This document summarizes Luciano Pesci's lecture on interpreting data to create behavioral customer personas. It outlines the benefits of personas in improving customer experience and profitability. Pesci discusses using observational and survey data to develop personas through analytical methods like dimension reduction and clustering. He advocates creating personas based on customer behavior rather than demographics. The lecture recommends visualizing personas and collecting feedback in an agile process to refine understanding of customer segments.

1. Dawn of the Data Age Lecture Series
Interpreting Data Like a Pro

2. Hi. I’m Luciano Pesci, PhD…
Founder & CEO, EMPERITAS
● Team of economists and data scientists delivering bi-weekly Customer Lifetime Value
intelligence so our clients can beat their competitors for the best customers.
Founder & Director, Utah Community Research Group, Univ. of Utah
● Teach microeconomics, data science, applied research, & American economic history.
2

3. Today’s Lecture Outline
● Teach the benefits of using personas.
● Show a persona template and its data.
● Explain how to create behavioral personas.
3

4. 4Benefits of Using Personas

5. From Average To Individual
5
● The long-term goal should be understanding
each customer as a unique individual.
○ This allows you to customize your interaction
with them across their entire customer journey.
● Personas move you one step forward from
using averages when understanding customers.

6. Profit In Personalization
● With persona-level insight about your
customer you can accomplish 2 things:
○ Improve their customer experience
○ Create profit for your organization*
● Profit comes by knowing CLV** because this
focuses attention on profitable actions in:
○ Marketing, sales, product, & CX efforts
6
*Think Like An Economist: emperitas.com/lecture
**Calculating Your CLV: goo.gl/rpu7iV

7. Typical Persona Development
7
● HubSpot has a great tutorial for creating
personas within their platform.*
● You enter all the information, which begs
the question: where does info come from?
○ Right now mostly from the SWAG method.
*How To Create Personas in HubSpot: goo.gl/k8Fyzg

8. Base It On Behavior
8
● Math-based personas are the antithesis
of the SWAG approach.
○ They use data & formal rules (usually statistical)
to identify your customer personas for you.
● They’re most accurate (and predictive)
when based on behavioral data.

9. Why Use Agile Research?
9
● Agile methods work well because they’re
highly responsive, allowing you to pivot.
○ Proactively solve problems before they happen.
● Sprints keep you focused on what matters,
and that can change in an instant…
○ It requires you to make progress (sempre avanti).

10. Agile Analytics
● The entire agile research process was laid
out in Customer Research for PM’s lecture.*
● You’ll have a lot of observational data to
use which doesn’t require research.
○ It should still use a system of agile sprints.
10
*Customer Research for Product Managers: goo.gl/gfyUZP

11. 11Persona Template & Data

12. Visualize & Publicize*
12
● Visualize your personas (use pics)
to quickly express key information.
● Make these visible to everyone.
○ Have a system to collect feedback
about usefulness of these personas.
○ It’s not a one-and-done process.
*Emperitas Persona Example: goo.gl/TGk4t8

13. Organization Is a Must
13
● To create personas you need a system:
○ Be organized with the 30-Second Rule*
○ Be focused with SMART Goals**
● Today’s required reading (see slide 28)
outlines 5 methods to choose from.
*30-Second Rule (00:22:08): youtu.be/bZlNO9E-zz8
**SMART Goals (00:01:15): youtu.be/VqMCK7Whyd4

14. Audit & Inventory
14
*Data Mapping Customer Touchpoints: goo.gl/p33xv5
● Chances are you’ll pioneer breaking down
data silos to create profiles & personas.
● Inventory your customer touchpoints* at
the same time you hunt for usable data.
○ This will also require you identify DRIs, KPIs, &
milestones (all useful for your SMART goals).

15. Go With The Data Flow
15
● Personas require data (from silos) flow in to an
environment with clear access management.
● All customer data fits into 4 data dimensions*:
○ Product Usage Preferences
○ Price Sensitivity
○ Marketing Engagement
○ CX Research
*Creating Data-Driven Customer Profiles: goo.gl/o1v972

16. Potential Data Sources*
● Observational:
○ Website visits, email behavior
○ Logins to an app, feature usage
○ Social media posts
● Survey/Experiment:
○ App store reviews
○ CSAT & NPS surveys
○ Experimental design (divisor differentials)
16
*Interpret Data Like a Pro (00:04:13): youtu.be/SirK0SSBeZg

17. Visualize & Publicize*
17
● Visualize your personas (use pics)
to quickly express key information.
● Make these visible to everyone.
○ Have a system to collect feedback
about usefulness of these personas.
○ It’s not a one-and-done process.
*Emperitas Persona Example: goo.gl/TGk4t8

18. 18Behavioral Personas

19. Don’t Even Demographics
19
● Demographics are the worst thing to create
personas based on. Choice is much better.
● With a persona assignment made, then you
can describe that persona demographically.
○ Difference is, persona was built based on behavior.

20. Garbage In, Garbage Out
● Data quality is something you’ll have to
improve upon from day one of your project.
○ Use what you have, it’s better than guessing.
● With behavioral data you also have to
consider if what you’re seeing is construed.
○ People behave differently when they’re watched.*
20
*The Power of Being Watched: goo.gl/kzd6BS

21. Analytical Methods
21
● Regardless of the data available, 2 analytical
methods are useful for identifying personas:
○ Dimension Reduction - from a set of 20 variables, which 5
can be used to predictably capture the behavior of all 20?
○ Clustering - how can we find groups of customers who
are similar within group & dissimilar between groups?*
*Creating Data-Driven Customer Profiles: goo.gl/o1v972

22. Divisor Differentials
22
● What follows is an (updated) analytical method bequeathed
to Emperitas by the now extinct Modellers (RIP).*
● As a survey-based experimental design, it has assumptions:
○ Self-reported data (they aren’t lying or confused)
○ Choices used are clear trade-offs or are mutually exclusive
○ No fence-sitting is possible
○ Extremes are predictive of choice
*Modellers: hallandpartners.com/the-modellers/

23. Divisor Differentials
23
-5 -1 +1 +5Choice 1-A Choice 1-B
Below is an example divisor exercise. Choices from each pair are positioned on either side of
4-button options and the customer is asked to place themselves between the two choices. While
the coded values aren’t seen by the customer, an even number of options means no fence-sitting.
If a choice completely explains the customer, it’s given more weight in the analysis (-5, +5).
Divisor Differential

24. Not All Choices Matter
● You’re trying to measure how customers
make choices, not any single choice.
○ You can find a subgroup of choices that matter.
● Of 20 choices, 5 can be used for future
classification models (with a high hit rate*).
○ You can find them through dimension reduction.
24
*Hit Rate Defined: goo.gl/fxDhnZ

25. Next Frontier: SIMULATION
● Personas are identical to “representative
agents” in economics.*
○ Can be used in “agent-based modeling.”
● Through research you can get parameter
estimates for models of personas and
predict behavior with far more accuracy.
25
*Sales Hacks with Market Research: goo.gl/rPyk63

26. 26What We Covered Today

27. What We Covered Today...
● The benefits of using personas.
● A persona template and its data.
● How to create behavioral personas.
27

28. 28Required Reading

29. You Should Read - [5 Guides for Creating Customer Personas]*
Authored by Spencer Lanoue, Growth Marketing Manager at Buffer.
Cliff Notes: There are many approaches to developing customer
groups into actionable personas, but pick one that’s based
on data. This requires a combination of art and science,
and you can avoid common pitfalls by using a formal system.
29
*5 Guides For Creating Customer Personas: goo.gl/Ncp5f8

30. 30At The Next Lecture...

31. Thursday June 7th, 2018
emperitas.com/lecture