Why Is Ecommerce Marketing Attribution Difficult?

In the dynamic landscape of ecommerce, where the customer journey extends across a multitude of touchpoints, attribution remains a tantalizing enigma. It is the elusive key to understanding the true impact of marketing efforts, yet, it presents a complex puzzle that businesses struggle to solve.

In the words of industry expert Avinash Kaushik, “Attribution is the most complex, expensive, and perhaps unnecessary problem in all of digital marketing.” Now, why is this? What makes ecommerce marketing attribution such a formidable challenge?

According to the latest eMarketer report, an average online customer interacts with a brand 7.6 times before making a purchase.

Each interaction, or touchpoint, can range from an Instagram ad, an email campaign, a Google search, a promotional blog, a YouTube video, or a product review on a third-party site. Now, imagine deciphering which of these touchpoints contributed to the final decision to purchase and to what extent. The complexity becomes clear.

The difficulty of attribution in ecommerce marketing stems not from a lack of data but from an overwhelming abundance of it. However, amidst this sea of information, one fact remains unchanged: accurate attribution is crucial for businesses to optimize their marketing strategies and maximize their return on investment.

This article aims to shed light on the intricacies of ecommerce marketing attribution, highlighting the challenges businesses face and offering insights into possible solutions. Join us as we dive into the fascinating, complex world of attribution.

The Multiple Touchpoints Challenge

The first obstacle to effective ecommerce marketing attribution is the sheer number of touchpoints a customer interacts with before making a purchase. Unraveling this tangled web of touchpoints becomes a herculean task for marketers.

To illustrate, imagine a potential customer’s journey:

They discover your product through a Facebook ad, then read a review on a third-party website, then click on a Google search ad, and finally receive a promotional email that persuades them to make a purchase. Each of these interactions plays a role in influencing the customer’s decision, but determining the exact weightage of each touchpoint is far from straightforward.

Furthermore, the challenge is amplified by the fact that not all touchpoints are easily trackable. For instance, word-of-mouth referrals and offline marketing efforts can be difficult to quantify and incorporate into the attribution model. This added complexity makes it even more challenging to assess the impact of each marketing channel on the customer’s journey.

The multiple touchpoints challenge highlights the need for a sophisticated and comprehensive approach to marketing attribution. By developing a clear understanding of the customer journey across all touchpoints, businesses can better allocate their marketing budgets and optimize their strategies for maximum impact.

The Role of Multi-Device Shopping

Customers often switch between devices during their shopping journey, adding another layer of complexity to marketing attribution.

Consider a typical scenario: A customer initially discovers your product on their smartphone through a social media ad. Later, they research more about the product on a laptop, perhaps clicking through a PPC ad. Finally, they might complete the purchase on a tablet after receiving a promotional email.

This transition between different devices presents a considerable challenge in accurately tracking and attributing the contribution of each marketing touchpoint.

Moreover, each device a customer uses may have different cookie settings, making it harder to maintain a continuous link to the same customer across devices. Some customers might also use ad blockers or regularly clear their cookies, leading to incomplete or disjointed data about their interactions with your brand.

On top of this, privacy regulations and technology changes, such as the deprecation of third-party cookies, are making cross-device tracking more difficult. For instance, Apple’s recent iOS updates emphasize user privacy and limit marketers’ ability to track users across different apps and websites.

Given these obstacles, businesses need to find innovative ways to track customers’ journeys across multiple devices. Solutions may involve leveraging first-party data more effectively, utilizing deterministic or probabilistic cross-device tracking, or embracing new technologies like Google’s Federated Learning of Cohorts (FLoC).

The Data Overload Dilemma

Each customer interaction across various touchpoints generates a data point. Multiply these data points with the number of customers, and the number of interactions they have with your brand over time, and you quickly have a staggering amount of data to sift through.

This extensive dataset could potentially offer valuable insights, but the challenge lies in extracting relevant information without getting overwhelmed.

Moreover, data collected from different sources may be structured differently, further complicating the analysis. For instance, data from social media platforms, email campaigns, and website analytics each have their unique formats and parameters. Aggregating and harmonizing this data into a unified format for analysis can be a daunting task.

Additionally, not all data is of equal importance. Understanding which metrics are critical to your business objectives and focusing on them is key to avoiding being swamped by irrelevant information. This requires careful planning and strategic thinking.

Businesses must be equipped with the right tools and strategies to filter, process, and interpret the colossal amounts of data they collect. Only then can they truly unlock the power of this data to enhance their marketing strategies and boost their bottom line.

The Limitations of Traditional Attribution Models

Traditional attribution models, such as Last-Click or First-Click, often oversimplify the customer journey, making it challenging to accurately measure the impact of different marketing channels. These models, while easy to implement and understand, have inherent limitations that can lead to skewed results and misguided marketing decisions.

Let’s consider the First-Click model, which attributes the entire conversion value to the first touchpoint a customer interacts with. While this approach acknowledges the role of brand discovery, it completely disregards the influence of subsequent interactions that may have played a significant part in the final purchase decision.

On the other hand, the Last-Click model, arguably the most widely used, attributes the entire conversion value to the final touchpoint before purchase. This model emphasizes the ‘closer’ in the sales process but overlooks the crucial role of earlier touchpoints that nurtured the customer along the journey. This could result in underinvestment in top-of-the-funnel marketing activities that are essential for customer acquisition.

Moreover, both these models fail to consider the complexities of the modern customer journey, which often involves multiple touchpoints across various channels and devices. They provide a linear, oversimplified view of the conversion path, which can lead to a distorted understanding of your marketing performance.

The Impact of Privacy Regulations and Data Fragmentation

The rise of privacy laws like GDPR and CCPA, along with the increasing fragmentation of data, add further complications to the attribution process. As businesses adapt to a more privacy-focused world, they must grapple with the restrictions these regulations impose on data collection and use, which can significantly impact their attribution efforts.

Let’s start with privacy regulations.

Laws like the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States have fundamentally reshaped the way businesses collect, store, and use customer data. These regulations mandate businesses to obtain explicit consent from users before tracking their online behavior, among other things. As a result, businesses may face difficulties in obtaining comprehensive and granular data needed for accurate attribution.

Moreover, the upcoming deprecation of third-party cookies, led by browsers like Google Chrome and Safari, adds another layer of complexity. Third-party cookies have been a cornerstone of online tracking and attribution. Their disappearance will pose significant challenges for marketers in maintaining a clear view of the customer journey.

Simultaneously, data fragmentation – the scattering of customer data across various platforms and databases – further complicates the attribution landscape. For instance, a customer’s data may be spread across your CRM system, email marketing platform, social media channels, and website analytics tool. Consolidating this data to create a unified customer view is a significant challenge, but it’s essential for effective attribution.

Advanced Attribution Methods: Pros and Cons

To tackle the challenges of ecommerce marketing attribution, several advanced attribution models have emerged, including Data-Driven Attribution and Multi-Touch Attribution. These models offer a more nuanced perspective of the customer journey, but they come with their own set of advantages and limitations.

Data-Driven Attribution, as the name suggests, relies on machine learning algorithms to analyze vast amounts of data and determine the impact of different touchpoints on the conversion path. Unlike rule-based models, it doesn’t assign credit based on pre-set rules but learns from the data to assign credit dynamically. This allows for a more accurate representation of the customer journey. However, the downside is that it requires a large volume of data to function effectively. Small businesses or those with limited data may not be able to leverage this model fully.

On the other hand, Multi-Touch Attribution models distribute the credit for conversion across multiple touchpoints. There are different variations of this model such as

Linear (equal credit to all touchpoints)

Time-Decay (more credit to touchpoints closer to the conversion)

U-Shaped (more credit to the first and last touchpoint)

and W-shaped (The first, middle, and last touches get the most credit, while the rest share it)

These models acknowledge the role of multiple interactions in the customer journey, providing a more holistic view than single-touch models. However, they can be more complex to implement and manage. Moreover, deciding on the best variant of Multi-Touch Attribution for your business can be a challenge in itself.

In essence, while advanced attribution methods offer a more comprehensive understanding of the customer journey, they also bring about their own set of complexities. It’s crucial for businesses to evaluate their unique needs, resources, and data capabilities before deciding on the most suitable attribution model.

The goal is not to find a perfect model – because there isn’t one – but to find one that provides the best possible insights given your circumstances.

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Conclusion

The future of ecommerce marketing attribution will be characterized by advanced technologies, privacy-centric approaches, and an increasingly sophisticated understanding of the customer journey. While the road may be complex, the rewards for those who can navigate it effectively are immense.

As the ecommerce landscape evolves, so too must our strategies for understanding and leveraging the impact of our marketing efforts. Adaptability, innovation, and a relentless focus on the customer will be the keys to success in this exciting journey ahead.

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