What happens when you finally close the marketing loop, and how do you get there?
by Toni Cejas
The marketing strategies that life sciences organizations employ progress and mature over time based on the way they understand their customers’ needs. What often starts as best-guess tactics becomes more finely tuned as client insights and data are organized and become actionable. Most organizations are able to move from strategies informed by anecdotal success to some data-driven tactics. However, the most successful organizations take their marketing operations a step further by integrating their insights and data into the omnichannel adoption of a Closed Loop Marketing system.
Expected Insights and Outcomes of a Closed Loop Marketing System
A Closed Loop Marketing (CLM) system begins with a series of hypotheses about target customer behavior and preferences, which inform marketing strategies and their associated tactics. In practice, it essentially means shared communications and alignment between sales and marketing teams.
New analytics technologies utilizing AI make it possible to ascertain the outcome of these tactics in near real-time, giving marketing and sales teams the ability to continuously perform a reality check of their hypotheses. Based on actual customer behavior, these AI-powered tools then issue specific recommendations for adjusting marketing tactics to achieve better results.
Changes informed by a CLM system range from updating messaging to refocusing entire strategies. This near-real-time impact data can also help uncover customer profiles that far outperform others, allowing you to quickly shift promotional efforts to segments with the best response rates.
The outcome from a CLM system will deviate significantly from traditional marketing efforts. Instead of having a marketing plan that is slow to react to real market responses, a CLM system with automated AI-based recommendations enables you to “change the flight plan” quickly, adapt to customer preferences and needs and ultimately optimize the return on marketing efforts.
Key Design Principles of an AI-Driven Omnichannel System That Can Close the Marketing Loop
There are three key design principles that must be front of mind when establishing an AI-driven omnichannel system to close the marketing loop.
Design Principle #1 – Flexibility
The system should be flexible enough to adapt to changing strategic objectives and optimize for corresponding KPIs. For example, six months prior to product launch, your key objective might be gathering mindset information about target HCPs. Then, in the first weeks after launch, the objective could be to grow as quickly as possible. Once the product is mature, protecting against new competitors entering the market may become your primary objective.
By selecting the right KPIs for your current objective, commercial teams can see daily results from their marketing tactics. Even in the absence of sales or prescription data at the HCP level, the system should identify behavioral changes as a result of commercial activities. This in itself is a way to measure ROI.
Consider, for example, building a “Customer Engagement Index” as a way to measure customer behavior with all the available data at the HCP level (e.g., number of calls per cycle, number of open and clicked emails, attendance to congresses, attendance to webinars, etc.) The Index would measure how each variable changes as a result of the sales and marketing activities. Changes in our customers’ behavior that result in better engagement should naturally lead to an increase in prescriptions or sales.
Design Principle #2 – Evolution and Improvement
The system should evolve with you as your company progresses on the AI and omnichannel maturity curve. Adoption is easiest when the system is open and modular, allowing organizations to “turn off” suboptimal parts of the process and “turn on” others as the organization improves its data and analytics capabilities. For example, one part of the process should determine the customers targeted by the marketing effort. In the first phase, this step could be based on the knowledge and experience of the marketing and sales teams and, in the future, complemented with recommendations from predictive analytics based on the actual behavior of each individual customer.
The evolution and improvement of the system should not be costly or traumatic; it should be designed in a way that allows adding or replacing process steps as they become available.
Design Principle #3 – Transparency
Avoid the black-box syndrome. Each step of the CLM process can be complex (e.g., an AI algorithm to determine the best message to use for each interaction), but complexity must be “encapsulated” in a transparent process. Transparency in your systems means that the end-user knows exactly where AI and business rules are being used respectively.
In other words, it should be easy to understand if the key messages suggested for the next interaction are coming from a predetermined human decision or from an AI model. The same should apply to the suggested timing of the interaction, the suggested account, and more. The AI thought process is complex, but a powerful analytics system that condenses it into clear, easy-to-understand language simplifies decision-making and bolsters user confidence.
What Else Do Organizations Need to Make Closing the Marketing Loop Possible?
Understanding how to design your AI-driven omnichannel marketing system does not guarantee that your organization will be able to bring your marketing operations full circle and close the marketing loop. Organizations must also possess a few operational traits to fully take advantage of the opportunities presented by CLM.
First and foremost, organizations adopting a CLM system must cultivate internal teams with strong analytical and business intelligence skills. AI and machine learning will deliver powerful insights, but humans are still largely responsible for navigating the last mile between recommendation and action. Decision-makers in your organization still need to have a deep understanding of your customers and keen business acumen in order to drive the best possible experience for HCPs.
Secondly, the marketing production process must be agile, so a modular approach to creating marketing materials is a must. Approaching marketing materials this way enables quick changes that don’t require additional MLR reviews. This allows for insights gained from CLM systems to be adopted in marketing practices very rapidly without external barriers.
Closing the loop between your sales and marketing teams is the ultimate goal for any life sciences organization. A CLM system ensures you’re always employing the best possible marketing tactics based on your own data and insight. With the right technology and an evolving system-design approach that follows the key principles above, you’ll be well on your way to breaking down commercial team silos, activating a more agile go-to-market strategy, and delivering a more impactful customer experience to the HCPs you support.
Looking for more resources to navigate the industry shift to intelligent and personalized omnichannel engagement? Download our new Frequently Asked Questions about Omnichannel guide.