Juice's Data Monetization Workshop helps product managers and business leaders consider the opportunities and challenges of transforming their valuable data into customer-facing products.

1. Turning Data into Dollars
Juice Analytics
October 2014
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4 Steps to
Data Monetization
3. Designing a data product
2. Data meets audience
1. Data as a product
4. Launching a data product

3. TOPIC 1 DATA as a PRODUCT
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Competing on Analytics (Analytics 1.0)

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Turning data inside out
3.0
marketing
operations
finance
sales
suppliers
partners
customers
Extended
enterprise
Enhanced
solutions
New data
products
Analytics 1.0
the organization

6. Not something new…
In healthcare alone, there is an estimated $300B to $450B in annual
cost savings that can be achieved through data applications (1)
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The Data Product Market
(1) The big-data revolution in US health care: Accelerating value and innovation,
April 2013, McKinsey & Company

7. Feature-rich
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Data products vs. traditional analytics
Web based
Research report
Dashboards
Internal External
Delivered as...
Interactive
Static
summary
Target audience...
Reporting
Data products
Interactive, web-based
solutions sold
independently or delivered
as part of another solution.
Solve
specific
problems

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Data Product Types
New
product
Product
enhancement
Direct Revenue Indirect revenue
Overt Covert
Directed Exploratory

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US News: New revenue stream

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LinkedIn: User engagement
Volume

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ADP: New revenue stream

12. Exercise 1: Data as a product
Brainstorming data product concepts for you or your organization. For each
one, describe the problem it solves.
Data product concept What problem does it solve?
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13. TOPIC 2 DATA meets AUDIENCE
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Many audience, unique needs

15. Raw materials Finished product
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Make them a hero

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How do you make your customer a hero?
How do they socialize data?
How can they impress
their boss?
What can they do in
their role?
What can’t they do?
How does data fit into
their everyday work?
How can data influence
decisions in their part
of the organization?

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Benchmarks, comparison, and context

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Making the data valuable
3rd party data sources
Predictive modeling
Calculated metrics
What’s this calculated metric?

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Data Readiness Checklist
Do you have rights to the data?
Can the data be accessed in an automated fashion?
Is there personal data that should be obscured?
Can the data be presented at a level of summarization/granularity that
the audience will find most useful?
Does the data need to be transformed to make it useful?

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Example: Predikto

22. Exercise 2: Data meets audience
Fill out the table for each of your favorite 2-3 data product concepts
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Data product
concept Who is it for? How can the
data help?
Is the data
ready?
How to add value
to the data?

23. TOPIC 3 DESIGNING your data
product
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24. purposeful design
leading to action fit users workflow
right-sized
socialize-able
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What goes into the design of a product?
form follows
function
guidance and
storytelling
Self-serve, not self-solve

25. Interesting < Useful < Actionable
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Designing for action
“neat” “good to know” “I’ll get right on that”

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Guidance and storytelling

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Apply Context
284 +4.5% v. last month
-2.8% v. goal
in Q3 2009
the new “Vortex” marketing campaign drove
new leads after a slow Q2
400
300
200
100
0
Goal Actual
Q1 2009 Q2 2009 Q3 2009 Q4 2009

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Example: US Chamber of Commerce

29. Exercise 3: Designing your data product
For your one or two of your favorite product concepts, answer the following
questions:
Concept 1 Concept 2
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In what form would the data
product be delivered?
How does the product fit into
in the user’s workflow?
What specific actions can it
drive?
How can you help users
share the data/insights?

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TOPIC 4 Launching a data
product

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Selling
Volume
How will it be sold/distributed?
What’s needed to help sell it?
How will you explain it?

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Delivering
Volume
How will user receive it?
Who is delivering?
How do we make delivery successful?

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Supporting
Volume
Who will answer questions, comments, etc.?
How will the product be maintained?

34. “Data isn’t like kids you don’t have to
pretend to love them equally.”
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Amanda Cox, NY Times

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Think Lean
Volume

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Lean Canvas
Volume

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Volume

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Example: The Essential Economy

39. Exercise 4: Launching a data product
Provide a brief description of the data product that is most promising and
describe the key features that represent the minimum viable product.
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Data product description
Minimum viable product
features

40. Data Product Examples
DataSift - licenses data from Twitter to aggregate, process and deliver social data to enterprises about
their brands.
ADP - provides a monthly snapshot of U.S. nonfarm private sector employment based on actual
transactional payroll data
Payscale.com - links individuals and businesses to the largest salary profile database in the world.
Glassdoor.com - Influence job seekers at the moment they are making the decision whether or not to
work for you
Indeed.com - Reports for economic forecasting or stock trading models.
Compete.com - compare publishers to buy the most efficient traffic
Alexa.com - compare publishers to buy the most efficient traffic
Quancast.com - compare publishers to buy the most efficient traffic
Zillow.com - Home values (real and estimated) for all houses in US
ProjectHoneyPot.org - Black lists of IP addresses or email addresses used in fraud, Botnet activity or
forum spam.
FICO - Selling scores, such as click scores or any other scores. FICO was one of the first companies to
do so.
Skift - provide you with the latest intelligence on travel trends.
SuperData Research - Data-driven market intelligence on online, mobile and digital games
Factual - Data on over 600,000 consumer packaged goods in a UPC centric database with ingredients
and nutrition information
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Additional reading
1. Data Jujitsu by DJ Patil.
2. The evolution of data products by Mike Loukides.
3. John Foreman blog - MailChimp data scientist
4. Juice Analytics.com Product Manager’s Checklist

42. A decade of guiding businesses through the process of designing,
creating and launching new data products
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About Juice Analytics
Design and launch
data products
Our B2B clients (from
start-ups to Fortune 500)
have launched profitable
products with our design
and technical guidance
Fruition:
Data Product Platform
Data visualization
components designed for
non-technical end-users
Features for sharing and
collaborating on data
insights and analysis
Recognized thought
leaders
Available early
November

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Thank you
@juiceanalytics
info@juiceanalytics.com