Data Analysis for the Retail Industry (Part 1)

Whether you love, hate or remain indifferent towards data, it’s impossible to deny its importance in today’s business landscape.

Businesses across all sectors and industries collect data and perform data analysis, to better understand their customers and business processes, in an effort to boost productivity, reduce expenditure and gain competitive advantage.

For this reason, Business Intelligence (BI) tools – which report on, analyze and transform these masses of organizational data into understandable and actionable information – are growing in popularity and importance.

In this mini blog series, we’ll explore some of the uses and benefits of data analysis when applied to a range of distinct industries and departments.

Today, we examine BI’s usefulness and potential within the retail industry.

BI in the retail industry

As the international retail market becomes increasingly competitive with mass offshore production and global retail conglomerates driving down prices, the ability to optimize your supply chain, react quickly to market place opportunities and satisfy customer expectations has never been more important. Therefore, accessing and maximizing the knowledge within retail data sets has never been more important.

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

Basic customer-centric data that retailers should capture and analyze with their BI solution includes:

  • 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


What are the key data sets that BI tools should be used to analyze and report on in the retail industry?

There are several common data sets critical to the retail industry that BI tools should be used to report on and analyze. These include:

  • Sales data
    • Point of sale data
    • Gross margins and revenue
    • Turns
    • Gross margin return on inventory investment
  • Market data
    • Market share
    • Competitor pricing
    • Competitor product lines
    • Competitor market share and customer profile
  • Promotional and marketing data
    • Success of past promotions/customer feedback
    • Total cost of promotion
    • ROI on promotion
    • Pricing offers
  • Customer-centric data
    • Demographics
    • Frequency
    • Loyalty
    • Etc (see above)
  • Supply chain and operations data
    • Demand for product types based on region, demographic, time of year, etc
    • Identify profitable products
    • Keep track of units sold (total or by category)
  • Merchandising data



Developing accurate customer profiles and in-depth operational understandings allow retailers to predict future customer behaviors and industry trends, allowing them to:

  • Predict customer likelihood to purchase a new product offering
  • Identify highly profitable customers by:
    • Total value of sales, number of sales, estimated lifetime value
  • Identify ‘lost’ customers
  • Identify troublesome customers (return policies, etc)
  • Identify customers responsive to promotional offerings
  • Determine which customers will be more responsive to specific types of marketing
  • Recognize which customers will remain loyal to your product despite changes to certain product variables (price, availability, etc)
  • Increase profits
  • Develop or purchase new product lines with confidence
  • Develop highly targeted and successful promotional campaigns based on data collected from past campaigns and customer feedback
  • Achieve accurate allocation (type and quantity) of stock across channels and stores leading to improved efficiency at the supply chain level as well as increased sales and profitability


Establishing measures

Careful data analysis of the above areas can also enable retailers to develop effective measures for:

  • Assessing the components and success of marketing campaigns
  • Evaluating and determining buying patterns
  • Finding optimal mix of product types and quantities via region, store, season
  • Deciphering optimal pricing strategies for product/category types
  • Improving supply chain procedures


Moving forward

Retail operators not already using a BI application will need to consider the benefits of analytic and reporting tools to remain competitive and eliminate wastage in an industry where being in-tune with shifting customer and market trends is paramount.

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