Impact of data driven decision making in Retail


The power of data is not hidden anymore from enterprises across the globe. Big Data has encompassed all fields from healthcare, finance, manufacturing or retail and many more. Data has strong potential to transform the way enterprises carry out business.

Most Retailers are fairly experienced with web analytics providing them with the necessary data insights, but they are still struggling to gain insight into offline channels to reduce costs or to improve the value proposition that they are making to their customers. But it certainly seems that data is the key of tying up offline and online channels.

Today the retail industry is quickly transforming and those enterprises that are slowly adapting to making fact based decisions using modern data science methods too have started gaining a competitive advantage over others. Data-driven decisions in the retail industry need to stop depending on traditional analytics and start their big data journey towards making quick as well as informed decisions taking full advantage of big data. With the help of hi-tech analytics and modern mechanisms retailers can understand their consumers better, they can track consumer behavior as well as their purchasing patterns / frequency. Retailers can take advantage of knowing these facts and promote the right products where necessary.

How do retailers predict their consumer’s behavior?

Retailers need to understand their customers thoroughly so that it becomes easy for the retailers to decide prices, promote products and face the competition in the market. Retailers need to analyze the behavior of their consumers and make quicker, smarter and efficient decisions. In order to make the right decision retailers need to have a 360 degree view of their customer base from all channels. To have updated 360 degree information of customers, the retailers need to have an accurate collection of relevant customer attributes and their behavior to rightly predict consumer behavior. Data-driven decisions made by retailers depend on the relevancy and accuracy of underlying collated consumer data.

A few prerequisites of consumer attributes before going in for advanced data modelling:

This list of consumer attributes is a prerequisite prior to advanced modelling taking shape. Below is an example list of measurable consumer attributes:

decision making analytics in retail

Below are a few key points that the Retailers need to keep in mind to be able to make the best possible data driven decisions:Points to consider for making data driven decisions:

  • Do not rely on historical reports to make decision of the future, as they do not guarantee future performance. Look at the past only to model the future.
  • Consumer behavior changes frequently and is heavily influenced by latest market trends. For accurate prediction of consumer behaviors, retailers should leverage the potential and power of data science
  • Retailers must prepare multiple relevant data sources for data modelling. They must take into consideration volume of data, internal expertise, and preparing multiple data sources.
  • With multiple sources of data the retailers need to take care of the privacy and security of data that comprise the key factors in deciding the appropriate data modelling course. Outside data sources or external data may be useful to enrich the current dataset based on the type of models required. Data strategy should be clear, retailers should consider various models when trying to predict the future trends and consumer behavior
  • Retailers must use Recent Frequency Monetary (RFM) models that help clearly segment consumers based on monetary value. RFM uses the most recent transaction, frequency, and monetary value to decide the relative score of a consumer. Some more advanced consumer segmentation models may be put to use in order to gain a better understanding of consumer’s behavior
  • Retailers may also use response models to predict if consumers will have a positive or negative impact by promotions. There are Lift model’s that help to predict how much impact the marketing campaigns can have on consumer’s purchase patterns
  • The benefit of utilizing more than just one data model is that retailers can link results of various models to enhance results. For instance, using the results of RFM models with retention models to find if the most valuable consumers will have a negative impact to further email promotions. Linking more number of models gives a better, clearer and wider picture and helps retailers predict their consumer’s reaction to promotions and new products in the market.


Advantages of data driven decisions:

Big data helps in taking a major leap, by making real-time personalization possible. Retailers can easily track the behavior of individual customers from Internet click streams, update their preferences, and model their likely behavior in real time.

Based on data, the Retailers have the ability to recognize when customers are about to make a purchase decision and when they could possibly push the transaction to completion by combining preferred products, that are offered with reward program savings. Hence this kind of real-time targeting, would also utilize data from the retailer’s membership rewards program that can surely increase purchases of higher-margin products by its preferred or most valuable customers.

Retailing is the right and appropriate place for data-driven customizations because the quality, variety and the volume of data available from social-network conversations, Internet purchases, and more recently, location-specific smartphone connections have risen to a great extent in the recent times
Most of the modern retail companies have already started gaining large scale advantages due to right data driven decision making process and data driven customizations. Retail enterprises believe that advanced analytics services cut down on the time required to create sales from a few months to just a week, it boosts the effectiveness of campaigns making them target oriented. Data driven decisions have definitely improved overall sales and marketing performances with improved customer service.

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