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7-Eleven Insights – The Future of Loyalty is Intelligent and Automated.

Paula Thomas, Chief Content Officer, Liquid Barcodes

16/10/2021

For so many people, their favourite mornings begin with a great coffee they pick up on the way to work and perhaps a delicious baked croissant. It’s the perfect morning treat, and they enjoy it even more when they find an exclusive offer that encourages them to drop in store even on days they might not have planned to!

Creating these “extra reasons to visit” is one of the exciting possibilities that convinced Jesper Østergaard, the visionary CEO of 7-Eleven in Denmark to take a huge step in to the future of loyalty, leveraging the latest artificial intelligence and machine learning technology to harness the power of his data.

The key challenge 7-Eleven faced was the need to become more efficient and effective managing their offers in the incredibly busy world of mobile marketing, and one thing we know for sure about this award-winning convenience retailer, is that they love to take on a challenge and show the world what’s possible.

Jesper Østergaard & 7-Eleven Denmark – NACS International Convenience Retailer of the Year Award.

The Challenge

Planning, promoting and managing offers manually to ensure the best ones are seen by customers is very time consuming and challenging for marketers. Many marketers simply don’t have the opportunity to continually think about the best positioning for their loyalty offers, with the result that they can become less relevant and ultimately less compelling for both consumers and retailers.

Here at Liquid Barcodes, we realised the power of adding artificial intelligence, applying sophisticated algorithms and machine learning to deliver relevant personalised offer sorting at scale, instead of relying on the busy marketing team as we did in the past.

Now we can use loyalty data to intelligently decide what offer to show each customer on every app visit – the ones which will be most exciting and relevant for users. And the results from our incredible recent trial with 7-Eleven in Denmark prove that this a better way to manage offers – a MUCH better way.

We created a new product to leverage the power of machine learning allowing us to match the needs of 7-Eleven customers with more relevant offers, while at the same time delivering an even more convenient and delightful experience in store. It was a huge win from a marketing perspective, and the transaction results were compelling too.

This article explains how the traditional model for managing offers compares with a data-driven, machine-learning and automated approach which delivered the incredibly exciting results shown below.

Jesper explained why this is so exciting for his loyalty team and the whole business:

“These machine learning capabilities are creating new ways of testing and learning what offers create the best customer experience in store, helping us nurture loyal customers who simply don’t want to shop anywhere else.”

Your customer offers are usually managed manually by your marketing team, but our automated model is much more effective.

Traditional Approach – Managing Offers

With FMCG brands supporting many convenience retailers to help them drive brand awareness and purchases, your offers are a great tool to help brands be seen by your convenience store customers. As a result, customers often feel overwhelmed with so many offers, and can struggle to find the one they want to use when they’re ready to buy.

Having too many offers is challenging for customers and retailers, with some not being seen at all as there are so many competing for their attention.

On the business side, the marketing team have to use their personal judgement when deciding which offers are placed in the most prominent spots, which means their position stays fixed until someone has the time to manually update or change them again. No matter how good your marketing team is, their time is limited, so this is exactly the reason that a powerful machine-learning approach makes sense.

Offers are created by busy marketing people who manually have to decide which ones to include in your top positions.

Unsurprisingly, it turns out that the best offer to show a customer changes all the time and it’s certainly not fixed!

In particular, the time of the day, the season, the weather each day as well as the customer’s product preferences and many other factors affect what offers you should show them in order to ensure relevance and customer engagement.

It’s a big task, perfect for a powerful machine-led algorithm that can automate, optimise and continually improve the position of, depending how customers respond and react in real time.

Using Artificial Intelligence to Automate Your Offers

Different customers engage with different offers at different times for different reasons.

Relevance is crucial, so our machine-learning algorithm combines key insights about the user, as well as the time of day they are viewing the app, and other factors such as the season and weather, in order to intelligently decide what coupons should be shown in each moment for each member. Richer data sources can also be added, such as receipts data, NPS scores, past purchases and redemptions – any data your customer has on their personal profile.

By combining customer data, market data and transactional data such as the members redemptions history and receipts, the Liquid Barcodes loyalty platform can intelligently decide what offers should be presented to each members in what order. As a simple example, given my love of a coffee and croissant each morning, I would only see a croissant offer in the morning, not the afternoon.

In the afternoon, I might see offers for perhaps a nice snack or an easy meal to take home for dinner. And so, every other member sees similarly relevant offers for their favourite products at the times they are most likely to want to buy them.

Such personalisation at scale, delivered so quickly in such an intimate and intelligent way, is the reason this machine-led approach has delivered FIVE TIMES the usage of the top three offers in the 7Eleven programme compared to our control group.

Why This Matters

  1. This saves your customers time in store! They love the efficiency of quickly finding the offer they want with just one or two swipes.
  2. Certain day parts and seasons have fundamental differences in their relevance and appeal. Croissant offers are simply not as important in the evening as they are in the morning. As marketers, our job is to delight our customers with offers they love every time they open they app.
  3. As always, we used the power of A/B testing and randomised control groups in order to assess the true impact of our machine learning product. The numbers prove how much more relevant members found their offers to be.
  4. While many retailers focus exclusively on the transactional results (or sales) from new marketing campaigns, it’s also extremely important to realise the non-transactional value of each app session. The improved user experience is hard to measure in monetary terms but is clearly seeing a huge impact throughout this project. By simply saving customers time at the till when they are ready to engage, their satisfaction levels with your programme will inevitably grow.
  5. We were also delighted to be able to confirm some assumptions that many of our campaign concepts that are only available at limited times such as our “happy hour concept”, are even more effective when supported by the power of machine learning. No more wasting customer’s attention showing them an offer that’s not available. Instead it will become prominent when it is live and active for use, with no need for the marketing team to manually re-position it. And with time-sensitive offers like this, 7-Eleven saw an improvement of nine times usage of happy-hour offers compared with their control group. Even though we expected a better performance, it was truly amazing to see how much better the actual uplift was!

What’s Next for 7-Eleven Denmark’s Leap in to the Future of Loyalty

With this important trial completed so successfully, Jesper shared how excited the 7-Eleven team in Denmark is about the future roadmap for machine learning. From this initial success, he expects they will use even more of the capabilities that artificial intelligence promises.

By realising the power of intelligently sorting offers, we see a future when marketers will rely on algorithms to optimise the operations of their loyalty programmes. Some ideas include the ability to set the perfect timing of communications to members individually. Even more exciting, we believe that artificial intelligence and machine learning technology will eventually be used to deliver entire loyalty programmes without the need for ongoing human intervention.

Here in Liquid Barcodes, we are thrilled to be supporting 7-Eleven with this latest innovation and no doubt their passion for excellence will be rewarded with even more awards and customer loyalty in the future.

About Us:

Liquid Barcodes is a leading global loyalty and digital marketing technology company specialising in the convenience store and foodservice industries. Our proprietary cloud-based technology platform allow retailers to create and manage their digital marketing campaigns with a proprietary process we call the “customer connection cycle’ to engage, promote and reward customers activities in real-time across digital and media channels.

How we do it:

We have developed the most advanced loyalty and digital marketing technology platform specifically for convenience store and foodservice retailers globally.

Retailers use our self-service dashboard to create and manage loyalty driven marketing campaigns that increase purchases with their existing customers, as well as effectively target and acquire new customers through partners or paid media channels.

One core component of live loyalty is gamification. We have gamified branding, loyalty and promotions. We believe this approach is essential in order to get customers’ attention and ultimately truly engage them with repeatable actions thereby winning their loyalty.

Check out some of our exciting/proven results here:

www.LiquidBarcodes.com

About Me:

Paula Thomas

Chief Content Officer, Liquid Barcodes and Loyalty Podcaster

With over twenty-five years marketing experience, I specialise in loyalty marketing articles and podcasts. In addition to working with Liquid Barcodes, my clients have included Telefonica O2 Priority, Three Mobile, Electric Ireland, Allied Irish Bank and The Entertainer Group (UAE), as well as Avios – the global points currency for some of the world’s top airlines. I am also a former judge for the Loyalty Magazine Awards and proud host of the “Let’s Talk Loyalty” podcast – the industry’s first podcast for loyalty marketing professionals.

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