The health care system has become fragmented and increasingly complex. Adding to this complexity is the volume of data brands have to sift through to generate insights about what customers want. Despite this complexity, consumers still expect experiences with brands to be customized and personalized to them as individuals. Here are a few ways health brands are making sense of it all, and using data and analytics to create exciting omni-channel experiences for customers.
KOFI ANNAN: How can data be used for a better customer experience? Data does contribute to the better customer experience but it’s really not about just the data, it’s about the digital experience and how we could create a communication and an experience that meets the — the customer’s needs. At the end of the day, customers really want experiences that are simple, smart and seamless, and data can help inform that.
By gathering the right data and focusing on the right data points, we could gather insights on their behaviors and their preferences and they could actually provide that to us voluntarily with the expectation that the communication and how we communicate with them really matches their preferences.
Data does contribute to the customer experience, particularly as it informs the — how you can get the right message to the right person at the right time through the right channel.
In the quest to create the right customer experience and to use digital tools to gather that data, we run into a problem where we have a lot of data. If you’ve heard the term kind of Big Data that’s been floating around, that’s really what it is. It’s about having so much opportunity to gather data that you’re overwhelmed by the influx of data and aren’t able to generate those insights.
Because of digital interactions, we do — we are able to collect that data. But once we put the lens of what really matters in the — the data collection, which kind of data points really matter to that customer experience, aligning that to the customer journey, then we really start getting some guidance around what we’re trying to achieve, what the expectation is from a customer side and what we need to deliver.
One of the main trends that we’ve seen here at South by Southwest that really helps make that process a lot easier and a lot more helpful is artificial intelligence, so taking machine learning, taking machines and processing that data so that humans don’t have to pretty much go through every — every Excel spreadsheet, let’s just say, processing that data, being able to gather insights from that so that we — it can inform the communication and the channel mix.
And going even beyond that, machine learning and artificial intelligence is al–could also inform and help create a model for predictive ana–analytics. So not just knowing what customers want now, but anticipating what they will want in the future. And that’s where we’re able, as a brand and brands, we’re able to create experiences that surprise and delight the customers, even based on the core data, the data that they didn’t know could bring forth those insights and that kind of experience. And that brings the brand and the customer closer together.
One example that we’ve seen down here at South by Southwest is a company called Optum. They’re part of United Healthcare and the nature of their business model or their service model is really focused on health outcomes. What they do is that they actually take the terabytes and terabytes of data that customers give them all the time, kind of health records and all the different preferences and behaviors, and they apply a lens of analytics and data on it to really start trying to understand what makes for a better customer experience and how that experience can lead to better health outcomes.
They really want to — they’re really focused on personalizing those communications, making the individual feel like they understand their health and they’re empowered to change and — and have an impact on that. The process that Optum uses is really focused on the data sources, so which data sources are really meaningful and provide those insights into the optimal customer experience, and then they’re also — they also take the — or use artificial intelligence and machine learning to really crunch those numbers and really start generating some of those insights that — that the — the internal teams can now start poring over to really start understanding channel mix and preferences.
They also have a pro–a process of predictive analytics, so how can the current behaviors of their customers impact the future behaviors and — and what different things can — can they do and — in changing their behavior so that it could really start leading to those outcomes. So machine learning helps that predictive modeling.
With the predictive model and with the goal of creating better health outcomes, they then look at — take the channel preferences and the behaviors and they look at what is the optimal channel mix for that individual. How do you personalize the communication based on what they want to see, where they want to see it and the — and the channel that they want to see it in.
And those predictive — and that omnichannel mix really is personalized to really drive those interactions, not interactions just from Optum going out, so outbound engagements, but really inbound engagements. And that’s one metric of success that they use internally to really see that their — their work is having impact. How — how often do customers reach out to Optum is one met–one KPI that they look at for success.
So the data does really inform how they engage with the — with the customer, but what they do which is really smart as well is that they take some of those insights and feed it back to the customer. They give their customer access to that data through data visualizations and da–different data points that they feel could, once the customer sees those data points and understands those visualizations and see their future self, they can start working and changing their behaviors to really affect that. And they want to be — and the customers will be motivated to do better. So that’s just one example that we’ve seen — seen here of how a company is using a lot of data, in this case terabytes of data, and really whittling it — whittling it down to really create in–and inform a customer experience that drives better health outcomes.