Data overload is paralyzing brand strategy instead of improving it. With retail media networks expanding rapidly, the ability to turn massive amounts of shopper information into actionable decisions is the dividing line between market growth and wasted ad spend. Laura Weiderhaft, Director of Product at Crisp, joins the show to unpack how brands can stop hoarding metrics and start building scalable intelligence.

We sit down to dismantle the current state of retail media measurement and how teams are actually utilizing shopper data. We cover the danger of cherry-picking analytics, the OMG (Objective, Measure, Goal) framework for promotion planning, and how to properly flight budgets by Walmart week based on geographic sensitivity. Laura also reveals a highly effective philosophy on automation, explaining why marketers need to approach AI like software engineers by organizing tasks into specific "jobs to be done" rather than treating the technology like a basic search engine.

Managing all of this information is incredibly tedious, and the messy reality is that most teams aren't willing to be held accountable for the bad decisions currently hiding in their reporting. Listeners will walk away with a clear understanding of how to automate mundane data entry, the importance of aligning commercial and brand teams on a single source of truth, and why tracking secondary indicators is vital for long-term category share.

If you care about retail media execution, competitive promotion planning, and operationalizing AI, you’ll get a lot from this conversation. Please remember to subscribe and share this episode with your team to help us continue bringing practical insights to the forefront. What is the most frustrating, manual data task you are ready to hand over to an AI workflow?

0:00 Intro & Laura's E-Commerce Background
3:53 Navigating Shopper Data and Retail Media
20:47 The OMG Framework for Measurement
41:25 Building AI Workflows Like a Software Engineer
56:41 Bold Vibes & Closing Thoughts

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