Display and Programmatic Channel Performance Analysis

Project Overview

In the dynamic landscape of the US financial services industry, our client, a leading entity, continually seeks innovative performance marketing strategies to maintain its competitive edge. Alongside utilizing established digital marketing channels like Bing and Google Ads for Search and Native networks such as Taboola, Outbrain, and Yahoo Gemini (before its merger with Taboola), the exploration of new avenues led us to Display and Programmatic advertising through channels like Verizon Media and MediaMath. Our team was responsible for building performance analytics reporting for one of the Programmatic display advertising channels, MediaMath, aiming to enhance brand recognition and audience conversion intent by strengthening upper-funnel marketing strategies.

Problem Statement

The reliance on a last-click attribution model posed a significant challenge, particularly in accurately evaluating the impact of Display and Programmatic channels on the conversion funnel. Given MediaMath's role in the upper funnel, assessing its value based on last-click conversions risked undervaluing its true contribution to the marketing mix. This challenge necessitated a shift towards a more nuanced approach to capture the full spectrum of customer interactions.

Strategy

To address these challenges, our strategy embraced Multi-Touch Attribution (MTA) as a core component of our performance analysis framework. This approach was designed to:

• Overcome the limitations of last-click attribution by distributing value across major touchpoints in the customer journey.
• Provide a detailed understanding of how Display and Programmatic channels contribute to awareness, engagement, and conversions.

Execution

To construct the most effective performance analysis model for our client, we utilized the following data sources and inputs:

• MediaMath Performance Data: Detailed information on costs, impressions, and clicks at an impressively granular level.
• Visitor Tracking ID: Each impression or event was linked with a visitor tracking ID, integrated with internal lead data, allowing for precise tracking and attribution.
• Internal Sales/CRM and Marketing Data: Essential for a comprehensive view of the customer journey and channel performance.

Given these data sources, we undertook a multi-faceted approach to build an MTA analysis report for the programmatic channel:

• Identification of Last-Click Channels: Utilizing MediaMath's log data, we pinpointed the final interaction before conversion, merging this with internal reports to trace back the origin of each conversion.
• Weekly CPL Calculations: We assessed the cost per lead for each channel, incorporating these figures into MediaMath's performance reports for ongoing assessment.
• Granular Performance Reporting: Our reports delved into ad or publisher-level data, integrating MediaMath spending with last-click channel expenditures to evaluate overall performance.
• Continuous Performance Review: This in-depth analysis facilitated weekly updates, allowing for tactical adjustments to optimize channel performance.

This deep dive into data, utilizing IDs and log data alongside a created ID graph, enabled a nuanced understanding of conversion paths and attribution.

To sum up, our approach to integrating more sophisticated analytics into our evaluation process involved:

• Multi-Touch Attribution Data Integration and Report Building: We initiated our process by establishing a comprehensive reporting system capable of integrating MediaMath's impression-level data with cost and click data from other managed channels. This system was designed to correlate directly with leads data from the CRM/1P data, enabling a holistic view of the marketing funnel.
• Comprehensive Data Analysis: Leveraging the integrated report, we meticulously analyzed the performance across channels. This analysis aimed to capture the nuanced contributions of each touchpoint to the overall marketing objectives, focusing on MediaMath's role within the larger ecosystem.
• Cohort Analysis for Deeper Insights: Evaluating the impact of upper-funnel marketing efforts on brand awareness and conversion rates through detailed cohort analysis.
• Ongoing Report Refinement and Optimization: Leveraging insights from MTA analysis to continuously refine our campaign strategies for enhanced performance and ROI.

Conclusion and Results

While our move to Multi-Touch Attribution reporting offered significant insights into Display and Programmatic advertising's effectiveness, it's crucial to recognize its limitations. Although far superior to last-click models in illustrating programmatic channels' influence on final conversions, the report did not prove incremental performance stemming from upper-funnel marketing efforts.

Nonetheless, this approach proved more efficient than previous methods, laying the foundation for targeted short-term channel optimizations and enhancing the management of creatives, audiences, and publishers.

Prompted by these findings, we've embarked on further analysis to devise a method for accurately gauging the channel's impact on elevating potential lead purchase intent, continuing our commitment to refining our marketing strategies.

Seeking Advanced Analytics for Your Marketing Strategy?

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