Most companies have a goldmine of data, yet lack the ability to know what to do with it. In this talk, Monica shared perspective on how to evaluate data, package it, and turn it in to additional revenue streams. Main takeaways: - Identify use cases for data. - Turn those use cases in to product offerings. - Create a pricing model & collect revenue.

1. How to Turn Raw Data into Product Revenue
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7. Monica McCool
TONIGHT’S SPEAKER

8. Turning raw data into
revenue
How to evaluate data, package it, and create additional revenue streams

9. The problem
Company
Any company that stores
and/or exchanges data.
Context
Leverage data that is a by-
product of doing business
to create new value.
Sometimes known as “data
exhaust” or “dark data”.
Problem statement
Most companies have a
goldmine of data, yet lack
the ability to know what
to do with it.

10. Applications
Companies
● Retail
● Transportation/
logistics
● Utilities/telecom
● Healthcare
● Financial services
● Software
● Business services
● Insurance
Data types
● Customer
● Transaction
● Product
● Location
● Weather
● Social media
● Telemetry
Outcomes
● Personalizing
experience
● Benchmarking
competitive position
● Optimizing process
● Predicting outcomes
● Mitigating risk
● Improving sales

11. Ideas into action
Research the market
Determine the job to be
done & focus on outcomes
● Step 1: Plan outcome-based
customer interviews
● Step 2: Capture desired
outcomes
● Step 3: Organize the
outcomes
● Step 4: Rate outcomes for
importance and satisfaction
● Step 5: Use the outcomes to
jump-start innovation
Create the product
Build the right “it” before
you build it right
● Step 1: Build pretotype
● Step 2: Validate pretotype
● Step 3: Design product tiers
& pricing
● Step 4: Get the fine print
right
● Step 5: Deliver minimum
loveable product
Take it to market
Launch in waves
● Step 1: Prep internal teams
for launch
● Step 2: Release to friendly
design partners
● Step 3: Refine & optimize
● Step 4: Expand features, to
friendly design partners,
refine & optimize
● Step 5: Go big!

12. Where do I begin?
http://radar.oreilly.com/2013/04/why-why-why.html

13. No really, where do I begin?
While most companies innovate by
trying to improve their existing
products (creating a better quarter-inch
drill), the innovation process is
dramatically improved by instead trying
to find better ways to create a quarter-
inch hole (to get the job done).
The implication of this thinking is
profound: stop studying the product and
instead study the job that people are
trying to get done.

14. Jobs-to-be-done template
https://www.linkedin.com/pulse/20140929071128-6657924-the-customer-jobs-to-be-done-canvas-
prototype/?trk=object-title&trk=object-title

15. Pricing
Inputs
Inputs in to determining
price:
● The cost to build/maintain
your product without data
(COGS - cost of goods sold)
● The cost to provide the
data/analytics
● The price you currently
charge for your product(s)
● The price your best
competitor charges for
data/analytics (if this exists)
Model it
Fit data/analytics into
your existing pricing
models, such as:
● Flat, recurring fee
● Transaction volume
● Per user
● Percentage of asset base
Test in market
Provide options that will
fit in to a good - better -
best model:
● Field(s)
● Scope of data
● Age of data
● Pre-built calculations,
summarizations,
visualizations
● User types - e.g. viewers,
readers, etc.

16. Case Study
Vertafore
Disrupt the
Market
with
Analytics
.

17. The problem
Company
Vertafore is the leading
provider of insurance
software and technology,
connecting independent
brokers with carriers at
every point of the
distribution channel.
Data source
With the foremost
comparative rater in the
US, Vertafore delivers
real-time personal lines
insurance ratings .
Solution
Leveraging its wealth of
big data, Vertafore wanted
to launch a premium data
analytics product to
provide agents and
carriers with KPI
visualization and
competitive intelligence to
help them manage against
competitive threats.
https://youtu.be/bRxOt6_Whp8

18. The data ecosystem
● 20,000 agencies
● 1,600 carriers
● 230m+ policies
● 123m+ real-time
transactions
annually and
growing...

19. Deriving value from data

20. Jobs to be done
Personas:
Carrier
● Actuaries
● Analysts
● Product managers & marketers
● Business execs
Agency
● Producers (Sales/sales management)
● Marketers
● Agency principals (business execs)
Questions all personas had in common:
● How am I performing now?
● How do I compare to peers?
● Where do I grow?
What matters:
● Geography
● Accuracy
● One version of the truth
● Timeliness
● Ability to know what next step is

21. Adding a face to data
“The greatest value
of a picture is when
it forces us to notice
what we never
expected to see.”
-John W Tukey (exploratory data analysis)

22. The solution

23. Example - estimate market size

24. Example - see how I am performing

25. Pooling data in the lake

26. Putting the tech stack together

27. How we actually priced it
Raw data My data Me vs. the market
1 Good
2 Better
3 Best
Rule of Thumb:
Data that has been
cleaned, standardized,
enhanced, visualized, etc.
is worth 2X of raw data

28. Impact
“Being able to take the data, put a
face on it and deliver it at scale, has
helped customers expand their
understanding of the value of
Vertafore’s footprint in the insurance
industry.
Vertafore Analytics provides a
common ground between both
business and technical users and
provides a vehicle for insight in
whatever format users want ... with
one version of the truth.”

29. Outcomes
Increase in average deal size
Carrier market average deal size increased
on average more than 50%, including
annual recurring revenue
50+%
Validated business model &
technical approaches
Starting with a limited focus allowed both
business and technical teams to work
iteratively, test, and validate along the way
100%
Room for growth on other
side of the business
With the carrier market firmly established, it
could continue with care & feeding while
turning focus to the agency market,
leveraging what was already built
100%

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Create value
Arm customers with data that gives
them an edge in competing in the
marketplace - and make it easier than
going to their internal IT groups to get
the information. In short, speed the time
between insight to action.
Keep it legal
Partner with legal to
mitigate risk
Deliver uniqueness
Figure out what you have
that no one else does
Make it high quality
Ensure data is clean & valid
Don’t share it all
Hold on to “special sauce”
and use it for consulting
engagement
Implications

31. Q&A

32. Thank you!
Sources
● http://radar.oreilly.com/2013/04/why-why-why.html
● https://www2.deloitte.com/insights/us/en/focus/tech-trends/2017/dark-data-analyzing-unstructured-data.html
● //www.birst.com/resources/cs/vertafore-bi-product-insurance/
● https://blogs.thomsonreuters.com/answerson/five-lessons-learned-data-exhaust/
● http://www.pretotyping.org/
● http://knowledge.wharton.upenn.edu/article/turn-iot-data-exhaust-next-competitive-advantage/
● https://hbr.org/2016/07/using-digital-exhaust-to-improve-sales
● https://www.birst.com/wp-content/uploads/2015/11/Birst-Beyond-the-Technical-the-complete-guide-to-embedded-analytic-products.pdf
● https://www.birst.com/wp-content/uploads/2016/10/Birst-Monetizing-the-Data-Exhaust-Top-10-Industries.pdf
Contact
https://www.linkedin.com/in/monica-mccool/
mccoolmonica@gmail.com

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