𝐠 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐫𝐞-𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 when customers showed 𝟑+ 𝐝𝐢𝐬𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬—led to a 𝟏𝟓% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐜𝐡𝐮𝐫𝐧 𝐢𝐧 𝐬𝐢𝐱 𝐦𝐨𝐧𝐭𝐡𝐬. 🔹 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐅𝐢𝐭 Guessing what users want is a waste of time. Predictive analytics showed us which features had a 𝟒𝟎% 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 before launch. The result? No wasted dev cycles, no misfires—just 𝐝𝐚𝐭𝐚-𝐛𝐚𝐜𝐤𝐞𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. If you’re still relying on past data to drive strategy, 𝐲𝐨𝐮’𝐫𝐞 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲’𝐬 𝐠𝐚𝐦𝐞. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐛𝐚𝐜𝐤. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭. #PredictiveAnalytics #MarketingStrategy #DataDriven #Growth

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.

The pressure is on: 90% of us are doubling down on prospecting this year. Simultaneously, so many marketers are still stuck using outdated targeting, hoping for the best and crossing their fingers on every campaign. I built Postie because I knew we could do better than ‘hope’ as a strategy. Here’s how we’re helping brands mail with confidence, not luck: 1/ Lookalike Audiences, Evolved. The classic LAL (lookalike) model is table stakes. You feed in your seed audience, and out comes a list of people who “look” similar. But that’s just the starting line. The next step is ranking every prospect by their actual likelihood to convert using propensity scores. 2/ Models on Models on Models. We don’t just run one model and call it a day. Postie builds, tests, and calibrates multiple ML models using a blend of customer and non-customer data, cross-referenced with third-party datasets (Axiom, Experian, Epsilon). Data fidelity matters. The more models, the more variables, the more accurate your predictions - and the higher your campaign performance. 3/ Calibration: The More You Mail, The Smarter You Get. Think of your model like a muscle: the more you use it, the stronger (and smarter) it gets. Brands mailing 10MM pieces a year calibrate faster and get sharper results than those mailing 1MM. By rotating models, you avoid performance decay and keep your campaigns fresh and effective. 4/ Propensity Score: Mail Smarter, Not Harder. Every audience we deliver comes ranked with propensity scores baked in. That means you know exactly who to mail first, who to deprioritize, and when to pivot to a new model as performance shifts. No more wasted budget. No more guessing games. Just data-driven, high-confidence prospecting. 5/ Plan for Decay. All models eventually run out of steam. The trick is to anticipate performance decay and have your next model ready to go. With Postie, you can seamlessly pivot and keep your results, and your ROI, on track. Stop mailing with crossed fingers. Mail with confidence, backed by models that learn, adapt, and put your budget where it matters most.