Rand Merchant Bank (RMB) ran an advert a few years ago, showing identical twins born 10 minutes apart. The advert shows how this small difference had a great bearing on their lives, with two very different personalities developing, and, of course, both eventually finding contrasting but equally fulfilling careers at the bank.
I was reminded of this recently when discussing the differences in behaviour within very similar customer segments. No matter how granular and analytically-advanced your segments, there are minor differences between the individuals in each one that cause them to behave differently from their peer group. In the words of the advert, they are “identical in every way, except the way they think."
Data-driven marketers are trying to overcome this problem with a segment-of-one approach. Listening to, understanding and then acting on the unique nuances in behaviour of each individual means truly personalised experiences.
Will we be seeing truly personalised experiences in the near future?
Imagine an online fashion retailer. Two identical male shoppers, in the young professionals segment, interested in smart suits and accessories, both shopping online, both presented with the same offers for the latest Italian designs . . . but one day, one of these shoppers clicks on the kids’ clothing section, not the men’s.
Is this a mis-click? Or does this young man have a new niece or nephew, or maybe a newborn son we didn’t know about?
And more importantly, what does this mean for the way that we handle his visits and make offers to him? Do we move him into the new parents segment? How do we know if this is a permanent change in behaviour? Can we triangulate this behaviour with any other that we’ve seen, to provide clues?
The technology is available to do this. We have big data processing power and the analytical capabilities to sift through this data to uncover relevant patterns of behaviour. We can even do it in real time, or close to it.
But the complexities in data collection and integration slow these efforts down. Companies are overwhelmed by the volume of the data, and struggle to identify common threads across multiple sources. Organisational silos, channel isolation and segment-based thinking all hamper company-wide efforts to develop the elusive 360-degree view of individual customers that would allow real-time analysis of their behaviour.
How can organisations realign around a segment of one?
I suggest that customer-centricity is developing a new meaning. It is now understood that realigning the organisation’s data, people, processes and technology around its customers is the only way to achieve truly personalised experiences. It is also understood that these experiences will be the cornerstone to winning and retaining customers.
But there is another problem. Even if an organisation could collect all its data and analytics in one place and build an intelligent view of each customer’s unique past behaviour, it would struggle to react quickly enough to nuances in real-world behaviour. And this is what is required for true personalisation.
The most vital component for marketers and data analysts is a centrally-managed data, analytics and real-time decision-making engine at the heart of all marketing efforts. This centralised engine should act as the channel-agnostic and context-sensitive brain. It would be working in the background during all interactions across all channels and make real-time decisions for these channels about what messages to provide to each customer.
Many organisations make the mistake of building personalisation logic, but limiting it to a particular channel, usually the website or mobile channels. If these channels operate in isolation and don’t listen to (or feed) the centralised brain, insights and decisions made on this channel do not inform, nor are informed by, any other channel, whether it’s the call centre, in-branch staff or batch email marketer.
The work of the centralized brain
Let’s go back to our example. Our young male customer’s recent change in behaviour cannot be handled in isolation. As soon as this behaviour occurs, the central engine should move into action.
It is constantly listening for new contextual information, such as website or mobile app clickstream data. When it obtains new data, it runs it through a real-time process to decide if this new information should change our predetermined action for this person. This process considers all available data, such as:
- Engagements with the brand in the past minutes or hours (since the last batch analytical processes ran).
- Insights on social media using text analytics.
- Previous browsing history to check whether this is an isolated incident.
- Purchase history to see if he does this at the same time every year.
At the end, the brain will make a decision about whether to override or append the predetermined scores or segments. It will determine the best action to take for that individual in that moment (our segment of one), and this action will immediately be available to all other channels, brands and data sources.
Is this really worth all the effort, time and expense? Well, SAS’ customers think it is. One mobile operator is able to detect real-time context in airtime balance thresholds. Their problem was that they could not send personalised offers until a few hours after the threshold was reached. And by then, the offers were often no longer relevant. The company was hovering at a 5 percent response rate to its offers no matter what it tried.
But when the company introduced real-time centralised decision-making with personalisation, response rates rose to 24 percent, generating tens of millions in incremental revenue per year. This was far beyond expectations and will only improve as the company’s capabilities mature.
Time to change
Is your organisation treating your customers like they are all twins? Improving your segmentation abilities is an evolutionary journey, and I urge you to start immediately with what you have. The white paper by Suneel Grover, Analytics in Real-Time Online Marketing, discusses how your organisation can take the first steps to detect, analyse and respond to the rich data that your customers are already giving you on digital channels.
This SAS eBook is another great primer on the concept of contextual marketing.