Improving Marketing ROI Through Data Analysis

Using data correctly can add tremendous value to your company. Most in the list and database industry wouldn’t consider this to be groundbreaking news. In fact, it’s rather elementary. The real key is the techniques to find and use this data to your advantage. That was the message offered during the morning session last week at the DMA’s List Day conference in New York City. A panel consisting of Erik Findeisen, co-founder/CEO of FC Data; Janette Barret, director of marketing services and analytics for International Masters Publishers; and Hao Chen, senior product manager at Experian Marketing Solutions each spoke of ways to help drive up your ROI through the use of data and analytics. Here are some of the tips they provided.

* Acquire relevant data. Data needs to be mined according to your customer-base. “Know who you’re speaking to,” Findeisen said. He advised this can be done through several methods, including: demographic studies (age, gender, income, marital status, etc...); transactional (RFM data, transactional purchases), which he feels drives success in modeling elements; lifecycle (new homeowners, marriages, birth); psychographic; attitudinal, why would the customer be interested in your products or services over another?; and ethnic, highlighting the need to pay close attention to the rapidly-growing Hispanic market.

* Reactivate old customers by cross-selling them a new product. “Reactivations work best with offering old expires new products within the same affinity,” Barret said. As an example, she cited a past campaign at International Masters Publishers that used matchbacks from older house files as targets for reactivation. These past customers had ordered a cycle of wildlife cards from 1999-2001. Mindful that they wanted to reach this customer-base within this affinity, they offered them a wildlife DVD series. The campaign proved very successful, netting a 40 percent response index and a 29 percent retention rate. In her testing she sought to narrow the affinity down as close as possible, which she considered a major reason for the successful campaign.

* Prioritize level of data importance through testing. “Data is critical pieces of information,” Chen said. “Certain types are more relevant than others, it all depends on the application.” He provided a series of models that Experian has implemented in their testing, including three-channel tests (direct mail, e-mail, mass-media), three-offer tests (regular price, free shipping, discount price), three-creative tests and three-contact frequency tests. In Chen’s results, demographic, transactional and behavioral data proved to be the highest level of importance in maximizing list performance.

* Search out non-traditional data sources. Findeisen stressed the importance of online lead generation. While public sources of information are shrinking, other non-traditional sources can be leveraged. He referred to this as unique “designer data”, where you’re not looking to sell name brand, but are selling an attribute.