Decoding Sensitivity Analysis: Optimizing Marketing Attribution Strategies

Decoding Sensitivity Analysis: Optimizing Marketing Attribution Strategies

Sensitivity Analysis in businesses today often encounter the challenge of measuring the efficacy of various advertising channels. The heart of this lies in attribution - the analytical science of assigning credit to the touchpoints a customer interacts with before converting. One of the most enlightening yet intricate parts of this puzzle is understanding how sensitive different channels are to various attribution methods. Today we delve into the world of Sensitivity Analysis in marketing attribution and offer insights into optimizing decision-making based on nuanced data interpretations.

The Chameleon Nature of Channel Performance Metrics

Take, for instance, a fashion brand analyzing its performance. The company sees a total of 74,000 orders, split almost evenly between new and returning customers, with a total media spend of roughly $2 million. Specifically, they funnel $1.3 million into Facebook for new customer acquisition. But here's where it gets tricky: you may see drastically different conversion and cost-per-acquisition (CPA) numbers depending on what attribution window you use!

For example, at a one-day click window, Facebook may appear to have driven 17,000 new customers. However, with a 28-day click window, that number could inflate to 27,000. The CPA across these windows fluctuates significantly, from as low as $50 to over $230, indicating a dramatic variation in perceived efficiency.

The fashion brand in question used to measure Facebook's performance with a one-day click and view, which showed Facebook as driving 17,000 customers at a CPA of $57 - closely aligned with their overall customer acquisition cost. However, an overlooked aspect was that not all reported conversions were from new customers, despite exclusion lists being used to separate out existing customers due to match issues and inaccuracies.

Upon integrating data from Shopify, which employed UTM-based attribution, it was discovered that only 67% of Facebook's attributed orders were from new customers. This revelation recalibrated the new customer CPA from $57 to $85. Without even touching upon advanced attribution methodologies, this illustrated the sensitivity of the calculated performance metrics to the depth of data scrutiny.

Advanced Attribution Multiplier: Reassessing Facebook’s Value

The journey to refine the marketing attribution model further involves applying an advanced attribution multiplier, offering a more sophisticated perspective on a channel’s true incremental value. For example, when adjusting Facebook's numbers with a benchmark multiplier for mid-funnel initiatives such as lookalikes and broad audiences, the CPA climbed even higher to $120. This new figure stands in stark contrast to the initial $57 CPA, underscoring how significant the impact of attribution adjustments can be.

Using this more rigorous model, Facebook's contribution to new customer acquisition drops to 29%, a more realistic portrayal of its true contribution to the business. This analytic progression showcases the variance in a channel’s performance based on the lens applied, emphasizing the need for marketers to adopt a multi-faceted approach to attribution.

Cross-Channel Analysis: A Holistic View of Attribution

While the example provided focused on Facebook, the principles of sensitivity analysis apply across all channels. By combining data sets from various platforms (such as Facebook, Shopify and other eCommerce platforms, or Google Analytics), and employing consistent methodology, businesses can assess the collective impact of their media efforts on both new and existing customer orders.

In doing cross-channel analysis with this advanced approach, businesses can ascertain the unique and overall contribution of each channel to the company's success. This becomes especially crucial in environments where the attribution can often be skewed by customer behavior, platform biases, or simplistic analytic models that fail to capture the multifaceted nature of digital marketing.

Sensitivity analysis is not just about number-crunching; it's a strategic beacon guiding marketers through the fog of complex digital interactions. It aids in deciphering the encrypted messages behind raw data, transforming numbers into narratives that inform smarter business decisions. As the attribution landscape continues to grow in complexity, the value of this analysis only deepens, becoming an essential compass for navigating the dynamic and often murky waters of marketing investment and performance.

Businesses should regard sensitivity analysis as more than a retrospective audit of what has occurred; it's a forward-looking tool, a precursor to adaptive marketing intelligence. By continually questioning and calibrating the data at hand, companies can ensure their marketing strategies remain robust, responsive, and, most importantly, profitable in the long run. 

Learn more about Sensitivity Analysis in our Advanced Attribution Foundations short course.