As the economy continues its slow and steady rebound, so too has merger and acquisition (M&A) activity. Both private equity and corporate investors once again are looking for opportunities to grow through targeted acquisitions.
While most pre- and post-merger activities typically focus on finance, operations, and technology, advanced and predictive analytics also can help maximize the value of these transactions. In short, there are two ways that analytics can be a critical component in ensuring that acquisitions are accretive to an organization’s balance sheet – by maximizing revenue or minimizing costs.
Protecting and growing revenue
First, advanced analytics can be an enabler to protecting and growing the combined organization’s customer revenue. Customer retention modeling, cross-sell and up-sell propensities, and lifetime value modeling can provide valuable customer insights that enable targeted marketing and sales activities that support those goals.
Advanced analytics can also drive the strategic direction of the combined organization. How closely aligned are the two companies’ customer profiles? Depending on the product or portfolio strategy, very similar customer profiles may indicate that the products and services of the combined firm will complement and appeal to each other’s customers. On the other hand, a strategy of acquisition into a new and different customer segment can also present growth opportunities into growth markets. Finally, are there opportunities to move into an untapped market or customer segment where both companies are underpenetrated? In any case, deep customer analysis can help understand the growth opportunities that exist within the combined customer universe.
In order to gain these deeper customer insights and propensities, one must first invest in building a consolidated “single view” of customer data. This can be time consuming, so it is important to build a road map that delivers a one-off view for initial analysis and then a longer-term data strategy for maintaining this customer view over time.
The following examples illustrate the risk of not investing in a “single view of the customer” and developing deep and predictive customer insights.
Minimizing costs and increasing margins
While protecting and maximizing revenue is usually most important, advanced and predictive analytics can also look “inwardly” at the combined companies’ operations to minimize costs and increase margin.
Fortunately, today’s companies capture more and more data than ever in their manufacturing, operations, and supply-chain processes, enabling root cause analysis and predictive modeling and forecasting to identify operational inefficiencies or failures.
For example, in manufacturing, business or operational analytics enable deep, exploratory analysis that identifies inefficiency or breaks in the combined companies’ manufacturing or supply chain processes. Such breaks can negatively affect customer satisfaction and sales in the short term and significantly erode margins in the long run.
Service-oriented firms such as banks or insurance companies can use predictive analytics to uncover fraudulent or risky transactions and adjust business processes to correct these problems. Likewise, deep analysis of customer touch points can identify opportunities to streamline operations and reduce costs without undue impact on the customer experience.
Why is this so important?
In the last decade, it’s clear that market leaders have made a commitment to analytics and data-driven decision making to grow revenue and improve profitability. Merger and acquisition activity should be no different. Applying the same level of analytic rigor and data management to these activities positions the combined company to realize its maximum value.