Unlocking Your Brand's Hidden Potential Through Dynamic Targeting

At the most recent PMSA conference, Analytical Wizards associate principal Sreya Chatterjee and advisor James Lin gave a presentation that turned traditional targeting methods on its head. In "Unlocking Your Brand's Hidden Potential Through Dynamic Targeting," they outlined how brands can achieve deeper and wider results through dynamic targeting.

In case you missed it, here's a recap of their "dynamic" presentation.

Business Challenge

It all began when a customer came to Analytical Wizards with what has proven to be a common challenge. The pharmaceutical client wanted Analytical Wizards to look into more effective and innovative ways to address two growth challenges of a buy and bill brand:
  • Low conversion. Only 17% of the accounts in the market prescribed the brand.
  • Sales concentrated within a small portion of the prescribing accounts. Just 15% drove 80% of the sales.


The client wanted to enrich their current targeting by blending quintile-based segmentation with a driver analysis for adoption and growth. Goals included:
  • Accelerate sales. The client wanted to expand the breadth and depth of prescribing.
  • Smarter targeting. Focusing promotional efforts on the right accounts (i.e., emerging adopters and growers).
  • Maximize conversions. Greater impact by identifying the right promotional levers (drivers) for the targeted segments.

Examine Targeting Methods

The first step in tackling the challenge of increasing those low conversion rates and capitalizing on the opportunity for growth shown in just 15% of accounts driving 80% of the sales was to look into the client's targeting methods. Chatterjee found they had been using traditional volumetric targeting.

Chatterjee and her team uncovered some problems with volumetric and quintile-based targeting:

Traditional volumetric targeting is not designed to drive adoption or prescribing growth. While easy to communicate, the traditional volumetric targeting approach results in sub-optimal effort allocation because it does not differentiate promotional effort between:
  • Two high-market volume writers with diverging adoption likelihood (high vs. low) for the target brand.
  • Two high prescribers of target brand with different growth trajectories (growers vs. decliners).
"It is possible for a physician to be a 'high writer' but not prescribing your brand," she notes. "High prescription volume does not mean they'll prescribe yours." Similarly, quintile-based targeting does not differentiate promotional effort between two high-market volume non-prescribing accounts with different adoption likelihoods.

"We saw that there was a high prescription volume, but our modeling ranked those accounts as medium or low," she explains. "To convert those to 'high,' a change was necessary."

Solution: Dynamic Targeting

The goal for Analytical Wizards was to:
  • Expand breadth — grow the base of prescribing accounts.
  • Increase depth — more prescriptions within writing accounts.
Rather than simply try to get different results by using traditional volumetric or quintile-based targeting, Chatterjee sought to expand the breadth and increase the depth of the targeting method itself. The result: Dynamic targeting.

Dynamic targeting doesn't simply look at volume alone. It looks at other factors as well, factors that may change, grow, diminish or otherwise move in different directions. Some of those factors included:
  • Volume
  • How many competitors the physician is writing prescriptions for
  • What types of promotions the physician has been exposed to
  • What types of promotions they have responded well to
  • Promotion variables including sampling, calls, non-personal promotions
  • The type of account itself
  • Insurance factors
  • Patient pool
  • Claims information
  • What stage of disease the patient is in
Using dynamic targeting, Chatterjee and her team were able to give recommendations for the client to achieve its goals, but, as with any change, they experienced some pushback at first.

"It's not an easy task to convince companies and sales teams to change the way they target," she explains.

Her solution? Combine the approaches. "We told them, keep your volumetric targeting. Overlay our dynamic targeting with that." The client began to see how many factors, other than volume, came into play.

"The market itself is dynamic," she says. "Everything is changing. If your targeting mechanism doesn't factor that in, you're setting yourself up for failure."

With the dynamic targeting approach, the client was able to track which of their accounts were most likely to prescribe, and model accurately. Looking at the factors dynamic targeting takes into account, the client received a more accurate view, giving them a stronger strategy to achieve their goals.

Pharma Forecasting Evolution: From PMSA 2019 to M4

If you attended the PMSA 2019 conference, you may have seen the presentation by Srihari Jaganathan, associate director of advanced analytics at UBC, Inc., about forecasting patient persistency rates. Titled "Simple Probability Models for Predicting Aggregate or Sparse Data: An Empirical Analysis of Projecting Patient Persistency," it won the Best Podium Presentation award.

If you missed it, here is a recap of the presentation, along with new information about Sri's triumph at the M4 competition. As you may know, the purpose of M4 is to identify the most accurate forecasting method(s) for different types of predictions. These competitions have attracted great interest in both the academic literature and among practitioners, attracting hundreds of entrants from countries all over the world.

More on M4 later. (Spoiler: Sri killed it.)

First, a bit of background on forecasting patient persistency, from his PMSA presentation.

Long-term data on patient persistency is critical for constructing patient flow models in pharmaceutical forecasting. But there's a problem with that. Persistency rates are typically available only for short durations such as 12 months or 24 months due to availability of data. Moreover, the data sometimes are available only at the aggregate level. (Example: persistency rates by month and product.)

A much longer duration of persistency rates is required in the analysis of patient flow models. Typically projecting patient persistency is achieved by using simple curve fitting techniques in spreadsheets on aggregate persistency data. This would sometimes provide sub-optimal and irrational projections.

Lee, Fader and Hardie (LFH) (Foresight, Issue 8 Fall 2007) proposed very effective and simple probability-based approaches such as shifted Beta Geometric and Beta discrete Weibull models to project patient persistency rates.

The main objective is to empirically analyze LFH models on persistency data from diverse disease states such as diabetes, epilepsy, osteoporosis, immunology, hypercholesterolemia and hypertension.

Guiding Principles for Modeling Customer Retention (or Any Analytical Problem!)

Aggregate Data
Models should account for sparse or aggregate data:
  • Data are often not available or sparse
  • Data is available only at aggregate level
  • Cannot access individual data
Missing Data/Heterogeneity
Most important variables will be missing in real world data; a model should be able to account for it:
  • Example: Disease Severity in RA is an important predictor of Adherence but is missing in claims data
  • Modeling should account for unobserved heterogeneity
Simple and Replicable
Models should be understandable to those with legitimate interest and should be replicable:
  • Can be easily programmed in standard software such as R or Excel
Projecting long-term persistency is critical to understanding patient flow modeling. Persistency rates are typically available only for short durations, usually 12 or 24 months, due to availability of data. A much longer duration of persistency rates is required in the analysis of patient flow models, usually five- or 10-year horizons.

Often, projecting persistency is achieved by using simple curve-fitting techniques, but results can be sub-optimal and irrational. That's why probability-based models, like the Beta-Geometric model, for projecting persistency leads to better accuracy. Beta-Geometric fits the validation data like a glove and gives the best forecast.

But, Geometric models assume a sequence of independent binary trials. Many expect that there should be some type of positive momentum or inertia over time when people are more familiar with the medications they're taking.

That leads us to modify the Geometric distribution to a beta discrete Weibull distribution or a k-latent class discrete Weibull distribution.

Modeling Development Process
  • Use only aggregate monthly data.
  • Consider each patient's refill decision at each month, but try to to explain the decision. Individual factors are usually uncaptures in aggregate data and unknown in the forecasted periods. The model is a mathematical approximation of the behavior.
  • Recognize that difference exist among patients.
Probability-based models (BG, BdW, and LCdW) clearly outperform Excel based trend models.
  • Having an accurate forecast of persistency can allow companies to plan future strategies accordingly
  • When using these models, we can extract additional diagnostics about the characteristics of the patients’ behavior
Forward thinking:
  • Patient-level variables might not be the causal factors for persistency, but they remain of interest to the analyst to understand how they are associated with persistency
  • Future research will explore the possibility of incorporating these observable covariates in the process of modeling

A Report from M4

The purpose of the annual M4 competition is to identify the most accurate forecasting method(s) for different types of predictions. These competitions have attracted great interest in both the academic literature and among practitioners, attracting hundreds of entrants from countries all over the world.

Several researchers have shown that combination-based forecasting methods are very effective in real world settings. Sri aimed to evaluate the effect, if any, of combination-based methods on the accuracy of forecasting when using both statistical and machine learning-based approaches. He proposed two types of combination-based forecasting approaches: evidence-based and optimization-based. He found that simple combinations of forecasting models performed competitively well.

Until now, people have been using spreadsheets to forecast persistency rates. For M4, Sri took a model that's not in healthcare domain and translated it to healthcare. He empirically showed that this model provides more accuracy in forecasting. Why is it important? Even a small shift in accuracy has huge implications. If one person misses one unit of medication per day, multiply that by 1,000 people on the same medication and that's huge.

M4 is a cutting edge leading competition attracting 200 entrants from 17 countries. Fifty of those entrants finished. Sri came in 2nd on monthly forecasting and 4th overall.

The software Sri created for this model, Foretell, has been downloaded some 4,000 times. Download it here.

Machine Learning Techniques Deliver Granular Insights to Enable Improved HCP Marketing

Companies have turned to analytics to help them drive more effective campaigns, and to rules-based programs to help reps decide when to execute different tactics. The challenge with most measurement programs is that they suffer from aggregation bias, washing out granularity as they estimate the overall response to a given tactic. They also can be challenged to provide critical insights around differences across specialties and geographies. When we look at rules-based execution aids, they are inflexible and do not tie to changing responses over time.

At the recent PMSA conference, Jane Portman, VP Health Analytics at Merkle, and Brian Demitros, VP of Analytics at Merkle, delivered a presentation designed to educate conference attendees on what's being done out there in terms of using machine learning and analytics to track the behavior of HCPs in response to past and current marketing efforts in order to predict future behavior, and also to gauge the efficacy of those efforts.

The goal for marketers is to find the optimal solution to evaluate and optimize the performance of new digital tools and a new addressable marketing ecosystem designed to provide continued support to sales reps within a multi-channel customer experience. Traditional measurement would not be able to get the level of insights that marketers need. We took a page out of our Digital Media Measurement practice to apply supervised machine learning techniques to the challenge to rapidly and consistently deliver granular insights to enable improved sales force execution and optimal multi-channel customer experience.

Why is it important? Because pharma marketers are facing common challenges in the industry:
  • Customers are saturated with promotional messages.
  • Outbound promotional engagement and access is declining.
  • Customers have channel preferences and propensity.
  • Value-add content and beyond-the-pill programs spark interest and brand engagement.
One key to addressing those challenges is to use a "marketing ecosystem" of various digital tools, including:
  • Digital leave behind
  • Targeted banners
  • Interactive sales aids
  • On-demand content
  • Virtual portals
  • Alerts/SMS
  • Rep-triggered EM
These digital tools require new methods to optimize the customer experience for multi-channel effectiveness, interaction effects of numerous tactics, and content-level optimization.

Meanwhile, existing or traditional marketing methods and approaches have their shortcomings, which machine learning can solve. The problem is, pharma has been slow to adopt it. According to 2018 data by Burtch Works LLC, healthcare and pharma has a 60% rate of machine learning use, which is lower than financial services, gaming, consulting, corporate, retail, general advertising and marketing, and technology. Why? Much of the reason involves pharma industry constraints, like medical legal approval, lack of CTAs or offers, limited content variation, and low campaign activity.

Key takeaways from the presentation?

Pharma marketing has changed to become more addressable. Given constraints in the industry, the analytics hasn't evolved at the same rate, but there's an opportunity in taking learnings and best practices from other industries and applying them to pharma. In the end, it's about reaching each individual HCP in the right way, at the right time, with the right message. Using machine learning, that goal is easier to achieve.

Targeting and Segmentation in Specialty Pharmaceuticals with Calibrated Messaging

At the PMSA 2019 conference, Mert Sahin, CMO of GE Healthcare and Ashish Patel from CareSet Systems, presented a session dealing with targeting and segmentation in specialty pharmaceuticals with calibrated messaging.

With capped resources for sales teams, modern sales force sizing and alignment decisions must artfully blend subjective knowledge captured from field experts with objective measurement gleaned from improved access to big data. Among the most critical exercises in planning is building a list of physician and account targets, leading to the prioritization challenge. How can sales management optimize both the physician’s and the field representative's time?

Here are some highlights:
  1. Among a vast portfolio of products, GE Healthcare manufactures injectable pharmaceuticals used to diagnose neurological disorders. Like other pharmaceutical firms, those diagnostic injectables are “buy and bill,” and face the same challenges, including:
    • Medicare datasets have no blackout markets or accounts.
    • Neurology, Cardiology, Oncology, Immunology and conditions prevalent in 65+ age group.
    • No proprietary identifiers. Physician/hospital NPI is backward/forward compatible.
    • First to obtain data from CMS, quarterly, with quarter lag.
    • Generating and publishing CMS PUFs like teaming.

  2. Market access can leverage affiliations by building data-driven plans and tactics:
    • Identify local key opinion leaders (KOLs) who utilize the brand.
    • Expose all organizational affiliations between the target and hospitals, medical groups and other physicians.
    • Invite KOL to local payer account conversations.

  3. Three targeting strategies:
    • Mass marketing: Targets the whole market and ignores segments. The products focus on common customer needs.
    • Segmented: Targets several market segments within the same market, products are designed and targeted at each segment, requires separate marketing plans.
    • Concentrated: Focuses on smaller niche segments or customers, aims to achieve a strong fit within niche.

  4. Putting it all together: How do we use all of the data collected?
    • Capture ROI with your favorite CRM.
    • Maximize ROI with natural language generator.
    • Dynamic content enables niche messaging at scale.
    • Contextualize each target for the representatives.
    • Hyper-localize messaging for market access.

    It is a concrete example in the field of neurology, revealing a method and segmentation framework to help organize physicians to optimal lead allotments. Results and impacts expected include a demonstration of targeting refinement to build messaging, KOL identification by local territory, and an example of how KOLs can power tactical market access in active negotiation with local payers.