6 Biggest Challenges in eCommerce Attribution

Since Google announced that it is considering a cookie-less future, eCommerce attribution has become crucial for running a successful online business. 

With 73% of customers using multiple channels during their shopping journey, accurately attributing sales to the appropriate marketing touchpoints is essential for effective resource allocation and ROI optimization. 

Knowing this importance, 98% of retail business owners consider attribution an essential component of their MarTech stack. It allows them to identify the channels driving the most sales.

However, eCommerce attribution is a complex process that comes with various challenges. Despite its significance, implementing an accurate attribution model is easier said than done. 

In this blog, we’ll examine the challenges of eCommerce attribution :

1. Multiple customer touchpoints make attribution complex

2. Traditional models oversimplify the customer journey

3. Data fragmentation hinders analysis

4. A mix of traditional and digital marketing makes attribution tougher

5. Attribution limitations with strict privacy rules

6. Correlation bias in eCommerce attribution

Businesses need an eCommerce analytics to tackle these challenges

Let’s dive right in! 

1. Multiple customer touchpoints make attribution complex 

The biggest challenge in eCommerce attribution comes from the unpredictable behavior of customers, who interact with an unlimited number of touchpoints before making a purchase. 

Customers seamlessly navigate across platforms, encountering your ad campaigns, retargeting campaigns, emails, and various other marketing efforts. Each of these touchpoints holds a certain level of influence in their decision-making process, but quantifying the precise weightage of each interaction is a different ball game. 

For example, a customer might initially discover your product through a social media ad, followed by visiting your website, receiving a retargeting ad on a different platform, and eventually making a purchase after reading a promotional email. 

While all these touchpoints contribute to the final conversion, determining the exact impact of each interaction presents a significant challenge in eCommerce attribution. 

Complexity also arises from highly individualized customer journeys, with different touchpoints and sequences that shape their purchasing decisions. Attributing credit accurately becomes increasingly difficult as the number of touchpoints increases, making it challenging to pinpoint the specific interactions that influenced the customer's final choice the most. 

2. Traditional models oversimplify the customer journey 

Traditional eCommerce attribution models, such as last-click or first-click, oversimplify the complex customer journey by giving credit to a single interaction before conversion. This approach fails to acknowledge the reality that customers are exposed to numerous touchpoints, including ads, social media interactions, and reviews, all of which influence their decision-making process. 

These traditional models neglect the crucial role played by touchpoints that educate or build brand awareness, such as informative blog posts or social media interactions. While these touchpoints may not directly lead to a sale, they play a vital role in shaping the customer's perception and journey. 

Many models struggle to reflect the time customers spend researching and considering products, a stage heavily influenced by factors like reviews, comparisons, and brand reputation. 

By focusing solely on single interactions, traditional models may overinflate the importance of certain touchpoints while undervaluing others. This oversimplified approach makes it difficult to understand which touchpoints are most effective at different customer journey stages. 

3. Data fragmentation hinders analysis 

Customer data is often scattered across various platforms, including website analytics, customer relationship management (CRM) systems, and email marketing tools. This fragmentation makes it challenging to obtain a unified view of the customer journey and hinders accurate attribution analysis.

Data inconsistencies between platforms can lead to inaccurate attribution results, while missing or duplicated data can further complicate the analysis process. Merging data from multiple sources requires significant time and resources, posing a barrier to efficient attribution analysis. 

Fragmented data creates blind spots, making it hard to understand the full sequence of customer interactions before making a purchase. Without a complete view of the customer journey, businesses may miss crucial touchpoints or fail to spot patterns and trends that could improve marketing strategies.

4. A mix of traditional and digital marketing makes attribution tougher 

While online marketing channels have been the primary focus of several eCommerce marketing attribution tools, businesses cannot afford to overlook the impact of traditional advertising methods. 

The focus on digital channels is understandable since eCommerce businesses often struggle to prove ROI and justify online marketing budgets. However, traditional advertising methods like print, TV, and radio also significantly influence customer journeys and purchase decisions.

A customer might have been initially exposed to a brand through a billboard advertisement, which then prompted them to search for the product online, leading to a series of digital touchpoints before eventually making a purchase. 

Without considering the influence of the initial offline touchpoint, the attribution model would fail to capture the complete customer journey, potentially undervaluing the role of traditional marketing efforts. 

By failing to integrate traditional marketing channels into attribution models, eCommerce businesses risk creating significant information gaps in their attribution reporting. 

5. Attribution limitations with strict privacy rules 

With privacy regulations tightening with GDPR and CCPA, third-party cookies, traditionally used for tracking user behavior across different websites, are becoming less reliable, reducing the data available for attribution analysis. 

Moreover, many cookies have short lifespans, failing to capture the entire customer journey, especially for those considering products over an extended period. 

Cookies can also be shared across devices, further complicating accurate user attribution. For example, a customer might browse products on their desktop, continue research on their mobile device, and eventually make a purchase on their tablet. This cross-device behavior makes it difficult to accurately attribute actions to specific users, potentially leading to misinterpretations of customer behavior. 

6. Correlation bias in eCommerce attribution 

Some eCommerce business marketers express concerns that attribution models may be skewed due to in-market or correlation bias, meaning that the predictions made by these models could be inaccurate. 

This skepticism arises from the belief that customers who are already in the market for a particular product or service may exhibit behavior patterns that could be misinterpreted as being influenced by specific marketing touchpoints. 

However, it's important to note that attribution models offer transparent, raw data directly within the platform, allowing marketers to analyze and interpret the information objectively. While attributing credit for every optimization should not be taken at face value, having access to this data can serve as a valuable compass, helping marketers in their decision-making processes. 

For example, if an attribution model consistently shows a high conversion rate for a specific marketing channel or touchpoint, it could indicate that customers who engage with that touchpoint are more likely to be in the market for the product. Rather than dismissing this information as correlation bias, marketers can leverage these insights to refine their targeting strategies or invest in optimizing the touchpoints that resonate with their target audience. 

Businesses need an eCommerce analytics solution to tackle these challenges 

Every customer interaction generates data points across various touchpoints. As customers engage with your brand through multiple channels over time, the amount of data accumulates rapidly, creating a vast and complex dataset. 

While this extensive data holds the potential to give valuable insights, the challenge lies in extracting relevant information without becoming overwhelmed by the sheer volume. 

Attempting to analyze and interpret this data manually is not only time-consuming but also prone to errors, which can significantly skew the results of the attribution analysis. 

This is where an eCommerce analytics solution like Graas comes in. It addresses these challenges by automating the data integration from all your eCommerce sales and marketing channels, and it's real-time, so you can quickly attribute sales and find opportunities to increase your conversion rate.

With Graas' eCommerce attribution models, you can see the distribution of each marketing channel's impact on your final sale. The platform consolidates data from various sources, providing a unified view of the customer journey and enabling accurate attribution analysis. 

By using Graas, you can overcome the challenges of eCommerce attribution and gain a competitive advantage in your industry. Sign up for free today!