Ocular has dedicated retention screens built for cohort analysis — more powerful than Chart Builder for these questions because they track cohorts over time, show period-by-period drop-off, and support acquisition vs. retention splits. The hierarchyDocumentation Index
Fetch the complete documentation index at: https://docs.ocular.dev/llms.txt
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- Sales Performance → New vs. Repeat Customer Trend — quick snapshot of new/repeat mix
- Purchase Retention Cohort — definitive tool for repeat purchase, CAC payback, LTV, split-by capabilities
- User Activity Cohort — site/app return rates from GA4 events, before repurchase
Are customers coming back to buy again?
Trigger: You need to know if you’re building repeat demand or relying on one-time buyers — for forecasting, LTV, and acquisition-vs-retention budget decisions. Start with: Sales Performance → New vs. Repeat Customer Trend. Shrinking new share with stable volume → acquisition slowing. Shrinking repeat share → first purchases aren’t converting to second. Then go deeper: Purchase Retention Cohort — tells you where and why. Configuration: Measure = Customers · Granularity = Month · Value Representation = Percentage.| Pattern | Meaning | Action |
|---|---|---|
| Steep M0→M1 drop (only 10–15% return) | Most first-time buyers never come back | Re-engagement at Day 30–60 — welcome-back offers, product education, replenishment |
| Flattens after M2–M3 (stable at 5–8%) | Loyal core; churn happens early | Invest in post-purchase flows, not late-stage win-backs |
| Recent cohorts retain worse than older | Retention degrading — channel quality or PMF | Split cohorts by channel or discount code to isolate |
| One cohort dramatically better | Something worked — campaign, product, onboarding | Identify and replicate |
Which acquisition channels produce customers who actually stick?
Trigger: Spending across Meta, Google, influencers, organic — and you need to know which bring high-LTV customers vs. one-time buyers. Go to: Purchase Retention Cohort → Split By: Discount Code (Acquisition) or Sales Channel Configuration: Measure = Customers · Granularity = Month · Split By in Breakout mode. What to look for: Big M0 + steep drop by M2 → channel delivers volume, not value. Smaller cohort + flatter curve (15–18% still active at M5) → most sustainable acquisition source. Cross-check with profitability: Switch Measure to Accumulated CM1 per Customer to see which channels produce profitable repeat buyers after COGS. A channel might retain well but at low margin if it’s discount-dependent. Decision: Which channels scale (high retention, high LTV), and which to cut (attract deal-seekers who churn)?How long does it take to recover my customer acquisition cost?
Trigger: Finance or leadership wants proof that acquisition spend is sustainable — month-by-month cumulative revenue vs. CAC, per cohort. Go to: Purchase Retention Cohort → CAC Payback toggle Configuration: Toggle CAC Payback on. Measure = Accumulated Sales per Customer (or Accumulated CM1 per Customer for profitability-adjusted). Cells turn green once accumulated revenue surpasses CAC.| Payback timing | Meaning | Action |
|---|---|---|
| M1–M2 | Healthy — recovers quickly | Safe to scale |
| M4–M6 | Acceptable, watch cash flow | Tighten CAC targets, improve first-90-day repeat rates |
| Beyond M6 or never green | Unsustainable | Cut budget; check if discounting is inflating CAC without building LTV |
Are users coming back to my site — even before they buy again?
Trigger: You want to track engagement retention, not just purchase. Site return rates dropping is a leading indicator — purchase retention will follow. Go to: User Activity Cohort Tracks GA4 event-based retention. Unlike Purchase Retention Cohort (counts purchases only), this counts any event — session starts, add-to-cart, product views. Configuration: Start Event =add_to_cart (or session_start for broader engagement) · Return Event = session_start · Granularity = Week · Split By Entry UTM Source.
Cross-reference with Purchase Retention: Strong site return + weak repeat purchase → problem isn’t engagement, it’s conversion. Users come back but don’t buy. Check pricing, availability, checkout friction.
Decision: Which channels/audiences need better post-visit engagement (email, retargeting, content), and is the drop-off at site-return or repurchase?
Who are my highest-value customers, and what do they have in common?
Trigger: Building segmentation, planning a loyalty programme, or trying to acquire more of your best customers. Go to: Purchase Retention Cohort → Measure: Accumulated Sales per Customer or Customer LTV Configuration: Measure = Accumulated Sales per Customer. Use Split By: Shipping City (Acquisition), Product Category (Acquisition), or Discount Code (Acquisition) to find what high-LTV cohorts share. Quick snapshot: Sales Performance → City Performance Across Price Buckets — shows where high-value orders concentrate geographically. Decision: Which customer profiles for lookalike audiences, and which first-purchase products/channels to prioritise for more high-LTV acquisitions?How does AOV differ between new and returning customers?
Trigger: Are returning customers spending more (loyalty signal) or less (discount dependency) than first-time buyers — and does it vary by channel? Go to: Purchase Retention Cohort → Measure: Net AOV or Gross AOV Configuration: Measure = Net AOV · Granularity = Month. M0 = first-purchase AOV. Later months = repeat AOV. If AOV declines later, repeat buyers may be cherry-picking discounts or buying lower-value items. Split By Sales Channel to see if the pattern varies. Channels where new customers spend more than repeat → discount-heavy acquisition inflating first orders. Quick snapshot: Sales Performance → Discount Affinity by Customer Type — discount usage by new vs. repeat by category. Decision: Is retention generating more valuable repeat purchases, or are repeat buyers being conditioned to buy only on discount?Next
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