Big data · Data Analytics · Retail · technology

A Revolution Of The Retail Sector – Data Analytics



In today’s world, consumers are enabled to buy and shop in completely different ways.

The Retail Industry

With a broad assemblage of dealers and merchandises at the tip of their fingers, customer experience is a fundamental differentiator. Retail has become one of the fastest growing industries. Latest trends are rising and competition is growing, notably in the online market.

The retail aspect looks very diverse today than it did even ten years ago. The way that customers make buying choices has drastically altered—they visit stores, use their smartphones to compare product and price reviews; friends and family immediately pull in on purchasing decisions via social media; and when they’re set to buy, an ever-growing catalog of online retailers cart products straight to them, sometimes even on the same day.

A Shift In Trends

Industry observers have forecasted that these shifts may lead to the end of retail as we know it and that the extinction of brick-and-mortar stores is not far off. There is an actual precedent for this kind of change, which recasts the industry’s champions and failures. We see that within the last century, local niche stores gave way to department shops and supermarkets, then to shopping malls, and then to big-box retailers. Each of these transformations unfolded quicker than the one that preceded it.

Direct customer contact is a privilege but also creates heavy amounts of data. The new online communication accessed by both retailers and consumers on a regular basis is creating more data to be stored. Extract relevant information from all current data in order to satisfy customer demands, improve business performance, increase sales and improve business performance.

Big Data Analytics

Big Data analytics, specifically retail analytics, are now being implemented every step of the retail process. Industry players work on finding out what the popular products will be.

They do this by predicting trends, determining where the need will be for those products, optimizing pricing for a competing edge, recognizing the customers seeming to be interested in them and figuring the most valid way to address them, getting their money and eventually find out what to sell them next.

Predicting Trends

Retailers today possess a wide array of tools to figure out what the seasons “must have” items will be, whether it be designer dresses or children’s toys. Trend forecasting algorithms scour through social media posts and web browsing practices to find out what’s creating a buzz, and ad-buying input is analyzed to understand what marketing departments will be selling.

Marketers and brands involve themselves in ‘sentimental-analysis’ using complex device learning-based algorithms to ascertain the context when a product is reviewed, and this data can be used to precisely predict the top selling products in a category.

Forecasting Demand

Once there is an evaluation of what products people will be buying, the retailers work on understanding where the demand may be. This requires a collection of economic indicators and demographic data to develop a theory of spending habits across a targeted market.

Price Optimization

Algorithms track inventory levels, demand, and competitor activity and automatically react to market fluctuations in real time, enabling steps to be carried on based on insights in a matter of seconds.

Big data analytics also helps in determining when the prices should be lowered—known as “mark-down” optimization. Earlier, most retailers would just decrease prices at the end of a season for a particular product line, when the demand is saturated. However, analytics has also shown that a gradual decline in price, from the minute demand starts to drop, induces increased revenues.

Customer Identification

The key is deciding which of the consumers want that selective product, and the best way to go about this is to put it directly in front of them. Till date, retailers depend heavily on suggestion engine technology online, data obtained through transactional reports and support programs online and offline. Demand is predetermined for specific geographic domains based on the demographics they have about their clients in those areas. This means that they receive the orders more promptly and efficiently.

Data on how specific customers communicate and make contact with retailers is applied to determine which is a reliable way to get their concentration on a product or promotion – be it email, SMS or a mobile alert.

Grabbing The Money

Data analytics has unveiled that a vast number of customer visits to stores online fail to convert at the last moment when the buyer has the product in their basket but doesn’t go on to confirm the purchase. Speculating that this is because clients can’t find their debit or credit cards to confirm the details to the billing, where customers can pay with cash once it is delivered. Advanced fraud blocking analytics are used so that the operation can’t be manipulated by those with shrewd intent.

Guiding Marketing with Better Data

With just the right blend of data, the vendor can guarantee that they are delivering the most optimal outcomes for your business. In order to build the comprehensive marketing strategy, they need to fully learn who their prospects and customers are. This insight must go beyond data such as name, address, phone and email Id. Customers demand manufactures to know who they are, what they want, which routes they like to shop in, and the best time to interact with them.

Must Pay Attention to Data Quality

All marketers talk about good data and the importance of it, but in reality, most of the records contain wrong or incomplete data. The records may be missing data elements such as name, phone number, or email address.

Reject Ineffective Draw-Ins

Diversified product offering and promotions and are assumed to draw in new customers, but many of these allurements fall tasteless. Unpopular inventory ends up on the clearance counter constantly, and some offers don’t seem to draw in the crowds looking for a good deal that retailers are expecting. Big data analysis of both in-store sales and online can benefit in finding the broader insights that can be hidden if the data hasn’t been looked at as a whole.

Social Media Marketing Tactics

Big data makes  a personalized type of marketing—individual interests of each customer and reaching them at the right place and time. They make this possible by using clickstream data and social media to figure out what the individual is thinking about at this minute.

Integrate Data Across Both Offline And Online Channels

Today’s purchasers use various channels from the primary research of a new product or service to closing purchase. Nevertheless, consumers don’t distinguish between channels and expect their purchase experience and every brand communication to be seamless. So with data analytics, it is easier to reach them across a variety of channels.

Ways to Transform the Retail Business

Go “Omni-Channel”

A study earlier this year by Mastercard found that eight out of 10 buyers now use a tablet, smartphone, computer, or in-store technology while shopping. Omnichannel retail shows no indications of quieting down. In order to keep up, retailers must merge their digital and physical systems to assist the present ‘omni-shopper.’

Beacons Are Making An Impact

There has been considerable hype about the beacon technology, but this year has been a breakthrough for this technology, prompted by the necessity to enhance in-store experience and give appropriate offers in store. Beacon technology is composed to transform the way customers communicate with brands, making devices helpful and the way retailers measure the offline impact of online ads, is a game-changer.

Retailers – small and large – have been collecting the benefits of analyzing structured data for ages, but are only just beginning to get a handle on unstructured data.

Great perks will come from innovative thinking and advances to analytics, rather than those who collect as much data as possible and then see what happens.

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