Monday, April 18, 2011

Predictive Analytics in Retail

When we look up a definition of Predictive analytics, we get that PA encompasses a variety of techniques from statistics, data mining and game theory that analyze current and historical facts to make predictions about future events.

For Business Intelligence we use predictive models to learn and exploit patterns found in historical and transactional data and identify risks and opportunities. These models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

Predictive analytics is used in actuarial science, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.  However, with recent market changes in the Real Estate on both commercial and residential areas we can use Predictive Analytics to help identify strategic retail location placement and target client specific geographical information.

Retail Location Selection & Market Potential through Predictive Analytics

Increased competition and shrinking profit margins in the retail industry have necessitated a demand for sophisticated predictive analytics. Organizations want to know what they can expect from a new or existing location over the long term, and what strategic investment is required to maintain a profitable market share.

Market Potential Analysis is used to forecast the potential sales for a location within a given trade area. The forecast is based on the analysis of critical information, such as historic purchasing behavior, current market conditions, local demographics, consumer expenditures and competitive activity.

Retail Predictive Analytics can deliver a clear picture of who the ideal customers are and can show where to find more just like them. Applying this technology help predict new opportunities, anticipate needs and proactively engage customers and prospects in winning CRM strategies that increase traffic, improve loyalty and drive sales.

Retail Predictive Analytics can determine:

 

  • Where to lease / buy property

 

  • What products to carry

 

  • What segments to target

 

  • How to position different brands in the marketplace

 

  • How a location will fare in the face of competitive threats

 

  • Which areas to target with offers / advertising

Applying Retail Predictive Analytics to comprehensive supply side data delivers valuable business intelligence retailers and location analysts can leverage to manage the business of capturing and holding profitable customers and ultimately growing market share.

Utilizing Retail Predictive Analytics, retailers can optimize location selection based on:

 

Cost

 

Revenue

 

Profitability

 

Market Share

Employing Retail Predictive Analytics Retailers can:

 

Analyze the profitability of existing locations and predict the success of new locations and marketing mix scenarios.

 

Build comprehensive customer segmentation systems for effective consumer targeting, marketing communications and branding.

 

Analyze competitive activity, cannibalization and distribution channels

 

Deliver data-driven demographic, expenditure and behavioral analytics to refine product mix and pricing strategies.

 

Profile markets to maximize the effectiveness of media purchases, direct-mail response and cooperative as well as internet strategies.

 

Deliver customer segments and profiles retail marketers can understand and quickly leverage.