The Digitalization of Deal Sourcing: A Chat with Kai Hesselmann of DealCircle

This interview with Kai Hesselmann of DealCircles focuses on the transformation of deal sourcing from traditional to digital methods and its impact on M&A.

Kai Hesselmann has a career spanning more than 20-years in the investment industry, with roles in M&A advisory, as a corporate executive, and as a partner of a private equity fund. He is the Co-founder and Managing Partner of DealCircle, a digital tool that supports M&A advisors and investors in deal sourcing, using a big data approach. In this interview, we sat down with Kai to discuss the transformation of deal sourcing from traditional to digital methods and what that means for M&A practitioners today.

TOUCHPOINT: Before we discuss the digitalization of deal sourcing, let’s step back and take a look at traditional deal sourcing. Given the continued importance of relationships and relationship networks to source deals, was there an underlying problem with deal sourcing before digitalization? What were some of the challenges involved?

Kai Hesselman: Deal sourcing is traditionally characterized by strong personal relationships and a large network. And that is in principle a good thing.

However, the small and lower midcap M&A market in particular has evolved considerably in recent years. Many new players have appeared on the market, both on the advisor and the investor side. As a result, the market has become even more fragmented and less transparent.

Thus, it is no longer as easy for investors to see all the relevant deals in the market. Vendor advisers no longer have a chance to follow the acquisition strategies and investment profiles of the buy-side on an ongoing basis, while also lacking an up-to-date overview of the portfolio companies.

As a result, the closing rate in this market segment is only 20-25%. In other words, a large number of fundamentally attractive companies are not sold.

TP: Given the challenges you’ve outlined, market fragmentation, gaps in information and low closing rates, what digital tools have made the most impact to address these challenges?

KH: I think anything that makes life easier for the stakeholders—that increases their efficiency and deal probability—helps. In deal sourcing, platforms or marketplaces can increase the deal flow. And tools like DealCircle use a big data approach to create the highest possible transparency about which buyers are currently looking to invest and where.

This knowledge is made available to advisors and they can identify suitable buyers from a vast database where, in many cases,  there are buyers they have never heard of before. DealCircle thus takes on the role of a meta-deal sourcing tool for the buyer side.

Of course, the same applies to virtual data rooms. We all remember the days and weeks spent in physical data rooms in dark basements. That’s where virtual data rooms naturally bring an extreme increase in efficiency—not only due to convenience in the cloud, but due to advanced features like Q&A tools, redaction, and other automated functions.

TP: How does the M&A advisor’s role change with digital deal sourcing? What stays the same?

KH: Finding suitable, interested parties is an important task for an M&A advisor. This is where digital tools can create rapid transparency. However, identifying potential buyers is only the first step.

The essential task for M&A advisors lies in creating a market around a company, which presents a deal at a good price. Digitalization can only support this service in some ways, but not in its entirety.

M&A tools can relieve research work, provide assistance in the acquisition mandate, and increase the closing probability of difficult deals. But they do not replace any of the advisor’s core services.

TP: Looking ahead to the next decade, what do you foresee as major trends or changes when it comes to deal sourcing?

KH: Today, big data sets are already analyzed and investment profiles are derived. However, we are still at the beginning of real artificial intelligence in the evaluation of this data.

My vision is that in 10 years, intelligent matching algorithms with semantic searches will be able to identify which buyers are suitable for which transactions. Perhaps even before the buyer knows what they want to buy.


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