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Big Data and Retail
Big Data,  Ecommerce,  Machine Learning,  Technology

Big Data and Retail: How to Improve Business Goals

Big data and retail

The characteristics of the retail sector make it an ideal terrain in which the use of Big Data combined with predictive analysis techniques make an excellent match for the business. Remember that predictions with Machine Learning improve when you have many data, of different types and that are updated over time (some like to call this the 3 “V” of Big Data: volume, variety and velocity).

In retail these circumstances occur:

  • Transactional systems generate large amounts of data, not only in volume but in variety.
  • Granular data is generated daily at each point of sale.
  • A large number of references or SKUs are available.
  • You can extract behavioral data from the information of the clients identified in a large number of cases through the Loyalty Programs.
  • Positioning systems at the Point of Sale generate information on the location of customers.

Most companies currently exploit their data using Business Intelligence, which is certainly highly recommended, but it is overlooked that:

  • No predictive and diagnostic techniques are used, just description.
  • Traditional statistical techniques analyze a limited number of variables, while Machine
  • Learning can detect patterns with hundreds or thousands of indicators.
  • The current marketing strategies are based on market studies and not on predictions based on company data and customer behavior.
  • The strategies of the supply chain are based on linear and non-logistic regression algorithms.

The business improvements that Machine Learning provides in retail are multiple:

  • Prediction of customer abandonment.
  • Increase of the customer’s Lifetime Value.
  • Customer segmentation based on behaviors against the offer.
  • Prediction of demand.
  • Control of stocks.
  • Linear redesign based on purchase data.
  • Increase customer loyalty by offering a personalized offer.
  • Increase of efficiency of the promotional activity.
  • Analysis of the shopping basket: cross-selling, up-selling.
  • Fraud detection.

Conclusions

Many companies are collecting data without a defined criteria on what to do with them. Others, on the other hand, know that with the data they already have, they can do very interesting projects with real value for the business. For example, predict when a customer will stop buying (and therefore to increase the profits of the competition). It’s not trivial. The efficiency of the marketing strategies that derive from this knowledge are manifest.

The improvements in the business objectives offered by Big Data, together with Machine Learning, in the retail sector are very significant. What before only the Internet greats could do (Google, Facebook, Amazon, etc.), is now available to everyone.

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