For many retailers, developing a marketing database can be a daunting task. Your vision is a single source of data that’s accessible to manage, analyze and report customer interactions.

The reality is that developing this asset can be time consuming and expensive. Furthermore, once in place, there’s no guarantee that the database will be used or produce return on investment. Unless you have a clearly defined path to ROI, it can be a risky proposition.

Those companies are better off adopting a more conservative approach. With current cloud-based technology, brands can turn to a “skinny” solution. There’s no reason to plow $1 million or more into true enterprise CRM when most of the targeting and analytics functionality that’s desired can be achieved through combining customer data, sales transaction data and relevant third-party data inside a cost-effective data mart.

This data mart can be the proving ground to define ROI. In most cases, retailers never need to scale beyond this approach and are able to save millions by never truly implementing enterprise CRM and an expensive campaign management solution.

Major Retailer Gets Skinny
One major wholesale club had all the information it could ever want on the purchase habits of its members. After all, you couldn’t buy anything in the club without providing your membership card. And yet, the marketing department was constantly hampered in attempting to quickly access the data due to operational priorities and ad hoc data export costs. Rather than building a costly marketing database, the problem was solved by creating an analytic data mart that was updated weekly and provided marketing with the insights necessary to drive highly targeted campaigns with dynamic creative. The wholesale club did this all in a matter of months without implementing a massive CRM database.

The key? Stay single-minded on ROI and focus on critical content and functionality components. The retailer thought “skinny.”

1. Content

    • Attractiveness: Not all customers and prospects are created equal. Create metrics around revenue, profit or some combination of the two. The key is being able to rank prospects based on their potential ROI.
    • Addressability: This is the combination of “addresses” (physical, email, phone, online registration, etc.) for which a customer hasn’t opted out of marketing communications.
    • Responsiveness: The ways in which a customer has been approached with marketing efforts and whether the solicitation has produced a response. This illustrates which initiatives (e.g., direct mail, email, banner ads, etc.) are working and which are not.

2. Functionality

    • Access: Ability to extract data for analysis and targeted lists for campaigns. This is the single most important function of a marketing data mart platform. All “post-access” activity can be evaluated based on its likely ROI.
    • Updates: Ability to update the data mart with feeds from data sources on a timely basis. This is based on the schedule required to keep marketing efforts “current.” Monthly updates will generally suffice and put a minimal burden on internal IT resources.
    • Reports: This refers to the ability to publish simple reports. Simple data mart status reports are the lifeblood of accountability. Communicating the value of the data mart asset on a regular basis and illustrating the marketing activities that are being executed against it are keys to early success.

The wholesale club stayed focused on these critical factors and built a world-class solution with very little spent on technology. Faced with more data than ever before and more pressure to put dollars towards media, not infrastructure, more and more retail marketers are deciding it’s time for their marketing database approach to “get skinny.”

Scott Bailey is executive vice president of Target Data, which combines a powerful marketing optimization platform with data-driven campaign execution to let businesses quickly identify, attract and keep their highest value customers.

http://www.mytotalretail.com/article/retailers-think-skinny-data-just-big-data/