Optimizing Merchandising Using Big Data Analytics

Merchandising managers, for many years, have been grappling with the following three major challenges (among others), in making product management decisions:

  1. Understanding visitor & purchase behavior in the store (and online)
  2. Silos of product, purchase and customer data that are disconnected, disparate and unrelated with no easy way to analyze them in real-time and run ad-hoc queries to get answers quickly to make agile decisions.
  3. Ability to accurately predict store (local) demand for products, both existing and new, to avoid for example, understocking and overstocking, underpricing or promoting the wrong products.

These challenges have impacted store level profitability and competitiveness. Key product related questions such as – what products are moving currently, which products are selling with other products, what promotions (own and competition) are currently running in the marketplace, who is buying what product, how are purchase decisions being made – have remained unanswered for many years. Managers have primarily relied on past data analysis (after the fact) and “gut feel” based on years of experience to make decisions on product assortment, stock levels, customer promotions (mass market) and more.

Enter the Big Data era. These challenges are now even more complicated with large volumes of unstructured data from a much wider variety of sources as consumers have taken to social network recommendations, product reviews, mobile apps for comparison shopping, and more, in their path to purchase. In effect, this has spawned a new buyer behavioral trend. Retailers, are now subjected to what is called as “showrooming” – i.e buyers visit a retail store to see a product but instead purchase the product online from a competitor at a lower price, possibly with no added taxes and free shipping.

The good news is that, with today’s Big Data technologies, it is now possible to analyze (aggregate, correlate, associate, track) in real-time, entire datasets as they arrive while keeping the data sources wherever they are, and provide instant insights to managers on product movements, pricing trends, promotion intensity, etc., so they can decide (and predict) which products to stock, who will buy what product at any given instant of time, how much of each product to stock, when to reorder what products, and more.

Applying big data analytics in real-time (or otherwise) is not only necessary (because of the volume, variety, velocity and mix of data) but also crucial to answer questions related to merchandising decisions, as alluded to in earlier paragraphs, with more granular data available down to individual levels and to correlate them to market conditions and context. This presents an opportunity for merchandising managers to provide personalized experiences to individual customers at the store (and online). (Please read a related blog – Click- & Mortar mortar can pack-1-2 punch with insights from re...)

In summary, with Big Data Analytics, merchandising managers now can Optimize Merchandising (and provide personalized customer experience) in real-time, to increase profitability, attract non-customers (new) and retain existing customers by instantly applying various “out of the box” analytic models and techniques such as market basket analysis, shopping cart analysis, cohort analysis and (local) purchase behavior analysis.


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Tags: Analytics, Behavior, Big, Data, Merchandising, Real-time, Retail


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