How can digital pricing tools support pricing in B2B sales? In the following interview, Dr. Kajetan Zwirglmaier, Partner at Simon-Kucher & Partners, explains. He also highlights the role machine learning and artificial intelligence play in this context. Dr. Kajetan Zwirglmaier is a Partner in Simon-Kucher’s Munich office. He is part of the company’s Global Telco and Energy Practice and an expert in pricing, marketing and sales. He can be reached at kajetan.zwirglmaier@simon-kucher.com.

The Pricing Advisor, March 2019

What does digitalization actually mean for B2B sales?

Digitalization is as much of an opportunity as it is a challenge for corporate sales. Products in the telecommunications world have become more diverse, particularly in relation to the Internet of Things. Price pressure in the market is rising, and it is becoming increasingly difficult for companies to stand out from their competitors. At the same time, digitalized processes, CRM systems, mobile devices, and ever-growing quantities of data are opening up new opportunities to reduce costs, increase efficiency, and optimize revenue.

How do opportunities to increase sales fit together with growing price pressure?

In our projects, we have made three observations that can be applied to all sales teams. First, if you ask two sales agents to give you a price for an identical deal, you’ll get two different answers or more. All the deal analyses we’ve conducted in our project work demonstrate this. Second, companies already have all the information they need to find the best price for a successful, revenue-optimized deal, but the fragments of this knowledge reside across multiple people, which means it’s no use for everyday business. And third, it’s people, not algorithms, who are the only ones that can make the ultimate pricing decisions in B2B business.

What insight can we take from this?

Making the collective intelligence of a company available to sales agents via decision-making support tools will result in more deals at better prices. If systems analyze historical sales data and create peer groups using relevant price drivers, then target prices can be recommended based on specific criteria, such as the prices achieved in the top ten percent of comparable sales. The benefits of machine-based learning and artificial intelligence can also help improve price recommendations. With this approach, more revenue can even be generated in difficult market environments because target price recommendations are based on actual deals closed.

How can sales teams increase their sales numbers?

The first step is to set prices that are ambitious but achievable, so the sale isn’t jeopardized by excessive price expectations on the part of sales reps. Companies should also analyze lost deals to calculate the probability of completing sales with current price recommendations. Digital support makes this process even more effective, and machine learning and artificial intelligence can significantly increase success rates.

How exactly can machine learning and artificial intelligence improve pricing?

Machine learning and artificial intelligence provide sales with even better and more precise target price recommendations. Two aspects offer particular advantages in this context. Systems based on this technology not only use historical data to provide future-oriented price recommendations, they also incorporate the probability of success when setting target prices. In addition, dynamic models can adapt to market developments and take into account factors that may have not been considered before. This not only means the effect of individual drivers but also the way these drivers interact with each other.

How can a company help its sales team accept and trust digital pricing tools?

Sales agents, especially “alpha” sellers who are experienced and well respected in the team, must be involved in the project right from the start. They have to believe the tool offers important added value. This way, they become ambassadors for it within the sales team. In addition, running a pilot phase with a large group of sales reps is an effective method for getting sales to trust and accept the tool.

It has also proven effective to display the impact of each sale on the sales rep’s incentives, particularly if it’s shown next to the suggested target price. If sales have transparency on how the price quality of the deal affects their personal bonus, the result improves significantly. Reps aren’t only motivated to close the sale but also to keep price quality high. The final result is that the sales team wants to use the tool even more.

Are there any numbers that show how digital tools affect sales performance?

In my experience, introducing digital pricing support tools for sales leads to a two to eight percent increase in sales for the company. The effect on EBITDA is usually in the double-digit percentage range. Above all, it’s important to note this isn’t a one-time thing – implementing these tools consistently has a recurring impact on sales and EBITDA.

When should a company equip its sales team with a tool like this?

Can any company afford not to do this? But in all seriousness, implementing an effective sales tool is particularly worthwhile for companies that are about to digitalize their sales processes, such as those planning to introduce a new CRM system. It’s important to consider the big picture early on and integrate pricing support for sales right from the start of any digitalization project.