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How Automated Data-Driven Nudges can Help Retailers Make Better Merchandising Decisions

How Automated Data-Driven Nudges can Help Retailers Make Better Merchandising Decisions


A smartphone app reminds you to have a glass of water at regular intervals; another app reminds you to pick up your groceries from your local around-the-corner shop; and another app perhaps informs you about the right time to change your toothbrush! If you happen to be reliant on one of these or similar technologies, then it is safe to say that you have been nudged!

Today, the world around us is structured to push or ‘nudge’ us towards taking a particular decision or another. This concept, known as the nudge theory, simply means that positive reinforcement can influence people’s decisions on a daily basis. Now, take the same concept and apply it to retail. On a day-to-day basis, retailers are confronted by numerous decisions around merchandising, assortment, pricing, etc.



Decisions about keeping products that are popular with consumers, products going out of stock, pricing competitively and so on. And more often than not, retailers and sellers end up making these decisions based on instinct or partial data. The good news is that in the West, retail giants such as Amazon are using data driven nudges – based on sophisticated algorithms of processing data – to help sellers across a host of merchandising decisions, which has resulted in increased sales for the sellers on the marketplace.

A paradigm shift

The ecommerce revolution characterized by technological change, empowered shoppers, data explosion and hyper competition is causing a paradigm shift. Consumers today expect much more from retailers. Whether online or in-store, to offer products they want at competitive prices, with superior customer service and the right product information during and after sale.

But, for many retailers, it is unclear from where they should start. They are faced with the challenge of keeping pace with consumer demands, while having to deal with voluminous amount and not having a systematic, workable way to make sense of it. This is where nudges come in. Let’s look at some of the factors that can help retailers attract and retain customers:

  • Assortment: By synthesizing data, merchandisers can provide specific nudges which will help them in understanding which product works and which does not.
  • Local flavor: Helping retailers with data about local nuances such as weather, festivities etc. at a particular time.
  • Online marketing: Based on consumer sentiments, marketers are nudged to not spend too much on a product, for example if reviews of a specific product are not favorable.
  • Pricing: To compete, retailers need to constantly vary their prices since price plays a crucial role in customer buying decisions. To this end, nudges help them in understanding when to vary their price based on competition price.
  • Product content: Nudges can help retailers decide product content such descriptions, which can boost traffic and drive conversions.
  • Promotion: Personalized promotions can be done better, as timing of promotions can be prompted by data-driven nudges.
  • Seller: In order to maximize sales, retailers can make better decisions about which sellers to add.

The future of a more data-driven Indian retail environment

The Indian retail scenario is dynamic and is growing at a fast pace as witnessed in the entry of newer players, almost every other day. There is tremendous opportunity to use Big Data analytics to enable ecommerce players make better merchandising decisions.

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In the coming years, we expect to see retailers leverage analytics and invest in data driven nudges to make better marketing and merchandising decisions with greater certainty. Of course, human nudges will still continue to be a part of the decision-making, only enhanced by data-driven nudges.

Imagine, if an automated nudge can keep you on the fitness track, or even assist you in your daily chores, its impact on making retail decisions more competent and effective will be huge.


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