September 21, 2018
We caught up with Graham & Brown’s Ecommerce Executive - Customer Journey, Stephanie Nash to hear how the brand is driving ecommerce growth through leveraging AI and, in particular, Monetate’s Individual Fit Experiences.
To give some background, we went on a little break from Monetate back in 2016 as we replatformed to Demandware. After 9 months of letting the new platform bed in, we knew it was time to bring back Monetate. We needed a platform that could offer A/B testing, personalisation and clear, easy-to-understand analytics so we could see the full impact that experiences were having on our customers.
So we came back a year later, and this was when I first heard about The Engine and Individual Fit Experiences (IFE).
Whilst blown away by the potential, I found it difficult to picture how we could use the technology, and I thought that it would require huge amounts of content and other resources. This was until I attended a User Group; I truly bought into the idea after seeing it in action, in a context that’s relevant to us, and also saw how it would work with minimal creative input.
I knew we had to get The Engine. Although it seemed we were a few levels below being ready, I knew that we needed to push the boundaries and start to think about what we needed to achieve in a year’s time.
We understood that we had to create more personalised, segmented experiences to meet the targets that we had set. We’ve done some segmentation for our larger groups, and it worked very well, but we don’t have the resources to continue building content for and then serving those smaller segments—so we thought IFEs would save us time and effort.
Luckily, Graham & Brown is very open to innovation and trying new things. It may be scary at times, but ‘test and learn’ is definitely our approach.
We had quite a few ideas about the different channels and audiences that we wanted to focus on. This, alongside leveraging Mark (Client Success Manager) and Simon’s (Director, Global Strategy & Insights) expertise in how to approach this new method of experience optimisation, helped us hit the ground running.
We started by looking at old tests that had previously failed because the target audience was too niche. This was a great starting point and a perfect problem for IFE to solve.
Since then we have delivered an array of experiences, many focused on mobile. If an experience works for a particular region, we’ll push it out to other geographies.
We will still do two weeks as a standard test prior to creating an IFE to understand what the core metric should be. Setting the correct core metric, as the team will tell you, is crucial, so this method gives us more confidence.
It is definitely a significantly different mentality when using IFEs. Traditionally, you run an experience/test until you reach statistical significance and you then hard code or set to a full experience. With IFE, you can keep the experience running and it will adapt and change. This is the biggest thing: it’s not static, it’s dynamic, and will therefore adapt and change to ensure that the most relevant experience is being put in front of the right visitor at the right time.
The first experiences that we’ve done have been mobile-focused. We know that new visitors convert at a higher rate when they use the search navigation, but we want to keep that page space for other content for those that don’t need/want to use search. We put these two contrasting experiences into the Engine and let it decide which should be shown to each visitor.
We ran this experience for two weeks on the UK site and it yielded some very impressive returns:
As you can see, these are pretty nice stats for a first go! We’ve left this experience running which is very different with the Engine—leave the experience there and it will change and adapt.
Following the success of the experience, we rolled it out to US site and we saw successful results there as well:
Not only were the results impressive, we learned a lot about the types of context that influence whether a visitor is likely to react positively to an experience. For example, day of the week, time of day, and browser type were important in helping The Engine decide which experience to show to each visitor.
Another outcome has been that Graham & Brown is now looking to bring AI into other areas of the business following the success of The Engine.
As with most retailers, multichannel is our focus and as we sell premium designer wallpaper, we have a product that is very hard to visualise in your own home. Our completed and future projects aim to reassure and encourage customers along the purchasing funnel.
We have just launched our augmented reality app which combats this constraint to an extent as it allows users to see what their selected wallpaper will look like in their own home.
We’re now trying to tie up the customer journey for greater consistency across all of the touchpoints a customer has with us, from app to website to in-store. For instance, when should the customer get the app in their hands, and how do we tie this touchpoint in with grahambrown.com.