RETAIL

Data analysis that drives action can rapidly bring together and explore massive sets of structured and unstructured data to uncover hidden patterns, new correlations, trends, customer insights and other useful business information in the field of retail

Retail data is increasing exponentially in volume, variety, velocity and value with every year. Smart retailers are aware that each one of these interactions holds the potential for profit.

Understanding your customer base: What customer-centric data should BI tools analyze and report on?

  • Purchase types: Understand customer purchase history to help effectively promote future product offerings to customer-base
  • Personal information: Help to design effective personalized marketing and communications materials/offerings using customer name, date of birth, or address, etc
  • Customer feedback and sentiment: Help establish successful product types and improve future offerings

  • Frequency of customer spend: Determine the frequency and type of sales and marketing promotions – distinguish impulse buyers from infrequent need-based shoppers. Also detect ‘lost’ customers and formulate strategies to ‘win’ them back.

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