Business Analytics Holds the Key for Retail Professionals

“Business Analytics(BA) can be defined as the broad use of data and quantitative analysis for decision making within organizations” -- Thomas H Davenport

Why Should Retail Professionals Use Business Analytics?

The last decade has witnessed a sea change in the retail industry. Bar-code scanners and universal bar-coding have resulted in plethora of data.  Transactional data are captured easily at the point of sale itself. The use of credit card and loyalty card programs have resulted in anonymous transactions giving way to buying linked to individual customers. Purchasing pattern of customers in terms of demographic and transactional data give rise to phenomenally richer information that could pave the way for better strategic decisions.  Hence, retail professionals should look up to tools for converting data into insights, leading to effective decisions and strategy formulation. It is in this context, the role of business analytics (BA) looms large.

According to Retail Systems Research (RSR)  Benchmark Report 2014   “those retailers that understand the strategic importance of new data about the customer experience are already becoming the next generation of Retail Winners”.   Another recent survey of 2,037 managers cutting across all sectors conducted jointly by MIT Sloan Management Review and SAS Institute  points out that more than half of the survey respondents strongly agree that their organization needs to intensify the use of analytics to make better business decisions. 

Scope of Business Analytics in Retail Industry

The revolution of information technology (IT) coupled with the advent of internet have opened up tremendous marketing opportunities for retailers in the form of e-commerce.  The web provides a virtual store that offers endless opportunities for adapting shelf space to every potential customer in contrast to the shelf placement designed in the orthodox physical store. The massive data generated from the web, social media, mobile devices, digital sensors, point of sale and the like is known by the term “Big Data”.  Big data continues to be a challenge to the computing power both in terms of hardware as well software. Big data revolves around Volume, Velocity, and Variety.

Business Analytics is concerned with discovering patterns and trends while analyzing generally a large data set. The science of   business analytics (BA) embraces a variety of techniques built around modeling a business situation for making insights from data leading to effective decisions. The techniques could range from simple descriptive measures to sophisticated predictive modeling and data mining. BA involves predominately quantitative analysis of data but not confined only to directly measurable numbers. It includes qualitative data in the form of text known also by the term “Natural Language Processing”(NLP). Unstructured qualitative data is a big challenge to analyze and needs appropriate software when it comes to doing “Text Mining”.  Customer Loyalty is the driver of retail profitability. Realizing this, Walmart spends enormous amount on existing customers to enhance sales.   Business Analytics enables the retail professional to take timely action for retaining customers’ loyalty by continuously tracking their tastes, preferences, and attitude towards products and services.

Business Analytics Tools for Retail Professionals

Classification: Classification techniques helps in segmenting the customers into appropriate groups based on key characteristics. For example, using Cluster Analysis, a retail professional could easily segment the customers into Long Term Customers, Medium Term Customers, and Brand Switchers. Another application in this context is classifying customers into “Buyers and Non-Buyers.” Classification will help retail professionals understand the customer behavior and position their products and brands using appropriate strategies. 

Pattern Recognition: “A picture is worth thousand words” and it reveals hidden pattern in the data that could be leveraged by retail professionals. Pattern recognition techniques include Histogram, Box Plot, Scatter Plot and other Visual Analytics.  For example, histogram drawn for income of a particular class of customers may reveal a symmetrical bell curve pattern or may be left or right skewed. The pattern revealed has implications to the manager for budgeting and planning.  Relationship between age and expenditure could be captured using a scatter plot.  Box Plot enables retail professionals to sift outliers (extreme points) apart from providing the distribution pattern.

Association:  Association Analysis helps in determining which of the items go together. Association rules include a set of analytics that focuses on discovering relationships that exist. among specific objects. The objects might include visitors to a website, products in a store, or content items on a media site. In this context, market basket analysis refers to an association rule that generates the probability for an outcome.  For example, market basket analysis may lead to a finding that if customers buy coffee, there is a 40% probability that they also buy bread. A good association is likely to have predictive value. Association rules can be adapted by retail professionals to store lay out, items bundling, discount and sales promotion decisions, and cross selling among others. 

Predictive Modeling: Both customer segmentation as well as identifying and targeting most profitable customers can be facilitated by predictive models. Regression can be used for predicting the amount of expenditure on a particular product based on input variables income, age, and gender. Retail professionals can leverage on other advanced models that comprise Logistic Regression, Discriminant Analysis, and Neural Networks for predicting a target variable as well as classifying and predicting into which group the consumer belongs to.  For example, these models can classify and predict buyers and non-buyers, and defaulters and non-defaulters on credit card loan. 

Future Outlook

• Big Data Analytics will be the prime mover for discovering pattern and trends in retail business.

• Mobile Technology will be a major driver of marketing analytics that will be incorporated in retail strategy.

• Interactions between Social Media and Customers’ Perceptions will be a significant factor in determining retail margins.

• Technology and Software will facilitate decisions for retail professionals on real time basis.

Retail professionals must as soon as possible equip themselves with tools of business analytics so that their growth and survival is not jeopardized.

The future certainly belongs to retail analytics which will replace traditional way of doing business. Business Analytics courses offered by premier institutions will facilitate retail professionals to become winners in the marketplace.

Written By

Dr. P.K. Viswanathan
Professor (Analytics)
Director, PGPBABI
Great Lakes Institute of Management