Understanding Marketing Attribution Modeling – The Pros and Cons

As a savvy marketer, you certainly know how many channels there are for your customers to interact with you throughout the buyer’s journey. According to Salesforce, the average customer uses 10 channels to communicate with brands, which means the marketing space is more fragmented than ever.

As customers’ expectations rise, so do the complexities in marketing processes and measuring ROI.

Research shows that marketers’ top priorities include optimizing the marketing mix for the best ROI and advancing their MarTech stack. The same survey shows that 41% of marketing businesses are leveraging marketing attribution modeling, for instance, multi-touch attribution, marketing mix modeling, as a measure of ROI.

Marketing attribution modeling makes it easy to identify a set of events or digital touchpoints that have contributed to your desired outcome.

In this blog post, we’ll decode marketing attribution modeling and its purpose, and how to overcome challenges while implementing it.

What is Marketing Attribution Modeling and Its Purpose?

Marketing attribution modeling allows you to give credit to the marketing channels and digital touchpoints throughout the buyer’s journey that made a lead convert. By assigning credit to your digital touchpoints, you can improve your chances of converting more prospects by:

• Identifying areas of the buyer’s journey that you can optimize
• Determining the ROI for each channel or touchpoint
• Tailoring the most effective ways to spend your marketing dollars
• Curating your marketing campaigns and content to your unique audience personas

What are the Pros of Marketing Attribution Modeling?

Businesses face numerous challenges when it comes to understanding the complexities of customer interactions and the effectiveness of their marketing efforts. Traditional models often fall short of providing accurate insights, leading to misallocated budgets and missed opportunities.

Marketing attribution modeling, however, offers a data-driven solution to unravel the intricacies of the customer journey, enabling companies to make informed decisions, optimize their strategies, and achieve higher marketing ROI. Now, let’s delve into the other pros of this powerful approach.

1. Enhanced Understanding of Customer Journey and Touchpoints:

Marketing attribution modeling allows businesses to track and analyze customer interactions at various touchpoints throughout their journey. By understanding the sequence of interactions that lead to a conversion or sale, marketers can gain valuable insights into the customer’s decision-making process and preferences.

2. Improved Marketing Budget Allocation and Optimization:

Attribution modeling helps allocate marketing lean budget more effectively by identifying which channels and campaigns have the most significant impact on driving conversions. By focusing resources on the most effective channels and reducing investment in less productive ones, companies can maximize their return on marketing investment.

3. Identification of High-Performing Marketing Channels & Campaigns:

Attribution models reveal the channels and campaigns that contribute the most to the final conversion. This information empowers marketers to prioritize and optimize efforts on those channels that consistently deliver the best results, leading to higher conversion rates and overall performance.

4. Facilitation of Data-Driven Decision-Making:

Attribution modeling relies on data and analytics to attribute credit accurately to marketing touchpoints. This data-driven approach removes guesswork and biases, ensuring that marketing decisions are based on concrete evidence rather than assumptions.

5. Increased Accountability and Measurement of Marketing ROI:

With attribution modeling, marketers can quantify the impact of their efforts and calculate the return on investment (ROI) for each marketing initiative accurately. This level of accountability enables businesses to justify marketing expenses and make adjustments to strategies to achieve better results.

What are the Cons of Marketing Attribution Modeling?

Marketing attribution modeling comes with its share of challenges and limitations. Let’s explore some of the cons in more detail:

1. Complex Implementation and Data Integration Challenges:

Implementing marketing attribution models can be a complex and time-consuming process, especially for businesses with large and diverse data sources. Integrating data from various marketing channels, CRM systems, offline interactions, and third-party platforms can be challenging and may require significant technical expertise.

2. Lack of Consensus on the Right Attribution Model:

There is no one-size-fits-all attribution model that suits every business. Different attribution models (e.g., Single Touch, Multi-Touch, U-Shaped Multi-Touch, W- Shaped Multi-Touch etc.) emphasize different touchpoints in the customer journey. The lack of consensus on the most appropriate model can lead to confusion and discrepancies in measuring marketing effectiveness.

3. Incomplete and Biased Data Leading to Inaccurate Attribution:

Attribution modeling relies heavily on data accuracy and completeness. Incomplete or inaccurate data can lead to skewed attribution results, misrepresenting the actual impact of certain marketing efforts. Additionally, biases in data collection and interpretation may further hinder accurate attribution.

4. Limited Visibility into Offline and Cross-Device Customer Interactions:

Attribution models often struggle to capture and integrate data from offline channels, such as in-store purchases or phone orders. Similarly, cross-device interactions, where customers switch between multiple devices during their journey, can be challenging to track accurately, potentially leading to incomplete attribution.

5. Inability to Capture the Impact of External Factors and Brand Awareness:

Attribution models typically focus on the contribution of marketing touchpoints and channels directly. They may not fully account for external factors such as economic conditions, seasonality, or competitors’ actions, which can influence consumer behavior and affect marketing results. Additionally, branding and awareness efforts may not have immediate or direct conversions, making it challenging to attribute their long-term impact accurately.

And because of these limitations of marketing attribution models, even Google decided to phase out four models – First-Click, Linear, Time-Decay, and Position-Based attribution models.

How to Overcome the Challenges of Marketing Attribution Modeling

Remember that marketing attribution modeling is not a perfect science, and no approach can completely eliminate challenges. However, by continuously refining data processes, leveraging advanced analytical techniques, and combining various data sources, businesses can improve the accuracy and reliability of their attribution models, leading to better marketing decision-making and improved overall performance. To overcome some of these challenges, businesses should:

• Invest in marketing automation and data analytics platforms like DiGGrowth that offer robust attribution capabilities and streamline data integration processes.
• Prioritize data cleanliness and accuracy by regularly auditing and cleansing datasets.
• Consider using a hybrid or custom attribution model that combines the elements of multiple models to suit your specific business needs.
• Regularly monitor data sources for accuracy and completeness, setting up alerts for potential issues.
• Leverage customer surveys and feedback to gain insights into the impact of offline and cross-device interactions on the customer journey.
• Incorporate external data sources, such as industry trends and competitor analysis, into your analysis to gain a broader perspective on marketing effectiveness.

The Bottom Line

As customers venture into new channels and businesses embrace cutting-edge marketing technologies, attribution becomes pivotal in aligning your entire marketing organization toward the shared objective of revenue generation. Though the most effective attribution models remain a subject of debate, customizing your approach to suit your business needs, while ensuring seamless integration of data with your CRM and technology stack, holds the key to maximizing lead conversion opportunities.