Frustration and analytics can go hand in hand. After spending hours exploring your data, running tests of difference, finding measures of association, and building predictive models you’ve hit a wall: how to turn all this information into actionable insights everyone can understand. This Lecture Will: -TEACH YOU HOW TO USE ANALYTICS TO ANSWER BUSINESS QUESTIONS. -EXPLAIN THE BEST PRACTICES WHEN VISUALIZING COMPLEX DATA OUTPUT. -SHOW YOU HOW TO PRESENT INSIGHTS THAT YOUR TEAM WILL UNDERSTAND AND ACT ON. You can watch this lecture here: https://youtu.be/JH-MCo-Xh5g

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

2. Hi. I’m Luciano Pesci…
Co-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 most profitable customers.
Founder & Director, Utah Community Research Group, Univ. of Utah
● Teach microeconomics, statistics, applied research & data analytics, & American economic history.
● Teach data science for Westminster and developed their MBA emphasis in data science.
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3. Today’s Lecture Outline
● Teach you to use analytics to answer biz questions.
● Explain best practices when visualizing complex data.
● Show you how to present results your team will use.
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4. 4FROM ANALYTICS TO ANSWERS

5. Have a Goal (already)
● The best way to ensure you deliver
insights that will be useful is to have
VERY specific business goals for the data.
● This is explained in our “Getting To Quick
Wins With Data” lecture using the
S.M.A.R.T. goals method.
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6. Use Frameworks
● Analysts get tunnel vision because they take for
granted all the data details they already know.
○ Like the context, correlations & differences.
● Using a Framework forces you to organize your
results into a clear narrative that non-analysts
can understand.
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7. Most Effective Frameworks
● Three highly effective frameworks for
organizing analytics into impactful narratives
(that people can act on) are:
○ Customer Lifetime Value (next month’s lecture)
○ Customer Personas (this lecture)
○ Customer Journey Mapping (this lecture)
■ Also explained in our “Hacking Your Customer
Journey” lecture (available on YouTube).
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8. 8VISUALIZING COMPLEX DATA

9. Tell A Story With The Data
● Thinking Broadly – Capture all relevant info & data.
● Mining Deeply – Use powerful analytics.
● Explaining Simply – Translate into plain English.
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10. Simpler is Better
● Avoid stats-y jargon & synthesize everything to it’s
absolute minimal form (without losing any truth).
○ Use plain english (or whatever verbal language of your choosing)
○ If you can’t explain your results to an 8-year old it’s too complicated.
○ Don’t show p-values just state if the results are reliable or not.
○ Brevity is best.
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11. Use Clear Visuals
● 2 of 3 people (including those on your team)
will be visual learners.
○ People can process visual information 60,000x
faster than text.
● Use visualizations like charts or images that
reinforce the conclusions of your analysis.
○ This is explained in “Interpreting Data Like a Pro.”
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12. 12PRESENTING USEFUL RESULTS

13. Quality Signals Reliability
● Quality is one of the few things you can
completely control, it’s also very impactful.
● No matter how great your analysis is, if your
deliverable is ugly your team will discount
the information (if only subconsciously).
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14. Be Explicit
● Explicitly tie your results to
the original business goals.
○ These are the rows from the “goals sheet.”
● This will remind your team what they
wanted to learn, & how to use the info.
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15. Build To Outlast Yourself
● Whatever results you present should be easily
understandable to anyone who won’t have you
there to explain it to them.
○ This is the real test of your tunnel vision.
● Everything necessary to replicate & reference
your results should be available to your team.
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16. Always Make Recommendations
● Don’t take an arms-length approach to analytics.
○ It doesn’t make you objective, it just makes you less
effective for your team.
● The ultimate act of synthesizing analytics to
answer business goals is making clear
recommendations for action.
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17. 17WORKED EXAMPLE (continued)

18. What’s the Biz Goal?
● A festival organization needs to learn what
drives their customer lifetime value, and how
to increase it to drive more profit.
■ They’ll change marketing targets, spend, and
channel mix based on this info (if they get it
before next season).
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19. What’s The Data Used?
● The example data comes from a survey of festival goers (aka customers)
and was linked to observational data about their
lifetime ticket sales.
● It’s a cross-sectional sample (n=3,834) since we
don’t have every festival customer’s feedback and
the data was captured at a single moment in time.
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20. What We Already Know About CLV
● Most festival customers have been attending for less than 10 years, but
there’s a small group that’s been coming for more than 20.
● Festival customers are unlikely to come alone, they’ll buy 4 tickets,
and virtually all are likely to recommend the festival.
● The average CLV is $486 and 80% of all CLV
comes from just 20% of festival customers.
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21. Picking A Profitable Persona
● The “Pareto Persona” was chosen
because it represents customers
with the highest CLV (and profit).
○ 80% of all CLV is attributable to these
individuals, who make up 1 in 5 customers.
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22. Pareto Persona Profile: Paula
● Paula is female over 65 years of age and has a Master's Degree.
● Her Median Household Income (HHI) is $125k.
● She’s been attending for more than 20 years.
● Her CLV is $3,500 and she attends in August.
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23. Predictive Persona CLV Model
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24. Customer Journey: Awareness
● Paula finds out about the festival through email,
the festival website, and printed brochures.
○ She’s on Facebook infrequently, but does
recall seeing ads there.
● She finds out about the festival
schedule primarily through the website.
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25. Customer Journey: Purchase
● Typically buys 8 tickets per visit, and has a
Customer Lifetime Value of $3,500
○ This is ~10x higher than the average customer.
● In addition to festival tickets, she buys
backstage tours.
● She donates $100 to the festival regularly.
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26. Customer Journey: Growth
● She’s 99% likely to attend again next year.
○ The present-discounted value of her future ticket sales is $392.
● Paula is highly engaged with the festival and
recommends often (a non-monetary add to her CLV).
○ Her Net-Promoter Score is 90%.
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27. Actionable Recommendations
● Increasing CLV
○ Offer a discount on 2 additional tickets when she purchases 8.
○ Incentivize her to recommend in exchange for a free backstage tour.
○ Ask her for a second $100 donation each year.
● Targeted Marketing
○ Use facebook for targeted ads based on Paula’s demographics.
○ Create & optimize an online sales funnel through the website schedule.
○ Email her in June & July to maximize likelihood of an August attendance.
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28. JOIN US FOR THE NEXT LECTURE
Calculating Your Customer Lifetime Value, Thursday November 9th 2017
emperitas.com/lecture