From Analytics Into Actionable Insights - Dawn of the Data Age Lecture Series
- 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. 2
- 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. 3
- 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. 5
- 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. 6
- 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). 7
- 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. 9
- 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. 10
- 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.” 11
- 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). 13
- 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. 14
- 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. 15
- 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. 16
- 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). 18
- 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. 19
- 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. 20
- 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. 21
- 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. 22
- 23. Predictive Persona CLV Model 23
- 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. 24
- 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. 25
- 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%. 26
- 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. 27
- 28. JOIN US FOR THE NEXT LECTURE Calculating Your Customer Lifetime Value, Thursday November 9th 2017 emperitas.com/lecture