A lot of decisions can be made through data analytics: Ugam CIO Mihir Kittur

K Giriprakash Bengaluru | Updated on March 12, 2018

MIHIR KITTUR CIO and co-founder, Ugam

Ugam is a global leader in managed analytics that helps retailers, brands and market research firms transform big data into valuable insights. The 1,700-strong company counts five of the top 10 US retailers as its clients. In an interaction with BusinessLine, Ugam’s CIO and co-founder, Mihir Kittur, shares the company’s insight into the Indian retail industry:

How is data analytics helping e-retailers come out with better business decisions?

In the past, a retailer would carry about 50,000 SKUs and there would be 500 merchants. In India alone, Amazon has 20 million SKUs and in the US, they have has 580 million SKUs.

The challenge is more about which product you should carry. You can’t rely on your gut alone and past data and on talking to supplier or competitor.

For example, the e-retailer, who has the largest inventory of biking accessories, is not Flipkart or Amazon but Paytm. They have about 19,000 of them. So, most of the time the retailers don’t even know whom they are competing with. If you carry a lot of stuff people are not looking for, you are not relevant in that category. So, the consumers may not come back at all. So, lot of decisions can be made through the use of data analytics.

Most e-commerce companies are directing consumers to visit their mobile sites more and even give away freebies to attract them. Flipkart has even claimed that during the Big Billion Day(s), most of their business came from their mobile app.

Our analysis says that on certain days, the prices of products on the mobile apps were higher than those on the website. Likewise, in several categories, Amazon was winning while Flipkart was a clear winner in others.

But how can there be a price difference because the mobile app is more of a convenience and all are anyway directed to the same website?

Not necessarily. The reason someone creates these apps is to drive far more personalisation. If my analysis through data location tells me that the customer stays back during the night at a certain location while he visit another location during the day, I know that probably one location is his home and another is his workplace. So, it helps me customise the products and the price for him. I will get far more information from you if I am using the app than PC. I will encourage you to do that so that I will personalise the mobile app for that particular consumer. If you are coming from a mobile app, I can direct it from different servers.

In the US, for example, if I am logging on from say, Florida which is a very hot place, then I won’t sell blankets to you. But from Boston which is a very cold place, I would.

How much more personalisation can e-commerce sites create for their customers. Rather, how far can they take it?

Several ways I can do. E-commerce companies can find out whether you are an iPhone 6 user or something else. They will check browsers like Safari and Chrome to find out more. I know in the past what you have bought . All of this when I synthesis and then I can then personalise and use algorithm to find out whether you are more sensitive than the other consumer. Another important thing is today if I put a certain price to a product, Flipkart will crawl it, Amazon will crawl it. There is no personalisation in it. If I put it on your app, you think it is a price for you. It is very difficult for Amazon or the competitor to know what price I have given you. It is like me telling it into your ears.

So, what you are saying is that even the pricing can be customised or personalised.

Yes. Not just that, certain e-commerce sites allow the customer to bargain as well. So, many a times in Amazon, the price you see is not the price you buy. What is shown is the listed price. Once you put it in the shopping cart, the price could change. It could get one more level of personalisation because all competitors can reach that price but will need a log-in to reach the next price.

What has happened in the US is that they have gone one step further. In some cases, they will say you make a counter offer and as more people do that, you get more data and behaviourial pattern of the consumer. So, you are basically making smart machines compete with people who are not using data and analytics.

For example, if you have gone six times to a site to buy a camera, then don’t change that price. Or, for example, everyone else has stocked out but you are the only one carrying that stock. I will tell my client that keep the price or raise it. The customer will surely come.

We write a lot pricing algorithm or rules. For example, if your two competitors, drop price but it has no impact on your sale, then you don’t have to drop the price. This is called competitive price algorithm.

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Published on November 20, 2015
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