Marketing Analytics Accelerator 2024

How is artificial intelligence transforming marketing analytics?

• Are AI-driven applications improving the effectiveness and speed of data acquisition and quality control; is it opening up the use of more unstructured data?
• Do models using AI techniques (e.g. neural networks, deep learning) solve legacy modeling challenges and provide new capabilities?
• Are AI techniques making insights more actionable and accessible (e.g. more accurate forecasts, more effective optimizations)?
• Is AI impacting the marketing analytics professional’s role in the marketing organization and the flow of insights throughout the organization and its marketing partners, considering changes in factors like speed, latency, frequency, granularity of insights and especially opening unmediated access to insights for non-technical marketers)?
• Do the rewards outweight the risks? Risk considerations could include algorithmic bias, the challenges of adoption and the shock of markedly different model results. Reward consideration could include improved productivity, cost-efficiency, speed, less reliance on historical data and more focus on the future, new ways to drive the business such as outcome guarantees for media.

What solutions are proving effective in this challenging privacy-first environment?

• How will attribution overcome its data challenges:
  • Audience cohorts?
  • Contextual targeting?
  • Geographic markets?
  • New identity solutions?
• How can MMM adapt to deliver:
  • Audience-led strategies?
  • Tactic granularity at the activation level, including tactics of growing importance such as influencers/endorsers, sponsorships, consumer experiences, and the like?
  • Creative evaluation?
  • Path to purchase, consumer journey, sequential analyses of any sort?

Is marketing analytics improving its ability to support business building and growth, via:

• Variables like customer lifetime value in an audience led strategy?
• Full-funnel analyses considering synergies/interactions among tactics at all levels from branding to conversion?
• Measurement of long-term effects and trading-off long-versus short-term returns?

How can analytics bridge the increasing number of walled gardens? Considering:

• The growth in legacy digital media
• The transition of television to streaming

How has analytics grown to more completely integrate externalities?

• Is there evidence of the changing contribution of factors like social sentiment, trends, futures, regulatory and corporate policy (e.g. DEI, ESG and data privacy and security), climate change, global conflict?
• How accessible, granular, accurate and predictive are these factors?

Where is industry action needed?

• Is there a role for data standardization, data governance around quality, security, transparency and privacy and data organization such as definitions, taxonomies and formats?
• NOTE: this topic will be the one exception to our rule requiring brand case studies. Given the forward-looking nature of this topic, it will be better addressed by papers advocating for or against industry or government action.  If this topic is included in the Accelerator program, the papers will be discussed by their authors in an in-depth panel discussion and distributed to attendees following the event.