Know your Customer to serve them better- Analytics Driven Solution

From the international Parisian fashion streets to India’s local markets, Customer Experience is considered as the secret of every successful business. With such an obvious route to success, theoretically, every business that is committed towards improving customer service should reap the maximum benefit out of the market. But in reality, only a handful manages to do so because their focus towards achieving ultimate customer satisfaction is a lot more meticulous, especially with their data.

Satiating customer requests with the choicest of data input streams is not a product of the digital age. Innovators across markets have always trusted their premium customers’ inputs and their very own instinct to arrive at crucial business decisions. But it is this information age that has truly taken data-driven analytics into mainstream decision-making that is meant for the masses.

Big Data, Bigger Outcomes

Robert Hetu from Gartner once observed, “Consumers have taken control of the shopping process and there is no sign that they plan to let go”. The evolution of retail to its present day avatar with customer centricity as its forte didn’t happen overnight and the rapid shape-shifting nature of the market is highly unlikely to get battered down.

The good news here for the retailers is while more avenues open up for the consumer to explore the alternatives, the more inlets get created to study their patterns to understand them better. With IoT penetrating commercial markets with a projected 50 billion connected devices by 2020, innovators are creating intuitive big data discovery and data analytics tools that can be deployed and run by anyone anywhere. Retailers can utilize this innate ying-yang dynamics of the market and invest in the following analytics driven avenues to understand and satisfy their customers better.


While the perks of running a business in an innovation-aggressive era commercialized cutting-edge tools for anyone to elevate their game, the downsides have shown them that, from a customer’s point of view, trends are highly volatile. The profile of a Gen Y consumer, irrespective of the age, can be best described as someone who is extremely articulate about their opinions and are always on the lookout for better options and flexible solutions. This evolution has pushed retailers to invest, more than ever before, on sentiment analyses derived from feedback data from the customers.

The biggest caveat here is that the data that gathers sentiment to understand the pulse are not served ready with analytics for the retailers. In fact, a 2016 e-commerce estimate suggests that companies lose over $10 billion worth of revenue because of their inability to accurately parse customer data (from social media posts, reviews in forums, ratings data etc.) and adequately push for changes from within. By investing in technologies like Natural Language Processing (NLP), text mining and other sentiment analysis techniques retailers can ensure that they process the outcomes better and retain their target audience much longer.


Another niche area where analytics becomes the foundation of customer engagement is crafting personalized experience based on historic data. People who are active on social media couldn’t have missed pop-up notifications of advertisements about the products they were browsing just a few minutes ago. Top brands have already started becoming more observant in understanding and predicting what the customer might want in the near future.

At the 2016 edition of Consumer Electronics Show (CES), British tech firm Smarter showcased their latest innovation called “Smarter Mats”. These mats can be placed in the fridge or work station and gauges how much is left of the products that are kept over it. If the mat’s weight sensor detects a shortage, a notification is triggered to the owner’s smartphone through a wireless or 3G connection. The mat will also offer recipes based on the real-time availability of the goods.

But arriving at data-oriented business decisions comes as a double edged sword with outcomes transforming to troublesome branding (remember, the infamous Target’s story?). In a social-media driven environment where people are equal parts willing and sceptical to share their personal data to the world around them, brands have to be careful about how they extract and utilize their customer data. Retailers must manage walking on the tight rope between gathering more ways to understand intricacies in customers’ patterns and losing their trust when they feel that data has been exploited and not used.

In 2014, Kohl- the American departmental store that also runs a successful ecommerce site- designed real-time offers for the customers who subscribed to the brand with their mobile numbers. They created and sent personalized offers when they feel that a customer is indecisive over a purchase in a particular section. They believed that, “customers are more likely to respond to an offer when it is at the moment of purchase when they’re shopping”.

Reliving memories through Data
As a part of celebrating two decades in the industry, EasyJet- a British Airline- decided to surprise their customers with unique, personalized emails that were created from respective user data that the company accumulated over the years. The result was staggering as the email campaign proved to be much more effective than average promotional mails with 78% recorded positive sentiment, 25% higher click-through rate and over 1.1 million impressions in social media.


Irrespective of whatever era the retail world is in, the often over-looked yet ultimate index of customer satisfaction has always been how fast, secure and effective can a retailer deliver customer’s purchases. With the explosion of omnichannel retail experience, the inventory equation has arrived at a complex point where the retailers have more avenues to track and process the goods. The need for accurate data-driven analytics solution is in an all-time high in the supply chain world of retail.

In the case of analytics-based demand forecasting even preliminary data metrics like web-search data can help enterprises to manage their inventory better, especially when they deal with bulk inventories and are spread across geographies. For example, Microsoft had developed a forecasting model for a firm to predict auto sales, and corresponding derivations of how automobile companies can cope with their inventory woes, using data based on customer’s social inquiries and research patterns online.

So, from understanding, predicting and preparing for the rise and fall of trends with demand-driven forecasting, optimizing inventory channels with GPS-powered route management techniques to offering much more accurate real-time inventory tracking for the customers, analytics-driven solution can be that bridge through which retailers can not just meet but exceed the expectations of their consumers.

The spread of globalization across all markets in the world is likely to make companies to be much more open and competitive with their international peers. Data, and in extension analytics and discovery tools, can be considered as the single most powerful tool for the retailers to transcend boundaries, satisfy their customers and exceed their competitors even in their own turf.

Author Name : Bhargavi Seshadri, Research Analyst

Practice Head: Abhishek Mahajan, Digital Retail