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A/B testing

FORECASTING 2023: Managing Risk — How Businesses Can Get Better Visibility into the Near and Long-Term Future

  • Posted: July 26, 2023

Managing business risk involves having a rational, data-driven view of the future while simultaneously being as prepared as possible for external shocks — from a global pandemic and the ensuing supply-chain disruptions, to inflation, data signal losses, war, and great power competition. At our annual Forecasting event, held virtually on July 18, leading experts shared how businesses can adapt forecasting techniques to manage risk.

How Businesses Can Get Better Visibility into the Near and Long-Term Future

How Businesses Can Get Better Visibility into the Near and Long-Term Future

  • FORECASTING 2023

Managing business risk involves having a rational, data-driven view of the future while simultaneously being as prepared as possible for external shocks — from a global pandemic and the ensuing supply-chain disruptions, to inflation, data signal losses, war, and great power competition. At our annual Forecasting event, held virtually on July 18, leading experts shared how businesses can adapt forecasting techniques to manage risk.

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MODERATED TRACK DISCUSSIONS: Understanding Audiences

  • Posted: July 10, 2023

In a follow-up discussion for the “Understanding Audiences” track, Havas Media’s Peter Sedlarcik delves deeper into the ways the panelists are measuring for their clients, from the challenges of creating custom platforms and how technology’s rapid advances are affecting how they reconcile data, to balancing rigorous methodology with dynamic measurement approaches.

Leveraging A/B Testing to Understand Consumer Behavior

  • Posted: July 10, 2023

Vidyotham Reddi shared insights from Mars’ approach to A/B testing as their gold standard of learning. Framing it within the timely “virus” context, Vidyotham reinforced A/B testing’s dependability, versatility, and precision for marketing and understanding consumer behavior as part of Mars’ overall strategy.

Day 4 Panel Discussion & Closing Remarks

  • Posted: June 5, 2023

Maggie Zhang of NBC Universal invited all the presenters back to a wrap-up session called “Attribution Pivot,” where she asked what challenges marketers are facing and how they are meeting them. Each provided insight into important attribution challenges that they as a marketer or their client is facing. Limitations include lacking the ability to do A/B testing, privacy issues and the looming issue of cookie depreciation. It is also difficult to determine long-term lift, such as lifetime value.

Leveraging Look Alike Models when A/B Testing isn’t an Option

  • Posted: June 5, 2023

It isn’t always possible to perform A/B tests when it comes to evaluating the impact of paid media campaigns. Caroline Iurillo and Megan Lau of Microsoft outlined the company’s development of a strategy which matches campaign exposure data with a customer database and then creates “look-alikes“ for non-exposed customers to make audiences as comparable as possible. Lifts in perceptions, behaviors and revenue can then be compared (in aggregate) amongst exposed customers and their non-exposed look-alikes to determine the effectiveness of a campaign.

Panel Discussion

  • Posted: June 5, 2023

Carl Mela (Duke University) helmed a panel of the day’s presenters to further review the “vanguard work of MMM in 2022.” Granularity inspired the most debate among the panelists, with other topics including causality, cadence of modeling vs. decision-making, false trust in priors, marketing mix model (MMM)’s worst mistakes and lack of precision, and methods for long-term ROI and branding meriting discussion.

Attribution & Analytics Accelerator 2022

Attribution & Analytics Accelerator 2022

The boldest and brightest minds joined us November 14 - 17 for Attribution & Analytics Accelerator 2022—the only event focused exclusively on attribution, marketing mix models, in-market testing and the science of marketing performance measurement. Experts led discussions to answer some of the industry’s most pressing questions and shared new innovations that can bring growth to your organization.

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Optimizing Interventions Along the Customer Journey

Optimizing Interventions Along the Customer Journey

  • MSI

Random controlled experiments for A/B testing help improve things like a company's marketing or customer service. However, individually optimizing interventions may not always capture interactions across the entire purchase decision journey. To optimize interventions more holistically, use a Bayesian reinforcement learning model. It can integrate multiple historical experiments, which can improve both current impact as well as future learning.

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