A few years back, a huge debate had many the fate of many retail stores hanging by a thread. Many chains were wondering if retails store were a boon or just a liability. And where we are standing currently, the answer is pretty clear. But a more pertinent question now haunts us. ‘What next?’
The rapid transformation that retail has gone through positions it at a very prime spot.
With technologies like AI, machine learning and big data readily available, retailers have a
huge opportunity to tap into the market like never before.
One of the first new tech available at a retailer’s disposal is predictive shopping. In theory, it means that based upon your purchase history patterns and on the levels of demand and supply of products in the market, your desired item will appear on your doorstep without you having to think about it.
Why is predictive shopping the next big thing in retail?
Amidst all the buzz relating to new tech, retailers are now turning to AI and machine learning.
But before they dive headfirst into the shiny new tech that’s populating the market, they need
to carefully evaluate the challenges they might encounter on the road to predictive shopping.
The consumer is more short on time than ever before. Brands and retailers are constantly battling for consumer’s attention. With such a highly competitive scenario, convenience is the most important benefit that predictive shopping can bring to the table.
Additionally, anticipatory shipping is the next step. Unsurprisingly, Amazon already exploiting the concept to the fullest. Anticipatory shipping means that retailers send out a few selected items for a few regular customers to the shipping hub, to place them in the pipeline even before the customer has actually placed that order. This not only increases the efficiency but also cuts down on the delivery time.
In this context, the shipping largely hangs on the recommendations made by the retailers based on the analysis of data. For example, many retailers pre-populate a customer’s shopping cart based on the previous purchase history and wishlist items.
A great example of the same is Amazon’s ‘your recommendations.’ With such features that reduce the friction in order, the e-commerce giant is now edging closer to predictive shopping.
Based on the predictive analysis, consumers will soon be able to shop for upcoming events and holidays from a specially curated list of items to match their taste and preferences.
5 ways retailers can use predictive analysis
The main aim of predictive analysis is to use big data to make better business decisions. It does not guarantee an outcome, but it can certainly help minimize the risk and uncertainty associated with any market scenario.
A few ways retailers can use big data are-
Improve engagement: Traditional analytics tells you what happened and why predictive analysis on the other hands informs one about what is the next most likely outcome a customer might take. By tracking and analysis browsing patterns, purchase history and other forms of engagement, predictive analysis can give recommendations about what products a consumer should purchase and their predict their browsing behavior. A result from a study done by Invesp shows that 45 % of shoppers have a higher chance of shopping from a site that offers personalized recommendations.
Better target promotion: A good marketing campaign draws on the facts based on insights about its target audience preferences. Predictive analysis collect various forms of data like market size, algorithms etc. and asses it all to assess how successful the next or potential marketing campaign is going to be. By determining if the promotions are going to work for a particular shopper or not, marketers can effectively pinpoint the most effective campaign message for every individual and then target them with it in real time.
Predictive search: A smooth and user-friendly shopping experience is always well received by the customers. To a retailer, it may sometimes look like a small thing that can be taken for granted, but it can make a world of a difference to a consumer since a website is a primary way a consumer interacts with the brand. Backed up with a thorough analysis of consumer history, behavior preferences, and browsing behavior, a predictive search function can predict what a consumer is searching for, even when they have just typed few letters in the search bar.
Optimize pricing: Prices of a product play a very important part in determining the sale of a product. The price needs to be just right or else it can severely affect the profits and sales. The predictive analysis looks at the old prices, consumer interest, competitor’s pricing, inventory and margin targets to decide upon the most optimal prices for a product. For example, on Amazon, sellers use algorithms to set their prices, taking into account sales, customer feedback etc.
Predictive inventory management: Overstocked and out of stock is an ever-present problem for retailers. But with predictive analysis, smart inventory management is possible. The predictive analysis takes into account existing promotions, markdowns forecast, and multiple store allocation to deliver the most accurate forecasts about demand, allowing retailers to allocate the right products at the right places.
Looking at how predictive analysis and predictive shopping can help you climb up the ladder for your business? Contact Datahut, your big data experts.