By: Fred Milman (fred@anchorcomputer.com)

Data and Analytical Needs Retail SectorUnlike many business sectors, the retail sector is inundated with customer data from POS transactions, credit accounts, on-line sales activity, customer inquiries and more. In dealing with this massive potential marketing asset there are 3 major activities:

  1. Collecting the data
  2. Gaining access to the data
  3. Analyzing the data in a timely manner

Collecting data today is not an issue. Modern POS systems are very good at capturing everything from customer name and address, SKU’s purchased, method of payment as well as some self-reported demographic information such as age gender, presence of children, marital status and email address, telephone number and permission for SMS texting.

The challenges for retailers lie in organizing and gaining timely access to the massive amounts of customer and product data that retailers generate and then making sense of the data and analyzing it for marketing decision support and merchandising efforts.

Many retail companies have a business data warehouse systems that provide predefined reports. Most of the reports consist of aggregations along multiple pre-defined dimensions and are adequate for some of their needs. However decision making using market basket, measuring the effect of promotions on sales, sales slowdown in stores and matching orders prior to sales are some are just some areas that are often very difficult to accomplish in a timely manner.

Retailers need to have a fast, user friendly decision support system that can handle ad hoc queries and produce analytical datasets for timely analysis that support the decision making process. Direct marketers long ago recognized this need which has led to the development of several fast, flexible database management tools that can meet these needs.

Anchor currently hosts the databases of many mid-market Cataloger and Retailers. Using several database management tools, Clients are able to:

  1. Measure the impact of an event on a market basket. Baskets can be selected by size and contents and metrics can be reported by time prior to the event and time after the event.
  2. Customers can be divided into income bands and then find correlations with age and the number of a specific item purchased around specific holidays.
  3. Show basket statistics (average number of items, average revenue, number of baskets) by day for 7 days before an event and 7 days after an event
  4. Provide KPI’s for baskets

These are just a few. Each retailer has their own specific needs which can be met using Anchor’s data management and data hygiene resources.

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