In this column, Snehamoy Mukherjee explains how using big data analytics can enable better decision-making across a retail company’s operations
The world over, analytics is shifting to ‘Big’ while in India we haven’t even started thinking ‘Small’. Every day, one hears about progress being made in the world of ‘Big Data Analytics’ and how it is going to change the way businesses are run. In India, businesses are not even keen to discuss the importance of using data that is already being collected by their existing IT systems and not being put to good use. It is time that the senior leadership in corporate India woke up to the urgency of using data in their tactical as well as strategic decision making process.
In most companies, data lives in many different systems and this makes the job of uncovering insights from the data all the more difficult. Thus, organisations are left trying to come up with strategies based upon gut feelings and intuition alone – not an effective way to root the innovative risk-taking needed, to shape a market.
Analytics takes the data from these disparate systems, joins them together and makes forward looking predictions, like how customers will act in the future or where, when and how a market will shift. Gartner Inc. describes analytics as the “combustion engine of business” and notes that the companies that use predictive analytics to analyse voluminous structured and unstructured data will grow 20 per cent more than their competitors
Let us look at a few sectors in India and see how they are sitting on a gold-mine of information and not putting the same to good use.
To my mind, the organised retailers in India are the biggest culprits of not putting their data to good use. Let us look at a few different aspects of retailing one by one and see where the Indian retailers have got it all wrong.
Long before the age of organised modern retail, the retail business was extremely localised and the customer experience was highly personal. Shopkeepers knew that their ability to connect with customers would be instrumental in driving them back to their stores for repeat purchases. For many successful shopkeepers, this translated into knowing their customers at a personal level, carrying a knowledge of what products they typically liked to buy, giving expert advice on individual products, the pros and cons of each, and sometimes even their personal recommendation on what the customer should buy.
What has happened to this breed of shopkeepers who cared so much about customers? Well, technology, the advent of big-box retailing and now e-commerce has converted retailing into a largely impersonal, buying experience where everything including the customer experience is mass produced.
Growing the loyalty of customers: Most retailers today have reconciled to the belief that customer loyalty cards have addressed the most important aspect of modern retailing. However, they fail to understand that every other competitor is issuing a loyalty card. How then, do they differentiate themselves from the other competitors? Unfortunately, the desire to excel, to stand out from the others and innovate is not in our genes.
Using data to understand who their loyal customers are, and then rewarding the loyal customers through a well thought out loyalty strategy is the need of the hour. Creating a customer contact program and incentivising the loyal customers to come back to their store to buy one more product, one more time, has been the key to success for a majority of retailers around the world. Why then, in this age of personalisation, do we still see the one size fits all-advertisements in mass media?
What is evident is the lack of ambition to serve the customer better. The retailers want to sell what they want to sell and not what the customers want to buy. But, to understand what customers want to buy, they need to look at the customer’s buying behaviour and understand what they are buying. For doing so, the retailers need to use analytics. I have been visiting a leading retailer’s branch in Gurgaon for the last two years, and I have seen many instances where old broken toys and furniture have been stacked up on displays, which no sane customer will ever buy. The reason for that is simple; no one in the store has ever bothered to look at the list of 100 bottom selling SKU’s (stock keeping units) or inspected the store to remove items that have been damaged. These damaged items create a negative impression in the mind of consumers and a customer will rarely want to pick up a similar product even though it is undamaged.
How much space should be allocated to each category and within each category to different SKUs – this is a question which needs to be answered through analytics. Even though tools like planograms help in tactical decision making on assortment, the larger strategic decisions need to be taken after a detailed analysis.
One of the biggest inferences through analytics has been that when you have too many products in a range, you end up saturating the category and give it more space than is required. The biggest challenge in a retail store is space. When retailers don’t make the most of the space that is available and have the right number of products in each category, they are leaving money on the table.
I can go on and on, but, I have a better idea. During my childhood, there used to be a book called “Tell me why” and it fascinated me with its repository of questions and answers. In India, our education system and social fabric has a way of curbing the innate curiosity in an individual so that when he grows up he stops asking questions. Analytics is all about asking questions and rummaging through the data to find answers. So, for the rest of the article, I will pose questions which business houses need to ask themselves and their organisations and use data to answer them. Hopefully, some of the curiosity will come back and as a reader, you will be tempted to ask the same of your organisation.
What is the optimal number of SKUs that should be kept in each category?
Pricing: Which products or SKUs should undergo a price decrease or increase? Can I analyse the data and identify the relevant SKUs?
Which products are bought by price sensitive customers and which are bought by affluent customers?
Can I segment my customers based on the price perception of products?
What has been the impact of price increase or decrease on revenue and number of items bought for products which have undergone a price change?
Has any retailer bothered to analyse the impact and ROI (return on investment) of their mass media campaigns and subsequent discount campaigns?
Does this mass discounting lead to sustained loyalty?
Do the retailers even analyse their repeat or loyal customers and try to identify what they usually buy and try and customise the discounts to the wants and needs of the customers?
Product bundling is a great concept, but do we do a market basket analysis to see which products need to be bundled together based on customer buying behaviour?
How does one store compare against the other in terms of various metrics?
Can we identify which stores are doing very well and why?
Can we adopt the best practices from the top performing stores and implement them in the poor performing stores?
For this article, I will limit myself to the retailers, but the hotels, online travel agencies, restaurant chains, banks, insurance companies are all losing out on a golden opportunity to use data to drive more revenue and improve their customer service. They need to ask themselves a host of questions and find answers by analysing their data. I will discuss them in detail in a subsequent article.
It is truly a bitter irony of fate, that, in spite of possessing one of the largest talent pools in analytics in the world, most of them work for foreign companies or have foreign companies as clients. The time has come for some of that analytics talent to be used for the betterment of the Indian industries and Indian companies.
Snehamoy Mukherjee is a part of the Strategic Leadership Team at Axtria, a New Jersey based Analytics firm, where he is responsible for business development, solution development, delivery leadership and strategy formulation. Prior to this, he used to head the analytics practice at Technopak Advisors and has over a decade of experience in the analytics industry having worked in multiple domains like retail consulting, FMCG/CPG, insurance and market research.