Firms have always looked to
mergers and acquisitions to grow or redefine their businesses. Pre- and
post-M&A diligence has historically focused on operational, tech, and
financial aspects. But with big data emerging as the newest and biggest
revolution in data management, companies are seeing how predictive and advanced
analytics can affect a merger or acquisition by identifying internal problems
and offering better solutions.
For example, a large bank
merger recently went awry when customers began taking low-interest loans from
the acquired bank and investing them back in higher-earning fixed asset
products of the acquiring bank’s investment division. The acquiring bank
discovered and addressed the issue more than one year and millions of lost
I spoke to Joe DeCosmo who is Director of Advanced Analytics
and Insights at management and technology consulting firm West Monroe Partners. With over
20 years of experience leading analytics teams and projects, Joe was able to
discuss the true value of advanced analytics in M&A.
Toni: Can you give me an example of how
poor information architecture can hurt an acquisition deal?
Joe: There are two ways that poor information
architecture can hurt a deal – financial and strategic.
From a financial perspective, there can be instances where
the information architecture of the two companies are so out of sync that it
will be simply too costly to update and integrate systems, data, etc. We have
seen plenty of examples where the target firm’s architecture is woefully out of
date and it will take a large investment of time and money to modernize. If the
investment becomes too large, it impacts the financial return of the overall
deal and the acquirer is either unwilling or unable to make that investment and
carry that financial cost.
Strategically, in the world of big data and advanced
analytics, data and information has become a critical differentiator for many
companies. No matter how good a firm’s performance may be, or how intriguing
their business plan, if the underlying information architecture is not sound,
if it cannot scale effectively, an acquiring firm may walk away. In other
words, no matter how effective the company is right now, if the information
architecture isn’t built to have scale and leverage IN THE FUTURE, the business
value will be diminished as it will become a limiting factor on the firm’s
We have absolutely seen deals fall apart and buyers walk
away from seemingly good businesses because of poor data and information
Also read: IT
leaders: How to survive a merger
Toni: Can you give me an example of how
predictive analytics can increase the likelihood of a successful merger or
Joe: Sure. At the most basic level, predictive
analytics adds value by protecting or growing revenue. If a bank is being
acquired, you can imagine that many of the bank’s current customers may
consider leaving rather than stay on with the new, combined bank. You can use
predictive modeling to understand which customers, based on their past
relationship, account history, etc., may have a higher probability of leaving
than others. Once a model is in place, you can use it to proactively target
these at risk customers with targeted customer care to retain their business. Likewise,
if the merging companies have complementary products, you can use predictive
modeling to identify the best target customers for each firm’s products. In
this case, you treat the new company’s customers as a prospect universe and use
predictive modeling to mine that data for the best sales opportunities.
The bottom-line is that predictive analytics can be used to improve
the overall financial performance of the new, combined company and help ensure
that the acquisition adds value as quickly as possible.
Toni: How can predictive and advanced
analytics minimize costs and maximize revenue?
Joe: I’ve described some of that above. On the
revenue side, it comes down to retention and growth. Predictive analytics for
customer retention, cross-sell, and up-sell is well-understood and has been
used successfully by many companies for many years. In the case of a merger or
acquisition, the twist is that the focus becomes the acquired firm’s customers.
That is, use analytics to predict how the acquired firm’s customers are most
likely going to behave and how best to target and treat them for long-term
With regard to improved operations and cost-savings,
predictive analytics can be used for everything from improved demand planning
or forecasting for a more efficient supply chain, or to identify the root cause
of failures or cost drivers in manufacturing, customer service, etc. In these
cases, you’re using the data from the combined companies to gain a deeper
understanding of the companies’ core operations and using modeling to identify
inefficiencies and cost-saving opportunities.