A Beacon of Hope: Making Shopping Personal With Big Data

From online retail stores to apps that let you do your own billing, we’ve come a long way from traditional marketplaces where there was a fair degree of human interaction. In this age of fast-paced information sharing, consumers know exactly what they want. And if they don’t, there are enough tools available at their disposal to help them make an informed choice without relying on storekeepers.

We’ve reached a stage where retailers are toying with the idea of sending out deliveries via drones. And at the pace with which technology is restructuring the retail industry, the day you are greeted by a hovering delivery drone at the door is not too far away.

If we were to closely take a look at how the retail industry is changing, the role big data is playing is quite evident. The use of retail data analytics is one of the biggest factors that is making shopping a lot more convenient and customized. Major players are applying big data at every step of the retail process to sell more and make customers keep coming back to buy more.

Heineken, one of the largest beer manufacturers, analysed consumer behavior across their stores. They even used 3D sensors in Walmart stores to see where customers are most likely to pick up six-packs. This exercise gave them actionable insights for better shelf placement and product visibility to drive more sales.

Walmart themselves have spent millions on real-time merchandising systems. They are in the process of building the ‘world’s largest private cloud,’ as they call it, which will track transactions across all their stores, as they happen. Retail data tools, in the form of customized software and algorithms will then track demand, inventory levels, and even competitor activity. These tools will then let them respond to market changes in real time and allow Walmart to take action based on hard statistical data, all in a matter of minutes.

You can take a look at the Intelligence Node blog to see how big data is revolutionizing the retail industry across the globe. With the help of big data analytics, retailers can now take data-driven decisions on things like figuring out which products are popular at what time of the year, where can these products be placed for maximum sales, what do customers usually buy along with those products, and a host of other interesting metrics to elevate the retail experience for their customers.

A deeper understanding

Perhaps the most advantageous use of big data in retail is understanding what your competitors are doing and how you can do it better. By integrating big data into their processes, retailers can scrutinise their competitors’ promotional strategies, analyze pricing, and gain insights into what drives customers to their stores.

They can receive instant alerts telling them things like whether their competitors have run out of stock or if they’ve launched a new item. This allows them to take decisions on which items they can promote and how they can optimise in-store product locations to capitalize on the situation.

Catalogue analysis helps retailers identify brand-level gaps in their catalogues, allowing them to always stay head-to-head with the competition.

The biggest advantage that big data provides retailers and brands is the power to make operational decisions based on hard facts and statistical data. This greatly reduces the pressure of trying to gauge your competition purely based on instinct and data from comparatively unreliable sources of information.

Retailers today have access to a multitude of tools to make shopping a truly immersive and personalized experience for every customer. Developers have worked out trend-forecasting algorithms which sift through social media platforms and search engine queries to figure out what people are looking for and where. Brands can use this and the data from ad conversions to figure out which products the marketing team needs to focus on.

They can also make use of sentiment analyses which employ complicated machine-based learning algorithms to determine contexts where products are discussed, and use this information to predict the top products across categories.

Going beyond numbers

Retail data analysis is not bound by the limitations posed by intangible metrics. To elucidate this statement, let’s take a look at something known as kinetic mapping. Essentially, this allows retailers and brands to track customer and employee movement through their stores. By looking at these patterns, they can position their product displays more optimally and make sales faster and more dynamic.

Interactional analysis takes into account how customers and employees interact. The results of this analysis can be used to design training modules to help employees understand customers’ needs better. Employees can be directed to focus on different aspects of the same product while talking to different customers, and sell more just by altering their sales pitch to suit the situation.

Knowing which customers want and which kinds of products and where they look for them is key to running a successful retail business. And when it comes to online retail, there’s nothing more important than understanding demand and supply across locations. This involves examining heaps of demographical data and spending habits to know what sells where.

Retail analysis platforms take all this into account and even include factors like weather, holidays and popular events. Fast food joints almost always run promotional offers and lure customers with attractive schemes during game nights and days with high temperatures. They know that people are going to stay at home and want to do little or no work when a match is in progress, or spend a lot of time in a hot kitchen during the summer.

More than just relying on customers who are already in the store to drive sales, retailers and brands are constantly looking for ways to attract more customers. To phrase it more accurately, retailers and brands are constantly looking for ways to attract the ‘right’ customers. With all the money that goes into advertising and marketing, the cost of conversion goes up with each customer that does not make a purchase. This is especially true when it comes to online retail stores who have no way to ensure that someone who clicks on an ad will actually buy something. By analysing the habits and preferences of their current consumer base, brands and retailers get to know exactly which demographics they should be spending time and money on.

Making the right moves

Big data and retail analytics can do wonders for brands and retailers who want to optimize their processes to save money and beat the competition while they’re at it.

With the right tools, organizations can run smart retail analytics which help them tackle real-time challenges at different levels. Retailers and brands can use big data analysis to optimize inventory operations and inventory management. They can even make use of real-time data gathered from markets across demographics to get data-driven suggestions for optimal merchandise assortment in their stores. By analyzing transactions across their stores, some retail analytics software can even recommend discounts and identify associated products that you can promote to increase your customers’ basket sizes at checkout.

Since there is little to zero human interaction when it comes online retail stores, it’s essential for e-retailers to personalise their customers’ shopping experience as much as they can. This ensures that customers feel like they are as important to the retailer as the retailer is to them. With the help of data collected from different transactions made by the same customer, retailers can gain an insight into their needs and buying habits. This will allow them to promote the right products to the right customer and even suggest alternatives if a particular item is unavailable.

It’s quite evident that big data is completely transforming the way we buy and sell in today’s world. And the faster businesses integrate big data in their operations, the faster they’ll grow, and the more they’ll sell.