April 7, 2021
Put simply, customer churn is when customers decide to go somewhere else to get what they need. In other words, it’s the percentage of customers that stop buying from you in a set period, either a month, quarter or year.
Maybe the customer was price-driven and left for a cheaper option. Or a new buyer was hired and they preferred a different vendor. Sometimes, the customer couldn’t find what they needed on your website and went to another’s. Maybe, more significantly, your team failed to save the day when the customer needed it most and a competitor stepped in.
Whatever the cause, it probably boiled down to a poor customer experience. It doesn’t usually happen all at once. It can be a slow defection, which is what makes customer churn so difficult to track and measure. Measuring customer churn matters. Keeping your customers from going somewhere else will drive long-term profitability because it costs less to keep and grow a customer than to attract a new one.
Distribution Strategy Group says retained customers:
According to the book Leading on the Edge of Chaos, a 2% increase in customer retention has the same effect as decreasing costs by 10%. Research from Bain & Company shows increasing customer retention rates by 5% increases profits by 25% to 95%.
The data is clear: Losing customers is expensive.
But there’s a problem. Many distributors measure churn, but traditionally customer churn rate has been a lagging indicator. It’s looking back at what’s already happened, which makes any effort to get that customer back less likely to succeed.
Few distributors try to predict customer churn. Those that do, get it wrong because of how they define churn. The problem lies in how they define a “lost customer.”
Some customers buy all the time and some only buy every few months. In this simple example, if you define churn as someone who doesn’t buy for 90 days, you’ll miss at-risk customers. That’s why traditional churn management practices aren’t effective. Distributors’ churn models assume all customers are behaving equally. But in distribution, there are some customers worth worrying about if they stop buying for five days because they usually buy every day. And others we should worry about if they stop buying after 90.
Missing timely reorders for products such as a filter is also a red flag. That’s a data point not typically captured in the standard calculations because churn in distribution is slow, category by category, product by product.
In addition to missing at-risk customers, distributors are also missing visibility into potential problems with customer service, user experience or even the products they’re selling. A high customer churn rate could also be an indicator that your sales team is targeting the wrong types of customers.
Distributors are focusing on the wrong things in part because they don’t have great visibility into churn. The complexity of distributors’ sales channels complicates matters further. Data operates in siloes: ecommerce, inside sales, customer service, field sales, catalogs, mobile apps and more. And as customers move to digital channels to shop and buy, it can be even more difficult to track. After all, if your website doesn’t have what they need –and they need it right away –it’s easy for them to leave and buy elsewhere, with no one in your company the wiser.
Distributors need AI to model churn. By analyzing data across sales channels, an AI model can predict when a customer is going to leave based on their behavior and sales history. Then, a distributor can take steps proactively to avoid losing that customer, deploying field sales or inside sales to reacquire or reactivate them. For example, if the customer missed an expected reorder date, a sales rep can reach out with an offer for that product.
If you’re not measuring the true extent of customer turnover in your business, you can’t fix it. The solution is not one size fits all. With AI, distributors can get ahead of the churn.