Reactivation Targeting
“Among dormant users, which will reactivate if we send a personalized offer?”
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A real-world example
Among dormant users, which will reactivate if we send a personalized offer?
Sending reactivation offers to all dormant users is wasteful — most would never return regardless, and some would return without an offer. What you need is the incremental lift: users who will reactivate because of the offer and would not have otherwise. For a platform with 3M dormant users, targeting only the persuadable segment saves $4M in offer costs while doubling reactivation rates.
How KumoRFM solves this
Relational intelligence for customer retention
Kumo's ASSUMING clause enables counterfactual prediction — comparing the probability of reactivation with and without a personalized offer. The difference is the true uplift. By learning from the relational graph of past offer responses, user behavior patterns, and social connections, Kumo identifies the persuadable segment that traditional A/B testing takes months to find.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
USERS
| user_id | plan | last_active_date |
|---|---|---|
| U701 | Pro | 2024-11-15 |
| U702 | Basic | 2024-10-02 |
| U703 | Pro | 2024-12-08 |
SESSIONS
| session_id | user_id | duration_min | timestamp |
|---|---|---|---|
| S7001 | U701 | 35 | 2024-11-15 |
| S7002 | U702 | 8 | 2024-10-02 |
| S7003 | U703 | 52 | 2024-12-08 |
OFFERS
| offer_id | user_id | type | discount_pct | timestamp |
|---|---|---|---|---|
| OF901 | U701 | reactivation | 30% | 2025-01-10 |
| OF902 | U702 | reactivation | 20% | 2025-01-15 |
| OF903 | U703 | reactivation | 25% | 2025-02-01 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(SESSIONS.*, 0, 14, days) > 0 FOR EACH USERS.USER_ID WHERE COUNT(SESSIONS.*, -90, 0, days) = 0 ASSUMING COUNT(OFFERS.* WHERE OFFERS.TYPE = 'reactivation', 0, 1, days) > 0
Prediction output
Every entity gets a score, updated continuously
| USER_ID | True_PROB (with offer) | True_PROB (without) | UPLIFT |
|---|---|---|---|
| U701 | 0.68 | 0.22 | +0.46 |
| U702 | 0.19 | 0.15 | +0.04 |
| U703 | 0.55 | 0.41 | +0.14 |
Understand why
Every prediction includes feature attributions — no black boxes
User U701 — Pro plan
Predicted: +0.46 uplift (high persuadability)
Top contributing features
Prior offer response rate
4 of 6 redeemed
30% attribution
Pre-dormancy engagement level
35 min/session
24% attribution
Connected users who reactivated
3 of 5
20% attribution
Days dormant
110 days
15% attribution
Plan value vs usage at churn
High gap
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A platform with 3M dormant users that targets only the persuadable segment saves $4M in offer costs while doubling reactivation rates — turning counterfactual prediction into measurable incremental revenue.
Related use cases
Explore more retention use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
