Year after year, measurement solutions consistently rank among the top business priorities in any marketing category. This is because every performance-driven brand in advertising shares the challenge of identifying the right questions to ask and acquiring the necessary data, technologies, and methods to address crucial marketing performance issues.
If we know that getting closer to reliable attribution requires a collection of methods and tools, how do you decide the best path for your measurement journey?
Every approach you take needs to start with a clear goal including objective function/ KPI(s), reporting frequency, precision, and comprehensiveness.
Determining your best path also requires an understanding of possible measurement approaches, their strengths and weaknesses, and how these align with your measurement
goals.
In the quest for reliable attribution, it’s essential to understand that no single method or tool will provide all the answers. Instead, a collection of approaches, each with its strengths and weaknesses, is necessary to achieve comprehensive insights. Here’s a condensed guide to help you navigate your measurement journey effectively.
Leveraging Different Tools and Approaches
At Ocean Media, our client’s goals are our goals. Most commonly, we expect frequent data-driven media optimization to achieve maximum performance efficiency that will drive business growth. The timeliness of media optimization, juxtaposed with the precision required to make efficient investments and drive business outcomes, requires varied measurement tools.
When used together, they fulfill each of the stated goals while attempting to rely on any of these measurements individually would fail to deliver on any one of them.
Tracking: Unlocking User Insights Beyond Surface-Level Analytics
The Tracking pillar offers a pixel-based intra-channel measurement solution, providing granular insights into user interactions on platforms, websites, or apps. This approach goes beyond traditional web analytics by capturing detailed user behaviors, such as navigation paths, feature engagement, and pain points. These insights can be used to optimize campaigns, improve product development, and create personalized user experiences, though it remains less accurate in absolute attribution.
With access to real-time data, marketers can experiment with different ad copies, website layouts, or features and immediately observe the impact on user behavior. This enables a process of iterative refinement, allowing for quicker optimization of marketing strategies. By understanding individual user behavior, marketers can segment audiences into more granular groups, delivering personalized messages that increase engagement and conversion rates.
However, despite these advantages, the tracking tools have significant limitations in assigning attribution accurately. Each marketing channel, like social media, email, or search, operates within its own platform, often leading to isolated analytics that can’t be compared across channels. This creates a “walled garden” effect, where each touchpoint assigns full credit to itself, leading to over-attribution and making cross-platform measurement challenging.
Experimentation: Marketing Incrementality Through Strategic Testing
Geo-based controlled experiments are considered the gold standard for measuring marketing incrementality. By isolating the impact of specific marketing efforts on desired outcomes, these experiments provide invaluable insights for optimizing marketing investments. Unlike platform-based measurement solutions, controlled experiments offer a consistent methodology, enabling direct comparisons across channels and eliminating over-attribution biases. They are also robust to privacy changes and can be replicated to validate other measurement approaches.
However, implementing geo-based, inter-channel experiments comes with significant challenges. These experiments are often time-consuming and disruptive to business operations due to the need for data collection, analysis, and modeling. Additionally, achieving statistically significant results may require substantial budget adjustments or extended test periods. The potential loss of revenue during test periods, known as dark periods, represents another opportunity cost associated with this method.
Surveys: Bridging the Gap Between Data and Insights
Surveys are essential tools in marketing, offering valuable insights into consumer behavior, preferences, and the overall effectiveness of marketing strategies. While survey results are often quantitative, the metrics they measure tend to be more qualitative, helping to understand factors that contribute to long-term business success. Surveys also provide detailed, context-rich feedback, such as in “How Did You Hear About Us” surveys, which can inform attribution and enhance marketing tactics.
Despite their usefulness, surveys have limitations. While they excel at capturing customer perceptions and motivations, they may not effectively measure secondary KPIs like cost to acquire a customer, time spent on a website, or other behavioral metrics. These insights are crucial for understanding consumer perceptions but may not provide a complete picture of customer actions or website engagement.
Additionally, survey data can be costly and is often collected intermittently, leading to gaps in the data that marketers rely on for timely decisions. Additionally, there is a potential discrepancy between what respondents say in surveys and their actual behavior, which can affect the accuracy and reliability of the data collected.
Media Mix Modeling: Leveraging Data Science for Efficient and Accurate Media Analysis
Media mix modeling addresses a fundamental marketing challenge: optimizing marketing investments for maximum impact. By analyzing complex relationships between marketing activities and outcomes, these models offer data-driven insights into resource allocation and predict the consequences of investment changes. Leveraging various data sources, including internal and external information, media mix models provide a holistic view of the marketing landscape and uncover potential opportunities or threats.
Advances in data science and accessible modeling tools empower marketers to analyze vast datasets efficiently, making media mix modeling suitable for intricate marketing programs with multiple channels and strategies. As data privacy and measurement challenges grow, media mix modeling gains popularity over channel-specific attribution methods. However, its reliance on correlations can introduce biases and errors.
While media mix models offer valuable insights, they have limitations. The requirement for substantial historical data and specialized expertise increases the barrier to entry compared to other measurement approaches. Additionally, these models provide periodic snapshots of performance over extended periods, limiting real-time decision-making. Despite these challenges, media mix modeling remains a powerful tool for strategic marketing decisions when used judiciously and complemented by other methodologies.
Why are measurement strategies so impactful when used together?
Each analytical approach has its own strengths and weaknesses, but they complement each other. Where one approach may lack precision, another can provide it, albeit at a higher cost of time and money. One approach is expedient while another is accurate.
No single measurement approach can fulfill all our measurement needs. However, you can achieve a balanced and reliable attribution strategy by combining different methods and tools and with a clear understanding of their strengths and weaknesses. This comprehensive approach allows for informed marketing investment decisions, driving business growth and optimizing performance efficiently.