1365
N. Scottsdale
Road
PMSA
Symposium
2020 Speaker Presentation
Suite
100
Title:
Dynamic Targeting Solution for Field Force
Scottsdale
Authors:
Karthik Somadri, Ankit Chhabra and Robin Varghese, CustomerInsights.AI
AZPresenters:
85257
Ankit Chhabra, Sr Director, CustomerInsights.AI
Abstract
Even with the emergence of new technology-based marketing tactics, “Field Force Detailing” continues to dominate the
promotional landscape for Pharmaceutical & Biotechnology companies, accounting for the largest bucket for promotional
spend. Analytics around Field Force Targeting appears to have been beaten to death over the years, but it still remains as the
primary challenge for Life Sciences companies to ensure they maximize their ROI on their largest promotional spend.
Through our two decades of experience in this space, we have come to learn that there is value in moving away from a Static
Once-A-Year Targeting solution (majority of Pharmaceutical & Biotechnology companies follow this today) to a more Dynamic
Targeting solution, that is flexible to emphasize or de-emphasize targets based on changing conditions on the ground.
Why should a Pharmaceutical or Biotechnology company consider moving towards a Dynamic Targeting solution?
• Improve ROI by calling on the “right targets” at the “right time”
• Minimize wasted efforts, especially with trial-error process on Medium & Bottom Tiers which typically accounts for
50-60% of the overall call volume
• Create tailored messaging based on ground reality for each physician
• Identify and engage new potential HCPs in an ever-dynamic market
Our Data Science team has developed a three phased solution to enable Dynamic Targeting for Field Force:
• Integration of New Data Sources
• Real-time Insights
• Predictive Insights using ML/AI
Phase 1: Integration of New Data Sources
Traditional approaches rely heavily on prescription sales data, prioritizing physician based on brand and market sales
performance. But this never tells the complete story. Understanding the disease and journey of a patient in its entirety is very
critical in determining the true potential of a physician. This can be achieved by leveraging newer data sources like Claims
data, Biosimilars data, Lab data, Payer-plan data, Clinical data, Formulary data, Medical devices data, Social/Digital data,
Hospital data etc.
We work with our clients to integrate as many data sources as possible to build a rich history of activity against every
physician. This is then followed by a detailed exploratory data analysis and a complex index scoring methodology using the
integrated information to evaluate the relative importance of each physician. Finally, this entire process is “operationalized”
through a Big Data platform to auto-update as frequently as data is made available. HCPs may get tagged with a new priority
index over time based on real-time factors like changing payer landscape, competitive changes, changes in HCO
ownership/integration, shift in HCP behavior, changes in patient volume, etc.
PHYSICIAN
DEMOGRAPHICS
•
•
•
•
•
•
Territory, Zip and State
related information for
HCPs and Patients
•
PATIENT PROFILE
•
• Age
• Gender
• Primary payment type
(Commercial, Medicaid, Medicare,
Assistance Programs, Cash)
Physician Specialty
# Total MDs and Visits
# Drug X MDs and Visits
# MDs w/ Drug X Call
Difference in visits/MDs in last
6M vs. Prior 6M
Primary Physician Gender and
Experience
Avg. Physician Decile
CLINICAL TRAITS
•
•
•
•
All Diagnosis (Dx) codes
All Procedure (Px) codes
All Prescription (Rx) codes
Lab test results
Page 1 of 3
1365
N. Scottsdale
Road
PMSA
Symposium
2020 Speaker Presentation
Suite
100
Title:
Dynamic Targeting Solution for Field Force
Scottsdale
Authors:
Karthik Somadri, Ankit Chhabra and Robin Varghese, CustomerInsights.AI
AZPresenters:
85257
Ankit Chhabra, Sr Director, CustomerInsights.AI
Phase 2: Real-time Insights
This phase focuses on tracking multiple events of interest on real time basis that can directly impact the priority of a physician
in the already established targeting schema (usually derived from Phase 1). These events are set up to be agile to change over
time based on current business priorities. For example, an event could be to track activities around a competitive launch
situation and track patient profiles of interest and their associated physicians to keep a check on competition.
Some examples of patient related events include:
a. Patients switching to competitive drug
b. Confirmation of diagnosis
c. Specific lab results
d. Rejection of your product’s claim or competitor product’s claim
e. Undergoing certain procedures
f. Experiencing side effects
Some examples of physician related events include:
a. HCPs writing Biosimilars
b. HCPs with an undesirable Source of Business Mix
c. Important HCPs with lower than expected call plan adherence
d. High writers with declining NRxs
e. HCPs/Practices facing higher payer rejections
f. HCPs New To Market or New To Brand
g. HCPs highly affected by recent Managed Care win for Pull Through
As done in Phase 1, this phase is operationalized through a Big Data platform to auto-update as frequently as data is made
available. The Big Data platform, which also has “specific” field level views, is used to inform and empower the Field Force
with ground intel. The views are also made interactive to easily get actionable call lists for each type of event which helps the
Field Force customize their messaging for each HCP.
Page 2 of 3
1365
N. Scottsdale
Road
PMSA
Symposium
2020 Speaker Presentation
Suite
100
Title:
Dynamic Targeting Solution for Field Force
Scottsdale
Authors:
Karthik Somadri, Ankit Chhabra and Robin Varghese, CustomerInsights.AI
AZPresenters:
85257
Ankit Chhabra, Sr Director, CustomerInsights.AI
Phase 3: Predictive Insights using ML/AI
Majority of today’s targeting solutions are all centered around available data. With the emergence of new technologies, we
can now leverage advanced ML/AI techniques to confidently predict future events in the data. In this case, ML/AI techniques
can be used to predict key events in the journey of a patient even before they have happened by learning from past behavior
of other similar patients in the universe. Through this predictive process, one can better understand the “true potential” of a
physician which is not completely understood through any of the other phases.
We have a comprehensive approach for model building & validation against real time data. We run hundreds of models across
a wide variety of ML algorithms like Linear models, Tree based models, Support Vector Machine based models, Ensemble
models (Random Forest, XG Boost, etc.) and customized blending/stacking methods and evaluate pros & cons against each
other. Our Data Scientists have built models with a prediction accuracy of 98% in some client instances. Finally, insights from
this phase get integrated with Phases 1 & 2 to empower the Field Force to take smart decisions on the fly.
PU Classification Engine
Dataset Creation
Client
dataset
Drug X patients
Analyzing PreDrug X
pathways
Open Target
(patient pool)
Working dataset for
classification model
Patients to HCP mapping
Drug X Patients are
assigned positive
label, while others
from patient pool are
labeled negative
Assigning patients to HCPs
Unlabeled dataset
Stratify overall patient pool
Create Positive
Unlabeled dataset using
selected features
Feature
Engineering
Training set
(80%)
Testing set
(20%)
Model
Training
Model
Testing
Model
Evaluation
FINAL Model
Prediction set from
patient universe
Identify Drug X markers
List of current &
potential Drug X
patients & their HCP
mapping
Drug X
patients
Potential Drug
X patients
Aggregate at HCP level &
bucket into HML
High Potential
Medium Potential
Validate Predicted Drug X
patients from the pool
Low Potential
To conclude, competitive & constantly changing marketplace calls for advanced strategies for the Field Force. Traditional
Once-A-Year targeting solutions (irrespective of the complexity of the model) are just not effective in today’s environment.
Real benefits will be realized by clients who move to a Dynamic Targeting solution that can stay in tune with reality on the
ground. To realize the benefits of this approach, a client’s entire system must be able to
• Adjust any target up or down the entire system based on market conditions & its impact
• Adjust messaging mix for each individual target physician based on his or her specific ground reality.
Don’t be surprised if this type of thinking and engagement gets a lot of pushback from the Field Force who is used to following
a set plan in their respective geographies. Changes of any kind takes patience and collaboration with the Field Force. We are
doing exactly this for one of our clients. During the actual presentation at the Symposium, we will walk through an actual
client case study sharing insights on all three phases to help event attendees gain some valuable knowledge on the approach
& challenges that they can bring back to their organizations for immediate reference.
CustomerInsights.AI
Founded by Life Sciences & Technology leaders with 25 yrs of experience, CustomerInsights.AI is an Artificial Intelligence company that
builds innovative ML/AI solutions with strong emphasis on self-service, stateless execution through automation, speed to insight, and
lower costs. We believe that true value of AI will be realized by Pharmaceutical & Biotechnology companies when AI becomes part of
one’s everyday plumbing and not a one of event at the individual or project level.
Ankit Chhabra, Sr Director
With over 12 years of Predictive Modeling & Data Sciences experience, Ankit focuses on challenging status quo by leveraging advanced
ML/AI techniques to build innovative solutions to solve Sales Force Effectiveness problems for Life Sciences and Health Care clients.
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