Joachim Meyn has many years of experience in B2B sales and customer management. In this article, he shares his expertise on “modern customer classification”.

Classifying customers and potential customers is always a topic for heated discussions. Mostly, there is an agreement that sales teams can only look after some customers and potential customers with the same intensity.

To achieve the best possible results, it is, therefore, necessary to focus accordingly, i.e., to classify customers and potential customers, in short, to be selective.
The best-known form of classification, which has been tried and tested for decades, is the division of customers into A, B or C customers.

The Classic ABC Analysis – A Quick Overview

Here A-customers represent the most valuable customers. As a rule, the Pareto principle also applies here; 20% of the customers represent 80% of the result. That sounds simple at first, but as is often the case, the devil is in the details. It starts with the fact that you must first define a valuable customer. The most common parameters are sales volume, revenue and contribution margin.

Let’s take a closer look at these three parameters:

Parameter 1: Turnover Amount

You can determine the turnover of a customer effortlessly. But how meaningful is this parameter? Two points immediately stand out:

1. The amount of turnover says very little about how profitable the customer is for the company. Often, customers with a high turnover are also granted high discounts, which impacts the profit or contribution margin.
2. The level of sales with you says very little about the actual potential of the customer. You may have classified a customer as a C-customer based on his sales to you, but 90% of his sales are to your competitor because you are only classified as a C-supplier to him. For this reason alone, the customer might have to be organized differently.

Parameters 2 & 3: Yield and Contribution Margin

A pure focus on revenue or contribution margin can also be problematic:

1. You may have only a few small customers, but they are willing to pay a high price. That may result in the underutilization of your capacity and an increase in your costs.
2. If you pay your sales force a commission on sales, the conflict is pre-programmed. In their interest, the sales force will acquire customers with high sales but possibly low revenue or contribution margin.
3) The actual business needs to say something about the true potential of a customer. It may be that the customer only buys from you because a competitor’s capacity is insufficient, so you only supply “residual quantity”.

Of course, the remuneration of the sales force can be adjusted in all scenarios.

The “Probability of Closure” with the Help of AI

Until now, companies have hardly considered the “closing probability” point in customer prioritization. In most cases, statements about the probability of closing were essentially based on the gut feeling of the corresponding sales representative.

Today, however, you can be supported by modern and highly sophisticated AI systems. Used correctly, such systems take most of the analysis work off your hands and supplement the “gut feeling” with data-driven analyses.

A modern AI system provides you, based on your existing data, with powerful and accurate sales predictions to better assess or evaluate customers.

With predictive sales analytics software today, you can get forecasts on closing probability, cross-selling and upselling potential, customer churn (churn prediction), and price analysis, resulting in reliable customer classification and sales planning and management.

Keep in mind that no one knows the future. Such prediction tools use AI to calculate “probabilities” of future events. That does NOT mean 100% certainty because even the best software and AI system are not “magic crystal balls”.

But these probabilities are essential for sales and make classifying customers and planning sales actions more precise. With the help of accurate sales forecasts, the sales team can focus on the most promising customers and activities. Or wouldn’t you like to know the answers to the following questions: “Which customer is at risk of leaving in the next few months?” or “Which customer is most likely to buy which product?”

Implementation of Successful Customer Classification in B2B Sales

In all scenarios, the sales processes must be adapted so that some customers are not suddenly no longer served because there is a risk of losing customers. You can help customers in different ways. You should also consider measures to turn a B or C customer into an A customer.

The sales team should be supported when introducing AI-based predictive sales analytics software for sales forecasts. After all, if there is an early warning of a high probability of customer churn, the sales rep can’t just call them and say, “Hi, my software says you want to churn, is that right?” Again, small strategies (micro-strategies), depending on the prediction type, must be defined for the sales teams.


Classifying Customers in B2B Sales: ABC Analysis and Then? – Conclusion

Today, a modern ABC analysis includes the consideration and classification of customers based on the following parameters:

– Actual business
– Strategic potential
– Contribution margin

All three points should find entrance into the classification and be weighted accordingly. In addition, the classification should be flexible to realize possible undiscovered potentials.

Speaking of undiscovered potential, you should use AI-based sales forecasts as another factor for prioritizing customers and sales activities. Forecasts on closing probability, cross-selling possibilities, churn probability and pricing potentials. Such information is precious for sales and provides a significant competitive advantage.