This story originally appeared in the May 2019 issue of strategy.
Big data is a big part of modern-day loyalty, and there’s plenty to go around. Three SIA winners showed they were able to successfully distill reams of data, creating highly personalized shopping, dining and travel experiences.
Quebec sports-bar chain La Cage Brasserie Sportive was feeling a little outdated a few years ago, so it spent some time modernizing its brand, menu and restaurant ambiance. One of the things it tried to do more as a result of the rebrand was to adopt a more local approach.
La Cage already had a loyalty program, but it needed to be updated with that personalized positioning in mind. So, working with Lg2, it redesigned Club Cage to move away from being a classic loyalty program (with a standard points system) to something a little more customizable and, naturally, rewarding.
The restaurant made the new Club Cage program focus wholly on the customer. It created a data-centric program (which won a Gold in CRM & Loyalty) that made menu recommendations based on the data it had on its customers. La Cage can also determine where its customers prefer to sit, what their favourite beer is, or which sports team they like to watch on the restaurant’s TV screens, assisting them the minute they enter its doors.
Similarly, Le Club AccorHotels also wanted to move beyond a “collecting points” mentality with the launch of its new loyalty program. AccorHotel’s guests are wealthy world-travelers that value experiences over rewards. So collecting points for a free night stay is not the most compelling idea. Instead, the hotel designed something that would show it understands guests’ desires, even before they do. In other words, it set out to read customers’ minds.
Taking home a Silver CRM & Loyalty, Silver Experiential, Silver Original Idea and Silver Tech Breakthrough, “Seeker” was launched as the world’s first biometric discovery tool for destination planning. Working with Cossette, it used biometric data to reveal what hotel guests desire when it comes to travel experiences. At a live event, people were invited to wear a wristband and headset that recorded their brainwaves and pulse while being shown images. Their experiences were filmed and then used to promote the rewards program and how Le Club takes luxury to a whole new level.
While maybe not as flashy as biometric feedback, Loblaw’s consolidation of its loyalty program PC Plus (with eight million members) and Shoppers Optimum (with 11 million) was certainly a massive data undertaking.
Research showed that customers prefer the convenience of having a single loyalty program for all of their purchases, across pharmacy and grocery. The company needed to bring together the two programs in a way that would allow it to track customers’ shopping behaviour under its entire family of brands.
Eagle Eye and Loblaw worked to create the new PC Optimum, which now connects data across every single touchpoint: POS, online and social. The platform sends points, discounts or promotions specific to each individual, in real-time, based on shopper insights now available to Loblaw following the consolidation of the two programs. As a result, PC Optimum – which walked away with a Bronze in Data – is projected to process more than half a billion transactions annually and distribute $1 billion in loyalty value.
Real-time aversions and alerts
When allergies strike, nothing else matters except getting relief and getting it fast.
Using data from three media partners, Reactine showed it’s got allergy sufferers’ back by telling them when and where allergies were about to strike.
Reactine and UM redesigned The Weather Network app’s home screen to include a Reactine “Pollen Alert” (which won a Silver Integration) report to show local pollen levels. It also inserted an alert feed into Breakfast Television’s live Weather Report and targeted YouTube users in cities with six-second video bumper alerts.
Reactine strengthened its #1 share position at retail, widening the gap with its nearest rival as a result of the data-driven program.
