LTV scoring agent for acquisition and retention
Kohepets builds sharper marketing segments around future customer value.
Kohepets used lifetime value scores to prioritize acquisition and retention audiences and saw 28% higher customer LTV. The agent scores customers using purchase history and profiles enriched by external data providers.
The impact
28%
higher customer LTV
LTV scoring agent for acquisition and retention
Case study facts
- Industry
- Pet retail & pharmacy
- Team
- Marketing
- Systems connected
- 4
- Time to live
- 3 weeks
- Engagement
- AI implementation
The impact
Results for the team
- higher customer LTV
- 28%
- higher customer LTV
- The marketing team used the predictions to compare customer groups and focus acquisition and retention campaigns on audiences with greater long-term potential.
- more repeat purchases
- 21%
- more repeat purchases
- Repeat purchases track whether customers return to buy again, alongside the model's prediction of their future value.
- faster audience prioritization
- 4×
- faster audience prioritization
- Teams spend less time ranking acquisition and retention audiences because both use the same predicted-value signals.
Situation
One customer. Two views of value.
A first purchase says little about a customer's longer-term relationship with a pet retailer. Campaign reporting and retention activity can optimize separate short-term goals without a shared view of repeat demand.
Challenge
The first order hid the longer relationship.
Acquisition needed to compare the quality of new customers, while retention needed to prioritize existing relationships. Both required a consistent forecast horizon and a way to tell a reliable prediction from an uncertain one.
What we built
Enriched profiles. More useful marketing segments.
We built an LTV scoring agent that connects external data providers to enrich customer profiles, combining that context with purchase history, recency, frequency and product mix. The agent uses a predictive model to estimate future value over a defined horizon and keeps uncertainty alongside every score.
Marketing uses enriched profiles and predicted value to build acquisition and retention segments, compare customer cohorts and prioritize campaign audiences. Estimates are tested against realized value, not treated as guaranteed revenue.
AI tech stack
Amazon BedrockAI inference
Amazon SageMaker AIModel training and serving
- Unify and enrich profiles
Connect external data providers to enrich customer profiles, resolve identities and combine the context with purchase history, net of cancellations and refunds.
- Engineer value signals
Combine enriched profile context with repeat cadence, recency, frequency and category patterns for every customer.
- Predict future value
The model scores each customer over a consistent horizon, with uncertainty attached.
- Validate calibration
Predictions are checked against later purchases using time-based holdouts.
- Activate audiences
Marketing builds acquisition and retention segments using enriched profiles and ready-to-use predicted-value bands.
- Refresh continuously
Estimates update as customers buy again or their behavior shifts.
Evals and guardrails
Test predicted value. Keep spending under review.
Forecast evals
Test predictions on later purchases the model has not seen. Measure errors and how often actual customer value falls within the predicted range.
Reliability evals
Compare predicted and actual value across customer groups. Test limited purchase histories and exclude information unavailable at prediction time.
Guardrails
Check that customer data is current and audiences are eligible. Use a fallback for limited histories; require approval for budget changes.
Results
Growth decisions look beyond the first sale.
The marketing team used the predictions to compare customer groups and focus acquisition and retention campaigns on audiences with greater long-term potential. Repeat purchases track whether customers return to buy again, alongside the model's prediction of their future value.
Teams spend less time ranking acquisition and retention audiences because both use the same predicted-value signals.
- higher customer LTV
- 28%higher customer LTV
- more repeat purchases
- 21%more repeat purchases
- faster audience prioritization
- 4×faster audience prioritization
From Kohepets
“We have worked with Revensi for many years. In that time they have made vast improvements to how our business runs, and they are great people to work with. We highly recommend them.”
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