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Enhancing Marketing Analytics for Leading Public Interest Enterprise – Case Study

Developing robust Marketing Analytics capabilities to optimize omnichannel marketing investments for a leading nonprofit organization.

SITUATION

A $150 million public interest enterprise was struggling to gauge the effectiveness of its omni-channel marketing investments, leading to inefficient spending and an over-reliance on offline channels.

The organization relied on last-touch attribution, overlooking the influence of other channels in the customer journey.

With an annual revenue of $150MM, the client faced significant challenges in optimizing its marketing investments. The primary issue was the organization’s inability to accurately measure the effectiveness of its marketing efforts across multiple channels.

This inefficiency resulted in a substantial portion of the organization’s budget being allocated to traditional offline channels like direct mail and face-to-face marketing, which accounted for about 70% of its total marketing expenditure.

In contrast, digital channels received only 30% of the budget.

The organization relied on last-touch attribution, which failed to consider the influence of other channels in the donor journey. Approximately 50% of revenues attributed digitally were from unknown sources.

Predominantly dependent on direct mail and face-to-face marketing—which constituted about 70% of their total marketing expenditure—the organization allocated only 30% to digital channels.

Marketing Knowledge Graph displaying all elements of Customer Touchpoints
Marketing Knowledge Graph displaying all elements of Customer Touchpoints
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Knowledge Graph built using Neo4j displaying elements of a single Customer Journey with built-in attribution modeling

OBSTACLES

The enterprise faced multiple challenges, including incomplete attribution models, inefficient data systems, data privacy concerns, and high costs from third-party analytics services.

The organization faced multiple obstacles in enhancing its marketing analytics capabilities.

The existing last-touch attribution model was inadequate as it failed to provide a holistic view of the customer journey. This model overlooked the contributions of various touchpoints, leading to an incomplete understanding of how different channels influenced donor behavior.

ACTION

To address these challenges, the organization collaborated with Revology Analytics to develop a sophisticated Marketing Analytics capability.

This collaboration involved merging traditional Marketing Mix Modeling with advanced Multi-Touch Attribution (MTA) techniques to provide a comprehensive view of the customer journey.

Main menu of the client’s Marketing Analytic dashboard. Revify Analytics is Revology’s nonprofit analytics division.
Main menu of the client’s Marketing Analytic dashboard. Revify Analytics is Revology’s nonprofit analytics division.
Example of New Donor performance analysis module. Revify Analytics is Revology’s nonprofit analytics division.
Example of New Donor performance analysis module. Revify Analytics is Revology’s nonprofit analytics division.

RESULTS

The implementation of the Marketing Knowledge Graph and Marketing Mix Model indicated that digital channels were more effective than traditional offline channels, prompting a strategic shift in marketing budget allocation over the next three years.

 

The implementation of the new Marketing Analytics capability yielded significant results. The Marketing Mix Model indicated that digital channels were more effective than traditional offline channels. This insight led to a strategic shift in the marketing budget allocation over the next three years, increasing investment in digital channels to enhance overall marketing effectiveness.

A dynamic Power BI dashboard, built on the Knowledge Graph views, empowered the organization to answer pivotal questions related to customer paths, channel effectiveness, and customer acquisition strategies. This dashboard provided a comprehensive and interactive platform for analyzing marketing data and making informed decisions.

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