When it comes to financial due diligence in mergers and acquisitions (M&A), the traditional approach often involves some level of financial statement and historical trending analysis. However, with the rise of new technologies and better data sources, leveraging large scale operational (not just financial) data can help buyers gain informed insights into the target’s performance, customers, and operations. This helps ensure that buyers and sellers confidently agree at the signing table.

The real value of data analytics in an M&A environment is how it helps users see beyond the financials—what lies behind earnings before interest, taxes, depreciation, and amortization (EBITDA) rather than simply the EBITDA amount. Whether it’s the buy side or sell side, everyone can benefit from identifying hidden risks or underlying opportunities.

Below are four areas (customers, geography, product rationalization, and operations) in which data analytics can help in a deal context. These four areas are based on the article, “The Power of Data in Deal Making,” by KPMG Canada.[1] This article uses examples from announced transactions to further demonstrate the concepts in a real-life context.

Customers
What drives a company’s customers to stay loyal? Is revenue growth coming from price, volume, or both? What other products are interesting? Collecting and analyzing customer data can help the buyer make quicker decisions and capitalize on optimal post-close integration and value-creation strategies.

Take the example of RBC’s CA$13.5 billion acquisition of HSBC Canada, which was initially announced in late 2022. This acquisition will add roughly 130 branches, 4,200 employees, and 780,000 customers to RBC.[2] Among other considerations, a detailed analysis of customer data is likely to be front and centre. How many of those customers are already RBC customers? Will RBC be able to retain these customers post-close? What are some cross-selling opportunities?

Geography
A location’s demographics, foot traffic volumes, and transit/road connections can play a role in a brick-and-mortar business’s success. When analyzed in depth, this data can help to explain why certain locations have better performance than others or determine the optimum location for a new branch. This knowledge could impact the overall deal valuation and terms pre-closing. Post-close, this analysis could also help with integration and developing a clear improvement plan.

A good example of this in a recent deal would be Loblaw’s CA$845 million acquisition of Lifemark Health Group in 2022, which operates more than 300 physiotherapy clinics across Canada.^[3] When analyzing location-level information before closing, data analytics can help answer questions such as: Which locations are underperforming and why? How can these stores generate synergies with existing Shoppers Drug Mart locations nearby?

Product rationalization
For retail transactions, digging down into individual products and transactions can allow the user to pinpoint opportunities to improve the product, its margins, and pricing strategies. If the buyer is in the same industry, this analysis also allows for better benchmarking and pricing alignment post-transaction.

Such was likely the case when Empire Company (the parent company of Sobeys) bought Longo’s in 2021.^[4] With Sobeys looking to gain a larger foothold in Ontario via this acquisition, it would be more important than ever to drill down into individual products and analyze its pricing strategies in a competitive landscape. Where can they make bulk orders for the group to reduce costs? Which products have the highest margins? Which products are not doing as well and why?

Operations
Opportune, accurate, and in-depth benchmarking data on functional business areas, such as human resources, finance, or procurement, can reveal risks and prospects that could affect the valuation and reveal previously unknown operational limitations. These kinds of data, however, would need to be tailored to each transaction as every business and industry has its own specific metrics.

In the case of Rogers’ CA$26 billion acquisition of Shaw,^[5] initially announced in 2021, data analytics can make clear the significance of various operational data and industry-specific metrics. For example, purchase order data across both organizations can be used to improve the procurement process and generate additional synergies as a combined organization. Industry specific metrics such as average revenue per user (ARPU) can also be used in this case to gauge relative profitability of different customer segments or geographies.

Although the examples above may seem to be buyer focused, sellers can also take advantage of data analytics, which can provide them with a holistic view of their business when they go to market. This includes its value and potential growth and integration opportunities. This helps sellers to determine a fair price for their business, supported by transparent and reasonable analysis.

With the rise in data volume and increasing adoption by dealmakers, data analytics is now at the forefront of many M&A due diligence processes. Whether investigating operational data to expose competitive advantages or uncovering customer buying habits to reveal cross-sell opportunities, the use cases are plenty.