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Big Data Analytics in Retail - A sneak peek into disruption potential

Big Data Analytics in Retail - A sneak peek into disruption potential
January 11, 2018

Co-authored by: Karthick Nethaji

When was the last time your retailer did something for the first time? Especially for you!

If it is more than a month, maybe it is the time to shift your allegiance to someone who can make you feel special. Err, I am not talking about ‘candlelight dinner’ special but the likes of tailored offerings and personalized promotions from your retailer. When I feel hungry and tap my food app, I expect a recommendation – more than ‘your recent order’ and ‘today’s special,’ I expect a blunt salad to compensate for the feast yesterday and I expect a discount that encourages me to go for it, because health is wealth, isn’t it? 

I would not be amazed if a notification pops in my mobile while I am crossing Carnaby Street in London, suggesting a steal deal of my favorite denim blue jeans. A casual wear shop was opened around the corner a week ago? I didn’t realize at all!

I want to live in that world — and so do you, because we deserve better.

Embrace the Language. Period. 

Observe the trend. Everyone has a smartphone and they are living with it, literally. From ’60s Baby Boomers to ’90s Gen-Z, each generation is producing massive amounts of personal data and putting out their interests, fears, moods, and attitudes in the language of likes and tweets. This is the new language that every industry and business is striving to learn. It throws a huge opportunity at data experts to learn about the market needs and deliver ingenious experience.

A quick Google search indicates that a digital consumer has four connected devices; 500 million tweets per day, and every minute, we have 510,000 posts, 293,000 statuses updated, and 136,000 photos uploaded. ‘Like’ and ‘share’ buttons are viewed across 10 million websites daily. And in total, more than 2.5 quintillion bytes of data is getting produced every day.

Measure the Unmeasured

Metrics like lifetime customer value and customer satisfaction (CSAT) are considered true measures to assess the customer. No denying that; however, deriving the value from typical transaction data or rating scores could be misleading. 

Let’s recall a recent incident that happened with an airline carrier, Indigo. An angry customer expressed his dissatisfaction through this tweet – 

“Thank you for sending my baggage to Hyderabad and flying me to Calcutta at the same time,” and received a reply, “Glad to hear that.”

Boom! The CSAT has gone down!

The bot behind the tweet replied promptly but it did miss the context and didn’t understand the sarcasm. Had the bot read the key words – ‘baggage,’ ‘Calcutta,’ and ‘Hyderabad,’ and sensed that something went wrong, would it have been a success story?

Speak with Actions

Retail world tends to create a customer view on shopping behavior through various metrics such as average sales value, frequency of purchase, conversion rate, and total amount spent in store/online. But reports built on pure transactional data will provide only general insights of the customers that lack the vision to determine how these customers will react in the future.

Consumers have become smarter, so did the computing power and capabilities. Machine learning innovations are uncovering inhumane possibilities to understand unstructured data, and these algorithms can also get better overtime by themselves. Hold the thought of robots replacing humans and losing jobs! That discussion is for some other day. For now, retailers ought to tap the data deluge that is generated every second and produce actionable insights of the customer.

Keep Connected. Be Empathetic.

Back in 2012, the retail world was startled at Target’s infamous attempt to predict a teen girl’s pregnancy and delivery due month even before the customer’s father knew! Sure, it made the father uncomfortable of certain activities back home but helped Target send promotional coupons of baby products to the customer, who is most likely to purchase. 

Make an Offer One Can’t Refuse

Google Shopping accounts for 21% of an average retailer’s digital marketing budget. Companies like Office Depot are using LIA (local inventory ads) to entice customers while they are browsing for a laptop or printer by showing actual inventory at a nearby store. Now, couple this concept with Amazon’s patented ‘predictive stocking,’ which is to ship products to the doorstep even before the customer has bought it. Voila! You can now go and open your door to pay for the printer you are browsing a half-hour back. Shorter lead times, indeed.

Spot the Fraudsters. Keep Them in Check.

The key result areas of predictive analytics are extending beyond wallet share, cross-sell/up-sell to fraud prediction, and store shrinkage optimization. 

Today, online returns stand at 30%, much higher than the 7% of brick-and-mortar stores. The easy returns is encouraging many customers to bring out their devious intent and exploit the return policies. Imagine a tourist in the Nordics walking into Elgiganten store to buy a DSLR and H&M for a dazzling makeover. Clicked snaps, amassed a thousand ‘likes’ on Facebook, and returned those products on his way back to airport. There goes a loss of sale — at least an opportunity. 

Predictive analytics can also help to improve the retailer’s bottom line through spend analytics, sourcing analytics, warranty analytics, transport optimization, inventory optimization, etc. by reducing the operational expenses.

Big Data Analytics in Retail - A sneak peek into disruption potential

Goodbye Festive Seasons! Welcome Festive Days!

To all the retailers: Let’s not wait for Black Fridays and Boxing Days to roll out jaw-dropping offers or optimize operations to boost the bottom line. Consumers have become more expressive than ever before in every walk of life. Churn the Big Data in retail, leverage the superlative technologies, and create an incredible shopping experience for your consumers.