Integrating Digital Marketing Analytics

Integrating Digital Marketing Analytics

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  A Roadmap for IntegratedDigital Analytics or How to Put High-Priced Data Scientists to Work Productively D. Shea June, 2018
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  From Investment toValue • 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
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  Effective Marketing isa 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
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  Marketing works byappealing 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
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  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
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  Increasingly complex businesschallenges 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
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  All efforts providevaluable 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
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  All of themetrics 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
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  The framework canbe 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
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  Cockpit instruments tellyou 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
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  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
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  Analytics should notbe 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
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  A recent McKinseyreport 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
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  • 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
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  Ramping up capabilitiesand 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
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  Getting Started Thank YouFor 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
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  Site Valuation Modelsweigh 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
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  Attribution Models areused 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)
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  Demand Models alsoweigh 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)