Advanced Analytics Helps Emerging Pharma Company Hone Customer Target List
Beghou Consulting deploys machine learning to identify the 1,000 most likely prescribers
Challenge: FINDING THE MOST LIKELY PRESCRIBERS
THE COMPANY’S TRADITIONAL APPROACH HELPED IT IDENTIFY 2,000 HIGH-DECILE POTENTIAL PRESCRIBERS. HOWEVER, IT WANTED TO BE MORE REFINED IN ITS APPROACH TO THIS CAMPAIGN.
Emerging pharmaceutical companies may be more agile than Big Pharma companies, but they often work with smaller budgets. Therefore, they can’t afford to waste dollars on fruitless sales and marketing efforts.
This particular emerging pharmaceutical company planned to launch a new non-personal promotional campaign to complement on-the-ground field sales force efforts. It already had a list of 30,000 target health care professionals. To make the most of its campaign resources, the commercial team needed to pare this list of 30,000 down to a reasonable volume for prospecting.
Traditional targeting involves reviewing claims data and targeting physicians who treat the highest number of relevant patients. This approach can be effective, but it can also waste time and effort. After all, many variables impact a physician’s decision to prescribe – a drug’s formulary position, an HCP’s experiences with a competitor’s drug and, of course, a patient’s unique health history. The company’s traditional approach helped it identify 2,000 high-decile potential prescribers. However, it wanted to be more refined in its approach to this campaign.
So, how could this emerging company create a list of HCPs that it could target with confidence?
Approach: BUILDING A MACHINE LEARNING MODEL
Enter Beghou Consulting. The Beghou team drew on its experience deploying advanced analytics techniques such as machine learning to help emerging pharmaceutical companies hone targeting efforts. It also pulled from its knowledge of patient journeys and prescriber behavior in the category.
Beghou aimed to create a machine learning model that would accurately predict which HCPs within the larger target list would be most likely to prescribe the company’s drug– and therefore would be worthy targets for the promotional campaign. To accomplish this goal, the team needed to build a model that accounted for the many factors that impact prescription decisions.
The Beghou team gathered transactional claims data and built a model with fields to account for data on related diseases, the commercial performance of competing drugs, sales data for the company’s product, the company’s historical promotional activity and more.
With these levers in place, the Beghou team trained the model using past sales data. From there, Beghou used the model to whittle down the target list. The company had used its traditional targeting approach to segment prescribers based on varying degrees of potential. Using its machine learning model, Beghou could take any of these segments and identify the prospects most likely to start prescribing the product. The Beghou machine learning-based model identified the 1,000 most promising prospects out of the 2,000 HCPs the pharma company’s traditional model had classified as having the most potential.
THE BEGHOU TEAM DREW ON ITS EXPERIENCE DEPLOYING ADVANCED ANALYTICS TECHNIQUES SUCH AS MACHINE LEARNING TO HELP EMERGING PHARMACEUTICAL COMPANIES HONE TARGETING EFFORTS.
Results: PROVING THE IMPACT
The company decided to target all 2,000 HCPs that its traditional model recommended, but the 1,000 HCPs Beghou’s model selected generated significantly more prescriptions than the other 1,000 HCPs. Approximately 10 percent of the 1,000 identified by Beghou’s model started using the product whereas fewer than 1 percent of the rest of the target group started using the product.
An HCP’s decision to prescribe a drug can be multifaceted. To identify the most likely prescribers and maximize the return on sales and marketing investments, companies must combine in-depth knowledge of the competitive landscape and commercial data with advanced analytics techniques to ensure they focus efforts where they’ll have the most impact. With Beghou’s help, this emerging pharma company now has a more effective way to develop prospect lists.