Predictive Analytics to Understand Customer Behavior in the B2B Sector
For some B2B companies, predicting customer behavior is like guesswork. Managers sit together and try to make predictions about upcoming sales, future pricing or appropriate customer loyalty measures.
Often these forecasts are based on sales reports, sales representative’s own gut feeling and, Excel analyses created with a lot of frustration. Don’t get us wrong, the gut feeling of an experienced sales team can very often be right, especially when it comes to customers they have had a lot of contact with. But what about all the other customers?
No human can make accurate, data-based predictions for thousands of customers and products. The technology “Predictive Analytics”, can do this very well and very fast.
The field of sales forecasting is also referred to as “predictive sales analytics”.
In short, predictive analytics uses various methods, such as machine learning algorithms, to calculate probabilities for the future from historical data. You can read about exactly how the technology works in this article.
And this is how it works with predictions about customer behavior in the B2B sector: data from the past, such as sales transactions from your ERP system that serve as the basis for predictions. Algorithms then calculate the most likely behaviors of your customers.
Let’s now look at three use cases for predicting customer behavior in B2B. For each use case, there is another article that looks at the topic in more depth. You will find the link to it at the end of the respective case.
“Churn Prediction” or Customer Churn Forecasts.
In B2B customer churn, a rough distinction is made between “soft” and “hard” customer churn. Hard customer churn happens one blow at a time, with no great portents. This is the case, for example, when a company suddenly announces illiquidity. Soft customer churn is much more common and much more difficult to detect. Here, the customer very slowly buys less and less, or at longer and longer intervals, until they finally leave altogether.
For sales teams, soft customer churn is very annoying because there were signs, but they were noticed too late – namely when the customer has already left.
Churn prediction software that uses predictive analytics can send the sales team alerts about churn-prone customers up to six months in advance. This gives sales enough time to take appropriate customer retention measures and not be surprised by a “sudden” customer attrition.
If you are interested in how accurately predictive analytics uses artificial intelligence to predict customer churn, we recommend reading this article.
Next Best Offer – Discover Cross-Selling Opportunities.
The larger your product range, the more difficult it is to suggest a suitable product to your customers. It is also proven that product suggestions that are adapted to the customer’s interests lead to higher customer satisfaction.
You are probably already familiar with some of the best-known cross-selling applications in the B2C sector. Amazon suggests products “that you might also be interested in”. Or Netflix recommends series and movies that match your preferences. These are all cross-selling recommendations. And they work. Amazon’s cross-selling algorithm is responsible for 25% of Amazon’s sales.
We have good news for you: you can use the same algorithms for your B2B company to make matching product suggestions at the right time like these companies.
What Amazon, Netflix and co. use are predictive analytics methods based on machine learning.
For a deeper insight into cross-selling algorithms, we recommend this post.
Calculate Price Acceptance and Identify Pricing Potential.
Pricing is a pain point in many B2B companies. Most often, list prices and manufacturing costs are used for final pricing. Price adjustments are also a difficult issue. For example, it’s not uncommon for your customers to pay the same price for a product for years because they’ve simply been forgotten. There is also a fear that if you increase the price, the customer will be dissatisfied and churn.
Predictive analytics software is a valuable asset here. On the one hand, it makes pricing inconsistencies visible and on the other hand, it calculates a price range per customer and product based on your sales and customer data. Prices within this price range are most likely to be accepted by the customer.
Such recommendations can give your sales team negotiation confidence and provide justifications for a price increase in relation to historical trends.
If you are interested in data-based pricing decisions, please have a look at this article.
Predictive Analytics to Understand Customer Behaviour in B2B – Conclusion.
Three use cases for predictive analytics in B2B sales to forecast customer behavior are customer churn forecasting, discovering cross-selling opportunities, and identifying pricing potential.
All three use cases are important for sales success. However, you should know that these three areas are interdependent. A customer who is dissatisfied with pricing may churn. A customer who doesn’t receive product recommendations that match their interests might go elsewhere for better advice.
For this reason, we recommend that you don’t focus on just one of the three areas when using predictive analytics in sales. Go for a tool that includes all three types of predictions.