Tailoring Media Investment Optimizations to Individual Brands

As described in an earlier post, the Elsy Knowledgebase is a foundational and unique data asset which provides our algorithms (and users) with a comprehensive and granular understanding of the media marketplace. It is available turn-key in Elsy and is common to all Advertisers.

But to truly optimize media investment portfolios on behalf of individual Advertisers, we need to calibrate our recommendations to the Brands that we are planning campaigns for. No two brands will get the same outcomes from the exact same media investment; due to differences in campaign goals, target audiences, audience data availability, creative efficacy, financial profitability, seasonality, geographic footprint, industry dynamics, etc.

This requires the ability to efficiently and securely onboard proprietary data on behalf of those brands, and to leverage this data to tailor our optimizations accordingly. After all, would you really expect anyone to be able to truly optimize multimillion-dollar investments on your behalf, without full visibility into business impact?

This is achieved through the creation of Brand Datamarts - a feature of the Elsy Platform which allows us to efficiently ingest and maintain a variety of proprietary Brand datasets which subsequently feed into our planning algorithms.

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What type of data goes into a Brand Datamart?

A Brand Datamart typically includes fundamental brand characteristics and data such as business metrics, financials, audience characteristics, audience data availability, seasonality patterns, geographic footprint, content availability, media inventory restrictions, etc.

It also includes (subject to availability) attribution outputs from a range of measurement sources such as Marketing Mix Model (MMM), Multi-Touch Attribution (MTA), Brand or Sales Lift Studies, as well as performance reporting from various media channels.

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How is this data used in Elsy’s algorithmic planning approach?

All this brand data is used to:

  • Recommend potential media investment opportunities which are suitable for the brand and campaign brief, given what we know of the media marketplace and brand characteristics

  • Evaluate the potential scale and attractiveness of each media investment opportunity

  • Optimize budget allocation across candidate media investment opportunities to maximize expected business outcomes

The combination of media marketplace, brand profile and attribution outputs allow us to be a lot more predictive, granular and accurate in our media optimizations than traditional approaches to media planning.

How is this data shared across Advertisers?

It isn’t. This data remains confidential and separate from the Knowledge Base, being stored in a separate Datamart for each advertiser.

Multi-brand Advertisers have the option to cross-pollinate data across Brands when relevant, but there is currently no mechanism to ‘share wisdom’ across Advertisers.

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