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Issue No:07/03/2

Next Issue: Allocation of Marketing Investments
Previous Issue: Intelligent Systems



Rapid advancements in technology over the past decade have made it possible for retail managers to collect, condense and categorize information in a highly efficient manner. However, the ability of the decision maker to correctly interpret this information has improved very little. The accurate interpretation of information goes beyond the ability to correctly read rows and columns of numbers, percentages and absolute values. It begins with an understanding of where the information comes from, how it is obtained, and whether or not it is relevant to a particular business decision.

One problem retailers' face today is the impersonal, transactional nature of the shopping experience. In effect, customers pass through stores anonymously, forming little or no relationship with the retailer. Decision Support Systems (DSSs) offer a way to improve a store's effectiveness and efficiency and to build and direct their marketing strategy based on customer needs and preferences.

Analytics form the core of DSSs which help retailers realize advantages in inventory management, buying, sales and marketing, and store productivity.

Inventory Management
Since inventory is a major expense for all retailers, a reduction in cost of inventory management benefits both retailers and customers alike. Quick Response merchandising is a strategy which makes inventory decisions so flexible to align current forecasts and immediate future stocks. This implies having the right merchandise in the store on time, in the proper quantities, styles and colors and at the right price. This is based on a shared platform for visibility, communication and decision support between vendors, distributors and retailers. The benefits from such a strategy are increased sales, reduced markdowns, reduction in merchandise expenses, and reduction of interest expense on merchandise.

The key decisions relevant for a retailer pertain to management of the merchandise mix. Retail Managers need to make complex decisions on the brands stocked, the product depth, width and support. Modeling the merchandise mix will allow for such decisions to be made intelligently while ensuring that customer preferences are catered to, maintaining low inventories, and improving store productivity.

Buying functions
Retail DSSs provide an understanding of future demand patterns and sales trends. These systems provide more reliable forecasts, and increase in-stock position of high-demand items without raising inventory levels. These systems also facilitate Inventory Replenishment. Automation of this task reduces response time, and eliminates the tedious reordering tasks.

Marketing
Analytics helps a retailer's ability to engage in relationship merchandising. DSSs help retailers

  • Fine tune merchandise to the needs and wants of specific target markets
  • Develop highly specific advertising messages that clearly communicate particular product offerings, services, and benefits to individual target markets
  • Obtain feedback on the testing of new and innovative merchandise, display procedures, customer services, and creative selling initiatives.
  • Develop a monitoring system that can gauge the success of a marketing program in relation to the required investment and anticipated rate of return
  • Provide vendors/manufacturers with valuable customer information for use in fine-tuning their marketing efforts
  • Increase response to promotions, and guide investments in marketing mix
  • Use of sophisticated tools and techniques in data mining for the essence of such DSSs.

All in all, analytics will serve to be the backbone of intelligent retailing decisions, in this era of increasing competition and choice for the customer. While analytics ensures effective use of data which is collected, it also renders itself to the tasks of data collection and maintenance. The competitive advantage for retailers will be the ability to devise innovative means to capture data from customer touch-points cost effectively, and channeling such data into actionable insights through analytics.

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