Ana Carreira Vidal’s Post

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Media Effectiveness Measurement Specialist @ Google

Today’s marketers know that not all attributed conversions (conversions attributed to ads) are incremental conversions (conversions that wouldn’t have otherwise occurred without ads). So how can we optimize our Media Mix for higher incrementality? Liam Doyle works day in and day out on running incrementality experiments for the Google marketing team and today he is sharing a snapshot on how to use incrementality results for optimisations. Check it out 👇 and download the Modern Measurement playbook (https://lnkd.in/gHk7Zy2T) I created for full guidance.

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Digital Marketing at Google

You might’ve heard the old adage; “I know part of my spend is wasted on marketing. The trouble is, I don’t know which part”. When it comes to validating that a media strategy’s contribution is more than its cost; look no further than the recently published Modern Measurement playbook (https://lnkd.in/gHk7Zy2T), built to equip marketers to measure media contribution more precisely.  Today’s marketers know that not all attributed conversions (conversions attributed to ads) are incremental conversions (conversions that wouldn’t have otherwise occurred without ads). So how can we optimize our Media Mix for higher incrementality? Attribution models are an immediate way to gut-check your customers typical journey through your ad touchpoints, as highlighted in Brandon’s post (https://lnkd.in/gRMsGvgh). But don’t stop there -  if attribution-based decisions are level one for budgeting decisions, incrementality experiments are level two. Incrementality experiments capture the purest assessment of media contribution, and the results can serve to validate attributed performance and even inform your future optimizations through a front-end multiplier. Validate:  Let’s say you see a large difference in performance between Google Ads attribution and another measurement tool. This is often the case with view-based strategies like YouTube for Action, Demand Gen, and Performance Max. Use an incrementality experiment like Conversion Lift Studies to explore the true performance of the strategy in question, validating the incremental contribution with the rigor of causality.  Calibrate:  Now that you’ve learned the relationship between attribution and incremental performance for a particular strategy, you may choose to use a multiplier to help inform future views of performance. Note that Conversion Lift measures a specific campaign grouping in a specific time period, so it’s important not to assume multipliers apply to other campaign types outside of tested ones, and equally important to refresh results when making big budget decisions. I prefer to refresh results at least twice a year. Test and Learn, then Test Again:  Getting a sense of a strategy's current contribution is an invitation to improve. Optimize and test again at a cadence that works for you!

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