Autoship & Save | Liki24
Build one ROI-positive retention initiative for Liki24.de | By Yevhenii Holovei
Why This Initiative
Why Autoship & Save
Four structural advantages that make this the strongest retention lever
Alternatives Considered & Rejected
Why the other three directions were deprioritised
| Initiative | Decisive Weakness | Verdict |
|---|---|---|
| Yearly Membership7% discount | app-only | Mobile-only caps TAM. Annual commitment is a high entry barrier. | Rejected |
| Personalised UXTop-category experience | Improves experience but creates no obligation. Impact is indirect and hard to isolate. | Rejected |
| Smart Re-purchase TriggersBehavioural push/email | No customer commitment. Notifications decay in effectiveness over time. | Rejected |
| Autoship & Save10% off | web + app | Cannibalization risk -- manageable via A/B test with proper control group. | Selected |
Target Segment
Segment Definition: Product Repurchase ≥ 20%
Why product-level repurchase rate is the right filter -- not category volume
The Bestsellers file contains a Product repurchase % column -- the only signal that directly measures repeat buying behaviour at the SKU level. Customers with repurchase ≥ 20% are already self-returning; Autoship formalises and accelerates that pattern.
Why NOT category rank alone: Antiparasitic is #1 by order volume (1,824 orders) but repurchase is only 1–7%. Offering Autoship there is a pure margin discount with zero behavioural additionality.
Cohort sizing -- Jan 2026: 4,247 total → ~30% eligible (~1,274) → 15% activation → ~191 autoship subscribers
| Product | Repurchase % | Autoship Fit |
|---|---|---|
| Maraton Forte | 54% | Strong |
| Power V8 | 41% | Strong |
| Minus 30 | 33% | Good |
| Magiun afrodisiac | 27% | Good |
| Pharmatex | 22% | Marginal |
| Antiparasitic category | 1–7% | Exclude |
Core Hypothesis
LTV Uplift Hypothesis & Model
If users with repurchase ≥ 20% switch to Autoship at 10% off, frequency + retention gains will outweigh the discount cost
Incremental LTV =
AOV × (1 − 0.10) × freq × 1.10 × retention_autoship
− AOV × freq × retention_baseline| Variable | Value | Basis |
|---|---|---|
AOV | $51 | Average across cohorts |
freq_baseline | 1.05 /month | Jan 2026 cohort actuals |
retention_baseline | 4 months | Historical baseline |
retention_autoship | 6 months | Target (Amazon analogue) |
Discount | 10% | Activation optimisation |
Frequency uplift | +10% | Monthly order rate target |
= (51 × 0.90 × 1.05 × 1.10 × 6) − (51 × 1.05 × 4)Success Metrics
Northstar Metric
Primary Metric
LTV uplift > Discount cost + Cannibalization. Measured via A/B test. Large cannibalization will destroy gross margin even if subscription LTV looks positive on paper.
Early Stop Condition
Stop test immediately if gross margin in the test group drops >3% vs baseline.
| Metric | Target | What it signals |
|---|---|---|
| Autoship Adoption Rate | ≥15% | Offer resonance |
| Autoship vs non-Autoship LTV | +15–25% benchmark | Incremental value |
| Active at 1 / 3 / 6 months | Monitor churn curve | Retention quality |
| Orders/user (test vs control) | +10% vs baseline | Frequency uplift |
| Cannibalization Rate | <30% | Margin protection |
| Subscription Cancellation Rate | <35–40% | Product-market fit |
UX Flow & Product Design
Purchase & Subscription Flow
End-to-end user journey across 6 core states
ROI Logic
Revenue ROI Model -- Jan 2026 Cohort
Bottom-up from cohort sizing to net incremental value across three activation scenarios
All figures are Revenue LTV, not Profit LTV. Margin impact of the 10% discount and cannibalization must be modelled against actual category gross margin data before final go/no-go.
Key Risks
Risk Assessment
Five key risks ranked by combined probability × impact -- with mitigation strategies
Customers who would have purchased anyway receive a 10% discount -- pure margin destruction. Must be isolated via A/B test with a clean control group. Treat incremental orders, not total subscriber orders, as the numerator.
Even eligible products (repurchase 20–54%) still have 46–80% of customers who don't return regularly. Track cancellation rate by delivery cycle -- a spike at cycle #2 signals frequency mismatch.
10% may be insufficient if users distrust auto-billing. Mitigate with: prominent "Cancel/Skip anytime" copy, A/B test on discount size (0% vs 5% vs 10%).
Autoship creates predictable demand but requires predictable supply. Mitigate with proactive email 5 days before if SKU is at risk of OOS.
German law may restrict auto-billing for certain near-prescription categories. Mitigate with legal review before launch; start with clearly non-regulated SKUs.
Experiment Design
A/B Test Structure
Three-cell design to isolate the mechanism effect from the discount effect | Min. 8 weeks | 2 delivery cycles
| Population | Repeat buyers of qualifying SKUs, ≥2 orders in last 90 days |
| Randomisation unit | User ID -- prevents cross-group contamination |
| Duration | 8 weeks minimum (covers 2 full delivery cycles) |
| Traffic split | 34% / 33% / 33% |
| Expected cohort | ~2,000–4,000 new eligible users/month |
| Significance threshold | p < 0.05 for primary metrics |