What are some common pitfalls or biases to avoid when analyzing media attribution results?
Media attribution is the process of identifying and measuring the impact of different media channels and touchpoints on a desired outcome, such as a conversion, a sale, or a retention. Media attribution can help you optimize your media strategy, allocate your budget more efficiently, and improve your return on investment. However, media attribution is not a simple or straightforward task, and it requires careful analysis and interpretation of the results. In this article, we will discuss some common pitfalls or biases that you should avoid when analyzing media attribution results, and how to overcome them.
The quality of data can significantly affect your media attribution results. Poor data quality can lead to inaccurate or misleading attribution results and can erode your trust in your media strategy. To ensure data quality, it’s important to define clear and consistent data collection and processing rules across your media channels and platforms. Additionally, reliable and compatible data sources and tools should be used to capture and integrate your media data. You should also validate and clean your data regularly to remove any errors, duplicates, or outliers. Lastly, you should monitor and audit your data quality periodically to identify and address any issues or gaps.
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In my experience, ensuring data quality is pivotal in analyzing media attribution results accurately. A common pitfall is overlooking discrepancies in data sources, which can introduce bias and skew interpretations. Ensuring consistency in data collection methods across channels helps mitigate this. Another critical aspect is filtering out noise—irrelevant data that can cloud judgment. Regular audits to validate data accuracy and relevance are invaluable, as they help maintain the integrity of analysis, ensuring decisions are based on solid, unbiased information.
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From my POV one of the biggest challenge for brands is to have a clear understanding of consumer experience end to end, orchestrated and aligned with retention and loyalty objectives. If the full experience is orchestrated each individual touch point will play a role and data cleaning can be a smoother process.
When analyzing media attribution results, the attribution model you choose can have an impact. An attribution model is a set of rules that determines how much credit each media channel or touchpoint receives for a given outcome. Common models include last-click, first-click, linear, time-decay, and position-based attribution. Each model has its own advantages and disadvantages, so it’s best to use multiple models to compare and contrast your results. Additionally, custom or data-driven attribution models can be tailored to your specific data and goals.
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When assessing attribution models I like to pay attention to: - Audiences funnel stage, e.i. A last click attribution model might work better to increase conversions. - How CX is connected and capitalized before and after the clicks. - MarTech capabilities to apply marketing automation, ML and AI to capitalize from each experience.
Confirmation bias is a factor that can affect your media attribution results. This is the tendency to seek out information that confirms existing beliefs or expectations, while ignoring or discounting contradictory information. This can lead to distorted or biased results, and prevent you from uncovering new insights or opportunities. To avoid this bias, be conscious of your own assumptions and hypotheses, and challenge them with data and evidence. Ask for feedback from different stakeholders and experts, conduct experiments to validate the results, and review your media attribution regularly and objectively. Look for any anomalies or trends that may require further investigation or action.
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Remember to take short-term trends and results with a grain of salt, particularly when working with smaller campaigns. If Channel A has a 0.02% higher conversion rate than Channel B, it doesn't mean you need to shift your entire budget toward Channel A immediately. While clients are always clamoring for insights and optimizations as quickly as possible, make sure you actually have a substantial sample size and that the differences in performance are statistically significant before making major changes. Be patient and understand that a stronger analysis will come from recognizing long-term patterns from a larger data set.
The fourth factor that can influence your media attribution results is the attribution window. This is the time period used to measure the effect of your media channels or touchpoints on a given outcome. For instance, if you use a 30-day attribution window, only media channels or touchpoints within 30 days before the outcome will be considered. The attribution window can affect your results, since different channels/touchpoints may have different effects over time. Some may have a short-term effect, while others may have a long-term effect. To choose an appropriate attribution window, you should understand your customer journey and behavior, as well as how long it takes for them to move from awareness to conversion. Additionally, ensure the window is aligned with your business objectives and media goals, and test different windows to see how they affect results. Finally, adjust the window as needed to account for any changes in your customer journey or media strategy.
A fifth factor that can influence your media attribution results is channel silos, which is the tendency to analyze and optimize your media channels or touchpoints separately without considering their interactions and synergies. This can lead to incomplete or inaccurate results and limit your ability to optimize your media strategy holistically. To overcome this, you should take a cross-channel and customer-centric approach to your media strategy, using integrated and holistic data and tools that capture and measure cross-channel performance. Furthermore, analyze and optimize your media channels not only based on their individual contribution but also based on their collective impact on the customer journey and the outcome. Lastly, experiment and test different combinations of media channels or touchpoints to find the optimal mix and allocation.
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