Should You Discount to Save the Customer? - Issue 324

Napkin math for estimating whether to offer a renewal discount - and how low to go.

One of the tools I integrate into the products I support or advise is a customer churn prediction model. For every customer, we estimate their probability of churning or downgrading, along with the expected timing, so sales and product teams can act early and try to retain them.

What often happens next is that the team targets the customers identified as “about to churn” and offers them a discount to encourage renewal.

The next challenge is determining the right discount threshold: how low can we go to retain the customer with the least possible revenue loss?

In this publication, I’ll walk through the framework I use to estimate that threshold, along with how to approach the underlying modeling and reporting.

Before we start, let’s align on metrics and definitions

It all comes down to NRR

Net Revenue Retention measures what happens to revenue from the same group of existing customers over time.

It reflects:

1. Upgrades and expansion

2. Downgrades and contraction

3. Renewal discounts

4. Cancellations.

Important: it excludes revenue from newly acquired customers.

How to read it:

• Above 100%: existing customers generate more revenue than before.

• At 100%: expansion offsets all losses.

• Below 100%: the existing customer base is shrinking.

Why a discount is important: A customer moving from $100K to $80K creates $20K of contraction. That is better than losing the entire $100K, but it is still a revenue loss.

The problem with renewal discounts

Teams tend to overvalue saved renewals. They see it mostly as logo (customer) retention: “We discounted, therefore we saved the account.” But it’s very difficult to model what would have happened without the offer. The customer might have:

1. Renewed at full price

2. Accepted a smaller discount

3. Reduced scope but stayed

4. Delayed the decision

5. Churned regardless of the discount.

First of all, no churn prediction model is perfect, and any model can be wrong for an individual customer. This framework relies on predicted probabilities, so probability calibration is especially important, and it requires ongoing maintenance. You should also monitor precision, recall, lift by risk band, and model stability over time.

Second, the problem with discounted renewals for the “about to churn” cohort is that even if a customer accepts it, they’re still more likely to churn during the next renewal cycle than customers not in this cohort. Even after renewing, these customers may remain at higher churn risk during the next renewal cycle, especially when the discount does not resolve the underlying product, budget, or value problem. Their future expansion potential may also be lower than healthier accounts.

Finally, teams often over-estimate renewal math: the customer was worth $100K. We renewed them for $80K. Therefore, we saved $80K!

This calculation assumes that without the discount, churn probability was 100%. But that is almost never known:

• If the customer had a 70% chance of renewing at full price, then the expected retained revenue without the discount: 70% × $100K = $70K

• If the discount raises renewal probability to 80%: 80% × $80K = $64K

So the company saves more customers but generates less expected retained revenue.

This is why this is difficult, and needs a village of data scientists to model it right. At the end of the day, it all comes down to retained revenue.

My framework for modeling discount threshold

For every proposed renewal discount, I model 2 things: