Kundenabwanderung vorhersagen | Predicting Customer Churn

For businesses selling ad-hoc, it’s hard for companies to predict customer churn. That is in contrast to as-a-Service subscription-based businesses for whom identifying at-risk customers is more accessible right from the initial sign-up.

SaaS businesses benefit from constantly updated, deep, live client usage statistics in the free trial and beyond when they become subscribers. They build AI-based churn prediction models on historical data tracking how often clients log in and what features they use.

Non-subscription B2B companies often only have data on orders placed by and emails/calls made to and from a client. That’s a little to base a customer churn prediction model on.

But it is possible. We work with many companies that have between 5,000 and 20,000 customers. Typically, 10% to 20% of their clients put regular orders in, with the rest purchasing sporadically. It’s much more complex.

In this article, we cover:

· How to define churn
· How to predict churn in a non-subscription environment
· Understanding why existing customers churn
· Ways to mitigate churn rate risk across your business.

Defining Churn for Businesses whose Customers order Ad-Hoc

When working with a new client, we first want to identify what “churn” means to them. When do active customers become lapsed customers? And do you count clients who have only ordered once as lost?

Defining what Churn Means is Individual to every Business

What if what you sell has a life span of 3 years and is used repeatedly on multiple jobs? Should you only consider a customer as churned three years later if they don’t order from you again?

What if your products must be reordered repeatedly because they are used on individual jobs? Let’s say that you have a client that installs heat pumps. They have a track record of ordering 10-20 units a month. Have you lost the customer if they don’t buy from you for one month? Or would you need to wait six months to consider them lost?

To truly understand the effect churn has on their revenues, businesses need to create a model that reasonably reflects how often customers need their particular products.

Customer Churn Prediction through Data Analysis

Now, data scientists can create machine learning models that can be trained to analyze customer data points to examine the likelihood of customer attrition.

– Data standardization

First, a data preparation team must standardize the fields on your customer database so that what the machine learning algorithms read is consistent.

– Data cleansing

Next, your database needs to be checked for accuracy. Over time, buyer names and contact details change. You may not have the correct customer email address, which leads to missing customer care, and the customer might churn.

List brokers offer data cleansing services where their data can be matched to your existing customer database and replaced if outdated. An additional benefit is that working from an accurate database will lower your marketing costs when retaining customers and winning back lapsed accounts later.

– Data appending

ERP data (historical sales data) are the essential basis for a customer churn model. But other data and data sources can be added as needed: To help the machine learning model perform better, add extra demographic data like company size, product return rates, service history, potential absences of signal (for example, website logins and interactions with your employees) so on. The more a model understands customer behaviour, the better it is to predict customer retention.

– Choosing what to monitor

They decide on one or more “target variables” to monitor – a target variable is something you want the algorithms to begin predicting.

– Going live

When activated, your software will predict churn. It will also provide real-time feedback on customer satisfaction on a client-by-client basis. That is a way to get customer feedback without approaching each client directly.

With machine learning tools, expect churn prediction accuracy to increase over time as it learns more from the data you add to your database and better understands the outcomes of its previous predictions.

Understanding Why B2B-Customers Churn and How to Mitigate Churn Risk

Key reasons why B2B non-subscription customers churn include:

· Product quality issues. Clients rely on the products they receive from B2B suppliers to work when on a job. When this doesn’t happen, it can delay the completion of work, adding costs and upsetting their clients.
· Limited ex-stock choice. Since the widespread adoption of online ordering, businesses expect same- or next-day delivery. If you can’t supply someone with what they need when they need it, they will look for and stay with a supplier that can.
· Pricing. Pricing is an essential factor and has excellent leverage on profits. It is not always that the lowest price wins! Customers often choose the supplier that comes closest to meeting price expectations.
· No special deals for the biggest spenders. Although companies are often well aware of their highest-spending and most profitable customers, few of them reflect that by offering ongoing incentives to stay.
· Poor customer service. If it takes too long for client questions to be answered or for disputes to be resolved, you are making it more difficult for your client to run their business.

With the help of AI-based customer churn models, you can more easily identify which of your customers you are doing an excellent job with and for which customers specific customer retention measures make sense to keep them.


Identify Customers at Risk of Churn with Qymatix

With the AI-based Qymatix Churn Prediction Software, you receive early warning signals to initiate proactive sales measures to retain customers. Customer retention is always cheaper and more profitable than customer acquisition because it costs 5-10 times as much to acquire a new customer than to retain an existing one.

Qymatix uses advanced machine learning methods developed by experienced data scientists to identify customers with the highest churn risk. You can instantly see the potential loss of revenue from customers at risk of churn. With Qymatix, you can increase the average Customer Lifetime Value your business receives from its customers.

To discuss Qymatix Predictive Sales Analytics Software for customer churn, feel free to make an appointment to get to know us!


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