Advertising analytics 2.0

•

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Advertising strategies needed to change as consumer behavior shifted, forcing marketers to rethink how they allocated budgets across mediums. Analytics 2.0 provides a solution by crunching the vast data generated over the past decade to gain insights into attribution, optimization, and allocation across interconnected marketing channels. It allows marketers to understand influence between mediums, optimize strategies without business risks, and accurately implement optimized allocation plans.

Marketing

But, consumer behavior
began to undergo a
paradigm shift.

This forced marketers to
rethink their strategies

They ignored the power influence one
mediumcan have on another.

complex and dynamic
inter-related phenomena?

A virtually infinite, granular reserve
of datahave been generated
throughout this past decade.

Crunching this data
is where Analytics 2.0
comes into the picture.

Analytics 2.0
includes 3 broad activities.

This is where the beauty of analytics 2.0 lie.

Thus, you have a a near accurate picture
of potential market behaviour,
without the
actual risks involved.

This is the last stage and involves actually
implementingthe course of action
after optimizing it.

Created by Prasid Mitra,
Jadavpur University

Today's video is a presentation given at App Growth Summit SF. In it, I detail how to use Excel's scatter plot trend line equations as a framework to predict whether your marketing activities are on track to turn a profit. Also included: * the difference between metrics and KPIs (don't use metrics to predict profitability) * which KPIs are best used as predictors (they're not all treated equal for every business model) * the differences between excel trend lines and which to use (hint: for most folks it's best to stick with more simple equations like linear) * assessing accuracy using the mean absolute percentage error (it's a better measure of predictive power of your model than R-squared) * caveats of using a prediction model: where it can break down (there are loads of reasons) * 3 other examples of how to apply the framework: > Predicting CPI > Long-term retention rate > Assessing whether week 0 ROAS is the best predictor

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