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Posted June 21, 2021

Your 'Churn Management' may be failing

by Benj Cohen

Benj CohenChurn is measured by the number of people who stop using your products and services within a certain time period. And because of the complexities surrounding when customers buy, why, and how, churn is especially challenging for distributors to track and manage.

Churn prevention is a key factor in long-term profitability. Most distribution markets aren’t growing rapidly, so protecting market share is equally as important as capturing new market share. Unfortunately, most distributors struggle to do this effectively.

Here are four reasons why your churn management system might be failing:

1. Most systems cannot account for purchasing behaviors at the account-level.
B2B customers’ buying habits and needs vary. One customer might purchase products every week cyclically, while another may buy every few months.

Most churn predictions are inaccurate because they are defined by rigid rules. For example, a common way that distributors predict churn is to raise a flag on accounts that haven’t purchased in a set period of time - say 90 days. With such a broad scope, you can see how at-risk customers that typically purchase from you more frequently could fall through the cracks, or how a customer that purchases every few months could be flagged as at-risk even if their purchasing behavior is perfectly healthy.

2. Churn can occur at different levels: customer, category, and product.
Churn can happen at the customer level (they stop buying from you altogether) or at the product/category level (for example, they keep buying cable splicing supplies from you but stop buying terminals and connectors). If your churn management doesn’t monitor and account for these losses both at the customer and the account level, you may lose wallet share without noticing.

3. When predicting churn, some data is left out of the equation.
The disparate sales channels used by most distributors — eCommerce, sales reps, customer service agents, mobile apps, catalogs (and sometimes more) — result in data silos. It’s important to know if your customer has been browsing your website but not purchasing any items. Alternatively, if your customer service agent answers a question about abrasives and their sales rep has no idea that this interaction occurred, they have no way to tell if they are losing business and the customer is going on to purchase abrasives elsewhere. If you’re not collecting data from all sales channels and using them to have a single source of truth for customer engagement, it’s impossible to identify early signs of customers at risk of churn.

4. Lack of “action items” when a churn-flag is raised.
The quicker you can give attention to a customer at risk of churn, the more likely you can prevent it. Unfortunately, many distributors don’t have the visibility into what’s causing churn or an automated way to intervene.

Distributors need a system that can identify an issue before they lose a customer and then trigger proactive intervention.

With the right AI-powered churn management system in place, all of your data can be used to predict churn patterns before they fully materialize. An ideal churn prevention tool will then go one step further - delegating a warning or trigger to the right resource (whether they be marketing, inside sales, outside sales, or customer service) to act before high-churn-risk customers walk away.

Benj Cohen founded Proton to help distributors harness cutting-edge artificial intelligence. He learned about distribution firsthand at Benco Dental, a business started by his great grandfather. He’s on a mission to supply distributors with an innovative technology they need to thrive in modern markets. Contact Benj at or visit


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