What does it take to persuade more shoppers to visit restaurants and stores in the digital age? Let’s consider the following scenario:
Lucy is a somewhat regular customer at Juice Shop— she has their app on her phone, she participates in their loyalty program, and right now she is eligible for a free drink. However, her schedule has been hectic recently, and picking up a glass of healthy juice has not been a priority. Today, she get a push notification from the shop saying:
Chances are, this message will catch Lucy’s attention and persuade her to plan a stop at her local Juice Shop the next time she is in the area. That’s because it’s not a general message sent to all Juice Shop customers—it is personalized for Lucy. It speaks to her loyalty status, her preferences, and her history as a customer.
This level of personalization is a powerful motivator, especially when it is delivered strategically for maximum customer influence. For example, what would happen if Lucy received the above notification while running errands near the Juice Shop? That’s even better! She is much more likely to stop in if it is convenient for her to do so. And once she enters the shop, what if they push her a follow-up mobile notification for a great deal on a dozen chocolate mini-donuts instead of just one? Now they’ve really got Lucy—at the right time, in the right place, and with the right message that is meant just for her. Now let’s explore how they’ve done it.
As consumers move their purchases online for more choices, better prices, flexibility and convenience, businesses that want to increase their in-store traffic have to do more than just display products and run price promotions. Even giant retailers, restaurants and convenience stores have been seeing footfall traffic declines lately. If these businesses want to bring consumers back through their doors, they must, like the Juice Shop, up the ante on the level of personalization they use to engage and interact with their customers.
This means going beyond inserting customers’ first names in emails or showing certain content on the website; it requires a real-time awareness of each customer’s:
Combined, these factors create the complete context or intelligence for each customer. It’s like the creating a Customer DNA, or the omni-channel click stream (activity) of every customer. Consider the power of real-time customer context in the following scenario:
Lucy is now browsing through leggings and trousers online. Her favorite brand takes notice, and immediately pushes her an in-session message while she is still visiting their site:
An understanding of Lucy’s context—including her current browsing activity, past transactions, and loyalty status—empowers the brand to deliver the message that is most likely to resonate with her in that exact moment.
Keep in mind, however, that context is not static. Lucy might be browsing a website on her smartphone one minute; she could be driving by their store the next. The ability to access all of a customer’s most up-to-date contextual data points at any given time is what enables brands to deploy hyper-personalized interactions—and when your goal is to increase and optimize in-store traffic, location is the name of the game.
While ecommerce and mobile devices are seen as the main causes of lagging store traffic, smart retailers and restaurants are embracing mobility as part of today’s omni-channel buying experience.
For instance, Nordstrom recently announced that it is using location-based ads on Facebook to help drive in-store traffic. Similarly, other retailers are exploring the use of analytics and proximity technologies such as geofencing, iBeacons, and WiFi tags to trigger personalized, location-based recommendations. Businesses can even guide customers to in-store departments that house products of interest—which is how the Juice Shop deployed a deal for Lucy’s favorite donuts as soon as she entered the shop. Consider this even more targeted scenario:
Lucy walks into a department store to pick up a dress she has ordered online. When she steps foot inside the store, she receive a mobile notification with a map that guides her to the pick-up counter. As she makes her way through the store, she also pass by the handbag section. Immediately, she receive an alert:
Instead of fighting online retail, this brand is leveraging Lucy’s ecommerce activity to get to know her better, and then using this personal knowledge to determine how best to influence her in-store behavior.
The technology that supports this level of influential personalization is a new stack; a new generation of systems that can enable an enterprise to touch every customer in a hyper-personalized manner at scale.
Machine Learning (ML) is at the heart of such personalized engagement. ML ensures that as the system ingests more and more customer data, it is able to learn and act on customer affinity and intent. These AI-powered tools then go on to determine a ‘zone of influence’ for each customer, or a situation in which the probability of a transaction is the highest. Once the influence zone has been determined, the most appropriate and relevant action is triggered—a personalized message, offer, or notification for the customer.
The possibilities of ML/AI-driven use cases in such a scenario are endless. Let’s revisit Lucy’s local Juice Shop to see how they could leverage customer influence zones to drive in-store traffic:
It’s the hottest day of the year, and Lucy is on her way to cool off at a local pool near one of the Juice Shop’s locations that is different from her typical stop. The temperature is nearing 100° as she approaches the shopping center where the Juice Shop is located. To her delight, Lucy sees a message pop up on her phone:
Now this will certainly make Lucy tap on the directions and head towards the shop! By learning from her past interactions, determining her current influence zone, and deploying a message designed to resonate with her, the brand has effectively gained Lucy’s loyalty—and her in-store business.
The ZineOne Customer Engagement Hub enables large enterprises to incentivize in-store traffic by understanding and responding in real-time to individual customer activity, solving the challenge of personalized engagement at scale.