The document discusses strategies for personalized next best actions in B2B account-based marketing for healthcare professionals (HCPs), focusing on understanding their preferences and decision-making journeys. It outlines metrics for measurement, suggests using reinforcement learning for optimizing marketing strategies, and emphasizes the importance of customer satisfaction and success metrics. Additionally, it highlights the need for ongoing evaluation and integration of technology to enhance user experience and improve marketing outcomes.

1. Next Best Action for
B2B account based
marketing
Edmond Sam • 2019

2. Edmond Sam | 2019
Framing the Business Problem
2
Stated Goal
● Personalizing Next Best Action suggestions based on the Healthcare
Professional (HCP)
● HCPs are individuals, thus have their own preferences
B2B marketing problem but
different
● Right content at right time to persuade all decision stakeholders
● Pharmaceutical not directly connected with patients
● Treatment information controlled
● Treatment consumption influenced by payer

3. Edmond Sam | 2019
Health Care Professional Journey
3
Metric Measurement
Coverage
How many protocol
decision
stakeholders?
Awareness
How many know
about treatment?
Engagement
How much time
spent? (e.g. webinar)
Reach Target / Attendees
Impact
Target business
outcome

4. Edmond Sam | 2019
Reference
Decision Model
For Next Best Action
4

5. Edmond Sam | 2019
1. Use cases that
could be
supported in
the component
5
Plan Build Run/Measure
“I want to get the
right story to the
HCP”
● Review funnel &
persona status
from system
● Define goals for
next marketing
iteration
● Set baseline
● Build & update
personalized
promomat to
persona and
organizations
● Measure HCP
funnel movement
● Use system data in
Marketing Mix
Model
“I want my team to
achieve business
goals.”
● Approve journey
policy for new
treatment in
system
● Set baseline
● Review system
journey policy
based on goals
● Update biz rule
● Review system
suggestions with
teams
● Review feedback
from HCP
“I want to help my
HCP” ”
● Update HCP
data
● Review and
prepare for
upcoming
messages
● Accept/Reject
suggestions.
Deliver content.
● Capture feedback
for system

6. Edmond Sam | 2019
2 - Traits and
preferences of the
HCP
personalization
6

7. Edmond Sam | 2019
3 - How these traits and preferences can be determined and calculated
7
HCP
data
available
● Reverse engineer path to successful
outcome
● Reinforcement learning for Markov
Decision Process
○ Individual with discrete states
○ Sequential decision making over
timeline (state, action)
○ Bellman optimality - max reward
○ Solve for optimal policy (π*)
No HCP
data
● Default persona
● Ask HCP, expert opinion by account
owners
● Look alike modeling using 3rd party
like Axciom or LiveRamp. “Find more
of this person”.
Inspired by Grid Dynamics Blog

8. Edmond Sam | 2019
4 - Evaluate the
on-going “validity”
or relevance of a
Next Best Action
suggestion?
8
Influenced
revenue
● Marketing Mix Modeling
● Multi linear regression
analysis
○ Sales, Media
○ Market (e.g.
population with
indication, payer
data)
● HCP touchpoints as media
● Revenue lift downstream
HCP funnel
movement
● Define expected next step
in journey after touchpoint
● Set baseline on variable
● Feedback or questions after
event
● Increase in time spent as
“engagement”?
● Regular survey for
sentiment and mindshare

9. Edmond Sam | 2019
5 - Success criteria
for the HCP
Personalization
Component?
9
External
● Financial targets: Revenue - R&D expenses
● Customer Satisfaction score
● Net promoter score (NPS)
● Case studies of quantifiable customer success
● Evidence of landing and expanding in target
vertical/geo
● Increasing # of pilots
● Robust pipeline on customer requests
Internal
● Team engagement (e.g. 360 review)
● “Internal NPS”

10. Edmond Sam | 2019
6 - How would you evaluate the value compared to the engineering time for this
component? Prioritization Scorecard.
10
Description Score
Roadmap
alignment
Innovation Overall solution: Agile marketing optimization in HCP
journey
● Does it enable part of process previously
unavailable?
● Does it improve existing step?
● Does it prove secondary option?
5 - Enable
3 - Improve
1 - Secondary
Customer
request
Measurement of pervasiveness
● Ideas portal with customer voting
● Veeva customer success team
5 - 50%+ customers
3 - 25% +
1 - 10%+
Integration ● Improves user experience in E2E process
● Non-functional requirement
○ Availability, Performance, Supportability
5 - Verifiable with client
3 - Quantifiable
1 - Unconfirmed
Ability to
execute
Technology
complexity
● Brand new infrastructure / technology stack
● Technology risks
5 - Same stack and data pipeline
3 - One is new
1 - New stack and pipeline

11. Edmond Sam | 2019
6 - Engineering
Time
2x2 Prioritization
11

12. Edmond Sam | 2019
7 - Additional
enhancements or
roadmap items
12
Plan Build Run/Measure
Studio to visualize and/or define:
● Funnel
● Decision model
● HCP Journey policy per
persona
● Persona discovery
Promo materials
● Auto scoring against
persona
● Suggestions on content
and format
Setup A/B testing
● Random sampling
● Test vs. Control variables
● Tracking of outcome
A) Prescription/consumption
data
- Collaborate with HCP &
pharmacies.
- Acquire app or license data from
existing consumer services.
-Solve known consumer pains in
exchange for data.
1. Upload picture of
prescription
2. Education on treatment
3. Reminder for treatment
4. Food compatibility
5. Auto refill
B)Trusted, “opt-in” benchmark
marketplace