Big Data and Analytics: Linchpins of an Omni-Channel Strategy

One of the biggest traits of the retail industry is its ability to dynamically adapt to changes in shoppers buying behavior. Prospective buyers and customers alike have begun to shop or browse for what they need across multiple channels. It is estimated that around 75 to 80% of shoppers have a habit of comparing prices online with a third of them using their mobile devices to compare prices while being physically present inside a store. This has created an immense pressure for retailers to stay competitive in order to survive by satisfying the ever changing needs of their customers irrespective of their presence – be it online or in person at the store. This has made retailers adopt an Omni-Channel approach to cater to their customers.

Major retailers have started to invest millions of dollars to enhance their customers shopping experience by transforming their stores into a smart and connected Omni-Channel fulfillment hub. By doing this they ensure that the customers enjoy the same rich interactive experience they are used to when they browse on the web, use their mobile devices and while accessing social media sites. The ability to provide such a rich and seamless in-store experience comprises of many different technologies. The adoption of cloud based solutions coupled with Big Data technologies and advanced Analytics has enabled retailers to capitalize on the changes in customer shopping behavior and ultimately stay competitive.

The retail industry has always been sitting on a vast amount of data that was waiting to be explored and insights derived out of it. One of the biggest challenges was to collect and analyze such huge volumes of data to derive meaningful information out of it. The advent of Big Data technologies has helped overcome this challenge of volume, variety, velocity and veracity of data. Retailers are now able to effectively use this data to predict customer behavior in real time and offer promotions based on their location within the store and personalize their marketing campaigns.

Let us look at a few use cases by which Analytics can help retailers to cash in on the Omni-channel opportunities.

Increase in-store orders and delivery times – Online only retailers are becoming hyperlocal by rapidly expanding their fulfillment warehouses to be located close to metropolitan areas to offer same-day delivery experiences. This makes it evident that though customers shop online they do not want to wait for long (read one day!) to get the products in their hands. It is estimated that Amazon spends close to $15 billion on setting up new warehouses to fulfill orders the same day. In fact Amazon was one the first retailers to adopt a robotic fulfillment approach to speed up the fulfillment process.

This presents an immense opportunity for brick and mortar retailers who have nationwide presence. Though limited on number of warehouses they can make it by the number of stores across the country. Using Big Data technology to analyze customer’s data starting from POS information to Facebook posts and Tweets as well as call center logs retailers would be able to use Analytics to gain insights on how much to stock to offer faster fulfillment timelines.

Enhance in-store shopping experience – With more and more customers walking into the store and using mobile devices to compare prices with competitors, retailers should turn this data to their advantage. Having beacons or mobile touch points around the store along with a mobile app would create an Omni-channel experience that would enable the retailer to derive insights from the customer behavior to offer personalized offers across multiple channels of interactions. A recent announcement from Wendy’s was that out of the $40 million investment this year, most of the money would be spent on POS and mobility technologies. Wendy’s management feels that this will help elevate the customers’ in-store experience – right from self-service kiosks to locations based offers.

Customers stand to benefit from this as they now have the power on their hands – mobile devices that provide contextual information based on their location within the store in combination with their past purchase data. At the forefront of all this would be Big Data and Analytics that would constantly collect, analyze and feed information about its customers in real-time.

Offer better Pricing – Retailers have to be competitive on pricing their products. Prices were traditionally arrived at using Bandit testing or A/B which was a manual process and as a result prone to errors. Predictive Analytics and Big Data technologies have now paved way for retailers to arrive at prices using inputs from a multitude of sources including but not limited to:

Product’s Historical Prices
Customer Preferences and Order history of the Product
Competitors price for the product
Available Inventory
Profit Margins expected from the Product

Amazon uses predictive analytics to gain competitive edge over Walmart’s prices. A study by a leading Price Optimization solution provider indicates that Amazon is able to undercut Walmart’s prices by as much as 4% in spite of Walmart matching 63% of its product prices with that of Amazon. A clear indication that Big Data and Predictive Analytics can help retailers to effectively manage their supply chain process and arrive at optimal prices for products by predicting demand and understanding customers’ path to purchase.

Traditionally Analytics was used for guessing customer preferences but that trend is rapidly changing to that of predicting a customer’s intent to purchase. This is indeed a huge shift from looking at the preferences from a transactional angle to establishing a more personal relationship with the customer.

The reading on the wall is clear for retailers as the lines are blurring between online and in-store shopping. The earlier retailers adapt to a data driven Omni-Channel approach the higher the chance for them to engage and retain their customer base.