Winback & pricing:
The most valuable lead is the ex-subscriber
Thousands of former subscribers already know your product - and nobody approaches them systematically. Uplift models show who returns with which offer and which price adjustment the base absorbs without cancellations jumping.
This module is part of a six-part model calculation about a fictional regional media house (55,000 print and 25,000 digital subscriptions). All euro amounts are derived from the documented assumptions of the sample publisher; the levers follow published industry benchmarks and deliberately stay below their best documented values. The values shown are not results of real clients and not a performance promise. What values are achievable on your real data is established only in a pilot project.
01 The problem - The ex-subscriber pool lies fallow, the price lies frozen
Between scattergun discounts and a price taboo, revenue is given away
In the sample publisher, around 12,000 addressable ex-subscribers accumulate within two years. The usual reaction is the scattergun: the same discount offer to everyone - expensive for those who would have come back anyway, useless for those who need a different product. Amedia achieved 12 percent reactivation with loyalty-sorted winback calls.
At the other end, the base price goes untouched for years out of fear of a cancellation wave. Mather Economics shows the opposite: segmented price adjustments - who gets how much, who gets nothing for now - achieved 8 percent more digital revenue at a US publishing group with only 0.4 percent additional churn; at Mediahuis Aachen, 6 to 7 percent price increases ran nearly churn-free.
Both without a single new contact: the lever lies entirely in data you already have.
02 The model - Uplift instead of probability
Uplift modeling separates who the offer convinces, who would come back anyway and who it does not reach at all
Dezil 1-3 (Kampagne lohnt): 3.600 Ex-Abonnenten Erwartete Reaktivierung Kampagnen-Segment: 9.5% Bestand mit tragfähiger Preisanpassung: 62%
A classic model says who comes back. An uplift model says for whom the offer makes the difference - only there is the discount worth it. The same logic carries the pricing side: price elasticity per segment decides which part of the base absorbs an adjustment and which needs more bonding first. Mather measures 2.5 instead of 5.7 percent additional churn for segmented versus across-the-board increases.
03 Business impact - Two levers, one data basis
The lever: 9.5 percent reactivation in the contacted top segment plus a 3 percent net effect from segmented price steering
No round number: every assumption comes from the sample publisher and is stored centrally. With your real figures only the input changes, not the method.
| Item | Value |
|---|---|
| Contacted top segment (deciles 1-3 of the 24-month pool) × 9.5% reactivation | 3,600 × 9.5 % = 342 |
| Reactivated subscriptions × 7 months × ARPU | €25,137 |
| Digital revenue / year × 3% net price effect | €94,500 |
| Lever: Sum of winback + price optimisation effect | €25,137 + €94,500 |
| Result: added revenue / year | €119,637 |
Assumptions of a sample publisher - in a real project your data replaces these values.
The 9.5 percent reactivation in the top segment sits below Amedia's 12 percent (by phone, loyalty-sorted), and only the actually contacted third of the pool is counted; the 3 percent price effect below Mather's documented 8 percent. Both levers use the same data basis - subscription history, usage behaviour, offer responses - and the same model framework.
04 Next steps in your publishing house
From the cancellation archive to ongoing base management
We structure the ex-subscriber base: cancellation reason, usage history, offer responses - the basis for both models.
The propensity-sorted campaign runs against a random selection. The uplift shows in direct comparison - split by segment.
Elasticity segments and a staggered adjustment plan for the base: who, when, how much - with the early warning from Module 01 as the safety net.