At the end of the second quarter in 1999, a division of one of the largest financial services companies in the United States had experienced a 40% drop in direct mail responses. Its mailing lists, which comprise 20 million direct marketing mailings annually, were performing sporadically at best. A couple lists would do well, and then they would drop off. The company couldn't discern any rhyme or reason for the changes. the lending firm decided to look for a new way to generate direct marketing leads.
Outsourced Business Intelligence
If the company could somehow tap its marketing data, which existed in several legacy applications and databases, it could potentially turn its mailing efforts around. After researching several vendors and consulting groups, it selected an outsourced data warehousing and business intelligence solution from Harte-Hanks. By outsourcing its data analytics needs it was able to lessen its deployment costs by more than half, plus it avoided hiring extra IT staff to manage the solution. The financial services company spent two months in the preplanning stage, determining all the user requirements. Phase two, which entailed building a data warehouse, lasted for the next four months. The data warehouse had to support more than 30 million records. And, it had to be updated bimonthly to give the company relevant results. Phase three, which lasted two months, entailed doing parallel testing to make sure data was being extracted, transferred, and loaded properly into the data warehouse.
It was the first time that its mailing data was integrated with its response data. Immediately, the financial services company experienced a 30% increase in its marketing campaign, which remained steady. The data warehouse included SAS Enterprise Miner and SAS Enterprise Reporter business intelligence tools, which enabled the company to spot important customer life events such as customers with newborn babies or customers with children approaching college age. Also, it helped them to spot duplicate mailings, which they were unable to detect previously. Within six months from its initial deployment date, the data warehousing and business intelligence solution had completely paid for itself.
In the future, the financial services firm plans to refine its marketing campaign further by adding additional channel data to its data warehouse, such as data from its sales prospects database. This will allow it to have a more accurate view of clients' lending needs, and help it to understand how customers prefer to be contacted.