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 revenueFirst, 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.
- A large credit card division put customer satisfaction and retention at significant risk when it merged with another card issuer. The combined firm had separate customer data warehouses and call centers, which confused customers when dealing with account, billing, or customer service issues. While analytics alone couldn’t have solved this problem, this case indicates how mergers can put customer relationships at risk when data and operations aren’t integrated.
- A large banking merger introduced a high degree of unexpected business risk. In this case, customers were taking low-interest home equity loans or lines of credit from the acquired bank and investing those dollars in higher-earning fixed asset products of the acquiring bank’s investment division. It took the acquiring bank more than a year to discover and address this activity. The lack of cross-company insight and ability to run simple consolidated customer reports led to millions in losses.
- A large consumer products company acquired a small company in Asia to increase its penetration into this growing market. A few years after the acquisition, the company realized that the acquired entity’s product was cannibalizing sales of the acquirer’s main product in the local markets. This realization came late because the company had no capability to run cross-product analyses (sales, vendor, third-party sales, etc.) in these markets. Again, investing the effort to profile the two companies’ customers and products would have helped uncover and even predict the cannibalization prior to the acquisition. This example illustrates the risk of product cannibalization that can occur when customer activity is not combined and modeled prior to or soon after an acquisition.
Minimizing costs and increasing marginsWhile 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.