Case Study: Precision Targeting Boosts Oncology Sales At a Top 5 Pharma

Within 6 months, increase of 15 – 25% in leading KPIs including new scripts

One of the largest Oncology Pharma divisions in the US, found itself with sales being flat for over a
year. Growth was startlingly low, and HCP churn was on the rise. Doubts have been raised as to
whether the traditional marketing and sales strategies being used are still practical in today’s complex
market, and the business unit in charge decided to look into alternative methods

Customer Overview

A large oncology business unit responsible for two leading brands of personalized oncology drugs. With an annual value of $110K-200K per patient, every single patient counts. The business unit has been working with a consulting firm to develop their go-to-market strategy and based on that, segment their therapeutic universe of 30,000 HCPs and generate executable target lists. Once a trimester, the consulting firm embarked on a periodical targeting project that would be used in the next four months. A salesforce of approximately 100 reps, was sent to the field based on these targeting lists. In addition, marketing initiated several non-personal promotion activities, with a handful of different message types, via a variety of channels.

As their brands’ performance was lagging, the brand managers realized that this traditional practice is rather futile in today’s highly dynamic market. They were looking for an automated dynamic solution, that will enable them to systematically take in fresh data, gain better understanding of the current HCP landscape, and quickly respond to risks and opportunities. Specifically, they wanted to be able to engage HCPs on a more personalized level, with timely
response to their varied needs, and better match HCPs with highly targeted drugs that are best suited for their patients.

After evaluating different approaches, they selected Verix, which enabled automation of a variety of painstaking tasks, with ease, precision, and repeatability as never before. In addition, Verix’s decision engine enables the evaluation of ‘What-if’ scenarios, and systematic A/B testing, which allows them to continuously monitor, adjust, and improve their strategy and tactics.

The challenge – quickly respond to market dynamics

Targeting lists manually tailored by the consulting firm were heavily one-and-done – collecting all data and painstakingly segmenting and generating multiple target lists for the following 4 months. A slow and very expensive process that all too often resulted in “too late” situations. The segmentation was rather simple, using two or three variables, such as number of prescriptions and number of patients. The resulting targeting lists were static, with no flexibility to adapt to the dynamics of the therapeutic landscape and to personalized engagements with specific target groups.

The brand managers realized they’re missing opportunities and do not timely attend to risks, as they’re working off 3-6 months old data and ignoring the market dynamics of recent months. They hired a leading research analysis group to thoroughly study their brands and pinpoint specific competitive advantages they could leverage. The study highlighted the strengths and weaknesses of their brands, yet their problem was, to micro-segment the entire therapeutic universe, find the relevant HCPs that would benefit from each competitive advantage, and be able to deliver the pertinent messaging that addresses the exact need of every HCP.

Verix Solution

Verix offers a dynamic micro-targeting platform that enables personalized messaging to every micro segment to address the exact needs of their patients.

Verix’s Tovanatm is a three-tier solution:

1. CDP – a robust Customer Data Platform holding a rich set of predictive attributes about each account in the relevant therapeutic universe, with ML-based micro-segmenting capabilities.

2. Decision engine – facilitates the training, maintenance, and delivery of dedicated ML models that
address specific business needs

3. Workflow studio – allows users to build tailored workflows that integrate into day-to-day business
processes

First, the CDP was implemented. Over 60 data sources were merged into the Verix CDP. A variety of customer tables with calculated measures and predictive attributes were generated to create a 360 perspective of all HCPs and patients in their therapeutic universe. All available data, internal sales and marketing data as well as competitive market data, was integrated into a dedicated data-mart. A repository of business rules has been added to calculate hundreds of attributes about every HCP in the relevant therapeutic universe. Machine-learning based technology provides fine-grain analysis of these attributes to add predictive measures.

Collaboration with the company’s data scientists

Verix joined forces with the company’s Advanced Data Science team to leverage the rich data in the CDP and produce an exhaustive, multi-dimensional segmentation of the HCP population. This micro-segmentation mechanism relates HCPs to over a dozen different groups of important business dimensions as well as life cycle stages: non-writer, new writer, continuous writer and abandoning writer. Two HCPs can be found similar on one dimension but dissimilar on another. The result is an elaborate web of groups that is rather impossible to process manually. Next, predictive scoring models were tailored to reflect the propensity of patients and HCPs to move from one life cycle stage to
another (e.g. become a writer, churn, increase market share, etc.)

The segmentation and predictive modeling, which constitute the analytic foundation, are deployed within Verix’s decision Engine that ensures the health and continuous delivery of the AI models. The decision engine analyzes the data and generates segmentation and predictive scoring. These analytical assets are used by the execution teams to translate the go-to-market strategy into an executable plan.

A holistic landscape analysis shows all important market aspects on one screen and allows brand managers to better understand the lay of the land and explore ‘what-if’ scenarios to select the most opportune tactics.

Verix’s decision engine includes a landscape monitor that presents the status of each brand within its market

Verix’s decision engine includes a landscape monitor that presents the status of each brand within its market

Results – within months, significant increase in new scripts

By using an in-house automated solution, both efforts and costs have been reduced. The dynamic nature of Verix and the accuracy contributed by the new models, led in the first 6 months to an increase of 15 – 25% in the leading KPIs, including new scripts.

Using Verix, targeting lists are now based on scientific models that optimize the effectivity of the lists rather than the old method of composing multiple hand-crafted business rules, which was performed by the 3rd party consulting firm. Instead of once a trimester, lists are generated once a month, and a probabilistic-based algorithm automates the prioritization between overlapping lists to maximize the expected outcome, rather than solving overlaps manually based on the analysts’ hunch. Lists’ alignment, which was another tedious manual task, requiring endless back and forth adjustments, are executed now automatically, with a click of a button.

Verix brought about a significant improvement in executing and calibrating the brand strategy. Before having Verix’s Tovana, the targeting strategy and tactics were not measured and evaluated on a regular basis. Now, systematic A/B testing groups are applied to ensure ongoing improvement of every process. The holistic landscape analysis module shows the latest market state of affairs and allows for a thorough understanding of the lay of the land.

Last but not least, replacing the old manual targeting and segmentation methods with an automated, scientific method, not only improves bottom line results but also, greatly reduces the stress, doubt, and manual effort, which were always part of the process, and brings about a high level of trust in the transparent and highly explainable process.

In addition to 20% increase in HCP engagements the brand managers felt that better understanding of the market, improved collaboration, and increased trust, played a great role in the overall success of implementing Verix.

Conclusion – traditional mass marketing doesn’t fit today’s dynamic market

In the oncology market, treatments are becoming more targeted and more personalized, to address the inherent variability of cancer. Subsequently, traditional mass marketing methods don’t work well in this highly variable market as they lack science-driven integration between data, strategy, and tactical execution to dynamically match market needs with product sales.

A science-based ML solution like Verix’s Tovana is needed to allow for dynamic, predictive, fine-grain targeting and segmentation of the therapeutic landscape and enable brand professionals to engage HCPs with focused messaging that addresses their exact needs with high precision.