Learn three ways to increase customer loyalty with algorithms and Predictive Sales Analytics in B2B wholesale.

Should computer programs be able to increase customer loyalty in wholesale? Yes. And no. We’ll discuss what is exactly meant by this in this post.

According to a survey by Roland Berger, customer loyalty was already a top priority for wholesale companies in 2016. However, 1 in 5 wholesale companies also believed that their efforts in the area of digitalization were not yet sufficient to survive the digital competition.

It is interesting to see that even then, sales was recognized as an important interface to digitization. The problem with this is also mentioned: The sales force sees advanced analytics as critical and invokes direct customer contact as the best measure for customer retention.

Which is right now? Both. Advanced analytics, like predictive sales analytics, will be essential in the future. They bring enormous benefits, and anyone who would have done this would have probably said that 20 years ago the Internet was a flop. But does that mean field sales staff have to give up their customer contact? No.

It’s a shame that not much has changed in many wholesale companies in Germany since 2016. Year after year, margins are shrinking, competitive pressures are increasing, and customer loyalty remains hugely important.

Here are three ways wholesale companies can successfully improve customer retention using their sales force AND algorithms:

1. Detect Customer Churn at an Early Stage

“I know my customers!” is a phrase often used by sales staff. We don’t deny that at all. We are sure that sales teams know their best customers, and they know them very well. But what about the middle and small ones? With thousands of customers, even the strongest sales teams can’t keep track of them all.

From time to time, do you notice when a customer is gradually buying less than before – or the intervals at which they buy are creeping up? These are early warning signs of a slow customer churn (“soft churn”), and in practice it is usually only noticed when the customer has already left.

For software that uses machine learning algorithms to predict customer churn, this pattern recognition in the buying behavior of its customers is a breeze. Predictive sales software can predict customer churn with over 90 percent accuracy and send early warning signs to your sales team.

As you can see, smart software is just a support for your sales reps to plan more targeted actions. Humans are still in charge. The software is not a sales machine – and that’s fine. However, it does give salespeople enough time to initiate appropriate customer engagement activities.

A study by Yael Karlinsky shows that sales teams that use AI-based predictive sales software in a hybrid model (collaboration with humans and machines) achieve the best results.

2. Suggesting Suitable Products at the Right Time

In the meantime, Amazon, Netflix & Co. have made us accustomed to having products suggested that match our interests. A recommendation system (Recommender) that is tailored to their customers is an additional service on their part which is well received by customers.

Making the right product recommendations at the right time are reliable measures to increase customer satisfaction and repurchase rates.

For wholesale companies, this function is almost indispensable. Some companies try to discover existing cross-selling potentials with Excel analyses. This is better than trying nothing. With thousands of customers and products, manual analysis is inefficient. With so much data, machine learning algorithms are best, such as the Apriori algorithm that Amazon itself uses.

3. Individual Pricing and Targeted Discount Campaigns

Just as with individual product recommendations to your customers, discount promotions and pricing should not be based on a scattergun approach.

There are many treasures hidden in your historical sales data that intelligent algorithms can uncover. This includes the past payment behavior of your customers. AI-based pricing software can calculate your customers’ willingness to pay per product.

For discount promotions, also use the above info on your customers: Is the customer at risk of churn? How likely is he to buy an additional product? Select discounts specifically.

But don’t forget: people are still in the driver’s seat. That’s why it’s imperative to use algorithms carefully in everyday sales.


You Can’t Do It Without the Right Approach to Algorithms in Sales

The best algorithms only deliver suggestions based on historical data. It’s also important to know that forecasts are never – really never – 100% accurate. It’s impossible, because none of us have magical powers and can see into the future. Neither do algorithms.

For example, if you use predictive sales software in sales, you will always get forecasts that you disagree with. Probably because you, as an experienced sales professional, have some information about the customer that the software does not.

But is it appropriate to distrust the software’s suggestions? No! Because for every “not accurate” prediction, you will find at least four predictions that come true! And you will not gain any benefit from these correct and helpful hints if you only focus on unrealistic recommendations.

This mindset is hugely important for the proper use of machine learning algorithms.

Let’s be clear, this whole thing does NOT mean, “Oh, turn a blind eye, the predictions aren’t perfect, but they’re better than nothing.” No, it simply doesn’t get any more perfect than that. That’s the nature of predictions. It’s a probability game and it’s impossible (today) to predict the future more accurately.

Our tip on how to properly use algorithms for sales predictions: sort them out quickly. Ignore recommendations that are rather unrealistic according to your information and focus on the recommendations that you consider realistic. Set strategies for your sales team on how to evaluate the predictions and when which sales action should take place.


Further Read:

Roland Berger: Digitalisierung im deutschen Großhandel: Drehscheibe der Wirtschaft in digitaler Not? (German language)

Big Data Made Simple (2018): How predictive pricing changes retail pricing optimization.

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Predictive Sales Analytics im B2B Großhandel