by  Valentyn Kropov

Big Data and Psychometrics: The Threshold of Change in Retail

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The retail industry is a major driver for the development of technologies connected with collecting, analyzing, and using big data. Research conducted by JDA Software Group and PwC among shows 86% of chief-level retail managers have already named big data as one of the priorities in their business strategy.

Big data can be analyzed to determine a customer’s personality based on the inputs of their actions in the digital space - social networks, search engines, purchases, mobile gadgets, GPS, smart devices can be used to determine the distinctive features of behavior, character, communication, geographical movement.

This data can supplement information about the person's lifestyle with insights into how a person makes decisions based on their psychotype. Are t hey impulsive or balanced in manner? Are they rational or emotional? Do they perceive information sensorily or intuitively? This allows us to not only to understand person’s motives, vision, and perception of the world, but also predict their behavior and change it.

However, to get the most accurate individual profile we must analyze online activities along with non-verbal language such as facial expressions, gestures, body language, and voice intonations. Building the most accurate predictions of human behavior requires psychometric information, and the retail industry is uniquely positioned to capitalize on this because it already has the most important data touchpoint for collecting information - physical contact with the customer in the store.

Big Data in Retail: New Opportunities

Retailers have the best contact with their target market both in online stores and in physical store outlets. But unlike online, brick-and-mortar stores are being visited by significantly higher amount of people, which assures the maximum possible coverage of the target audience. And thanks to this physical contact, they can get much more data about a person because they can see, hear, track person’s behavior and movements around the store. This gives a fairly complete picture of an individual’s psychotype, along with information about person’s financial situation and preferences.

This positions retailers to leverage some great opportunities to bot h understand and predict buying behavior and adjust their marketing and store layout.

In-Store Analytics

During just one visit to a store, a customer is able to generate thousands of unique indicators, captured by various cameras and sensors. Analyzing this data, one can understand where the person is going to move, what exactly attracts their attention, how the person makes their product decisions, how much time the individual needs to make a decision, and if they make purchases acco rding to a list or impulsively. This data can be useful for layout planning and for developing promotional campaigns and materials.

This is also able to prevent thefts. For example, by analyzing the body language of the visitor, facial expressions, and the peculiarities of thier movements around the store, the system can send warning signals to security or store personnel.

Forecasting Tends Before They Occur

Big data analytics make it possible to forecast trends and demand for certain categories and to plan purchases and supplies accordingly. Numerous factors that could influence demand are taken into account, such as sales info, messages in social networks, search requests, the current economic situation, and even weather conditions.

Retailers can predict trends and start developing and delivering new product lines to store shelves before demand starts gaining momentum and consumers will always be able to find the right product at a more affordable price that meet their expectations and tastes. Retail will dictate future global consumer trends as data brings a shift from producers to retailers.

A New Level of Interaction with Customers

Cross-platform analysis of consumers’ behavior and purchase histories makes it possible to develop clear recommendations and offer the most relevant products for each specific customer. This increases customer loyalty and their affinity for particular vendors.

For example, supermarket chain Target has developed a mechanism for recognizing pregnant women, by analyzing their regular purchases and minor, but typical changes. One of the signs may be that the woman began buying vitamins, personal care or cleaning products without flavoring. Based on the analysis of a massive amount of data on purchases of pregnant women, Target learned to determine the expected due date. Through this detailed analysis of customers, Target effectively hones their advertising campaign s with an understanding on the needs of women in a particular period of time.

Expense Optimization and Cost Reduction of Goods

The ability to monitor trends, demand, needs, and competitors’ activity in real time provides retailers with insights that are not based on assumptions, but on actual market situations. Companies can make decisions regarding orders and deliveries, and establish efficient operation supply chain much faster, which reduces the costs of acquiring and storing products. This reduces the cost of goods and increases the final gross margin.

With this data a vendor will be able to accurately predict exactly what each consumer will buy today, in a week, in a month or in six months. This will facilitate a qualitative change in the entire business approach - from optimization of purchases to reducing the cost of goods and advertising. This is mutually beneficial for both parties - buyers and sellers. Retailers will not be wasting storage space and loading shelves with goods, which are unlikely to sell. They will be able to better target ads and will understand which products must be produced in order to satisfy demand. And buyers will be loyal to those retailers, whose favorable prices will be combined with a thoughtful assortment and relevant advertising.

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