customer analytics 3 - yale school of management · 2019-12-30 · management and analytics 2. pick...
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Thomas H. Davenport Babson College/MIT/International Institute for Analytics
Customer Analytics 3.0 Integrating Big and Small Data for Fast Impact
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Big data begins at
online firms
& startups
No technical or
organizational
infrastructure to
co-exist with
Working wonders for
Google, eBay, & LinkedIn
…but what about
everyone else?
What happens in
20 big companies when
marketing analytics are
well-entrenched?
Findings show evolution
of a new analytics
paradigm
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“Big Data in Big Companies” Study
• How new? “Not very” to many –continually
adding data over time
UPS – Started building telematics capabilities in 1986
• Excited about new sources of customer data,
new processing capabilities
• Familiar rationales for customer-facing big data:
Same decisions faster – Macy’s, Caesars
Same decisions cheaper – Citi
Better decisions with more data – United Healthcare
Product/service innovation – GE, Novartis
• Need new management paradigm
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Customer Analytics 1.0 Traditional Marketing
• Primarily descriptive analytics
and reporting
• Internally sourced, relatively small, structured
data
• “Back room” teams of analysts
• Internal decision support focus
• Slowly-developed batch scoring models
• Warehouse-centric storage
1.0
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IT and Marketing are
enemies
How dare you question the
value of my campaign?
Our scores last forever!
Let’s give our segments
cute personas!
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Customer Analytics 2.0 The Big Data era
• Complex, large, unstructured data
about customers
• New analytical and computational
capabilities needed—i.e., Hadoop
• “Data Scientists” emerge
• Online and digital marketing firms create
data-based products and services
2.0
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2.0 Data Products
• Google—Search, AdSense, Books, Maps, Scholar…and now Nest
• LinkedIn—People You May Know, Jobs You May Like, Groups You May Be
Interested In, etc.
• Netflix—Cinematch, Max, etc.
• Zillow—Zestimates, rent Zestimates, Home Value Index, Underwater Index, etc.
• Facebook—People You May Know, Custom Audiences, Exchange
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We need to be “on the bridge”
Agile is too slow
Decision consulting = dead zone
We don’t need to talk to a customer
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Customer Analytics 3.0 Fast, Pervasive Analytics for Customer Decisions and Offerings
• A seamless blend of traditional analytics and big data
• Analytics integral to marketing and all other functions
• Rapid, agile insight and model delivery
• Analytical tools available at point and time of decision
• Analytics are everybody’s job
• Industrialized marketing processes
3.0
TODAY
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Customer Analytics 3.0 Competing in the Data Economy
• Every company – not just online firms – can create data
and analytics-based products and services
• Start with data opportunities or start with business
problems? Answer is yes!
• Need “data products” team good at data science,
customer knowledge, new product/service development
• Continuous, real-time customer analytics
• Customer analytics embedded into decision processes
and circulated widely
• More speed, more scale, more granularity of models
Products/
Services
Decisions
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Customer Analytics 3.0: Data Types
• Customer profiles
• Organization
contacts
• Billing
• Marketing
• Contracts/orders
• Shipping
• Claims
• Call center
• Customer service
• Purchase history
• Segmentation
• Customer value
• Purchasing behavior
• Recommendations
• Sentiment analysis
• Target marketing
• Satisfaction
• Customer
experience
management
• Service tiers
Clickstream logs
Images
RSS Videos
Hosted applications
Spatial GPS
Device sensors
Articles
Text messages
Cloud
Mobile devices XML
Presentations
Blogs
Website activity
Social Feeds
Documents
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• Heavy reliance on machine learning
• In-memory and in-database analytics
• Integrated and embedded models
• Delivery to multiple channels, specifically mobile
• Hadoop, EDW, marts, data discovery, etc.
• Blended data science/marketing/IT teams
• Chief Analytics Officers, IT as key Marketing partners
Customer Analytics 3.0 Technology & people
3.0
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•
• Caesars—real-time offers at slot machines
• CVS—over a billion customized, optimized
ExtraCare offers a year
• Microsoft—targeted Bing offers in 200
milliseconds
• Macy’s—repricing of all SKUs in 19 minutes
• P&G—”Decision cockpits” on 58K desktops,
with real-time social media sentiment analysis
for “Consumer Pulse” application
• Cisco—30,000 propensity models per year on
170 million companies
3.0 Customer Analytics Decisions in
High Gear
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• Monsanto—FieldScripts, ClimatePro, Precision
Planting
• Elanco—poultry productivity from AgriStats
• Nest—selling thermostat data to utilities
• Fitbit—bundling activity data for employers
• GE—predictive asset maintenance for turbines
• Intuit—data products on personal and small
business finance
• MarketShare—Planner for marketing
optimization comparisons
• Medidata—clinical trial productivity
3.0 Customer Analytics Products
and Services
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Corporate Executive Board survey of 800 Fortune 1000 marketers
• Marketing executives depend on data for just 11% of all customer-related decisions
• When asked what types of information supported a specific recent decision about customers, data was last on the list, after conversations with colleagues, expert advice, and interactions with single customers
• 6% of the marketers could answer five basic statistics questions, and 5% owns a stats textbook
The Biggest 3.0 Obstacle Marketers with blinders
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Recipe for a 3.0 World 1. Start with an existing
capability for customer data management and analytics
2. Pick a customer analytics target
3. Add some unstructured, large-volume customer data
4. Throw some product/service innovation into the mix
5. Add a dash of Hadoop and a pinch of NoSQL
6. Cook up some applications in a high-heat convection oven
7. Train your sous chefs in digital marketing, customer analytics
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Thanks! [email protected]