Real-time data science can provide valuable insight throughout all stages of an M&A transaction. For example, right from a deal's onset, we can leverage alternative data and predictive analytics to:
- Provide insights into the M&A market on an industry- or even company-specific basis to predict the likelihood of M&A activities as a buyer or seller
- Pinpoint and evaluate potential targets during the deal sourcing stage
- Equip potential buyers with an outside-in view of potential M&A partners and related industry/market insights (e.g., competitive position, value creation opportunities, potential liabilities, etc.).
- Create proprietary benchmarks from multiple data sources that identify and measure the potential short- and long-term value of a deal.
Data science is also proving critical throughout the deal evaluation stage. Here, we can take advantage of machine learning, automation, and visualization tools to quantify value creation opportunities and validate investment hypotheses to help buyers bid with confidence.
Take a multi-location retail business, for example. Internally focused metrics (e.g., foot traffic, loyalty programs, sales trends) can be combined with third-party data (e.g., local demographics, competitor activity, market trends) to produce an in-depth location score using spatial analytics. The resulting insights can then be used to pinpoint success factors, marketing and sales strategies, and untapped growth opportunities at a store level. What's more, that same information can be used to find similar M&A opportunities moving forward.