I recently came across an article by PricewaterhouseCoopers stating that M&A activity in 2009 would be fueled by Merger of Necessity. According to the article, “Troubled companies will look to align with larger, stronger players in order to survive, creating the perfect storm for mergers of necessity,” stated Robert Filek, a partner in PricewaterhouseCoopers’ Transaction Services group. Further, Greg Peterson, a partner in PricewaterhouseCoopers’ Transaction Services group, stated, “Historically, it has been during a downturn when strategic buyers and private equity firms have their best buying opportunities, yielding the best return.”
When an organization acquires another organization as part of a “Merger of Necessity” strategy, understanding the overlap and uniqueness of the customer and prospect bases of the merging organizations is pivotal to developing the up-sell, cross-sell, and cost reduction strategies that are the drivers of the merge.
Utilizing the matching processes and number schema from a Master Data Management solution, an acquiring company can achieve the expected return on investment in a timely fashion by:
Securing Market Share
Integrating the acquired organizations locations and contacts into existing database systems or brand new systems in a quick, organized, and complete fashion to ensure that the acquired customers are properly serviced and marketed to prevent loss of customers after the merger
Decreasing Costs
Properly allocating the appropriate number of resources based on the proper counting of customers and prospects of the two merging organizations
Increasing Sales
Developing a single customer view so that representatives can see information from the combined organizations utilizing information across multiple data silos to help up-sell and cross-sell
Executing Marketing
Understanding the overlap and uniqueness of the customer bases of the merging organization to develop target marketing campaigns based on the opportunities created by the merger
Data Quality
Identifying data quality issues such as duplicate records, building more complete and accurate records, develop master records for reporting, and other data quality issues