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Amazon Incrementality: Deep Dive Into How To Run Incremental Amazon Ads

Amazon Incrementality: Deep Dive Into How To Run Incremental Amazon Ads

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In this episode of Measurement Talks, Bryan discusses his experiences at a recent intimate marketing event in Aspen, where he had the opportunity to interact with C-suite level marketing professionals and founders from various brands. Bryan shares insights on the challenges of snowboarding in Aspen compared to the West Coast, but the focus shifts to the core discussions around media mix diversification, the search for low-cost per mille (CPM) advertising opportunities, and the importance of an omnichannel strategy. He delves into the complexities of managing ad campaigns across different platforms, including Meta, Amazon, and more, highlighting issues like overspending, the unpredictability of algorithmic ad placements, and strategies for ensuring incremental gains in brand visibility. Bryan also stresses the significance of using data effectively to inform advertising decisions and maintain a balance between achieving scale and building a resilient business foundation.
Timestamps:

00:00 Welcome to Measurement Talks: A Ski Adventure

00:13 Navigating the Slopes and Event Insights

01:21 Diving Deep into Marketing Strategies

03:21 Exploring the Complexities of CPM and Campaigns

06:57 The Meta Conundrum: Algorithmic Challenges and Solutions

19:04 Amazon Advertising: Strategies for Incremental GrowthOur promise to you: Every episode will have actions you take back to your business


Bryan was the Director of Efficacy at Kargo and directly helped answer this question for many brands through his work there. Now, he leads marketing for one of the fastest-growing, profitable DTC companies, Nood.

Rishabh led new business at LiveRamp, the largest provider of online digital identity, and directly helped scale many measurement and attribution programs for the ad tech ecosystem. He is now the CEO of Fermat Commerce, a commerce platform that enables content-native shopping that has no attribution loss.


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