Enhancing Marketing Analytics for Leading Public Interest Enterprise - Case Study

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

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SITUATION

A $150MM Public Interest enterprise struggled to gauge the effectiveness of its omnichannel 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.

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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.

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  The organization’s reliance on last-touch attribution further complicated matters.

  This attribution model did not account for the influence of various touchpoints throughout the customer journey, resulting in a skewed understanding of marketing effectiveness.

  Approximately 50% of revenues attributed digitally were from unknown sources, highlighting the need for a more comprehensive approach to track and analyze customer interactions.

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

ACTION

Revology Analytics developed a robust marketing analytics capability, merging marketing mix modeling with advanced multi-touch attributes and utilizing a marketing knowledge graph for efficient analysis.

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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.

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  A Marketing Knowledge Graph was constructed using neo4j, facilitating MTA by connecting customers to campaigns and channel interactions.

  This graph-based approach allowed the organization to answer critical questions more efficiently and accurately.

  Each marketing channel, campaign, and customer interaction was represented as nodes and relationships within the graph, providing a detailed customer journey visualization.

  We used a modified version of Facebook's Robyn algorithm to create a Marketing Mix Model.

  This model revealed that digital channels such as Facebook, Paid Search, and Instagram were more effective than previously believed, prompting a reassessment of the marketing budget allocation.

  To ensure privacy and accuracy, the customer journey was pieced together using hashed emails as anonymized identifiers. This approach integrated existing email data with website interaction data from GA4 and linked fa

Example of New Donor performance analysis module. Revify Analytics is Revology’s nonprofit analytics division.

Marketing Knowledge Graph displaying all elements of Customer Touchpoints

OBSTACLES

 The last-touch attribution model failed to provide a complete view of the customer journey, and traditional SQL-based systems were inefficient in representing complex customer paths while ensuring privacy.

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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.

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  Traditional SQL-based systems compounded the problem by not being efficient at representing and traversing complex customer journeys.

  These journeys often involved multiple interactions across various channels, making it challenging to piece together a coherent narrative of customer engagement.

  Furthermore, the need to maintain customer privacy and handle data available only in aggregate form added another layer of complexity to the situation.

  Without a comprehensive view of the customer journey and efficient data processing capabilities, the organization struggled to optimize its marketing strategies and allocate resources effectively.

Main menu of the client’s Marketing Analytic dashboard. Revify Analytics is Revology’s nonprofit analytics division.

RESULTS

The new Marketing Mix Model highlighted the effectiveness of digital channels, leading to a shift in budget allocation.

The Marketing Knowledge Graph enabled comprehensive multi-touch attribution and enhanced customer journey insights.

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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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  The Marketing Knowledge Graph enabled the organization to perform multi-touch attribution, considering the entire customer journey rather than just a single interaction.

  This comprehensive approach provided a more accurate understanding of how different channels contributed to donor behavior and revenue generation.

  Furthermore, the graph-based interaction data facilitated content optimization, customer segmentation, and crafting of personalized experiences.

  The insights from the Knowledge Graph helped the organization optimize its marketing strategies, improve donor engagement, and ultimately enhance its overall impact and efficiency.