• Jump to bottom
• 4:15 pm - Current session
• 3:30 pm - Customer Centricity & Lifetime Value
• 1:30pm -  Attribution
• 11:45am -  Measuring & Tag Management for Mobile
• 11:00 am - Tom Davenport Keynote
• 10:00 am - Product Updates
• 9:00 am - Opening Keynote

9:00 am, Opening Keynote: Paul Muret, Google

The Data-Driven Opportunity

Paul’s grandparents grew up in early 1900’s - picture shown of building from early 1900’s.  There is no Home Depot down the road.  But, this is not that long ago.  36% of Americans worked on farms doing it all themselves in the 1900’s, now only 3%.  Industrial Age Industry changed the workforce from doers to creative thinkers around the area of computer inventions.  Innovation.  Creativity.  Ideas.

Last decade in the digital age we see amazing progress especially around mobile devices.  This digital age has put the world on a new tragectory.  Create, Invent, Don’t just Produce.

Awesome picture shown of man on large clock wheel.

A Changing Consumer

Consumer Behavior has changed:

• Mobile Media Consumption up 500%
• 2.7 Billion Internet Users Today
• 400 Million Tweets per Day
• Ecommerce $224 Billion

It was once easier to literally see who our customers were because we saw them in person.  We could see if they came in alone, were in a hurry or had kids in tow.   Now people are doing their research and purchasing online.  We can’t see that anymore, we are left with bits of data in their place.  Paul makes the point “how can we possibly make intuitive business decisions if we can’t understand who our customers are?”

Access. Empower. Act.

These are the themes of the conference: Access, Empower, Act.  Google Analytics is:

• Built to "Access" - connect the right data to the right people.  Incredibly complex and tools like UA are built to help us do this.  Easy access enables bringing data to the right people in the organization.
• Built to "Empower" - by providing tools and techniques to analyze and make decisions
• Built to "Act" - deliver an ability to act on data and leverage insights

An underlying goal is a process of Analyze -----> Optimize ------> Automate.

The Data-Driven Opportunity

Key Impact Areas - from framework to practice

Three impact areas:

• Budget and Bids - ability to change what we invest in
• Campaign Dynamics - once we decide where we invest then we can optimize
• User Experience - What are we showing them?  What is the actual engagement we are providing?

Three Examples

First example: Fairmont Hotels Video by Barbara Pezzi - she was able to double their bookings through using GA.  She also learned that different countries had different social media preferences, which provided a key insight to their social media team.

Second Example - Baby Supermall by Joe Meier - biggest challenge is extremely long buying cycle.  Attribution modeling changed everything for them because they were able to see the path that lead to them.  The 5K extra that they were spending per week was bringing in many more thousands in ROI.  Were able to see upper funnel vs lower funnel and was able to shift.  They kept their CPA flat but expanded their business by 35%.

9:50 am, Product Update: Riding the Rocket - The trajectory of GA

Babak Pahlavan, Director of Product Management, Google Analytics

New Feature Announcements

#1: Google Tag Manager gets Automated Event Tracking

#2: Google Tag Manager gets an Enterprise SLA

#3: Universal Analytics Upgrade Path

#4: Account Management API for User Access Management

#5: New ABC Report (Acquisition Overview)

#6: Unified Segments Tool

#7: Demographic Reporting Built in to Google Analytics

#8: Audience data within unified segments

#9: BigQuery Integration with Google Analytics

#10: DoubleClick Campaign Manager (DFA) Integration with Google Analytics Premium

#11: DCM/DFA data in Multi-Channel and Attribution Modeling reports

#12: Google Play Referral Flow Reports

#13: Analytics Academy

#14: Improved In-Product Materials & Videos

11:00 am, Marketing Analytics 3.0

Tom Davenport - Professor, Author & Senior Advisor to Deloitte Analytics

History of Big Data

Tom conducted a Big Data in Big Companies Study to find out how big companies are and having been using Big Data.

Marketing Analytics 1.0

Traditional Marking:

• Primarily descriptive analytics and reporting
• Internally sourced, relatively small, structured data
• “Back room” teams of analysts
• Internal decision support focus
• Slowly developed batch propensity models (direct mail campaign)

Data Environment:

• ERP
• CRM
• Legacy
• 3rd Party Apps

Other Technologies:

• Spreadsheets
• BI and analytics “packages”
• ETL tools
• OLAP cubes
• On-premise servers
• Out-of-database/memory analytics

Keep inside the sheltering confines of the IT organization.

Marketing Analytics 2.0

The Big Data era

• Complex, large, unstructured data about customers
• New analytical and computational capabilities
• “Data Scientists” emerge
• Online and digital marketing firms create data-based products and services

Marketing Analytics 3.0

Fast, Pervasive Digital Marketing

• A seamless blend of traditional analytics and big data
• "Big data with small math!"
• 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 (not just the analyst)
• Industrialized marketing processes

• Every company - not just online firms - can create data and analytics based products and services that change the game
• Use "data exhaust" to help customers use your products and services more effectively
• Continuous, real-time marketing analytics
• Start with data opportunities or start with business problems?  Yes, to both!
• Need "Data products" team good at data science, customer knowledge, new product/service development
• Marketing analytics embedded within products and processes throughout the organization

• Create a data management environment (this is what Google Analytics + Google Cloud Services is becoming)
• Heavy reliance on machine learning
• In-memory and in-database anlaytics
• Integrated and embedded models
• Analytical “apps” by industry and decision
• Focus on data discover
• Blended data science/marketing/IT teams
• Chief Analytics Officers as key Marketing partners

Examples of Companies (all 100 yrs or older):

Procter & Gamble - 176 yrs old

• Primary focus on improving management decisions
• “information and Decision Solutions” (IT) embeds over 300 analysts in leadership teams
• Over 50 “business suites” for executive information viewing and decision-Making

GE - 120 years old

• $2B initiative in software, analytics, and “industrial Internet”
• Primary focus on data-based products and services from “things that spin”
• Will reshape service agreements for locomotives, jet engines, turbines
• Gas blade monitoring in turbines products 588 gigabytes/day - 7 times Twitter daily volume

Ford - 110 years old

• 25% of markeing budget on digital and social
• Ford’s Digital Analytics and Optimizatio team has full responsibility for al B2C channels and N. American business units

IBM - 102 years old

• $16B of investments in analytical software and service acquisition
• Internally heavy use of analytics for demand generation, sales management, etc.
• In demand generation alone, 3000 different and new predictice models per year at the product/account level

The biggest 3.0 obstacle

Marketers with blinders.  Corporate Executive Board survey of 800 Fortune 1000 marketers

• Marketing executives depend on data for just 11% of all customer-related decisions
• Teradata survey of 2200 marketers
• 75% can’t calculate ROI on marketing spend

Marketers have to think like Quants

1. Finding the problem
2. Review of findings
3. Modeling/Variable selection
4. Data Collection
5. Data analysis
6. Results presentation & action

Recipe for a 3.0 world

1. Start with existing capability for marketing data management and analytics
2. Add some unstructured, large-volume customer data
3. Throw some product/service innovation into the mix

11:45 am, In-app measurement: Going native

Russell Ketchum, Product Manager, Google Analytics

Universal Analytics

• Gain Access to your Data
• For Marketers - answers questions that matter
• Support Adwords conversion tracking in Apps
• Support Adwords remarketing in Apps
• Conduct Custom Tracking
• Do this all without updating your App!

EMPOWER

• Take ABC’s of Analytics to your data
• Understand critical user acquisition paths
• See new user breakdowns
• Access Google Play Referral Flow data
• Answer questions about behavior: How are they using your App?
• See if your users are doing what you expect
• Measure app crashes and exceptions

Live Demo

Q&A About Google Tag Manager for Mobile Apps

1:30 pm, Solving Marketing Challenges: How Attribution Can Help

Jody Sarno, Customer Insight Senior Analyst, Forrester

Trends in Measurement & Analytics

• Customer Segmentation
• Marketing Mix Modeling (MMM) - nothing new about this; statistical modeling
• Attribution - different but still statistically driven.
• Its all about the granularity - instead of just measuring, we are not able to dig deeper to understand the customer experience
• Channel Measurement and Optimization

• First/Last Touch also known as Single Source Measurement
• Rules Based also known as Fractional Assignment
• Algorithmic Modeling also known as Statistical Approaches

• 32% helps identify vulnerable partnerships
• 37%helps evaluate the customer purchase path
• 67% enable smarter marketing decisions
• 61% provides accurate measurement
• 6% don’t know/other

• Resources and data integration issues hamper extensive use of attribution models
• Deficient internal resources to support intense analytical efforts
• Difficult to access and connect data sources
• Limited funding available to support new attribution tools and services
• Lack support, knowledge and/or confidence from key stakeholders on an advanced attribution approach

Key Takeaways

2:00 pm, Making Attribution Work

Bill Kee, Head of Attribution Products, Google Analytics

What is attribution modeling?

Social to Display to Paid Search to Organic Search to Email all can play a role in getting ROI.  But, why do we care about attribution?

• Evaluate past decisions
• Predict results from the future actions…
• So we can better decide which actions to take!

Attribution Progress in GA

• Multi-Channel Funnels launched in 2011
• Attribution Modeling launched in 2012
• Data-Driven Attribution launched in 2013

Access

Break down data silos - Display then Search then Affiliates then Money.  Display is becoming truly "first class citizen" with these two key integrations:

1. Google display network which includes YouTube (trueview)
2. DoubleClick Integration - bring all of your display and trueview data into one place to get a full view of your spend and customer interactions

Tie the whole story together: Display impression -> Paid Search -> remarket Video -> Email -> Money.  Google Analytics is now able to provide a more complex view across digital channels - display and video.

Empower

With data driven attribution you can be empowered to actually make sense of all of this data:

• Explore the customer journey
• Compare rules-based models
• Create complex custom models
• Lookback Windows
• Impression Widget
• Building the right model is hard at the end of the day
• Introducing Data-Driven Attribution

“All models are ultimately wrong” - don’t take any of these models as absolute truth on blind faith.  Use them to build the picture.

2:15 pm, Live Case Study: Attribution

Melissa Shusterman, Strategic Engagement Director, MaassMedia

Act: by putting into practice

Outcomes of attribution modeling:

• 35% decrease in ad spend
• 145% increase in ROI

Why should we care about attribution?  So we can optimize Display campaigns for total sales.

Decisions have included

• Reducing upper funnel messaging while we try to find better performing creatives
• Eliminating some sites, increased buys on others
• Moving some incremental funds out of display

• Media Buyers review results with advertising sites
• Media Analysts report weekly on attributable leads
• Business Owners evaluate results monthly to determine future funding

3:30 pm, How can customer centricity be profitable?

Peter Fader, Professor & Co-Director of Wharton Customer Analytics Initiative at University Pennsylvania

Customer Centricity

Focus on the Right Customers for Strategic Advantage

• The traditional product-centric business model is showing some cracks
• Producing a product and then using the data to find better ways to sell it doesn’t work anymore.
• Customer centricity is a promising alternative but is not clearly understood
• Celebrate customer heterogeneity: distinguish the most profitable customers using metrics such as customer lifetime value
• So where do higher profits come from?

Show me the money!

Customer centricity can lead to improved profitability through greater effectiveness/efficiency in:

• Acquisition
• Retention
• Development

Mo Money

• Balancing acquisition, retention, and development requires considerable skill (and analytical insight)
• If you had an extra dollar to spend, which of these activities would you allocate to
• Let’s examine each tactic…

Customer Acquisition

• What metric do we use gauge and guard your acquisition?
• CPA (cost per acquisition) - BIG MISTAKE
• Question should not be “how little can we spend on our customer to get them?” - cheap CPA makes for cheap customers
• Firms should focus on VPA (Value per acquisition)...which is CLV - what is the maximum you are willing to spend on acquiring a customer
• Study example:
  • “Customer acquired from Google on average have a higher lifetime
  • value than customers acquired from other channels
  • The differences is even larger for whose first-time purchase was off-line

Summary

• You must actively avoid having a “CPA mentality” Focus on ceilings instead of floors…Value instead of costs
• Celebrate of heterogeneity by using CLV to drive acquisition strategies and tactics
• Be more patient/forward-looking when judging acquisition efforts

Customer Retention

• Churn/attrition rate is used by most firms to gauge and guide their retention activities
• Vodafone attrition rate example given of 17.7% attrition or 5.6 years
• Celebrate heterogeneity!
• How do the attrition rates vary across the company instead of the average we look at the distribution?
  • 70% of customer have a .06% attrition rate or 16.7 years
  • 20% of customers have a .35% attrition rate over 2.9 years
  • 10% have a .65 attrition rate or 1.5 years.
• Correct average lifetime: 12.4 years not 5.6 years
• “Because we can’t get accurate calculations with averages, we must work with the actual rates for each separate class of customers.”
• Serious money is left on the table if you are ignoring the heterogeneity
• Customer Retention:
  • There is no average customer
  • When heterogeneity is accounted for, the “attrition elasticity” is much lower than in the homogeneous case
  • Investments in reducing attrition will have more modest returns than expected
  • You must still spend but don’t spend on the wrong types of customers (spend a little on the ones that are making the least amount of noise)

Customer Development

• Due to massive customer heterogeneity, there is more opportunity to “move the needle” via acquisition than development
• Many development success stories are more about unlocking potential value that was there all along, as opposed to truly creating new customer value

Balancing acquisition, retention and development

Do we think this is going to be a high value customer or not?

• Consider the conventional wisdom: “it costs 5-10 times more to acquire a new customer than to retain one, so work hard to keep the ones you have…”
• This may be true, but it totally misses the point: focus on value instead of costs

Summary and Key Takeaways

• Celebrate Heterogeneity
• Improve profitability through “smart acquisition”
• Don’t overspend on retention
• View development as “icing on the cake.”
• Don’t have blind faith in conventional wisdom

4:15 pm, The future of Data & Marketing, Q&A

Neal Mohan, Vice President , Display Advertising, Google Paul Muret, Vice President of Engineering, Google Analytics

Huge trend this year is programatic buying - we have seen a huge increase in programatic buying and selling across Google products in the past year.  For the first time we can now take the information that we have spread across data sources and apply it to bid decisions programatically.

What are your thoughts on the DMC/GA Integration?

• It’s going to be transformative about how advertisers think about their businesses
• One of the big areas where we have been investing is in the notion of Doubleclick Digital Marketing - a comprehensive suite for managing all digital buying
• You can do both your purchasing and analyzing in the same platform

Silos between creative agencies and media agencies need to be broken down - everyone should be looking at the same data.