Call for Content: 2022 Topics for Consideration

1. Are experiments successfully filling the attribution void?
   • As attribution modeling is increasingly challenged, experiments are being chosen to play a similar role. How is this working?
   • Are experiments successfully guiding marketing decisions?
   • What are the comparative strengths and weaknesses of the two techniques?
   • Where do experiments fit in the marketing analytics tool kit?
   • How do various experimental techniques compare – RCT, ghost ads, online test/control, test markets, etc.?
   • What are best practices in marketing experiments?

2. Is in-housing on the rise?
   • How have marketers successfully transitioned from using a third-party modeler to bringing the practice in-house?
   • Is this impacting MMM, attribution, or experimentation more?
   • What are the benefits and the disadvantages?
   • What are best-practices in organizational structure for in-housing?

3. Are we experiencing a renaissance in marketing mix modeling?
   • Marketers have been adopting changes in their use of MMMs. Notably increasing granularity of model variables and frequency of model updates, among other things. How is this working?
   • What are the most useful advances and how are they best implemented?
   • What are the limits?

4. Is artificial intelligence, especially machine learning, working out?
   • Where has it succeeded? Why?
   • Where has it failed? Why?
   • What are best practices?
   • What work needs to be done to achieve the promise?

5. Have multicultural consumers been forgotten?
   • Marketers appear to be increasing their focus on multicultural consumers, but are they measuring the returns on those investments?
   • How is their ROI being measured? How is it done well?  What are the pitfalls?
   • What are best practices?

6. How have privacy-related regulations and policies impacted analytics, and what are the successful workarounds?
   • How is attribution evolving to meet these challenges, especially MTA?
   • The rise of first-party data
   • New pseudonymous identifiers
   • Clean room data integrations  

7. Is there progress in representing the brand in attribution, or measuring long-term effects in MMM?
   • How are marketers better equipped to make long-term versus short-term investment trade-offs?
   • How are these insights being applied in practice?
   • What analytic techniques have been most productive?
   • What metrics best represent brand value?

8. Have quality metrics added value for marketing performance measurement?
   • Are they helping maximize the performance of creative?
   • Or media tactics?
   • Or audience targeting?

9. Walled gardens proliferate; how can cross-platform attribution cope?
   • Streaming television services are the newest walled gardens. How can television attribution measure the impact of all ads delivered in all environments?
   • Have any of the developments offering peaks through the digital walled gardens been productive for brands?
   • How can cross-platform attribution deliver productive media or creative insights for brands?  

10. Creative has been found to represent 50-80% of advertising’s in-market effectiveness; how is this being leveraged by brands?
    • Which techniques have successfully provided the granularity, cadence and accuracy necessary to produce actionable insights?
    • What organizational processes and stakeholder coordination has proven effective for leveraging these insights?
    • Are new buyer/seller terms needed to enable timely activation of these insights?

11. Has the extension of attribution beyond digital benefited brands?
    • In recent years, attribution techniques have been introduced for OOH, television, radio, among other media. Have these been tools for the media, or have they benefitted advertisers?
    • Are the new data sources used for media attribution providing adequate coverage and accuracy?
    • Are performance guarantees practical?

12. Has the extension of marketing mix modeling beyond marketing factors benefitted brands?
    • Some modelers have expanded their focus to include non-marketing commercial factors such as sales force effectiveness, customer service levels, and the like; many continue to add to the list of uncontrollable environmental factors. Have these led to actionable insights and improved business performance?  

13. Are new data sources and data science capabilities enhancing attribution to the benefit of brands?
    • Do new data sources offer benefits that outweigh the potential discord with the brand’s legacy data?
    • How can a brand successfully, and respectfully, leverage its first-party data?
    • Are better identity resolution techniques resulting in better brand outcomes?
    • Do better target audience data sources drive more profitable in-market results?
    • Has better location data made a difference for retailers’ business performance?
    • Are we finally able to see the consumers behind the devices and has that made a difference for brands?

14. Beyond the data, beyond the stats, how do the people make a difference for analytics driven businesses?
    • What organizational structures and processes are most productive?
    • Does leadership matter?
    • How has organizational adoption been most successfully managed?
    • Where do we need industry support – data identifiers, standards, best-practices, audits?

 

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