Sales is the lifeblood of any organization, and in today’s increasingly data-driven world, sales teams are often the last to adapt and change to a data-driven strategy. The skepticism of sales teams is likely due to lack of data scientists failing to deliver insights that are digestible to sales teams and that sellers can take action from. Fortunately, becoming a data-driven sales team isn't impossible, it just requires the right mix of human data-detective work and a touch of automation to create a scalable system to deliver leads that convert at higher rates and at higher total value to an organization. This Lecture Will: -TEACH YOU THE ROADBLOCKS SALES TEAMS HIT WITH DATA. -SHOW YOU DATA TYPES & USES FOR BETTER SALES CONVERSIONS. -EXPLAIN HOW TO BECOME A DATA-DRIVEN SALES LEADER. You can watch this lecture here: https://youtu.be/noIjGerm3eE

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 you the roadblocks sales teams hit with data.
● Show you data types & uses for better sales conversions.
● Explain how to become a data-driven sales leader.
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4. 4Sales Data Roadblocks

5. A Critical Middle Position
● Sales sits at the critical midpoint in the
customer’s journey.*
○ It also provides revenue for the whole organization.
● But sales data is often messy, incomplete,
and unusable for finding actionable insights
& building predictive models.
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*Customer Journey Mapping: goo.gl/5wY3Sd

6. Got Data?
● Virtually all roadblocks sales teams
hit fall into 2 distinct groupings:
○ Problems resulting from the lack of data.
○ Problems using the data that’s available.
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7. Lacking Data Problems
● Many sales teams still run on gut alone and
don’t utilize data. Their usual roadblocks are:
○ They don’t believe in the power of data.
○ Everything they do happens offline.
○ They don’t know which data is worth collecting.
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8. Data Usage Problems
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● For sales teams with data, the typical
roadblocks they experience are:
○ Dealing with unstructured data.
○ Using form fields for entry to the CRM.
○ The time series nature of sales data.

9. Dashboard & AI Dependency
● Depending on the organization’s position
in the data maturity stages* there are 2
additional roadblocks with sales data:
○ Dependency on dashboard solutions.
○ Dependency on one-size-fits all AI solutions.
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*Data-Driven Plan of Attack for 2018: goo.gl/9iVvMb

10. A Siloed Mentality
● Sales may be the most critical stage in
the customer journey, but it’s only one
part of the overall customer experience.
● Creating information feedback loops with
other departments provides necessary &
better data to increase sales conversions.
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11. Another Way To Look At It
● Pushing through these roadblocks to become a
data-driven sales team creates exponential
gains for the entire organization.
● More details about each of these roadblocks
are laid out in my recent article.*
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*The Problem With Sales Data: goo.gl/ds1QdG

12. 12Sales Data & Use Cases

13. Differing Definitions of Data*
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*Data Types (00:02:34): youtu.be/SirK0SSBeZg
● There are many ways to define data, each
requires a different approach when utilizing it:
○ Origin - How it was created.
○ Totality - If it’s a sample or a census.
○ Scope - Whether it’s been captured over time.
○ Measurement - How it was quantified.

14. Data Measurement?
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● All data fits into 4 basic types
based on how it was measured:
○ Nominal & Ordinal = CATEGORICAL.
○ Interval & Ratio = CONTINUOUS.
● Identifying data types is a
critical skill to develop.
○ Analysis selection &
interpretation depend on it.

15. Data Use Cases For Sales
There are 5 powerful use cases for sales data,
each touching a distinct part of the process:
● Lead Qualification Scoring
● Outreach Cadence & Messaging
● Customizing The Pitch
● Predicting Conversions
● Nurturing Lost Deals
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16. Use Case #1: Lead Qualification Scoring
● Lead scoring can improve qualification by 80%.
○ You don’t stop contacting every lead, you just
do it in a strategic way.
● This requires marketing data from the
awareness stage of the customer journey.
○ You should also include publicly available data.
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17. Use Case #2: Outreach Cadence & Messaging
● Optimizing the message, channel mix,
and timing of your outreach can
lift conversions by 40% or more.
● This requires a lot of A/B testing
and benefits from adding competitive
intelligence and marketing data.
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18. Use Case #3: Customizing The Pitch
● Like with outreach, it’s possible to A/B test
everything about the sales pitch and find
the key drivers behind conversion.
● Behavioral data captured during the pitch
(via observation) is extremely powerful.
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19. Use Case #4: Predicting Conversions
● This is the ULTIMATE goal for your sales data.
● Ideally, you’d be able to predict a conversion
as early as the qualification stage.
○ This often requires multiple models because of the
differing availability of data at each sales stage.
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20. Use Case #5: Nurturing Lost Deals
● No matter how good you are, or how much
data you have, you can’t win every deal.
● Nurturing strategies can be optimized.
○ Requires data on the reason for a loss, which is
best done with exit interviews (this is the topic of
our next lecture).*
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*Sales Hacks with Market Research: emperitas.com/lecture/

21. 21Data-Driven Sales

22. Standardize & Automate
● The first and most important step is to use
automation to standardize the collection &
imputation of your data to the CRM.
● Bucket your data by what it impacts:
○ Product Features, Pricing, Messaging, & Channel Mix.
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23. Get A Handle On Time
● The sales process plays out in distinct
stages over time, which means you have
to start incorporating time into analysis.
● Seasonality by month, week of month,
day of month, day of week, and even hour
of day can all be found in your data.
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24. Deal With Unstructured Data
● Though it takes a special set of skills,
utilizing unstructured data offers a
massive return on investment.
● For text data in particular, use
natural-language processing (NLP).
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*The Value In Unstructured Data: goo.gl/RAr7wa

25. Using Frameworks
● You have to decide how to organize and interpret
your data or you’ll get lost in it.
● Frameworks like the Customer Journey, Personas,
and Customer Lifetime Value force you to focus
all your data on things with high impact.
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26. ● Total value of a customer from first purchase to
churn. Requires historical data & future prediction
(to get the present discounted value till churn).
○ Includes monetary & non-monetary components.
● Pareto Principle** means there’s a pareto persona.
Customer Lifetime Value*
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*Calculating Your CLV: goo.gl/rpu7iV
**Pareto Principle (00:04:16): youtu.be/pyNrxUB-tBc

27. Create Feedback Loops
● Every department in the organization
complains about their inability to
work effectively with other teams.
● Sales sits at the crossroads between
marketing, product/ops, and CX.
○ Lead the charge to create information sharing.
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28. 28What We Covered Today

29. What We Covered Today...
● The roadblocks sales teams hit with data.
● Data types & uses for better sales conversions.
● How to become a data-driven sales leader.
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30. 30At The Next Lecture...

31. Thursday March 29th, 2018
emperitas.com/lecture