Contribution vs attribution analysis: What is the difference?

Contribution and attribution analysis are two customer journey analytics models that help identify the friction points a customer experiences until they reach a desired action. They are based on customer behavior data collected through numerous customer interactions with a company’s website, application, or physical storefront.

Both types of analysis can help businesses map out their entire customer journey, revealing what marketing strategies influence a conversion or what pain points deter customers from making a purchase.This essential information enables organizations to create transformative customer experience initiatives that address issues at every point of their customer journey and improve their overall customer experience , boosting customer satisfaction.However, marketing teams need to know whether to use contribution or attribution analysis models as they perform journey mapping.

In this article, we define each type of analytical model, detail how they differ from each other, and indicate their importance in journey analytics.

What is attribution analysis?

Attribution analysis is one journey analytics model used in customer journey analytics that enables companies and their marketing teams to determine what key steps within a customer’s journey influence them to take certain actions.

Utilizing customer data, marketers with an attribution analysis model can determine whether a visit or transaction occurred due to a particular touchpoint within their storefront, website, or application.

Some examples of a touchpoint include:

• An online banner ad on a website
• A quick commercial on YouTube
• A blog post on a company’s website
• A print catalog of products
• Company social media across multiple channels

Marketing teams attribute values to each touchpoint to determine what produces the most desired business outcomes and is worth further investment.

What is contribution analysis?

Contribution analysis is another journey analytics model used in customer journey analytics to help marketing teams and organizations discover what points on customer journey maps affect customer satisfaction.

While performing a similar function to attribution analysis, contribution analysis considers the same array of touchpoints within a company’s marketing strategy and provides a weighted value to each, providing a visual representation of how much each one contributes towards a customer’s conversion.

Both contribution and attribution models in journey analytics provide a deeper understanding of how customers behave when interacting with your product or service.

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The differences between attribution & contribution analysis.

While attribution and contribution analysis are both meant to provide companies with actionable insights about their customer experience, each differ in how they measure and present metrics to your marketing team. Here are a few critical differences between attribution and contribution analysis:

How metrics are weighed.

One of the primary differences between contribution and attribution analytics is how each one weighs customer interaction data. Attribution analysis focuses on one touchpoint, or customer action, at a time to determine the performance of a particular marketing strategy.

For example, marketers using an attribution analysis model may measure the last click a customer makes before making a purchase as the determining factor of the sale. If the final interaction was a customer clicking on a paid ad, the marketing team would attribute the ad as the cause of the sale.

Contribution analysis, on the other hand, doesn’t involve monitoring just one touchpoint. It measures all at the same time. Using a contribution analytics model, marketers analyze each metric related to every point of the customer journey and weigh how much each contributed to their customers’ sales.

For example, a marketing team may find that paid search and banner ads each accounted for 25% of the sale, while organic searches contributed to 40%, and direct visits influenced 10%.

In a contribution model, all marketing efforts are considered factors that contribute to a customer’s purchase. Since some factors weigh more than others,marketing teams are able to quickly understand whether their current strategy is working and where they should invest.

Depth of customer journey analytics.

In addition to weighing data differently, attribution and contribution analysis models also differ in how they utilize customer journey data.

Attribution analysis bases business outcomes on singular actions, whether website visits, form-fills, or sales transactions, while contribution analysis will track customer behavior across multiple touchpoints, creating a well-rounded conclusion based on all sets of customer journey data.

Overall, this means that contribution analytics provides a more comprehensive perspective of the customer experience, giving a marketing team deep insights into what affects customers overall.

On the flip side, attribution analysis provides a brief overview of the customer journey but can quickly indicate pain points that a business should fix.

Number of customer journey analytics models.

One area where attribution analysis beats contribution analysis is in the number of available models it offers marketing teams and businesses to use.

While there may be numerous contribution models, they are more likely adjusted according to the specific industries or businesses they are used in.

However, there are several models of attribution analysis that vary in how touchpoints are measured. Here are a few to consider:

• First Point Attribution: A model that involves assigning 100% value of a sale or other action to the first touchpoint a customer interacts with before making a purchase.
• Last Interaction Attribution: The opposite of a First Point Attribution, where 100% credit of a sale or action goes to the last touchpoint a customer interacts with before making a purchase or conversion.
• Time Decay Attribution: A model that assigns credit to the touchpoint or touchpoints that a customer most recently interacted with, depending on how soon they did before making a purchase or conversion.

No matter how many model types one has over another, contribution and attribution analysis help businesses perform effective customer journey mapping and leverage customer journey analytics data to achieve market success by continuously increasing customer satisfaction.

The scale at which it is effective.

Finally, a critical difference that contribution and attribution analysis have is the scale at which each is most effective for businesses to use. On a smaller-scale number of purchases and customer interactions, attribution analysis provides an adequate view of an organization’s marketing performance.

However, for customer journeys on a larger scale, as well as ones that result in more significant purchases, contribution analysis provides more depth and scope of the trends and patterns affecting an organization’s customer experience.

As with all kinds of other tools, contribution and attribution analysis provide a solution to their own unique problems. By using them effectively, businesses can continuously enhance their customer experience, ensuring long-term customer satisfaction and success.

Unravel customer journeys with Quantum Metric.

Customer journey analytics enables companies to collect data about their customers’ needs and preferences. Through contribution and attribution, a business can develop a data-based solution informing marketing efforts and ensuring successful outcomes.

Furthermore, your organization can use Quantum Metric’s extensive analytics platform to align your marketing, product, customer service, and business teams by providing the same metrics and insights across all teams to foster collaboration and streamlined processes.

Try Quantum Metric‘s unrivaled digital experience analytics platform and unravel every detail of your business’s customer journeys today.