Big Data Analytics

Browse, click, and pay—this has become the new mantra for shopping in today’s world. With hardly any time to spare to check out products in retail outlets, compare the prices, and ultimately make a buy, most consumers today have switched to the internet for their shopping requirements. All they have to do is surf through a few e-commerce websites, compare the prices, and buy the product of their choice. The best part of it all is that they can do this on the go. Whether it’s sitting in front of the television watching their favourite sitcom, or in a cab on their commute, the customer doesn’t have to put in added efforts for shopping.

And this is just the beginning. The fast-paced evolution gives rise to dynamic retail strategies for the survival of most retailers. Money, quality, and price are the main factors that a consumer looks for when shopping. Customer convenience, the speed of delivery, and range of choice add on to the list. All these requirements can be met by an e-commerce store, which makes the sustenance of retail outlets questionable.

Big data analytics comes in handy when devising strategies to beat the retailing obstacles the modern world poses. It helps reveal hidden patterns, market trends, customer behavior and preferences, and other useful business information through price optimization software and specially designed software. Here are some methods that helps big data analytics come to the retail rescue in today’s digital era.

• Prediction Of Trends

A number of tools and algorithms are available to predict the shopping patterns that are likely to trend the following season. The algorithms gather information through social media sites and web browsing habits to identify the upcoming trends. Sentiment analysis also is implemented in determining the feedback of a product when mentioned on social media.

• Speculation Of Demands

Once the trending products are identified, the next step would be to determine where the demand would occur. For instance, Russian retailers noticed that consumers tend to buy more books as the weather gets colder. So, retailers increase the recommendations of books appearing in the customer’s feed as winter approaches.

Such data can be accumulated by studying the shopping patterns through economic indicators and demographic data specific to the market.

• Optimization Of Prices

With several companies like Oracle, Intelligence Node, Revionics, JDA, Nomis Solutions, and PROS launching efficient price optimization software, laying out a retail pricing strategy is made much simpler. The algorithms track consumer demands, levels of inventory, and competitor activities and make necessary changes to respond to the market trends. These software help in determining the customer’s response to varying prices on products through a thorough mathematical analysis.

• Targeting Customers

The main key in identifying the target audience for your product is studying the data collected through loyalty programs, previous transactions, and products that don’t make it from the cart to the checkout counter. The way a customer approaches a retailer helps determine the best platform to display the product to the specific audience of customers—through email, SMS, social media, a phone call, or even announcements in malls.

• Checkout Conversions

Analytics reveal that most consumers do not make successful checkouts and leave the products in the cart at the last minute due to simple issues like the inability to find their credit/debit cards at the time of checkout. Certain retailers follow a strategy of invoicing the product only once the checkout has been confirmed. This not only helps in making successful conversions but also in preventing fraudulent purchases.

There is no limit in the optimization of retail in the modern world through big data analytics. Even though retailers, small and big alike, have adapted to big data analytics, the depth of its usage is only at the surface. Through innovative thinking and smart strategies, the data collected through the analytics can vastly raise your profit margin.

References:

https://www.linkedin.com/pulse/big-data- shopping-how- analytics-changing- retail-bernard- marr

https://news.warrington.ufl.edu/alumni-friends/how- big-data- is-changing- retail/

http://www.forbes.com/sites/bernardmarr/2015/11/10/big-data- a-game- changer-in- the-retail-

sector/#1d73db678aad

Author Bio

This article has been written by Frida, she is working as a retail analytics researcher for about 7 years. She stayed on top of growing industry trends through market research. She also interacted with brands and retailers on pricing and merchandising analytics.

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