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Customer Intelligence

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RFM Segmentation for Shopify and Klaviyo Brands

RFM segmentation for Shopify and Klaviyo brands scores every customer on recency (days since last purchase), frequency (total order count), and monetary value (total spend), then groups customers into behavioral tiers. The top tier — typically 15–20% of the customer file — drives 50–70% of contribution margin in brands generating $15M–$100M in revenue. DAS applies RFM segmentation as the first step of every engagement, feeding the output into Klaviyo segment architecture, Meta custom audiences, and creative strategy to concentrate spend on the customers most likely to compound.

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What We Analyze

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RFM Segmentation

Recency, frequency, and monetary value reveal the behavioral reality of a customer file. Most brands have access to this data through Klaviyo’s built-in RFM scoring — Champions, Loyal, Recent, Needs Attention, At Risk, Inactive — but treat all six groups the same way in campaigns. That is not a personalization problem. It is an intelligence problem. RFM segmentation tells you which customers are accelerating (recent, frequent, high-value), which are plateauing (loyal but not growing), which are slipping (at risk), and which are already gone (inactive). Each group requires fundamentally different treatment — different messages, different offers, different cadences, different investment levels. The Champion segment is the most important finding. In most brands, Champions represent 8–20% of the customer file and 40–70% of contribution margin. Every downstream decision — creative, media, retention — should be oriented around identifying more people who look like Champions and retaining the ones you already have. Klaviyo surfaces this data in Analytics → Customer Lifetime Value → RFM Groups. Most brands look at this report once and move on. DAS treats it as the foundation of every brief.

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Cohort LTV Modeling

Cohort analysis tracks customers who started at the same time (same acquisition period, same campaign, same channel) and measures their behavior over time. This replaces blended averages with behavioral reality. Blended average LTV is almost always misleading. It combines customers acquired in good periods with customers acquired in bad periods, customers who came through high-quality channels with customers who came through cheap ones, customers who stayed with customers who churned immediately. The blend smooths out the signal you need. Cohort LTV modeling separates the signal. A cohort acquired through a specific creator campaign in Q3 2024 might have a 12-month LTV that is 2.4x higher than a cohort acquired through a broad prospecting campaign in the same period. That finding changes how you should allocate budget — but you cannot see it in a blended average. DAS builds cohort models on Shopify and Klaviyo data to identify:
  • Which acquisition channels produce high-LTV customers (versus volume)
  • Which campaigns produced cohorts that are still purchasing versus cohorts that churned
  • What the 90-day repeat purchase rate looks like for each major cohort
  • Where the payback period on CAC actually sits when measured against real cohort margin

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Contribution Margin Diagnostics

Contribution margin is not profit margin. It is not gross margin. It is the margin that remains after accounting for all variable costs associated with making and delivering a specific sale — COGS, shipping, returns, discounts, and variable marketing spend. Most brands manage to gross margin. That is a mistake. A customer who purchases with a 40% discount and returns a product has generated gross margin on paper and negative contribution margin in reality. A channel that looks efficient on ROAS might be producing customers with a 30-day return rate that destroys the economics downstream. An email campaign with a 40% open rate might be cannibalizing customers who would have purchased anyway — generating revenue without generating margin. Contribution margin diagnostics at the customer, channel, and SKU level reveal where margin is actually made and where it is silently lost. DAS runs this analysis as part of the Margin Diagnostic and as an ongoing layer of every retainer engagement.

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What This Unlocks

Customer intelligence is not a deliverable. It is a foundation. Once the RFM distribution is mapped, cohort LTV is modeled, and contribution margins are understood at the granular level, everything downstream changes: Creative briefs are written for the Champion segment, not a demographic average. Messaging reflects what high-value customers respond to — often different from what you would guess without the data. Email and SMS segmentation reflects behavioral tiers. Champions get retention-focused, exclusivity-oriented messaging. At-risk customers get win-back sequences calibrated to their purchase history. Inactive customers are either re-engaged efficiently or suppressed to protect deliverability. Media audiences are built from the customer file, not platform algorithms. Champion lookalikes outperform broad prospecting. Acquisition spend is allocated toward channels that produce high-LTV cohorts, not just volume. Retention strategy focuses on extending the Champion relationship and accelerating the most promising Recent and Loyal segments into Champion behavior — rather than trying to re-engage everyone uniformly. The compounding effect is real. Each decision made with customer intelligence builds on the learning from the previous one. The customer file becomes more valuable over time, not less. This is the opposite of what happens when campaigns reset to zero each quarter.