This post is the first in a three-part series on how pharma companies can achieve customer centricity.
“I know what to do, I just can’t do it.”
Sound familiar? Most life sciences companies have made, and continue to make, significant investments in digital promotion tactics, digital data, cloud-based customer databases, machine learning and artificial intelligence. These investments have enabled companies to capture more customer information, develop new and better insights into customer preferences, and obtain these insights faster than ever before. These new capabilities enable life sciences companies to take actions to better meet customer needs, delivering content via tactics at the optimal time.
But here’s the rub: The campaign management function in most life sciences companies hasn’t materially changed over time. It remains a manual, time-consuming and costly process, so while a machine learning decision engine generates insights to optimize the customer experience, the slow, manual campaign management process significantly slows down the speed to market, almost eliminating the value of those insights.
The solution is to move away from the tactic marketing process, which is a siloed, manual campaign management process that’s built around the campaign tool, and adopt an orchestrated marketing approach driven by a machine learning decision engine that matches channel, content and cadence with customer preferences. To understand the differences, let’s define these two terms more specifically:
The tactic marketing process:
- Leverages a campaign management tool to manually pull target lists for each tactic
- Uses channel-level insights to inform target list development
- Target lists deployed in “batch and blast” mode
- As more customization is added to campaigns, such as A/B testing, triggers, etc., the number of people needed to code the tool increases, as do the costs, the likelihood of errors, etc.
The orchestrated marketing process:
- An integrated and automated process from initial data capture to customer database updates to the data science decision engine to message and tactic deployment
- Optimizes the content, channel and cadence for each customer after updating the machine learning decision engine with all available information
- Enables more personalization and dynamic market adaptability by enhancing the data science algorithm
- Links the machine learning decision engine to the campaign management tool to apply suppressions and distribute target lists to relevant tactic owners
But how can companies move from a tactic marketing process to an orchestrated marketing process? There are four phases of this transition:
- The tactic marketing process
- The trigger marketing process, which enhances the first phase by including trigger capabilities, typically sending non-responders a follow-up email
- The customer journey marketing process, which enhances the second phase by developing and deploying customer journeys, which include multiple tactics over time
- The orchestrated marketing process
We find that most life sciences companies are in the second phase. The movement to phase four will be critical for life sciences companies to take full advantage of their investments in digital tactics, data, technology and data science/machine learning algorithms. In our view, the biggest challenge to doing this is organizational enablement. Can life sciences companies change to fully leverage the new capabilities? And how? Stay tuned. We’ll answer those questions in our next blog post.
WHITE PAPER: A Customer-Centric Approach to Promotion Planning