Digital analytics is complicated! Advanced analytics (data science & predictive modeling) are needed to extract the most knowledge. But how? Here is a framework and some tips from my experience peeking through the curtain.

1. A Roadmap for
Integrated Digital Analytics
or
How to Put High-Priced
Data Scientists to Work Productively
D. Shea
June, 2018

2. From Investment to Value
• Marketing is More Than the Sum of It’s Parts
• Marketing Analytics is Key to Improvement
• Advanced Analytics is a Strategic Asset
• Planning for “Marketing Analytics 4.0”
• Sign-on, Sign-up, Get Going
What is “Analytics?”
• “the method of logical analysis” –Merriam-Webster
• “the discovery and communication of meaningful patterns in data” –Wikipedia
• “the systematic computational analysis of data or statistics” –Oxford English Dictionary

3. Effective Marketing is a Big Team Effort
1. Marketing and advertising is a multi-faceted
business that requires a “divide & conquer”
approach
2. But, at the end of the day, efforts must
converge on a seamless customer
experience and provide demonstrable value
to clients and stakeholders
3. Some “siloing” is necessary amid evolving
complexity and increasing reliance on SMEs
and external partners
4. A key concern is to maintain alignment of
objectives and improve overall efficiency
over time and across groups
5. Analytics and advanced analytics is the key
to weaving the threads together to make
more effective decisions
3
Content
Effects Creative
AttributionMedia Reach &
Frequency
Message
Break-
through
StrategyCustomer
Segments
Site
StructureOperations Campaign
Execution
Making the whole more than the sum of the parts is the big objective

4. Marketing works by appealing to consumers’ preferences, feelings, and desires but
most business decisions are best made dispassionately
Marketing Analytics is the Objective Search for “Hidden”
Patterns and Truths About Consumers and Markets
Analytics doesn’t fill every knowledge gap, and subjective experience does matter,
but robust analytics is a differentiator in marketing success
Strategically, analytics is key to
becoming less reactive to events and
more proactive in determining
conditions & outcomes
Tactically, analytics helps level-set
executives around facts and
assumptions, minimizing guesswork
and biases in decision making
The Big Data/Business Analytics Grid

5. In past times, analytics was viewed as a “luxury item” by Ad Agencies and
Marketing Departments but, in an age of complexity and big data, analytics
provides more than facts and figures. It is the process by which progress is made
and measured
Marketing Analytics is a Business Asset
Analytics as a marketing discipline is poised to move from a support role of idea
confirmation to leadership in marketing innovation

6. Increasingly complex business challenges require analytics built on a solid
foundation of data, talent, planning, and organizational commitment
Advanced Analytics Builds on the Foundation of
Data, Reporting, and Business Analytics
Data and technology investments need to be directly paired with analytics
leadership and investment to create the greatest value

7. All efforts provide valuable information that must be harmonized to yield the
greatest value and maximum effectiveness
Discrete web and website work can be connected via broader objectives and those
relationships should be articulated and synergized
Advanced analytics
bridges strategy and
tactics
Digital Advanced Analytics Builds on a Foundation of Operational
Analytics to Address Strategic Challenges

8. All of the metrics and business analyses behind digital operations should be used
to paint a richer picture of the market and improve customer interactions
In addition to supporting the business with more sophisticated algorithms
Advanced Analytics connects the dots of business analytics and reporting
To Consumers, All Brand Exposures Are Related.
Marketing Analytics Should Reflect That Dynamic

9. The framework can be designed and developed that connects existing methods
and models with new ones to capture, distill, and inform
Start Small But Think Big: The Holy Grail is a Decision Support Platform
Based on a System of Models and Optimizers
What are often though of as distinct or isolated practices (silos) need to be logically,
organizationally, and algorithmically integrated

10. Cockpit instruments tell you how the flight is going but not how to fly, and warning
indicators are based on past mishaps. Knowledge management points out what
has worked (or not) and toward what might work better next time
Don’t Neglect Knowledge Management: It is the “Hippocampus” of
Organizational Memory and Continuous Learning
The conditions, recommendations, decisions, and outcomes of our marketing
efforts need to be “remembered” to be improved on

11. Coordinating management needs, operational capabilities, and knowledge
extraction is itself a process that improves with time
It’s a team effort and these roles reflect areas of professional
responsibility in organizational processes or structure
• People may fit within a main role (e.g., Predictive Modelers) but also need to be familiar
with adjacent ones (i.e., Data Science & Analytic Translation), and performance
improves with understanding of more “distant” roles
Communication Matters: Key Roles in the Analytic Workflow

12. Analytics should not be isolated from the business by over-centralization nor
diluted due to disconnected silos. A hub-and-spoke approach balances
operational concerns and analytic priorities
• Ensures subject matter expertise in, and business communications with, functional
areas (spokes)
• Enables better integration, standardization and quality control over business analytics
and reporting (hub)
• Provides framework for advanced analytics development and implementation
Organization Matters: Creating a Digital Analytics
Center of Excellence
Advanced analytics exists to both serve digital functions and to coordinate efforts in
the service of addressing broader business concerns

13. A recent McKinsey report highlighted signs of “failing” analytics programs and that
are best avoided with good planning and leadership
• Misunderstanding of what Advanced Analytics is vis a vis traditional marketing
analytics and reporting
• Lack of clarity around use cases or having few use cases developed
• Misunderstanding of analytic roles / poor job definitions, skill misalignments
and gaps (including “Analytic Translators”)
• Isolating analytics organizationally, distancing from decision makers
• Not monetizing or otherwise sufficiently demonstrating value from analytics
• Data strategy and design are not aligned with analytics use cases
• Running afoul of regulatory or ethical rules
Leadership Matters: Avoiding Common Pitfalls
Problems are just there to be solved and each example is an ongoing challenge
requiring vision, leadership, and commitment
Modified from: McKinsey & Company, “Ten red flags your analytics program will fail,” McKinsey Analytics, May 2018

14. • Organizational Design
• Internal Communications
• Training & Development
• Workplace Design
• Performance Management
HR Management is also gravitating towards analytics methods that promise to
benefit organizations through both employee satisfaction and fit
Administration Matters: Staffing Considerations
List Credit: Tom Hack, “10 Trends in Workforce Analytics”” https://www.analyticsinhr.com/blog/10-trends-in-workforce-analytics/, May 7, 2018
Analytics talent is a tapestry of skill sets, roles, titles and job descriptions.
However analysts and technical professionals are a non-traditional group to many
marketers adding recruitment, selection, and management considerations
The Chief Analytics Officer (or CDO) should work with HR to develop the job
descriptions and reporting structure to support both present and future analytic
strategies
• Work Processes
• Recruitment & Selection
• Talent Development
• Staffing & Succession Management
• Rewards
Staffing Considerations

15. Ramping up capabilities and organizing staff is complicated and time
consuming but necessary. A change management plan might include:
• Form an executive steering committee to guide and support change and key activities
• Interview digital personnel (analytic and technical) on day-to-day work tasks, collaborative
connections, skills and work preferences
• Perform job/task-mapping and management interviews and develop tentative organizational
structure (current state)
• Identify analytic initiatives & business use-cases to create an “analytics and insights agenda” for the
short-term and for longer horizons
• Identify enabling technologies and any gaps in capabilities and skills
• Engage project management and human resources in managing and executing plan
• Revise change proposal and submit to steering committee for feedback, leading to a final
recommendation and approval
Recommendations for Analytics Change Management
Implementation may be ninety percent of the effort but a good plan and broad buy-in
make all the difference

16. Getting Started
Thank You For Your Time!
Taking analytics to the next level requires a destination, a roadmap, and a lot of
help
1. Assess, Discuss, Listen
• Take stock of current & future workload compared to capabilities
2. Envision, Discuss, Listen
• Develop a vision of the future and the passion to advocate both up and
down the org chart
3. Act, Discuss, Listen
• Implement changes thoughtfully, strategically, and systematically

17. Site Valuation Models weigh site activities for value in predicting sales. They are
useful for gauging demand, making media spend comparisons, site analysis, and
simple 1:1 customer intervention triggers
• The thing predicted (Purchased Y/N or Sales Amt.) is used to contrast buyers’ and non-buyers’ site
activity patterns (owned and partnered) and assign values to them
• These values are then applied to volumes of activities (days, media buys) for business analytics, to
customer clickstreams for shopping engagement, or both
Integration Example: Linking Site Valuation & Media Attribution (1 of 2)
This “right-side of the equation” links customer behaviors to sales

18. Attribution Models are used to evaluate media placements and other touch-points
for relative value in driving traffic to owned properties
• A typical attribution rule credits a site visit to the most recent ad exposure
• Using purchase probability (or “visit value”) from a valuation model, media credit can be distributed
more accurately and even monetized
The left side of the equation links media investments to consumer behaviors on
the same scale, providing connectivity of models
Integration Example: Linking Site Valuation & Media Attribution (2 of 2)

19. Demand Models also weigh site activities for value but are “tuned” to the market
rather than individuals
• Econometric methods are used to estimate total future sales based on the elasticity between activity
volumes and sales volumes at a later date
• Provides an indication of headwinds or tailwinds to meeting sales objectives
• Natural searches can also be used as an “upper-funnel” demand metric
Integration Example: Estimating Consumer Demand
Demand models provide a high-level market view and a baseline for
understanding economic and seasonal factors and conversion efforts
Months to Purchase
TotalVisitsbyBuyers
Eventual buyers may first visit
your site anytime prior
Buyers tend to make return visits
closer to purchase time
“Waves” of shopping interest and intensity, when compared to sales outcomes, suggest your chances of meeting
objectives and can be harnessed as a leading indicator of success
Business WeekSales/Demand
Weekly Sales Obj.
Future Forecast Past Sales
Future
(Underlying Shopping Behavior)