Big Data Transforming Retail

March 29, 2022

A little bit of history

In the late 1990s, entrepreneurs from various fields of business realized that data was a valuable resource that, when properly used, can become a powerful instrument of influence. The problem was that the volume of data grew exponentially, and existing ways to process and analyze it were not efficient enough.

In the 2000s, technology made a "quantum leap". Scalable solutions appeared on the market, capable of processing unstructured information, coping with high loads, building logical connections, and translating chaotic data into an interpretable, human-understandable format.

In 10-20 years, big data will be the main means of capitalization and will play a role in public life comparable in importance with the electric power industry, analysts say.

Success formulas for retail

Modern shoppers are no longer a faceless mass of statistical data but rather quite delineated individuals with unique characteristics and needs. They are selective and can switch to any other brand without regret if the offer seems more attractive. That's why retailers use big data, which allows them to engage with customers in a targeted and accurate way, guided by the principle of "unique customer - unique service".

Personalized assortment

In most cases, the final decision "to buy or not to buy" happens already in the store near the shelf with products. According to Nielsen statistics, it takes only 15 seconds for a customer to find the right product on the shelf. This means that it is crucial for businesses to put the optimal range of product in a particular store and present it correctly. For the assortment to meet demand and for the display to contribute to sales, different categories of big data need to be examined:

  • local demographics,
  • ability to pay,
  • shopper perception,
  • loyalty program purchases, and more.

For example, a Zurich-based fast-fashion store used GeoCTRL solution to analyze: 1) audience in and around its location, 2) footfall traffic, 3) customer dwell time in certain areas of the store. Further, the data was correlated with the weather forecast, marketing activities, product purchase frequency, and financial data of the store and presented on an easy-to-use self-service platform. As a result, the store managers got a clear understanding of their audience and business potential. Based on the GeoCTRL data, they developed a new marketing and business development strategy, plan for store layout and product placement optimization, product line update, and working time correction.

Effective use of space

A separate area of solution development based on big data is the efficient use of space. It is data not intuition that merchandisers now rely on when laying out products.

In more advanced shops, the layouts are generated automatically, considering the properties of retail equipment, customer preferences, sales history of individual product categories, and other factors.

At the same time, the correct display and the number of products on the shelf are monitored in real-time: video analytics and computer vision technologies analyze the video stream coming from the cameras and select events according to specified parameters. For example, store employees get a signal that premium pasta is out of place or that shelves are out of greek yogurt.

Personalized offer

Personalization is a priority for consumers: according to Accenture research, 80% of shoppers are more likely to buy if a retailer makes a personalized offer or discount; moreover, 48% of those surveyed would not hesitate to leave for a competitor if the product recommendations are not accurate and don't meet their needs.

To meet customer expectations, retailers are actively adopting IT solutions and analytics tools that collect, structure, and analyze customer data to help understand customers and take interactions to a personal level. One popular format among shoppers, the "you might be interested in" and "this product is being bought with" product recommendations section, is also generated from analysis of past purchases and preferences.

Amazon generates such recommendations using collaborative filtering algorithms: a recommendation algorithm that uses the known preferences of a group of users to predict the unknown preferences of another user. According to the company, 30% of all sales are due to Amazon's recommendation system.

Personalized delivery

It's important for today's shoppers to get what they want quickly. Most large retailers and carriers have a multitude of sensors and RFID tags (used to identify and track goods) that provide enormous amounts of information: data on current location, cargo size and weight, traffic congestion, weather conditions, and even driver behavior.

The analysis of this data not only helps to create the most economical and fastest route in real-time but also provides transparency of the delivery process for customers, who have the opportunity to track the movement of their order.

But speed alone is not enough: today, everything is delivered "quickly". A personalized approach is also valuable.

Personalization of delivery is a key factor for the customer in the "last mile" stage. A retailer that combines customer and logistics data at the strategic decision-making stage will be able to promptly offer the customer to pick up the item from the pickup point where it will be fastest and cheapest to deliver. Same-day or next-day pickup and a discount on delivery will encourage the customer to go even to the other end of the city.

Amazon, as usual, went further than its competitors by patenting its proactive logistics technology, which works based on predictive analytics. The idea is that the retailer collects data

  • about past purchases,
  • items added to the cart,
  • about items added to the Wishlist,
  • cursor movements.

Machine learning algorithms analyze this information and predict which item the customer is most likely to buy. The item is then sent by cheaper standard shipping to the shipping hub closest to the user.

Today's customer is willing to pay twice for a personalized and unique experience with money and data. Only with big data, it is possible to analyze personal preferences and provide the right level of service. While industry leaders create entire organizational units to work with big data projects, small and medium-sized businesses rely on box solutions. But everyone has a common goal - to build an accurate consumer profile, understand consumer pains, and determine the triggers that influence the purchasing decision. All to accentuate purchasing lists and create a comprehensive, personalized service that will encourage people to buy more and more.

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