The Direct Customer:
Whom the message moves, not who is at risk
Direct sales are small, but they are the only channel in which the manufacturer sees the buyer. The common rule "whoever threatens to leave gets an offer" is often ineffective. An effect model targets the customers for whom the message changes something.
This module is part of a nine-part model calculation on a fictitious mid-sized chocolate manufacturer (€95 million net revenue, 140 items, 4 lines). All euro amounts are derived from the documented assumptions of the sample company. They are not results of real customers and not a performance promise. Which values are achievable on your data is shown only by a pilot.
01 The problem - risk is not effect
Whoever leaves anyway leaves with a voucher too; whoever stays anyway does not need one
The sample company earns €2,850,000 in direct sales, 3 % of net revenue: online shop, corporate gifts, resellers without a head office. Small in revenue, large in data: order rhythm, basket, seasonal reference per customer.
The usual rule sends offers to the customers with the highest churn risk. Research shows: that is often the wrong target group. What matters is for whom the message changes behaviour, and that can only be measured with a randomly held-out comparison group.
Without a held-out comparison group there is no effect, only a guess.
02 The model - two models, one difference
Purchase probability with message minus purchase probability without message, per customer; the target group is the top deciles of this difference
Mean effect top 30 %: 5.8 percentage points Mean effect random selection: 2.5 percentage points (example values)
Part of the customer base reacts to a message by turning away: a voucher nobody needs reminds customers that they can leave. The effect model shows this decile and leaves it alone. A risk model would write to it.
03 Business impact - small, clean, measurable
The lever: additional revenue in direct sales at the direct-sales contribution margin
Every assumption comes from the sample company and is stored centrally. With your figures the input changes, not the method.
| Position | Value |
|---|---|
| Base: Revenue in direct sales / year | €2,850,000 |
| Lever: Additional revenue through targeting by effect (assumption) | +5 % = €142,500 |
| Contribution margin in direct sales | 40 % |
| Result: savings / year | €57,000 |
Assumptions of a sample company - in a real project your data replaces these values.
Direct sales are the only place where effect can be measured cleanly because the manufacturer can form the comparison group itself. What is learned here about seasonal reference, basket and message later travels into the conversations with retailers. The amount is small and stays small; the method is the gain.
04 Next steps in your factory
From the campaign to the comparison group
From the next campaign a random part of the customer base is held out. Without this step there is no effect model.
After two campaigns with a comparison group the first effect model emerges. You see the effect per decile against the previous rule.
Before every seasonal campaign a list of the customers with the highest expected effect - and a list of those to be left alone.