predicting-customer-churn
Understanding and avoiding customer churn ( or attrition) in Business-to-Business(B2B) organisations can make the difference between a successful financial year or a miserable one.

No experienced sales leader will deny that at the end of the day, some of their current customers will churn or defect. That represents revenue missing from their yearly company quota. Surely, a B2B customer does not defect overnight, but several signals are indicating this risk. And once those signals are detected, swift action is of the essence. Both steps can represent a challenge for a key account manager splitting his time between finding new customers and hitting his quota.

How can a sales leader help her sales team spot a customer at risk of churning before it is too late? With the help of predictive sales analytics.

Churn risk: Let the Robot do the maths for you

Sales teams in B2B are confronted with an increased number of customers to serve. The pressure of hitting the yearly sales goal adds to this unlucky yet sometimes necessary combination. This tension creates a situation where complacency kicks in, key account managers tend to relax about the customers currently buying. A professional sales rep will not forget about her best accounts, of course, but an ever-increasing need to improve sales efficiency easily risk overseeing churn warning signals.

There is where predictive sales analytics and artificial intelligence can help sales controlling. Instead of waiting for a customer not buying again, with the help of advanced predictive algorithms, a sales leader can provide her sales team with timely advice. A warning signal, followed by action (such as a call or a visit) can prevent a customer from churning.

The financial numbers of the last sales periods, usually tracked on an ERP System, are one of the most valuable pieces of information to predict customer churn. Recent studies show that in Business-to-Business, behavioural variables (such as time between transactions) and financial variables (such as margin) had a significant effect on predicting customer churn. In simple terms: use your past sales data to make predictions about future customer churn.

How to do this with Qymatix? In the main sales insights view, a dashboard with the number of customers at risk of churning is presented. Instead of having to wait for a customer to defect, sales leaders can list their buyers based on a churn prediction.

main-sales-dashboard

Behind the first sales dashboard, we present you with a detailed analysis of customer churn risk. Here the sales leader can list and organise her customers based on churn risk. For example, in the first churn dashboard, the risk of churning is plotted against the numbers of sales plans or opportunities in the pipeline. The size of the bubble represents the sales. In conclusion, in one view, a sales leader can understand and analyse churn risk vs sales and open sales opportunities.

list-churn-risk-customers

 

Your churn prediction model? Dig deeper.

Once the sales leader has a focus on the customers at risk of churning, she can start digging deeper. What do they buy? When do they buy it? How is this customer performing, compared with the rest of the customers?

From the previous view, by clicking on each customer card, the user can access a detail view of each customer. In this third layer, the user can access the most relevant key performance indicators for a particular account. Also, here the user can compare how this specific buyer is performing versus the rest.

churn-ai

First, re-order your customers based on churn risk. Second, dig deeper into one of them. Last but not least, do something about it.

 

How to make your sales team successful? Take action.

Once the sales manager has identified a customer at risk of churning, taking immediate action is crucial. In Business-to-Business, where sales cycles are long, taking putting early attention on customers about do defect can make a significant financial impact.

Qymatix tool provides the sales leader with a straight list of the actions that her key account manager has undertaken in any particular customer. Has your sales rep visited this customer in the past? Is he pursuing any open project of sales opportunity?

sales-plan-churn

If a customer is a risk of churning, and your key account manager has not taken care of it in some time, the chances are that you are already late. Save a new sales plan and address this situation together with your sales team.

 

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Financial and behavioral metrics can be used to predict customer churn. Qymatix is a software tool that can help sales managers in business-to-business to detect customers at risk and to undertake actions to avoid them from defecting.
 
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Kuanchin Chen, Ya-Han Hu, Yi-Cheng Hsieh. “Predicting customer churn from valuable B2B customers in the logistics industry: a case study”. Information Systems and e-Business Management. August 2015, Volume 13, Issue 3, pp 475–494.

 

How to use Big Data to stop customer churn? See our Customer Churn Prediction Software for yourself.