Personalization is consistently ranked as one of the most important things customers want in a product or service. To successfully move beyond an average understanding of who your customers are, you need data-driven profiles. The more customer data you can gather for creating these profiles, the more accurate they can be using advanced analytics like clustering. But beyond the fancy math, with these data-driven profiles you can deliver the personalization your customers crave while making your organization more profit. This Lecture Will: -TEACH THE BENEFITS OF PROFILING YOUR CUSTOMERS. -SHOW METRICS FOR CREATING ACCURATE PROFILES. -EXPLAIN ANALYTICS USED TO CREATE DATA-DRIVEN PROFILES. You can watch this lecture here: https://youtu.be/g7SM9USK448

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

2. Hi. I’m Luciano Pesci…
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.
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3. Today’s Lecture Outline
● Teach the benefits of profiling your customers.
● Show metrics for creating accurate profiles.
● Explain analytics used to create data-driven profiles.
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4. 4Benefits of Profiling

5. What’s In A Profile?
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● It’s the most complete collection of data
you can create for an individual customer.
● It differs from a “persona” which is an abridged
version of the profile you relate to (and act on).
○ You don’t use all the profile data to create a persona.

6. Personalization is “Why”
● The reason you profile your customer is
to offer the personalization they want.
○ This is consistently ranked among the top
drivers of better customer experience (CX).
● If you go too far you can seem creepy.*
○ “Creeper threshold” differs for each customer.
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*Personalization Becomes Creepy: goo.gl/o1rHHu

7. 4 Customer Types
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● Profiling shouldn’t just be done for your own
customers. There are 4 customer types to profile:
○ Your customers (past & present)
○ Competitor’s customers (past & present)
○ Nobody’s customers (future)
○ Never customers (present)

8. Framework Effects*
● Using frameworks to organize & interpret
data ensures you capture all the benefits
of your customer profiling.
● CX teams are already familiar with two of
the best: Customer Journey & Personas.
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*Frameworks (00:42:35): youtu.be/fCbTTjvDXLE

9. CX Needs CLV
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● The single most important metric you can
calculate from your customer profile data
is Customer Lifetime Value (CLV).*
● It’s an actionable metric for optimizing
marketing, sales, product, & CX around
“best” customers (aka highest value).
*Calculating Your CLV: goo.gl/rpu7iV

10. A Quickly Moving Target
● Capturing CX benefits from personalization
using your customer profiles is neverending.
○ It’s not “one and done” process.
● Customer preferences are changing
constantly, and at an increasing pace.
○ In econ this is “intertemporal substitution.”
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11. 11Metrics for Profiling

12. Another Way To Look At It
● Net Promoter Score is a tyrant in CX metrics.
○ It should be in customer profiles, but shouldn’t be alone.
● More details about breaking NPS dependency
are laid out in my recent article.*
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*Data-Differentiating Your CX Strategy: goo.gl/VHfAZx

13. 4 Data Dimensions
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● All the data you collect for customer profiles
fits neatly into 4 data dimensions:
○ Product Usage Preferences
○ Price Sensitivity
○ Marketing Engagement
○ CX Research
● These all fit the Customer Journey Framework.*
*Customer Journey (00:31:37): youtu.be/g8UXdIchqrw

14. Product Usage Preferences
The data necessary to answer questions in
the product usage dimension can come from:
● National product data
● Observational events data
● Purchase & CRM data
● Technical support data
● Primary research data
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15. Price Sensitivity
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Data for the price dimension mostly comes
from proprietary systems & primary research:
● National pricing data
● Purchase & CRM data
● “Willingness to Pay” data
○ DCM & Q-Sort* experiments
*Qsort: wikipedia.org/wiki/Quicksort

16. Marketing Engagement
Marketing has significantly more data than
any other department for their dimension:
● Competitive intelligence data
● Digital ad performance data
● Social (profile) data
● Website pathing data
● Offline & Traditional ad data
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17. CX Research
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● Primary research (the act of generating your own
new data) is a workhorse for the CX data
dimension, and comes in two basic flavors:
○ Qualitative methods* can produce data faster.
○ Analytics (like DCM) require quantitative surveys.**
*Customer Conversations (00:09:05): youtu.be/CO_JlBDZgNs
**Step Up Your Survey Research: goo.gl/8622fc

18. 18Analytics for Profiling

19. Understanding the Individual
● The ultimate focus of your analytics is
understanding the complete customer
profile across the 4 data dimensions.
● This is why you profiled, and it will allow
you to personalize (without being creepy).
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20. Settling for Groups (for now)
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● You won’t be able to build complete profiles on
every single customer; you’ll always be sampling.
● The most powerful group you can create from
the customer profile data are personas.
○ You can run simulations with them.

21. As Same, But Different
● Even individuals are predictable.
● Actionable personas from your profiles can
be found with clustering methods.
○ These maximize similarity & minimize differences
across individuals (using the 4 data dimensions).
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22. Differing Clustering Approaches
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There are a wide range of analytical methods
available for clustering (and classification):
● Correspondence Analysis
● Ward’s (hierarchical)
● K-Means & K-Nearest Neighbor
● DB Scan*
*DB Scanned Simply Explained: youtu.be/5E097ZLE9Sg

23. Face-ing Personalization
● The groups that result from your
clustering need a human face.
○ Otherwise you’ll fail to act (rightly) on the info.
● This is where the persona framework
should be used to describe the data.
○ The bellwether of success is how much
empathy you can feel for the persona.*
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*Empathetic CX: cxuniversity.com/cxublog-back-to-basics-on-empathy/

24. Automated Profile Insights
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● Once personas are built from the profiles, you
need to share it with the rest of the organization.
○ The CRM is the first place to start.
● You can use the data from the 4 dimensions you
clustered to build real-time predictive tools
for continually assigning customers to a persona.

25. 25What We Covered Today

26. What We Covered Today...
● The benefits of profiling your customers.
● Metrics for creating accurate profiles.
● The analytics to create data-driven profiles.
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27. 27At The Next Lecture...

28. Thursday April 26th, 2018
emperitas.com/lecture