1. © 2013 Quant5, Inc.
How to apply
Predictive Analytics
to
Marketing Challenges
For the Planning-ness Conference
May 10th, 2013
Doug Levin | doug@quant5.com

2. © 2013 Quant5, Inc.
Agenda
Planning-ness Approach ~Time Allocation
(Minutes)
Teaching 45
Putting teaching into practice 45 – 60
Evaluation and discussion 20-30
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Teaching
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Challenges Facing Marketing
• Frequently make critical decisions without:
– The information they need
– The insights in their business & environment they need
– Access to data in other parts of their organization
– A supporting cast & crew (aka data scientists)
• Hours are spent each week searching for data
– “Connecting silos”

5. © 2013 Quant5, Inc.
Challenges Facing Marketing Departments
• Roadblocks to Success:
– Being asked to come up with brilliant new insights
• Shortage of data scientists to do statistics, math, etc.
• No tools
– An avalanche of data from mobile, social media and other
sources… and growing by the minute!
– Data located in legacy systems run by IT
• Have to connecting silos through organizational means not direct
reporting authority
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6. © 2013 Quant5, Inc.
How ambitious are you?
• Do you want to have a “data centric” business?
 Business decisions no longer based on gut instinct
• Do you want to have a “data centric” marketing dept.?
Fact-driven relying on
measurement & feedback
Real-time data
At the point of impact
Everyone’s
Involved &
Connected
Ubiquitous
optimization
Automated
Relying on predictive analytics & validation
Characteristics
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Data in your organization that can help you…
discover trends and opportunities
• The top 5 sources of data tagged for predictive
analytics:
• 54% Sales
• 67% Marketing
• 69% Customer
• 55% Product
• 51% Financial
In addition, 40% of companies surveyed indicated that “Social (Facebook,
Twitter & LinkedIn) had potential value in predictive analytics
Source: SAP Analytics 02/08/13
All related to revenue
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8. © 2013 Quant5, Inc.
Persistent, Deep Questions
Who are our
• core customers?
• frequent buyers?
• best customers?
• poorest payers?
• Best sales guys?
The Who?
How do we:
• Attract the best customers to buy
more?
• Reduce the cost of customer
acquisition?
• increase first purchase size?
• increase subsequent purchase size?
• increase cross-product purchases?
• Reduce fraud
The How?
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Predictive Analytics CAN help? A LOT!
• Predict market trends
• Predict customer needs
• Predict price volatility
• Create customized offers for
each segment and channel
• Predict changes in demand
and supply across the entire
supply chain
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Use Predictive Analytics…
When your spreadsheet runs out of gas
Data
Variables
Logic
Speed
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Predictive Analytics Solutions
Horizontal Vertical
Embedded
Database
Analytics
•Hadoop
•Unstructured and
Structured
Databases
Consulting Services
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Functional / Business Unit Outcomes
Goal: More new & incremental sales
Sales
Goal: ROI + efficiencies + incremental rev’s
Marketing
Goal: Better products, prices & competitiveness
Product
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• Customer Analytics
• Prospect analytics
• Sales cycle analytics
• Price analytics
• Competitive Prospects
& Intelligence
• Industry trends

13. © 2013 Quant5, Inc.© 2013 Quant5, Inc.
Functional / Business Unit Outcomes
Goal: More new & incremental sales
Sales
Goal: ROI + efficiencies + incremental rev’s
Marketing
Goal: Better products, prices & competitiveness
Product
13
• Market trends & Drivers
• Competitors, threats &
vulnerabilities
• Opportunities & Budget
Optimization
• Improving positioning &
messaging
• New products, markets
and partners
• Marketing activity
optimizations
• Business risks
• Threat detection

14. © 2013 Quant5, Inc.© 2013 Quant5, Inc.
Functional / Business Unit Outcomes
Goal: More new & incremental sales
Sales
Goal: ROI + efficiencies + incremental rev’s
Marketing
Goal: Better products, prices & competitiveness
Product
14
• Product Management
Analytics
• Actionable Product
Intelligence
• Competitive Analysis
• Partner Analysis
• Supply Chain Analysis
• Launch Plans & Positioning
• Price Analytics

15. © 2013 Quant5, Inc.
Steps to Successful Predictive Analytics
Design Implement Measure
• Goal setting
• Resource
Assessment
• Questions to
be answered
• Tests
• Deployment(s)
• Feedback
• Assessment of KPIs
• Improvements
• Validation
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Non-Obvious Knowledge
and Probabilities
Predictive Analytics for Business
Analyze current and historical
data in order to better
understand customers,
products and partners, and
identify potential risks and
opportunities
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Putting the teaching
into Practice
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Situation Analysis
• Lucy Couture:
– A 3-year old eRetailer
• High-end “Juicy” couture
– Bags, business attire,
dresses, intimate apparel,
parts, shirts, shoes, skirts
– Demographic:
• Women (25+)
• In College (19-25)
• Other (gift purchasers)
– Generates a couple of
millions in gross revenues
p/year
– Has 15 employees
– Limited data centricity
Marketer
What are
the prices sensitivities?
Product
Manager
What are the product
relationships?
Marketer
What are the
demographics (age
cohorts) of purchases?
Marketer
What sort of financial
data can be used?
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Situation Analysis
• You are the Director of Marketing
– With a marketing manager (“marketer”) with an MBA reporting
to you
• He/she is not a data scientist
• The Marketing Department
– Maintains the website
– Uses ConstantContact as a email campaign management system
– Has access to:
• POS data & the customer database
– Has a Limited budget
– Has limited data centricity but a desire to transform the
company into a data centric culture
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Your goals:
1. Increase revenue
2. Increase efficiencies of marketing activities
3. Improve customer communications
4. Evolve into a data centric organization
• Here are the steps involved:
1. Gather data from current systems
2. Determine the product relationships
3. Determine the customer set that is most & least receptive
4. Determine the next product and price to be promoted via
email
5. Integrate back into current systems
6. Measure & improve results
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Your Marketing Mix
4P’s
• Price
• Product
• Promotion
• Place
Which element(s) of the marketing mix
is most effective increasing revenue?
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Your Marketing Mix
4P’s
• Price
• Product
• Promotion
• Place
Which type of
promotion is
going to be
most effective?
 Direct or indirect sales
 Advertising
 Marketing Promotions
 Events
 Direct marketing
 PR
Email !
Lucy has email addresses from
all kinds of potential customers
and a relatively small number of
actual customers
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Which data to use?
• Do not use data from:
– Social Media
• Facebook, Twitter, Blogs,
surveys, etc.
• Customer Sentiment
– Mobile Data
– Machine Data
• RFID, sensors, etc.
– Images
• Video, audio, emails
– “Real Time”
• Use data from:
– Customer transactions
– Legacy systems
– Web site: Google
Analytics
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Can A Spreadsheet Do The Analytics?
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Prospect Customer Scores –
Step Two
• Data needed
– ( Any demographic data is ok here, we can take advantage
of a lot of disparate types of information)
– Customer ID
– Age
– Household income
Equation: Machine learning algorithm which mines
customer demographic and descriptive data to determine
which characteristics are indicators of success.
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Product Relationships –
Step Three
• Data needed
– Product transactions data
– Transactions in common
Equation: Determine which groups of products are highly
correlated and purchased together.
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Targeted Offers –
Step Four
Available Data:
• 10,880 email
addresses
• 1,360
customers
– ∑ 4,080
transactions
(3 per customer)
– x transaction =
$138.00 (2013)
• Days since last purchase
• Purchase prices by product,
category and SKU number
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Targeted Offers–
Step Four
• Equation: Determines the similarity of market
baskets by analyzing past customer behavior, and
determining which products are most likely to be
purchased next by each customer.
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Targeted Offers –
Step Four
• Promotional offer:
– Who should receive these targeted offers?
A select group of established customers
– How should the customer info be presented?
Customer names and customer IDs
– What other information would be helpful to know?
Lifetime value
Risk of Churn
• Validation
– Past KPIs (# of emails per period, opens, sales)
– Closed loop?
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Demo
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Discussion & Assessment
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32. © 2013 Quant5, Inc.
How YOU can apply
Predictive Analytics
to
Marketing Challenges
For the Planning-ness Conference
May 10th, 2013
Doug Levin
doug@quant5.com