This is Part 2 of our DTC Growth Intelligence series. In Part 1 we covered where customers come from and what they cost. Now: what happens after the first purchase.
Acquiring a customer is step one. The question that determines whether that acquisition was an investment or a loss is what happens next. Does the buyer come back? How quickly? And what made them return?
These four metrics cover the activation layer: the behaviors between first purchase and profitable repeat customer. This is where most DTC brands have the biggest gap between intuition and measurement.
Quick glossary: Activation = a first-time buyer completing a second purchase. Activation rate = % of first-time buyers who repurchase within a defined window. RPR (Revenue Per Recipient) = email/SMS revenue attributed to a specific send, divided by recipients.
Metric 3: Activation Rate
What it measures: The percentage of first-time buyers who make a second purchase within a defined window (30/60/90/180 days), segmented by acquisition channel, first product, and post-purchase engagement behavior.
Why this is the single most important DTC inflection point: After a second purchase, the probability of a third nearly doubles, jumping from roughly 27% to 49%. Everything downstream (subscription conversion, cross-sell, referral) depends on this first repeat.
Formula:
Activation Rate = Customers with 2+ Orders within [Window] / Total First-Time Buyers in Cohort
Benchmarks:
- Overall DTC: Average ecommerce repeat customer rate is approximately 28% (Shopify, 2024).
- 90-day second purchase rate for consumables: 20-30% is strong. Below 15% signals a product-market fit problem or broken post-purchase experience.
- 90-day second purchase for beauty: 15-25%, given longer replenishment cycles.
- Subscription conversion within 90 days: 10-20% of one-time buyers converting is strong (Recharge, 2024).
- Customers acquired through branded search have 2-3x higher activation rates than cold prospecting on Meta or TikTok. The intent gap shows up here more than anywhere else.
Where the data lives
The core activation query is straightforward: first order date vs. second order date per customer, grouped by whatever dimension matters (channel, first product, AOV band). Shopify has all of this in its orders table. The challenge is running it by segment, not blended.
For subscription-specific activation, Recharge or Skio track conversion from one-time to subscriber natively. Post-purchase engagement data (email opens, SMS clicks) lives in Klaviyo.
A warehouse (BigQuery + dbt) joins orders to engagement signals, letting you build activation rate by first product AND post-purchase email engagement AND acquisition channel simultaneously. That’s three dimensions the native platforms can’t cross-reference. This is Phase 2 work in the implementation roadmap.
Metric 4: Activation-to-Monetization Rate
What it measures: The percentage of “activated” customers (2+ purchases) who convert to high-value status: 3+ orders within 12 months, active subscription, cumulative spend exceeding 2x category AOV, or cross-category purchase.
The second purchase proves interest. The third and fourth prove habit formation. The probability of a third purchase jumps to 49% after the second, but the drop between second and third is still where most brands lose customers they thought they’d retained.
Benchmarks:
- 2nd-to-3rd purchase conversion: 40-55% should make a third purchase. Below 35% indicates product satisfaction or re-engagement problems.
- One-time to subscription conversion: 10-20% within 6 months (Recharge, 2024).
- Cross-category purchase rate: 15-25% of repeat buyers purchasing from a second category within 12 months.
- Top 20% of customers should reach 4-5x average first-order AOV within 12 months.
Where the data lives
Same sources as activation rate, extended over a longer window. The key addition is category-level product data joined to orders, which lets you measure cross-category behavior. Shopify product tags and collections provide this, but it needs to be structured consistently. In a warehouse, a dbt model mapping SKUs to product categories makes this a simple join.
Metric 5: Customer Quality Score
What it measures: A composite 0-100 score predicting which customers will become high-value vs. one-and-done, combining four signal categories:
Purchase Signals (40% weight): Order count, recency, AOV relative to category average, subscription status.
Engagement Signals (35% weight): Email open/click rates (personal, 90 days), SMS opt-in, loyalty program membership, review submitted, referral made.
Browse Signals (15% weight): Site visits in last 30 days, product pages viewed, add-to-cart without purchase.
Support Signals (10% weight): Support ticket sentiment, NPS/CSAT response.
Tier mapping:
- L1 (0-20): One-and-done risk. Minimal post-purchase engagement.
- L2 (21-45): Potential repeat buyer. Engaged with email but hasn’t repurchased.
- L3 (46-70): Likely high-value customer. Multiple engagement signals.
- L4 (71-100): VIP / Brand champion.
Target: Clear separation. L3+ customers should convert to repeat purchase at 45%+ while L1 converts at under 10%. If the tiers don’t separate, the score weights need recalibrating.
Where the data lives
This is the most cross-platform metric in the framework. Purchase data from Shopify, engagement data from Klaviyo, browse behavior from GA4 or your CDP, support signals from Gorgias or Zendesk. No single platform holds all four signal categories.
Building the composite score requires a warehouse. A BigQuery model pulls signals from each source, normalizes them, applies weights, and outputs a score per customer. The score then reverse-ETLs back to Klaviyo as a custom property for segmentation and flow triggers. This is Phase 4 work, but the payoff is immediate: your flows and campaigns target based on predicted value, not just past behavior.
Metric 6: Touchpoint-to-Revenue Attribution
What it measures: The correlation between specific marketing touchpoints and product experiences on revenue outcomes.
Marketing touchpoint analysis: Email flows, SMS campaigns, retargeting ads, direct mail, loyalty interactions, post-purchase sequences. These are the “features” of the DTC customer experience.
Benchmarks:
- Automated Klaviyo flows generate up to 30x more revenue per recipient than one-time campaigns (derived from $0.11 campaign RPR vs. $3.65 abandoned cart flow RPR).
- Highest-revenue flows: abandoned cart ($2.65-$9.86 RPR depending on AOV), welcome series, post-purchase, browse abandonment.
- Brands with 3-5 email post-purchase education sequences see 15-25% higher repeat purchase rates.
- SMS campaigns show slightly higher RPR than email ($0.12 vs. $0.11, Klaviyo 2024).
The gateway product concept: The best brands have identified a single SKU or bundle that converts the highest percentage of first-time buyers into repeat customers. Dr. Squatch: bar soap (the “You’re Not a Dish” video drove 120M+ views and 30x revenue growth). Liquid Death: tallboy variety pack. Magic Spoon: variety pack sampler. If you don’t know your gateway product, you don’t know your activation strategy.
Where the data lives
Klaviyo is the primary source for email and SMS attribution. It tracks flow performance, RPR, and attributed revenue natively. The limitation is that Klaviyo’s attribution is last-touch within its own channel. It can’t tell you how an email flow interacted with a retargeting ad that ran simultaneously.
For cross-channel attribution, the warehouse approach joins Klaviyo flow data with ad platform touchpoints and Shopify conversion events. First-product-to-LTV analysis (the gateway product question) is a pure warehouse query: group customers by first SKU purchased, calculate 12-month LTV per group, rank.
What These Four Metrics Give You
Activation rate tells you whether first-time buyers come back. Activation-to-monetization tells you whether repeat buyers become profitable. Quality scoring predicts who will do what before they do it. And touchpoint attribution shows you which levers actually drove the behavior.
Together, they close the gap between acquisition and retention. Most brands can tell you how many customers they acquired and how many they have today. These metrics explain what happened in between.
Next in this series: Expansion & Retention — detecting growth signals and preventing churn before it happens.
