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BlueLabs | Data, Analytics, Technology.
Changing Behavior with
Experiments and Modeling
Dan Porter
Co-founder & Chief Analytics Officer, BlueLabs
Driving Strategy with Data
www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology.
Media consumption is fragmenting
Digital is no longer a ubiquitous, single medium
Cord cutters are an increasingly large segment of the population
Companies and other organizations have more decisions than ever about
where to deploy resources to change human behavior
Current Environment
BlueLabs | Data, Analytics, Technology. www.bluelabs.com @blue_labs
Question: How can
organizations other than e-
commerce harness the power of
data to build engaging
individual level outreach
campaigns
BlueLabs | Data, Analytics, Technology. www.bluelabs.com @blue_labs
www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology.
Always be Testing: Rather than have a “test” followed by a “program”,
holdout groups should be incorporated into any outreach campaign
Track outcomes over time: Rather than testing a given thing on a given
day, constantly iterate and track how outreach affects behavior long into the
future
Measure efficacy at individual level: Use uplift modeling to measure how
much more likely an individual is to take an action as a result of receiving
outreach
Answer:
BlueLabs | Data, Analytics, Technology.
We leverage data to nudge
citizens/patients/customers
towards taking actions and
completing multistage processes
BlueLabs | Data, Analytics, Technology.
What is Persuasion (uplift)
Modeling?
In persuasion modeling, we are
measuring and predicting:
The likelihood of someone to take an action as a result
of receiving specific outreach
We are not measuring:
Current
intent
Issue
importance
Interests
There are 4 types of people we
identify for persuasion modeling:
The key is to find the people who will benefit most from a treatment or message:
Takes no action with
treatment
Takes action with treatment
Takes no action without
treatment
Lost Causes Persuadables
Takes action without
treatment
Do Not Disturbs Sure Things
Some People React Negatively to
Campaign Contact
Some People React Negatively to
Campaign Contact
Some People React Negatively to
Campaign Contact
www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology.
Real World Examples of “Sleeping Dogs”
• Message that undermines core brand to a
particular customer with the wrong message
• Inadvertently “reminding” somebody to cancel
an auto-renewal
• Sending too many emails and having the
recipient unsubscribe
• Trying to sell something to somebody too soon
after previous sale
Experimental Design
Persuasion Modeling
Goal: identify heterogeneous treatment effects
y=α + β’Xi + γTi+ σ’XiTi
Avoid
Persuasion scores target people
at the individual level:
Example Universe by Persuasion Score
Lift
Persuasion
Weak Targets Strong Targets
BlueLabs | Data, Analytics, Technology.
Digital Services Analytics
Many applications across industries
Healthcare Banking Automotive Many
Desired Outcome
Reduce Appointment
Flake Rate
Move Services
Online
Service car at
dealership
Opt into paperless
communication
Treatment
Reminder SMS,
Phone Calls
Reminder at
branch and on
phone
Online/offline
outreach, in car
alerts
Online outreach
with custom
message
Interesting Data
Input
Previous Flake Rates,
distance from office,
login to website
Failed attempts
using
website/app
Connected Car
Data
Lack of
responsiveness to
mail, online
engagement
iPhone released in 2007
$399
$80/month after fees
2 year contract
Good credit required.
Mobile broadband is now accessible
Android phone from
MetroPCS today.
$59
$30/month.
No contract.
+ Tethering allowed for computer
access.
BlueLabs | Data, Analytics, Technology.
By 2020, 41 million Americans
will be mobile only internet
users.
Confidential and Proprietary
Least Accessible Most Accessible
Mobile Web Desktop Web
Brick and mortar
locations
Brick and mortar
locations
Desktop Web Mobile Web
2007
2020
THE DIGITAL DIVIDE IS REVERSING
www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology.
Awareness: People generally understand how to interact with entities via
legacy means, but need to learn about the digital services available to them.
Multiple Sessions: Processes are completed over multiple sessions.
Agencies must develop strategies to bring users back to complete
processes.
Feedback Loops: Over time, platforms must adapt to the populations that
use them. Experimentation and predictive modeling are keys.
Continuous Engagement: To make the best use of a platform, users may
return more frequently than they do for in-person services. This opens
opportunities to build programs that are efficient and effective.
FUNDEMENTAL DISTINCTIONS OF DIGITAL
SERVICES
Confidential and Proprietary
ENGAGEMENT MODELING
Identify individuals who are either
at retention risk or can benefit
from additional program use
Use randomized experiments
trials to identify individuals most
likely to take action if contacted
Operationalize contacts across
channels, including mail, phone,
SMS, in person, and online
Continually measure program
impact via longitudinal holdout
group
Replicate process to improve
additional area of program
engagement
We create a positive feedback loop of using individual outreach to drive
program engagement. This process has significantly moved the needle
on program success.
www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology.
EXAMPLE MODELS
• Likelihood to complete a process if customer receives a particular piece of
outreach via a particular channel.
• Likelihood of opting out of an auto-renewal if a person receives a reminder,
vs receiving no reminder
• Preference for completing an application process online, on the phone, or
via digital service.
• Likelihood of unsubscribing from an email list if a person receives 3 emails,
but not if they receive one.
www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology.
Behavior: Time since last engagement, number of validation errors, incorrect password attempts,
modes of engagement (online, in person, or call center), average number of engagements per
completed action.
Circumstances: Drive time trelevant brick and mortar locations, household level information,
purchasing power.
Community: Census variables, program uptake within community, health metrics, education.
CREATIVE MODELING INPUTS DRIVE SUCCESS
MORE THAN ALGORITHM SELECTION
EXAMPLE MODELING INPUTS
y=α + β’Xi + γTi+ σ’XiTi
www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology.
TAKEAWAYS
• Organizations other than e-commerce can still target individually with uplift
modeling and experiments—generally high ROI compared with other types
of targeting
• Relevant content is important-–often driven much more by recent behavior
than demographics
• Creativity around what data is predictive is not strictly a statistical skill but
will make or break predictive models
• Multistage processes particularly conducive to such methods
BlueLabs | Data, Analytics, Technology.
Thank you!
Dan Porter
Co-founder & Chief Analytics Officer, BlueLabs
Driving Strategy with Data

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1645 track 3 porter

  • 1. BlueLabs | Data, Analytics, Technology. Changing Behavior with Experiments and Modeling Dan Porter Co-founder & Chief Analytics Officer, BlueLabs Driving Strategy with Data
  • 2. www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology. Media consumption is fragmenting Digital is no longer a ubiquitous, single medium Cord cutters are an increasingly large segment of the population Companies and other organizations have more decisions than ever about where to deploy resources to change human behavior Current Environment
  • 3. BlueLabs | Data, Analytics, Technology. www.bluelabs.com @blue_labs
  • 4. Question: How can organizations other than e- commerce harness the power of data to build engaging individual level outreach campaigns BlueLabs | Data, Analytics, Technology. www.bluelabs.com @blue_labs
  • 5. www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology. Always be Testing: Rather than have a “test” followed by a “program”, holdout groups should be incorporated into any outreach campaign Track outcomes over time: Rather than testing a given thing on a given day, constantly iterate and track how outreach affects behavior long into the future Measure efficacy at individual level: Use uplift modeling to measure how much more likely an individual is to take an action as a result of receiving outreach Answer:
  • 6. BlueLabs | Data, Analytics, Technology. We leverage data to nudge citizens/patients/customers towards taking actions and completing multistage processes
  • 7. BlueLabs | Data, Analytics, Technology. What is Persuasion (uplift) Modeling?
  • 8. In persuasion modeling, we are measuring and predicting: The likelihood of someone to take an action as a result of receiving specific outreach We are not measuring: Current intent Issue importance Interests
  • 9. There are 4 types of people we identify for persuasion modeling: The key is to find the people who will benefit most from a treatment or message: Takes no action with treatment Takes action with treatment Takes no action without treatment Lost Causes Persuadables Takes action without treatment Do Not Disturbs Sure Things
  • 10. Some People React Negatively to Campaign Contact
  • 11. Some People React Negatively to Campaign Contact
  • 12. Some People React Negatively to Campaign Contact
  • 13. www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology. Real World Examples of “Sleeping Dogs” • Message that undermines core brand to a particular customer with the wrong message • Inadvertently “reminding” somebody to cancel an auto-renewal • Sending too many emails and having the recipient unsubscribe • Trying to sell something to somebody too soon after previous sale
  • 15. Persuasion Modeling Goal: identify heterogeneous treatment effects y=α + β’Xi + γTi+ σ’XiTi
  • 16. Avoid Persuasion scores target people at the individual level: Example Universe by Persuasion Score Lift Persuasion Weak Targets Strong Targets
  • 17. BlueLabs | Data, Analytics, Technology. Digital Services Analytics
  • 18. Many applications across industries Healthcare Banking Automotive Many Desired Outcome Reduce Appointment Flake Rate Move Services Online Service car at dealership Opt into paperless communication Treatment Reminder SMS, Phone Calls Reminder at branch and on phone Online/offline outreach, in car alerts Online outreach with custom message Interesting Data Input Previous Flake Rates, distance from office, login to website Failed attempts using website/app Connected Car Data Lack of responsiveness to mail, online engagement
  • 19. iPhone released in 2007 $399 $80/month after fees 2 year contract Good credit required. Mobile broadband is now accessible Android phone from MetroPCS today. $59 $30/month. No contract. + Tethering allowed for computer access.
  • 20. BlueLabs | Data, Analytics, Technology. By 2020, 41 million Americans will be mobile only internet users.
  • 21. Confidential and Proprietary Least Accessible Most Accessible Mobile Web Desktop Web Brick and mortar locations Brick and mortar locations Desktop Web Mobile Web 2007 2020 THE DIGITAL DIVIDE IS REVERSING
  • 22. www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology. Awareness: People generally understand how to interact with entities via legacy means, but need to learn about the digital services available to them. Multiple Sessions: Processes are completed over multiple sessions. Agencies must develop strategies to bring users back to complete processes. Feedback Loops: Over time, platforms must adapt to the populations that use them. Experimentation and predictive modeling are keys. Continuous Engagement: To make the best use of a platform, users may return more frequently than they do for in-person services. This opens opportunities to build programs that are efficient and effective. FUNDEMENTAL DISTINCTIONS OF DIGITAL SERVICES
  • 23. Confidential and Proprietary ENGAGEMENT MODELING Identify individuals who are either at retention risk or can benefit from additional program use Use randomized experiments trials to identify individuals most likely to take action if contacted Operationalize contacts across channels, including mail, phone, SMS, in person, and online Continually measure program impact via longitudinal holdout group Replicate process to improve additional area of program engagement We create a positive feedback loop of using individual outreach to drive program engagement. This process has significantly moved the needle on program success.
  • 24. www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology. EXAMPLE MODELS • Likelihood to complete a process if customer receives a particular piece of outreach via a particular channel. • Likelihood of opting out of an auto-renewal if a person receives a reminder, vs receiving no reminder • Preference for completing an application process online, on the phone, or via digital service. • Likelihood of unsubscribing from an email list if a person receives 3 emails, but not if they receive one.
  • 25. www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology. Behavior: Time since last engagement, number of validation errors, incorrect password attempts, modes of engagement (online, in person, or call center), average number of engagements per completed action. Circumstances: Drive time trelevant brick and mortar locations, household level information, purchasing power. Community: Census variables, program uptake within community, health metrics, education. CREATIVE MODELING INPUTS DRIVE SUCCESS MORE THAN ALGORITHM SELECTION EXAMPLE MODELING INPUTS y=α + β’Xi + γTi+ σ’XiTi
  • 26. www.bluelabs.com Confidential and ProprietaryBlueLabs | Data, Analytics, Technology. TAKEAWAYS • Organizations other than e-commerce can still target individually with uplift modeling and experiments—generally high ROI compared with other types of targeting • Relevant content is important-–often driven much more by recent behavior than demographics • Creativity around what data is predictive is not strictly a statistical skill but will make or break predictive models • Multistage processes particularly conducive to such methods
  • 27. BlueLabs | Data, Analytics, Technology. Thank you! Dan Porter Co-founder & Chief Analytics Officer, BlueLabs Driving Strategy with Data