The way that customers make buying choices has changed dramatically over the past several years.
Today, customers visit stores; use their smartphones to compare product and price reviews; pull family and friends in on purchasing decisions via social media; and, when they’re set to buy, a growing catalog of online retailers deliver products straight to them, sometimes even on the same day.
Industry observers have predicted that these shifts may lead to the end of retail as we know it — and that the extinction of brick-and-mortar stores as a result.
We see that within the last century, local niche stores gave way to department stores and supermarkets, then to shopping malls, and then to big-box retailers. Each of these transformations unfolded quicker than the one that preceded it.
How big data is being used to improve the shopper experience.
Big Data analytics, specifically retail analytics, are now being implemented at every step of the retail process. Industry players like Upstream Commerce, Intelligence Node, and Wiser 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.
Using data today to predict what will be hot tomorrow.
Retailers today possess a wide array of tools to figure out what the season’s “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 ‘sentiment 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 to improve margins.
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.
Using algorithms to optimize pricing strategies.
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 in retail 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.
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Leveraging customer data to improve the shopping experience.
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.
Until now, retailers depended 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.
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 that manufacturers know who they are, what they want, which routes they like to shop in, and the best time to interact with them.
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.
Most retailers believe promotions will drive a growth in revenues, but many of these promotions fall flat. 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.
Key insight: integrate data across both online and offline channels.
Today’s consumers use various channels from the initial contact to the final 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.
How can you transform your 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. Omni-Channel 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.’
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Beacons are finally 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.
About the Author: Yasen Dimitrov is Co- Founder and Chief Analytics Officer of Intelligence Node, a Retail Analytics Company that helps brands and retailers to optimize their pricing, product and merchandising operations by using real time data.
