The first step is identifying and overcoming customer variances. Each organization identifies individual customers differently and therefore needs to evaluate customer data thoroughly at the outset of the acquisition process. For instance, company A might use account numbers, SSN or a unique, self generated ID number to define customer accounts while company B may choose to use elements of an address field such as name, address, telephone number or any combination to do so. When consolidating the data, organizations need to verify the customer is the same even if the individual customer ID is different, and then create a universal customer identification standard for that single customer. Otherwise, businesses risk incorrectly identifying a potential customer or, worse, failing to identify an existing customer. While the former may lead to a poorly implemented marketing campaign, the latter will lead to the loss of a customer and all of its associated revenue. It would be counterproductive to the purpose of the M&A to alienate the very customers just gained from such a business transaction.

Individual branded banks are often subsidiaries owned by larger players, with additions to the “family” being made all the time. Under this corporate umbrella, each subsidiary has a unique set of customers and a unique view of what those customers require. Without a solid data quality solution, managing the data between the parent company and the subsidiary would prove dangerous. The organization would have a difficult time identifying mutual customers and that confusion would threaten the very relationships they had worked so hard to develop.

Within banks that acquire other banks, for example, the parent must immediately be able to triage its new customer set into four new categories: those it had originally, those new to its services but veteran customers of the acquired bank, those new to both entities, and those that had been customers of both. With those new categories in place, the parent bank can then create and launch separate approaches to each customer set that will optimize retention of those customers.

Similarly, new acquisitions bring with them different sets of operational terminology – such as codes or labels applied to accounts, products, processes and services – all of which need to be converted and mapped to a single, standardized set of descriptions to ensure a smooth flow of business moving forward for all customers and employees.

Once all the information attained through focusing on the accuracy and integrity of data, organizations need to decide how to market to customers moving forward. For example, the automotive industry is highly susceptible to cross marketing, owing multiple varied brands. In order to leverage mutual assets while maintaining separate brands, the parent company must have a solid handle on where customers overlap, where there might be a gap in executing promotions and where entirely untouched opportunities lie. As they continue to acquire additional entities, a measured look at possible customers will provide numerous growth opportunities and additional direction on where the next acquisition may be.

Preparing for the organizational challenges of an M&A process can be a daunting task. Avoiding the data issues associated with merging companies can be an even bigger nightmare. Consolidating data and ensuring both its quality and accuracy from the start, allows businesses to benefit from the collective information that resides within each company. Leveraging these newfound assets can create a solid foundation on which to build a successful long-term company.

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