Engaging and interacting with customers on a 1:1 level is often feted as the holy grail of digital customer experience.
But in a multi-device and multichannel world fraught with data and privacy challenges, we know the road to such personalisation nirvana is long, bumpy and expensive. All without truly knowing how big the pot of gold is at the end of it all.
At Marie Curie, we’ve been experimenting with how situational data can help us get a meaningful foot up on the personalisation ladder, with some interesting results.
What is situational data?
If personalisation is broadly designed to provide a better customer journey through tailoring relevant messages, experiences and content to an individual, situational data is the aspect that helps identify customers based on the environment in which they are interacting with your brand at any given time.
Or in other words the when, the where and the how.
Acquiring personal data can be hugely beneficial, but it can also be really hard. Situational data (such as device, location or weather) is typically garnered from open data sources, meaning it is usually much easier to access than personal data, doesn’t have the same tracking or privacy issues as cookie based targeting, and crucially, has the potential to apply to the vast majority of your audience.
Every customer interaction always has a situation or context, but there is not always the means, or reason, to be personal.
The flip side is that situational data offers a very modal view of the customer. it is much more aligned to improving the more functional aspect of the interaction (how to perform the action), rather than enhancing the persuasiveness of the message (why should I do the action).
Situations are also transient and variable. Someone could change location and device multiple times every day, so it’s also important to ensure this type of targeting is adaptable enough to be relevant at all times.
How have we started to use situational data at Marie Curie?
One of the first scenarios we’ve started to explore is how one of the most basic situational data sets, device, affects our email donation appeals.
The nature of these appeals is that donations are fairly low value and are made very much ‘in-the-moment’.
Here is our analysis highlighting device as a key drop off point in the journey, with high mobile opens but low mobile donations (with very limited multi-device usage).
We figured there could be two broad reasons for this:
- People viewing on mobile didn’t want to donate at that time – there could be a number of different reasons for this, but the device they were interacting with us on is coincidental and of no importance in this context.
- They did want to donate but their situation (e.g. being on a mobile on the train) prevented them from doing so.
So, we set about trying to test the impact of the situational friction on the interaction.
In order to truly personalise the situation, we knew we had to go beyond just form (we already optimise design for mobile) and consider the interaction in context. If interacting on mobile was adding too much friction to the journey, we needed to find a way to reduce this without disturbing the experience on other devices.
So we created some adaptable content within the email that allowed us to change the creative treatment and donation mechanism, based on the device someone opened the email on.
If somebody opened on a desktop they would be prompted to donate online and routed to the website, but if someone opened on a mobile the same content would transform to ask them to text-to-donate, which when clicked launched a pre-populated text (from their native phone SMS message service) and all they’d have to do was hit send. A personalised experience tailored to a situation.
We then used three control groups to test the impact of this approach:
- Group A – standard online donation (existing method)
- Group B – non adaptable online and SMS donation (static messaging where both options appeared on both mobile or desktop)
- Group C – adaptable online donation or SMS donation messaging (message changed by device type opened on)
The results proved interesting…
- 90% uplift Group C vs. Group A for cumulative donations.
- Group B provided 48% uplift vs. Group A. Group C also provided uplift compared to Group B across both online and SMS conversions.
- While all broadly tracking at similar levels, Group B produced the lowest online donation total.
While this was an isolated test (and so the results have to be taken as such) the initial findings seem to suggest a couple of important things:
- Tailoring the interaction (and not just the message) by situation generated incremental conversions
- Tailoring the experience to each situation is potentially more effective than cluttering the experience with another option that might be inappropriate when taken out of context (i.e having online and SMS option together)
Personalisation is about creating experiences relevant to the matter at hand, and while some scenarios will be more situationally oriented than others, this type of data offers a relatively easy (but scalable) way to tailor and enhance customer interactions.
And with the rise of mobile, wearables and the ever-connected consumer, situational and contextual targeting could become an increasingly important aspect to integrate into the personalisation mix.
I’d love to hear your thoughts and any examples of using situational and contextual data to good effect, so please leave a comment below.