SIX TIPS FOR INTEGRATING HUMANS AND MACHINES IN PREDICTIVE MARKETING
Historically, pharma marketers have always had an abundance of marketing data. And - owing to the transactional, prescription nature of our business - for a long time we were well ahead of other industries in terms of our ability to know and track our customers. As a pharma sales representative, market researcher, and marketing director I continuously marveled at how much we knew about our HCP customers... long before the phrase "big data" was bandied about. In careers that have tracked the evolution of data from state-level dollar sales, to zip-code level prescription data, to months-delayed prescriber-level data, to near real-time prescribing behavior, many of us have seen the industry evolve from one that uses data to find out "what HAS happened" in the past to one that uses the data to see "what IS happening" now.
But the real trick for today's pharma marketer is to mine all of the available data for actionable insights that can be used to determine not just who the best customers are, how to reach them, and what to say to them... but ultimately use the data to predict "what WILL happen" in the future. Forward looking companies - in every industry - know that humans alone can’t do all the heavy lifting to find those insights, and are evolving from being "organizations with a marketing department" to "predictive marketing organizations."
In a recent article, Suresh Vittal - Vice President of Adobe Marketing Cloud - offers a six best practices for integrating marketers and machines to create a powerful, results-driven predictive marketing organization; using the machine’s analytics power to uncover customer insights and human ingenuity to piece those insights together to make better decisions. (Full article available on VentureBeat and the Adobe Blog.)
"With predictive marketing, we function as the archaeologists of the data discoveries, Interpreting the past and present." says Vittal. "Predictive marketing combines artificial intelligence (AI) and machine learning technologies, analytics forecasting, segmentation, and automation, together with human creativity, reasoning, and decision making."
Mr Vittal highlights six best-practices for integrating marketers and machines to create a powerful, results-driven predictive marketing organization:
1. Ensure alignment of business goals and analytics
When the business objectives and analytics strategy are not aligned, competing priorities may result in various departments and stakeholders being at cross-purposes. In pharma (and most industries) the traditional "what happened" or "what is happening" analytics approaches didn't have this worry, historically relying on dashboards and scorecards that simply measured how well identified goals were achieved. At the core of a predictive marketing organization is the alignment of all activities to predict what will happen, and drive specific activities that will achieve those goals. Metaphorically, predictive analytics goes from being your rear-view mirror to being your navigation system; if the system doesn't have the correct destination plugged in, the route that it tells you to take to get there is meaningless. As Mr. Vitall points out, if your analytics aren't aligned with your goals, you simply won’t be able to drive change or improve the bottom line.
2. Begin with the end in mind
You must be able to envision how you want to leverage the insights that predictive analytics, AI, and machine learning can generate. In pharma, we ultimately want to drive prescription/sales growth... but must also consider what the steps are along the way. Create a roadmap for how the information gathered will be used to automate decision making, engage with customers, or change some process or experience along the customer journey. Make sure to directly connect the output of your machine learning model to specific decision and execution points along the way.
3. Communicate the strategy and the plan
Top-to-bottom alignment within the organization is critical to the success of a predictive marketing organization. Beyond pharma's traditional need to ensure that the commercial functions of sales and marketing are on the same page, the predictive marketing mindset must pervade the entire organization... and be endorsed across all levels and departments. This will not only ensure that everyone is aligned with the analytics plan, but that they are also on-board if and when the data indicates that a change in strategy might be needed.
4. Decide what will be automated
For pharma marketers, this may feel like the hardest part, but it should really be one of the easiest. Don't think of automation as relinquishing control, but rather as delegation. We all delegate important tasks to team members based on their strengths, and this is no different. One of the key strengths of predictive marketing is the ability to automate processes; from consolidating data sources and segmenting audiences to personalizing ad units and message elements. Vittal recommends delegating the smaller, tedious tasks to machines in order to let the team focus on the larger, strategic decisions.
5. Establish a data democracy
As a predictive marketing organization, today's pharma companies must make data easily accessible to those who need it. Lower the barriers and eliminate the roadblocks to access standard, non-sensitive business data. Not pre-canned dashboard reports, but the actual data. Pharma-specific legal, regulatory, and healthcare privacy concerns make some of these barriers higher than in other industries, but we have to determine how to ensure that the people responsible for driving the business are able to get up to their elbows in the raw data, truly following the breadcrumb trail to see where it leads them.
6. Encourage creative, strategic thinking
Pharma companies need to empower everyone - at all levels - to tell data-driven stories. The tools and technology available to marketers these days make it easier than ever to uncover and leverage insights hidden in the data. With a little creativity, an understanding of the data has the power to spark new ideas and drive revenue growth.
"With humans and machines working together, we bridge the gap between the massive amounts of data provided by machines and the uniquely human experience. Using both, marketers can tell more meaningful stories, create more personal experiences, and make deeper human connections."
Obviously, pharma faces different - and often higher - hurdles than other industries when it comes to data collection and analytics. But this doesn't let pharma off the hook when it comes to predictive marketing. It simply makes taking a rigorous, best-practices driven approach to adoption and implementation that much more critical.
The full article - "SIX TIPS FOR INTEGRATING HUMANS AND MACHINES IN PREDICTIVE MARKETING" by Suresh Vittal, Vice President of Adobe Marketing Cloud - can be found at VentureBeat and on the Adobe Blog.