Amazon is now arguably today’s most popular and most shopped online retailer in the world, and is often looked upon as a personalization leader. This is in large part due to Amazon’s ability early on to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors.
Today’s shopper now has the expectation that all retailers will know them personally as a consumer and serve up personalized experiences the same way Amazon does. When a retailer communicates with them – whether online or in a physical store – they expect to receive messages, deals and offers that are relevant to them, at the exact time when the offer is most relevant, and in a manner they expect to be communicated with based on their personal preferences. In short, they want one-to-one personalization.
While physical store associates can provide great customer service, online retail has an advantage when it comes to personalization because the retailer can tailor its homepage and customer recommendations based on easily trackable clickstream paths and browsing data on recently viewed or purchased items. So how can retailers apply this level of Amazon-esque personalization in-store?
Enhancing Beacon Technology
Perhaps the easiest way to leverage personalization in-store is through beacon technology, which has grown in popularity over the last few years. Beacons can detect when a customer approaches or leaves a specific location, enabling retailers to push timely messages to shoppers that promote certain products or offer other useful information. While many retailers use beacons to entice customers to make a purchase with store-wide deals, this may not always result in a sale because the offer isn’t always relevant to all shoppers.
However, when beacon technology is coupled with data about consumers’ past behaviors, preferences or other valuable data about that specific customer and layered with predictive analytics, retailers can identify the messages that will resonate with each shopper and provide them with a truly personalized deal or offer.
Let’s say a customer is walking by a department store and receives a mobile notification on a 20 percent off deal on sneakers. The customer enters and roams the store, but ultimately decides not to buy anything. Perhaps they had just bought a new pair of sneakers, or the sneakers that were on sale didn’t include their favorite brand.
This is where predictive intelligence steps in. Now imagine the department store has all of this data on this same customer – data that reveals things such as:
• Transaction History: The customer always buys a new pair of shoes during the first week of each month, but never on days in-between;
• Likes: She particularly likes comfort over style, often buying sneakers, flats or low-heel boots over stilettos;
• Lifestyle: She is a working nurse, and spends 12-hour shifts 4 days a week at the hospital running around from room to room checking in on patients;
• Cause: She often contributes to charitable organizations that provide resources or aid to impoverished countries.
Knowing these data points – which provide context for when and why a consumer makes a purchase, and work in conjunction with the customer’s location – the retailer can now send a mobile offer that is truly tailored to the customer and is much more effective in enticing her to make a purchase.
Case in point: Perhaps the customer is in the store on Labor Day weekend. You now know she prefers to buy shoes at the beginning of the month, prefers comfort over style, is on her feet all day most days and has a soft spot for philanthropic endeavors. You know then that offering her a deal for 20 percent off a pair of TOMS would more likely result in a purchase than if you were to offer her 20 percent a pair of Sam Edelman heels. To top it off, the deal was sent to her at the optimal time she would buy.
Using this type of technology in conjunction with predictive intelligence allows retailers to personalize in-store offers to each individual shopper by speaking to their individual tastes – offering more compelling deals that have a greater chance of resulting in a purchase.
Delivering Deals Anywhere, Anytime, in Any Weather
Perhaps the greatest thing about predictive intelligence is that it’s not restricted to beacons or any one location. Predictive intelligence enables retailers to know the data points that are relevant in any purchase decision and send targeted offers based on this data, no matter where their customers are located. This not only encourages them to make a purchase, but can actually encourage them to come into the store if the deal is in-sync with when, where and why a customer prefers to make a purchase.
For example, a local coffee shop learns the following about one of its customers:
• Location: The customer works at an office two blocks away;
• Time: He usually makes a coffee run at 2 p.m., when he hits his afternoon work slump;
• Weather: He opts for a hot latte every time rain is in the forecast.
The next time it rains, the coffee shop can deliver a deal for this particular customer that is specifically tailored to his preferences and behavior – perhaps offering one dollar off a medium latte sent to his mobile just before two o’clock. They know they can get his attention due to the time, his proximity to the shop and his preference for hot drinks on rainy days, and they’ll be more effective in enticing him into their store because they were the only nearby coffee shop to offer a deal for what he wanted at a time when he wanted it.
Retailers can deliver this level of personalization anywhere their customers are located, whether they’re in an office, at home or even lounging on a beach. No matter where a customer is, what time of day it is or what the weather is like, predictive intelligence learns how to utilize the right combination of data points to drive a purchase. The possibilities are endless and the variables limitless; retailers just need to be armed with the right intelligence about their consumers’ unique preferences and behaviors.
Craig Alberino is co-founder and CEO of Grey Jean Technologies