In the second of a three part series on CLM, Timothy Moore, Hala Qanadilo and Rio Longacre from Capgemini Consulting Life Sciences explore the second pillar of CLM success, analytics, which has been a pain point for many companies. They provide pharma with a roadblock for analytics success and a way to slowly build up your CLM’s maturity to gain the deep insights needed to engage with customers.
In our past post The CLM Conundrum: Three Pillars of Success, we discussed why, for many pharma companies, CLM has often failed to deliver on its initial hype. We also discovered CLM’s first pillar, Personalization. In this post we turn to the second pillar, analytics.
CLM requires understanding not only who your customers are, but using that knowledge to engage with them in a continuous feedback loop on a 1:1 basis. In any CLM program, personalization refers to the physical act of deploying customized content to customers. Analytics, on the other hand, is the mechanism for learning about customers based on their actions and using that information to paint a complete picture of who they are, along with their preferences, behaviors and attitudes.
For pharma, CLM offers an analytic framework that is a dramatic departure from the past. For the first time the veil is lifted on what actually occurs during a salesperson’s call to a doctor’s office. In a mature CLM system, much of this information is gleaned automatically using tablets containing personalized content, tracking what slides are viewed, for how long and why. For pharma marketers, having access to this new-found data opens a world of possibilities.
CLM offers marketers an unprecedented opportunity to track customer engagement and understand their behavior, leveraging three distinct categories of feedback:
- Content-driven analytics—information on content use and engagement
- Sales representative-driven feedback—information that representatives provide on field visits
- Direct feedback from the HCP—information that is provided by physicians themselves
Combined together, these three sources provide powerful insight on pharma’s customers. Insights they generate can be used to develop robust customer segmentation models based on customer preferences, create personalized content for various customer types, and ultimately deploy the personalized content to customers using their channel of preference. Insights collected also measure the effectiveness of the sales and marketing materials, not to mention ensuring that compliance rules related to messaging and content are being followed.
CLM Analytics Pitfalls
Ironically, much of the promise and potential of CLM stems from the limited insight pharma currently has of its customer base. Unlike other industries, pharma depends almost entirely on data purchased from various third-party sources, such as IMS and others, to paint a picture of its customers. Some firms have attempted to supplement this information with market research or sales/marketing activities, though these initiatives are usually run in organizational silos and contribute little to building a complete picture of the customer.
Giving pharma marketers access to unprecedented data on their customers has been and remains one of the central tenets of CLM. Unfortunately, for many early adopters, analytics has been a frequently cited pain point. Despite being offered truly unprecedented data by CLM, for a variety of reasons many companies have been unable to use information generated by their program to meet even the most basic commercial objectives—forget about achieving any sort of demonstrable ROI.
In a recent benchmarking study, Capgemini Consulting discovered that nearly half of the companies surveyed rated their company’s analytics capability as “completely immature” or worse. What’s more, a surprising number of companies still do not track any interaction data from customers, despite having in place a flavor of CLM.
Reasons for this failure are legion. Some companies are collecting vast troves of data, but lack the people and process capabilities to analyze it. Several CLM pioneers we spoke with severely underestimated the volume of data that would be captured, and are now struggling to manage the large databases they have developed. In other cases it turned out that the data models initially deployed were far too simplistic for the data to be actionable.
For many CLM adopters, the lack of an easy integration between CLM and CRM platforms has been a major impediment. Many firms rushed to implement tablets for their field force, yet continued to rely exclusively on their CRM to log call data. The result has been two disparate sets of customer information, causing widespread discrepancies and failing to achieve the 360-degree customer picture envisioned by CLM.
For others, organizational capabilities have acted as a roadblock. Running a successful CLM program requires a new skill-set for many pharma organizations. Unlike other industries that have been using terms such as segmentation, attribution and ROI for years, in pharma the understanding of these concepts is less mature. To support CLM, many firms have thus been forced to build up new organizational units to support these needs. In some companies, analytics teams have understood the value and benefit of CLM analytics, yet had a difficult time selling the concept to the rest of the organization.
Building CLM Analytics Capabilities
Planning, developing and launching a robust CLM analytics program is complex. On the technology side, building analytics capabilities involves integrating disparate databases and technical platforms (CLM, CRM, BI, etc.), all the while maintaining data accuracy and consistency.
Technology aside, all analytics systems have strategic questions that must also be addressed at the outset. For example, what reports are actually needed and by whom? What will the system’s Key Performance Indicators (KPIs) be? Who will need to have access to the system’s reports, and what are their needs from a reporting requirements point of view? What training will the various stakeholders need in order to use the system effectively?
When it comes to developing and rolling out a program, Capgemini Consulting recommends a phased approach. In the diagram below, we chart out our CLM Analytics Maturity Framework, which provides pharma marketers a roadmap for success.
The Framework addresses CLM Analytics maturity, beginning at Basic Content and Channel Management and ending with Predictive Analysis. This approach provides a high-level framework for assessing analytics capabilities, as well as a continuum of capabilities that can be achieved along the maturity curve. This phased approach also makes the development and implementation more manageable, highlighting incremental gains along the way.
The first phase in the continuum, Basic Content and Channel Management, involves creating simplistic channel analytics. In this phase, pharma marketers focus on understanding what content is being used, and by whom. This phase requires that all in-scope systems be integrated, painting a detailed picture of what is occurring and where. It’s also during this phase that team members are trained on how to use the data, CLM systems are integrated with the sales force tools (for example, CRM), and an overall approach to customer segmentation is formulated.
For this initial phase, the focus is on what is being used and by whom. Common items that are measured include e-detail use by reps, geography/region and content. Metrics include data on what slides are being used, number of slides, etc. This stage of maturity focuses on gathering feedback from the customer, such as whether they like or are responding to the presentation.
The second and third phases take a much deeper dive into customer preferences and insights. It’s during the second phase, Customer Preferences and Insights, in which marketers begin to analyze channel and content preferences to develop more robust segmentation models. The third phase, Business Impact Analysis, takes the next logical step in making analytics meaningful to the organization, correlating content and channel usage to business impact measures and ROI measurements. Programs in the second or third phases have reported improved HCP access, enhanced message recall by HCPs, gains in intent to prescribe and, in many cases, a lift in actual prescriptions.
Predictive Analysis is the fourth and final phase. At this level of maturity marketers begin to forecast customer preferences and business impact based on historical performance. Insights gleaned by this phase enable marketers to make accurate predictions about not only the content that the customers want to see and will respond to, but also the channel(s) they want to use to see it.
Having access to Predictive Analysis tools gives marketers the ability to forecast which customers will respond to what content based on past preferences they’ve shown. In addition to vastly improved content, better channel engagement and enhanced customer experience, pharma companies can expect many other benefits once they reach this stage of analytics maturity. These ancillary benefits include a steep reduction or elimination of “message recall” and “intent to prescribe” market research, as well as a marked drop in the need for content testing.
Taking the Right Approach
When implementing a CLM Analytics program, it’s critically important to go slow and steady. Instead of trying to race straight through to Predictive Analysis, for example, marketers should focus on making incremental changes and fixing the basics, such as determining what content is being used by whom and why. Once it’s understood what content is being used, marketers can turn to making it more useful for doctors or reps.
Running a mature and effective CLM program means having both the willingness and the capability to provide customers with truly personalized content. On one hand, firms must be willing to listen to customers, measure their opinions of marketing activities and evaluate results. On the other, this is only possible when marketers change their mindset from a “push” to a “pull” mentality, which means letting customers tell you about themselves, rather than simply telling them what you think they should hear.
In terms of capabilities, firms who implement CLM must ensure their systems and processes can support the capabilities to collect, store, retrieve, evaluate and draw insights from customer feedback, and turn actionable insights into enhanced or new customer products/promotions. All the while, the focus must be on providing customers content they are interested in based on their feedback.
Our chart outlines the key success factors for developing and using robust analytics in a CLM program.
Why You Should Do It
Our experience has shown us companies that plan and build an analytics platform gain access to information far more valuable than that produced by more traditional methods, such as market research. If you believe the ultimate goal of your program is to develop a better customer experience and to give customers what they truly value, then you must start by identifying what the customer wants, which can only be done by monitoring their behaviors.
In an idealized state, pharma companies should aim to capture and analyze feedback from across every customer interaction and touch point, regardless of where it occurs, to paint a complete picture of the customer. This data can then be leveraged to enhance or modify the segmentation model, provide insights to drive brand strategy and messaging, gain a better general understanding of customers’ preferences and behavioral triggers, and of course deliver highly personalized content.
Ultimately CLM is about providing customers with more tailored content through the right channel. Once achieved, customer satisfaction goes up, promotional costs go down, and the relationship between the pharma company and its customers is radically transformed from a one-way street to a two-way conversation.
Without analytics, none of this is possible. Firms that have launched CLM without the proper analytics framework in place have learned the hard way, and been forced to recalibrate their approach after the fact. So if you have or are considering launching a CLM program, take the time to consider your analytics strategy. Without one, you might as well be driving in the dark.
That’s it for now. Look forward to more about the last pillar, Responsiveness, in a subsequent post. In the meantime, if you have any questions please feel free to contact us.
Timothy Moore is a Vice President at Capgemini Consulting Life Sciences, and is lead for the Closed Loop Marketing and Digital Transformation practices. Tim has more than 20 years of pharma experience, and he has led CLM transformations for over 50 brands. He can be reached at firstname.lastname@example.org.
Hala Qanadilo is a Principal at Capgemini Consulting Life Sciences, and lead for the Marketing and Sales Digital Transformation practice. Hala has extensive MCLM experience in pharma and has launched CLM programs for more than 20 brands. She can be reached at email@example.com.
Rio Longacre is a Managing Consultant at Capgemini Consulting Life Sciences. He is a core member of the Closed Loop Marketing and Digital Transformation teams, and brings extensive experience in MCM and personalized communications. He can be reached at firstname.lastname@example.org.