With big data emerging as the newest and biggest revolution in data management, companies are seeing how predictive and advanced analytics can affect - both negatively and positively - a merger or acquisition.
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 dollars later.
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 We have seen plenty of examples where the target firm's architecture is woefully out of date and it will take a large investment of time and money to modernize.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 growth.
We have absolutely seen deals fall apart and buyers walk away from seemingly good businesses because of poor data and information architecture.
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 acquisition?
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 revenue growth.
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.