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.

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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.

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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 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.

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

ACTION

Revology Analytics collaborated with the organization to develop an in-house Marketing Analytics capability, leveraging the client's existing tech stack to deliver comprehensive insights while achieving substantial cost savings.

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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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  Marketing Knowledge Graph and Marketing Mix Modeling

  • Constructed a Marketing Knowledge Graph in Neo4j: Facilitated advanced Multi-Touch Attribution (MTA) by connecting donors to campaigns and channel interactions. Each marketing channel was linked to specific campaigns, and donor journeys were mapped as sequences of activities within the graph.

  • Employed a Modified Robyn Algorithm for Marketing Mix Modeling: Revealed that digital channels like Facebook, Paid Search, and Instagram were more effective than previously believed.

  • Anonymized Donor Data for Privacy Compliance: Used hashed emails as anonymized identifiers to piece together the donor journey, integrating data from website interactions, face-to-face engagements, SMS, and phone calls.

  Development of a Comprehensive Marketing Analytics Dashboard

  Building upon insights from the Knowledge Graph and Marketing Mix Modeling, Revology Analytics developed a comprehensive Donor Analytics Dashboard in Power BI using the client's existing technology infrastructure. This eliminated the need for costly third-party analytics services, resulting in substantial savings.

  Key Features of the Dashboard:

  • Dynamic Donor Behavior and Marketing Analytics Insights: Utilized dynamic drill-down and drill-up capabilities to uncover key descriptive (what happened) and diagnostic analytics (why it happened). This allowed users to explore data at various levels of granularity, providing deeper understanding of donor behaviors and marketing performance.

  • Advanced Filtering and Segmentation: Enabled users to segment data by fiscal year, acquisition channel, donor category, age group, and other relevant dimensions to tailor insights to specific needs.

  • Comprehensive Donor Metrics Covered:

    • New Donor Analytics: Insights into the number of new donors, average first gift size, acquisition channels, and demographic breakdowns.

    • Donor Retention: Year-over-year and month-over-month retention rates, donor lifetime value (LTV), and comparisons between sustainer and one-time donors.

    • Donor Gift & Revenue Analytics: Distribution of donation amounts, monthly revenue fluctuations, and revenue contributions by source.

    • Sustainer Analytics: Sustainer revenue trends, retention rates, acquisition methods, and additional one-time giving beyond regular contributions.

    • Marketing ROI: Evaluation of marketing channels in terms of Return on Investment (ROI) and LTV, assessing customer acquisition costs relative to donor value.

    • Performance to Budget: Real-time tracking of financial performance against budgeted targets, providing transparency and accountability.

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

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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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  • Incomplete Attribution Model: The existing last-touch attribution did not provide a comprehensive view of the donor journey, overlooking the roles of various touchpoints.

  • Inefficient Data Systems: Traditional SQL-based systems were ineffective in representing and traversing complex, multi-step donor journeys.

  • Data Privacy and Aggregation Challenges: Piecing together the donor journey was difficult due to the need to maintain donor privacy and handle data that was available only in aggregate form.

  • High Costs of Third-Party Analytics: The client was paying mid-six-figures annually to a third party for foundational donor and marketing performance analytics, adding significant expenses without delivering the desired insights.

Main menu of the client’s Marketing Analytic dashboard. 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.

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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 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.
  

  Key Outcomes Achieved Through the Dashboard:

  • Substantial Cost Savings: By developing the donor and marketing performance analytics capability in-house using the client's existing tech stack, the organization eliminated the $250,000 annual expense paid to the third-party analytics provider.

  • Data-Driven Decision Making: Empowered stakeholders to make informed decisions by providing real-time insights into donor behaviors and marketing performance.

    • Example: Utilizing dynamic drill-down capabilities, the team analyzed donor retention heatmaps and identified a drop in retention among first-year donors, leading to targeted engagement campaigns that improved retention rates.

  • Optimized Marketing Spend: ROI analysis revealed higher returns from specific digital channels. The organization reallocated budgets toward high-performing channels and reduced spend on less effective ones, maximizing marketing impact.

  • Enhanced Donor Acquisition Strategies: Insights into the most effective acquisition channels and demographics allowed for refined strategies focusing on segments with higher conversion rates and lifetime value.

  • Improved Donor Engagement: Deeper understanding of sustainer donor behavior led to tailored engagement programs, increasing retention and additional one-time donations from sustainers.

  • Financial Transparency: The Performance to Budget section facilitated better financial management and planning by allowing real-time tracking against budgeted targets.

  Additional Benefits:

  • Enhanced Donor Insights: The organization gained a comprehensive view of donor behaviors, preferences, and lifetime value, enabling more effective segmentation and personalized engagement strategies.

  • Increased Retention Rates: By analyzing retention trends through diagnostic analytics, targeted initiatives were implemented to improve donor loyalty, particularly among new donors and sustainers.

  • Maximized Marketing Impact: Detailed insights into channel performance and ROI helped in reallocating resources to the most effective channels, resulting in improved overall marketing efficiency.