We’ve all been there. Those customer-journey work-shops that start with good intentions, dollops of Post-It Notes and a large, dauntingly empty wall. By the end of the session the wall is crammed full of flow diagrams of perfect customer journeys carefully maximising the KPIs.
The various segments and methods of personalisation will have been considered and the programmes are ready to be implemented. The job is nearly done.
Except it’s not. This approach can and does work for the brand. It can be useful and relevant to the customer, but it’s hit and miss – a game of chance at best. It’s not the best we can do for either the brand’s ROI or the customer’s experience (it’s not unknown for bad customer comms to suppress responses that would have happened in its absence). The issue is that the "flow-diagram" approach of comms programmes is fundamentally from the perspective of the brand, not the customer.
So rather than the wall full of Post-It notes, imagine this instead. Let’s take all the data we have about a customer – who they are, what they’ve done before, what we’ve sent them before, what they’re doing right now – and put it into a magic box. The magic box isn’t designed to spit out programmes; hundreds of emails that will spam the customer until one finally finds the mark.
It’s designed to output just one thing – the "What’s next?" Given everything that we know, what’s the next thing we should tell the customer that is of tangible benefit to both them and us? The magic box tells us what’s the best content, the best channel (including notifications), the best timing. At MBA we’re calling this magic box a Decision Lens. It takes the mind-boggling amount of data and focuses it purely on this one thing. A customer-focused breath of fresh air.
To create the apparent simplicity of the "What’s next?", the Decision Lens needs to be complicated. It needs to be able to take into account huge amounts of data from various sources and combine it effortlessly. The starting point for building the Lens is to think about the key moments for customers. When are they looking to buy? When are they going to be nervous about their forth-coming trip? Then we need to identify the context of those moments to be able to identify when they are happening and understand how to treat them. What signals are we able to detect that we can respond to with appropriate communication?
Once the customer moments are identified and contextualised, the task of the Decision Lens is to make a series of complex logic decisions that determines what the customer receives – a highly personal, if not unique, comms experience. The output of a Decision Lens may at times feel like a programme, but the comms can change as the context changes; it’s not simply blasting through a sequence of emails. This would not be possible without marketing automation.
A connected data architecture and integrated tech stack are essential to delivering work at this scale with this level of complexity. At MBA, we’re increasingly excited about the potential of embedding artificial intelligence. Amazon and Microsoft already offer APIs to their machine-learning algorithms and AI businesses are being snapped up by the martech giants.
Plugging in AI will enable real-time propensity modelling to tailor communications even further, choosing just the right proposition for just the right person at just the right time on just the right channel. We’re talking to several clients about AI, including Investec for the imminent launch of its Click & Invest product, where we’ve had the enviable task of building the CRM eco-system from scratch with no legacy issues.
Let’s move on from CRM as complicated diagrams of customer programmes. Let’s think of it in terms of customer relationship moments, delivering the perfect "What’s next?" for customer and brand alike.
The new CRM
- We need to move on from thinking about CRM in terms of a complicated series of programmes that blindly spits out communications at customers.
- Instead, a "Decision Lens" approach (using a data-fuelled, automated, logic engine) will suggest the next-best piece of contact with the customer, taking into account all we know, including their current context – the moment.
- Artificial intelligence will provide the next wave of martech integration, producing real-time modelling and personalisation.
- By thinking in terms of "customer relationship moments", we serve the customer better and will achieve improved returns.
Stephen Maher is CEO at MBA, and chairman of The Marketing Society