In my last blog, I talked about the technical challenges of getting customer data from multiple agencies all in one place. Oh yes, and the corporate culture challenges, too! Challenging but not impossible.
So now what? How do we go about making sense of this bolus of customer data? What’s the process for making data actionable? How can we help pharma brands change the relationship they have with their physician customers?
What we learned is that there are three steps to building a strategic framework.
First, expose the data and the underlying framework of customer behaviors. Second, build a predictive model based on that data. Finally, integrate the model back into the customer experience in a way that’s seamless and automatic.
1. Expose the Data
For decades, finding a link between advertising and customer impact was almost impossible. Direct response marketing offered a step in the right direction, but as soon as more than one marketing tactic was involved, it became difficult to attribute dollars spent to customers created.
This led to an industry-wide acceptance of marketing opaqueness. Very frustrating.
However, digital technology now underpins most marketing activities. With the ability to attribute responses from most marketing tactics to individual customers, true marketing analytics is possible. Making decisions based on truth is now possible.
So our first step after acquiring the data and getting it in one place is to expose it. Bring it into the light. Use visualization tools to reveal trends. Analytics can determine tactic effectiveness and calculate ROI on the individual customer level.
This move to data transparency can be unnerving to some. But it is a critical step on the journey to creating a digital organization.
Exposing marketing data and democratizing access to marketing insights will change the way marketers think about their spend. Budget holders will begin to see and understand the linkages between investment and return by channel, campaign and target customer. Through new tools such as closerlook’s Backstage® Duet, we can create a “rolling ROI” metric. This makes marketing planning and targeting much more dynamic and responsive.
Build a Predictive Model
Tying the exposed data patterns and their insights back to an individual physician opens up a world of new possibilities. For the first time we can build predictive models based on the underlying framework of behaviors. A predictive model considers how a physician is responding to a specific message, channel or campaign. It uses that information to predict how she might respond in the future.
But a predictive model can do more. It can help us create a “lookalike model” to find other physicians like the ones we’re studying. And this is where it gets interesting.
By creating models of our most valuable customers, we can sift through the many healthcare professionals that we don’t currently communicate with to find the ones most likely to respond to our message. We built Target Clarity™ to do this. Find the “lookalikes” and map them to a conversion journey. This provides the framework needed to develop personalized messaging. It also enables a financial model that can predict potential TRx growth as a result of more intelligent targeting and help us decide whether the investment is worth it.
Integrate into the Customer Experience
The final step is to integrate this level of detailed customer insight back into the customer experience. Now we not only know what kind of message to deliver to a customer, we can predict the response. And based on recognizing patterns over time, we can get better at predicting need and be able to deliver a more relevant experience based on that insight.
Customer intimacy has become central to consumer expectations. These elevated consumer expectations now extend to professional relationships as well. Data-enabled insight can help pharma become more responsive. But to appear seamless, we must integrate the data models into the customer experience. As we learn to embed these models into marketing and customer service automation, we build a process that is scalable and cost effective.
Consumer Examples of Data at Work
Two companies that use data in a predictive and seamless way to enhance customer experience are Nest and Tesla.
Tesla, the all-electric auto manufacturer, has created a responsive car. Its Model S car observes its owner’s driving patterns over time. When you get in the car in the morning, it predicts you’ll be heading to work and has already analyzed traffic patterns to let you know the best route to get there.
Same thing in the evening — when you’re heading home, it optimizes your route home. But the car’s system also links to your calendar. This means that when you get in the car during the day to go to a meeting it can recommend the best route and predict how long it will take to get there.
Tesla’s strategy is to use technology to capture personal data to expose the underlying patterns of your day. Then it looks at third-party data like traffic patterns. This allows it to build a predictive model that creates a seamless and optimized customer experience.
Nest, which manages the heating and cooling in your home, is a thermostat on steroids. The product learns your daily habits — when you wake up, leave for work, return home, etc. — and the temperatures you prefer. After about a week or two, Nest is able to predict your daily habits and adjust the thermostat accordingly. It will go into “away mode” during the day to save energy, bring the temperature back to normal before you arrive home from work, and drop the temperature when you go to bed.
Over time, Nest exposes the underlying patterns of your behavior. It builds a predictive model, and then without you even thinking about what it’s doing, it evolves your customer experience in a way that is seamless and automatic.
Both Tesla and Nest represent models for rethinking data and the customer experience.
A Strategic Framework in Three Steps
You start by using a comprehensive data perspective to expose underlying customer patterns. Using this insight, you build a predictive model. Finally you integrate the model back into a new customer experience.
This represents a strategic approach to data analytics. It’s much more than simple reporting. It’s using data to transform how we create and keep customers.
For pharma marketers, this is a framework for creating value for physicians and their patients. But it starts by investing in data and embracing transparency.
To get the data, we built a “master data vortex” to suck all that customer information into one place. It required a lot of plumbing, something we probably had no business doing. But it had to be done.
But that’s a story for next time…