🍯 True Classic Case Study: How We Increased CLV by 12% in Q4

Hello You DTC Savage,

If you’re not treating RPR (Repeat Purchase Rate), Average Time Between Orders, and CLV (Customer Lifetime Value) as a unified post-purchase strategy, you’re leaving serious money on the table. Here’s how we tackled all three for True Classic and delivered a 12% lift in CLV across all cohorts.

The Post-Purchase Trifecta

To drive results, you need to optimize:

• RPR: How many customers return to buy again.

• Average Time Between Orders: How quickly they come back.

• CLV: How much they spend over their lifetime.

You can’t focus on just one—each metric feeds into the other. Here’s how we approached it:

Step 1: Analyzing RPR Products

When cross-referencing products with high RPR, volume, price point relative to AOV, and PDP CVR% (Product Detail Page Conversion Rate), here’s what to look for:

• Volume vs. Price Point: The sweet spot is high RPR products that also sell at a price above your AOV.

  • Why? If their first post-purchase item is above AOV, the incremental lift is significant, and subsequent purchases are likely to normalize at AOV.

However, this isn’t always a given. You’ll need to test:

• High RPR + High AOV

• High RPR + Low AOV

• Optimal timing for these offers

The goal: Get customers to purchase a high RPR item within 20-30 days post-purchase. This ensures you capture incremental revenue while building momentum for future purchases.

Step 2: Optimize the Replenishment Cycle

As you analyze customer purchase behaviors, identify key replenishment opportunities. For True Classic, we looked at:

• Initial purchase timing (e.g., T-shirt buyers often replenish after ~30-60 days).

• Best-performing products with high RPR and relevance to prior purchases.

Execution tip: Slot replenishment reminders into your post-purchase flow with campaigns promoting top-performing best-sellers. Track performance and refine timing based on results.

Step 3: Testing & Balancing All 3 Metrics

Achieving perfection across RPR, Average Time Between Orders, and CLV is rare. But here’s what works:

• Start with high RPR products that align with your AOV goals.

• Test campaign timing relentlessly. (E.g., does a replenishment reminder at Day 30 outperform Day 45?)

• Let the data guide decisions while prioritizing small, incremental wins.

The best-case scenario: Customers buy a high RPR, above-AOV product quickly (Day 20–30). This not only hits Month 1 CLV goals but increases the likelihood of a faster third purchase cycle.

Key Takeaways:

• Treat RPR, Average Time Between Orders, and CLV as a single, unified strategy.

• Optimize your post-purchase flow to feature high RPR products with incremental lift potential.

• Focus on data-driven testing to fine-tune product selection, timing, and messaging.

This approach isn’t just about driving revenue, it’s about creating a system that builds long-term customer loyalty and compounds profitability with each purchase.

Are you optimizing all three metrics in tandem?

Lastly, I would love to chat with you on LinkedIn if we aren’t already connected.

Talk soon,
Feras