Correlation and causality in artificial intelligence: what does this mean for wholesalers?

“Yes, I understand the forecast, but why is AI telling me the customer will churn?” Please God, give me a euro for every time I’ve heard that question in the last 10 years. And my answer is always the same…. Read on, and you’ll know.

The world of technical wholesale has changed dramatically. In the past, relying on reliable business relationships and historical sales figures was enough to assert oneself against the competition. Today, the reality is different. While eCommerce giants are shaking up the markets and putting margins under pressure, managing prices and stock correctly is becoming a key survival strategy. Companies need to act faster and more intelligently; this is where artificial intelligence (AI) comes into play. What was once a vision is now a reality.

But before allowing ourselves to be dazzled by AI’s fascinating possibilities, we should understand a fundamental concept often ignored: the difference between correlation and causality. Why has AI recommended these products at this price? That is a perfectly valid question!

If you are a technical wholesaler with 5,000 to 10,000 customers and a product range of 20,000 to 100,000 items, you face a challenge beyond data analysis. You want to adjust prices and predict customer behavior dynamically – but without understanding the intricacies of correlation and causality, you could quickly find yourself on a dangerous wrong track.

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What are correlation and causality? Why is the difference significant?

OK, first, the definitions. Correlation means that two or more variables are mathematically related. This can be measured. For example, your AI can tell you that customers buying product A are likelier to purchase product B. Causality, on the other hand, means that one variable directly influences another. In other words, product A actively leads to the purchase of product B. Causality means a cause and an effect.

Do you ever confuse these two terms? Probably not. But many sales managers in wholesale do. “The customer buys from us because we have the largest range. “Because” is the key. Where can you prove exactly why the customer buys from you? Can this apply to all your customers?

OK, you can ask the customer; maybe they know, but perhaps they don’t. What you can definitely do is measure the correlation between the number of products your customers buy (as a proxy for “assortment”) and their churn rate.

Many companies confuse these two concepts and make serious decisions or miss meaningful sales opportunities on this basis. In practice, correlations lead to actionable conclusions, even if causality is not proven.

Your data shows that customers who buy a particular product are often willing to accept a significantly higher price. The best AI software would recommend exactly this course of action. Is this increase also causally justified? Or is it just a random correlation? Does it matter?

This is the key: AI (unlike humans) can identify the most significant correlations from millions and generate specific sales opportunities without “knowing” exactly why something is like this. Causality is AI’s greatest weakness, but this is where we humans are strong.

The role of artificial intelligence in technical wholesale

Artificial intelligence (AI) is increasingly being used in technical wholesale to create competitive advantages. It analyzes ERP data and recognizes patterns and predictions often hidden from humans. Pricing optimization at an item and customer level is especially challenging for a wholesale company that manages 100,000 items in its product range. The effort involved in manually analyzing thousands of customers’ sales data and purchasing habits is enormous. This is where AI unfolds its potential.

In B2B wholesale, AI allows you to identify products with the highest purchase probability for each customer, determine the optimal price range for a specific customer, and retain customers before losing them.

Your ERP data has long shown these correlations, but without AI, you would never have noticed them. To take real action – for example, a targeted cross-selling campaign – you don’t necessarily need to know whether a causal relationship exists. You must use it. Your competitor is already doing it.

Do I need to distinguish correlations from causalities?

Of course, they are not the same thing. But correlations alone are valuable and useful. Let’s be honest: how exactly do you determine causality in sales? Do you have to fixate on the “why”?

Wholesalers who recognize the difference between correlation and causality but still infuse their millions of correlations with AI for sales will be more successful than those who fixate on causality. Think about yourself. Do you still have an atlas in your car, or do you drive with a navigation system? And don’t you know exactly why this or that route is suggested?

Let’s think about your B2B wholesale sales again. Which specific applications of AI can be rethought from the perspective of correlations and causalities?

For example, pricing is based on correlations. Many companies’ lower prices because they believe higher prices do not work with specific customers or are not in line with the market. AI is often surprised with a sensible price adjustment for customer B and product C that would otherwise have been overlooked. Can AI explain the “why”? No. Who cares? You and your customers. In B2B wholesale, often those euros more or less make a financial difference.

Or let’s take the example of cross-selling opportunities: your AI can show you precisely that customer C could have a high demand probability for plugs, sockets, press fittings, or safety gloves. But why? With AI, this could be due to millions of possible correlations. Perhaps it is due to external factors – such as seasonal fluctuations, shopping baskets, or special project requirements. Nevertheless, you can take targeted cross-selling measures, even if you can’t explain the causal relationship. Why? Did you not click on your Facebook or Instagram ad today? Who recommended this post to you?

Of course, I need to understand that correlation and causality are different. However, I can still use AI-generated recommendations without understanding exactly how the AI arrived at that suggestion. Don’t fixate on causality and miss the sales opportunity hidden in correlation!

The importance of ERP data for successful AI implementation

The data you have at your disposal is the key to everything. Your ERP (Enterprise Resource Planning) data already contains huge amounts of valuable information – your customers’ behavior, purchase history, and preferences. This historical data is the foundation for the success of your AI initiatives. Can you recognize all possible correlations without AI? And how would you prepare this data for AI? How does your sales team use AI to derive recommendations from correlations when an explanation for causality is missing?

Experience in your industry with many similar projects plays a decisive role here. For SMEs in B2B wholesale, for example, cooperation with Qymatix makes a lot of sense.

Interesting: studies show that companies that integrate AI into their ERP data achieve up to 50 percent faster decision-making and up to 46 percent less customer churn. For wholesalers, this not only means a considerable increase in efficiency but also significant competitive advantages.

How AI is transforming wholesale

Here’s the key point: artificial intelligence can transform your organization if you use it correctly. Recognizing the difference between correlation and causality is important, but don’t let it hold you back.

Companies that use AI-supported price analyses, cross-selling tools, and predictive models for customer behavior gain a decisive advantage. They can react more quickly to market changes and proactively derive measures instead of reacting to outdated data.

 
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Conclusion: Act now – with AI and a clear strategy

In modern tech wholesaling, companies have to respond to a flood of data. But data alone is not enough—it needs to be used intelligently. Correlation and causality are key concepts that you need to understand if you want to use AI-supported systems successfully. Your ERP data holds enormous potential, but only with a clear strategy for interpreting correlation and causality can you derive real business decisions from it.

Wholesalers who harness the power of artificial intelligence will optimize their prices, increase customer loyalty, and fully exploit cross-selling opportunities. They will no longer react to data but actively shape their future.

Now is the time to invest in AI and take your business to the next level. The competition won’t wait, and customer competition is getting tougher. Act intelligently – and turn correlations into causal actions. Contact us if you would like to learn more about how AI can help you make sense of your ERP data to stay competitive.

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