Wholesale market leaders are investing a lot of resources and expertise in advanced AI technologies for pricing optimization to set their prices dynamically. How can medium-sized wholesale companies also benefit from dynamic pricing?

Optimizing pricing in B2B wholesale is a big topic. No wonder pricing has the greatest leverage on margins. In our private lives, we have all had experiences with dynamic pricing. For example, at peak times, Amazon changes some product prices up to 70 times a week.

In the meantime, dynamic pricing has also arrived in the B2B business. Market leaders in German wholesale, such as Sonepar, are investing heavily in intelligent pricing technologies. And they are doing so to incorporate one important clue in particular to pricing: past customer behavior.

In this article, we will discuss what dynamic pricing means and whether mid-sized wholesale companies can also supplement their pricing strategy with the help of new AI technologies.

What Does Dynamic Pricing Mean in B2B?

Dynamic pricing is a strategy used by companies to adjust their sales prices to current market situations.

Of course, this is relative because no price stays the same forever. Even the baker next door offers the rolls at a lower price in the evening to sell them before he has to throw them away. So, you could say that all prices are dynamic. Some are just more dynamic than others.

But in today’s world, where there is a lot of data, dynamic pricing refers to the fact that prices – as soon as new information (in the form of new data) emerges – are adjusted immediately. There are many methods and technologies available for this purpose.

Especially algorithms based on machine learning (a subfield of artificial intelligence), significantly increase the performance of pricing optimization.

The most advanced pricing solutions predict the impact of price changes even before they take effect.

In fact, a “Survey on the Importance of Dynamic Pricing in Stationary Retail 2021” by Statista found that 47 percent of all retailers consider such a strategy significant for the future.

The Old Way – Traditional Pricing in the B2B Business Sector

Some B2B wholesale companies find it difficult to accept automated pricing recommendations. This is because B2B distributors are very attached to traditional pricing methods. Their goal is to achieve the best possible margin while maintaining a high order volume.

So B2B wholesale pricing is heavily influenced by manual and rule-based criteria , which makes the whole pricing process very complex. The complexity is reflected in the many criteria that play a role, such as: the evolution of purchase prices, individual contracts, list prices, strategic considerations, and competitive pricing. If you are a professional, I’m sure that you can think of at least two additions to these examples right off the bat.

However, there are two essential problems:

1. The previous individual customer behavior is hardly or not at all taken into account when setting prices.
2. Such complex pricing is very error-prone and time-consuming – especially with thousands of customers and products. Price adjustments are forgotten, individual potentials cannot be exploited and it is difficult to keep an overview.

In contrast, automatic price adjustment by algorithms is primarily based on historical sales data. The algorithms find their own rules and patterns in the payment behavior of customers, according to which they create price proposals. It’s a completely different way of setting prices. How do large B2B retailers use this?

Dynamic Pricing – How do the big B2B companies do it?

Successful wholesale companies, like Sonepar, have adapted their pricing. They use powerful and expensive business intelligence programs to generate automatic price recommendations per customer and product. These price recommendations are based on individual customer behavior, or the customer situation.

The previous pricing strategies are of course not thrown away from one moment to the next, but the customer-specific recommendations are simply taken into account as well. For example, a price increase by the manufacturer is not passed on directly 1:1 to every customer but is targeted at those customers who are most likely to accept it.

Companies have recognized the enormous potential of AI-based pricing and are using it as an additional decision-making tool, rather than the only one.

Wholesalers who switch to AI-driven pricing solutions are proven to achieve higher profits and outperform their competitors.

Recommendation for Medium-Sized B2B Wholesalers

For medium-sized B2B wholesalers especially, the pressure is enormous: low margins, supply bottlenecks, competition from eCommerce and increasing transparency for customers are some factors for this.

For this reason, they should rethink their rigid and traditional pricing policies. Changes in pricing have a direct impact on margins. Targeted and data-based price changes, for certain products and customers that have a high probability of acceptance, are therefore enormously valuable and hold a lot of potential. In addition, the characteristic “customer behavior” should also play a role in pricing. This therefore means “away from the watering can principle”!

A frequent and understandable reason for the hesitation of wholesale companies is the high resource expenditure for data-based pricing: Either you have to set up elaborate business intelligence systems for pricing, which are not only expensive, but also require IT professionals to operate. Or, build your own AI system with automatic price recommendations. Both options are not affordable or very difficult to finance for medium-sized wholesale companies.

However, there is a third and fundable option: a standardized AI-based sales forecasting software – such as Qymatix. The software as a service gives a price range per customer and product that is most likely to be accepted. The forecasts are based on historical sales data. This makes price inconsistencies visible and gives the sales team an additional basis for decision-making when setting prices. Their historical sales data also shows their customers’ previous buying behavior. These are all transactions that your customers have already agreed to. This way, the AI algorithms can find similarities between your customers and their buying behavior and predict price ranks per product.

So, you don’t need valuable IT professionals to use such a tool, as the opportunities are played out directly to sales.


Is Dynamic Pricing in B2B Wholesale Financeable? – Conclusion

Yes, dynamic pricing is financially viable even for medium-sized wholesalers. In fact, it is essential to use the potential in your own data for pricing in order to remain competitive. It is already standard in the B2C market, and large B2B retailers are also investing heavily in data-driven and individualized pricing.

Artificial intelligence for dynamic pricing is now affordable even for mid-sized wholesale companies through appropriate predictive sales software providers . So it’s only a matter of time before your competitors start using it.

Our tip: be open to AI technologies that can support your pricing strategy in a customer-centric way. Analyze your data to determine the extent to which pricing potential exists.


Further Read (in german language):

eClear (2021): Was bedeutet Dynamic Pricing?

Eva-Susanne Krah (2019): Mit dynamischem Pricing auf Kundenfang. Ed.: Springer Professional

Statista (2021): Welche Bedeutung hat Dynamic Pricing für den derzeitigen und künftigen stationären Handel?