In today’s digital age, customers are exposed to multi-channel marketing and touchpoints such as social media, email marketing, paid advertising, and content marketing – and marketers work hard to maximize search engine optimization (SEO) so their businesses rise to the top of the list no matter what the browser of choice might be.

These touchpoints can occur across different devices, platforms, and stages of the customer journey. It is therefore essential for businesses to have a clear understanding of how each of these touchpoints contributes to the overall customer journey and the final conversion. More sales, signed contracts, donations, volunteer signups – won deals – is the ultimate goal.

By using multi-channel attribution, businesses can accurately allocate marketing budgets and resources based on the channels that have the highest impact on customer behavior and sales. This allows businesses to optimize their marketing efforts, improve the return on ad spend (ROAS) of their campaigns, and drive growth.

Multi-channel attribution is essential for marketing because it helps businesses to understand how their customers interact with their various marketing channels and campaigns, and how those interactions contribute to the ultimate conversion of a sale or a desired action.

How Multi-Channel Attribution Works

Multi-channel attribution – also known as multi-touch attribution (MTA) – provides an impartial view of marketing efforts. It identifies what is working, what is not, and which channels are working together; giving credit to ALL channels that are contributing. That knowledge is power.

Here’s a step-by-step look at what MTA collects and how it works:

• The first step in multi-channel attribution, or MTA, is collecting data about the customer’s journey. This data includes the various touchpoints the customer interacts with, such as email campaigns, social media ads, organic search results, paid search ads, and more.
• There are several different attribution models that businesses can use to assign credit for conversions to different touchpoints. These models include first-click attribution, last-click attribution, linear attribution, time-decay attribution, and more. Each model assigns different weights to the touchpoints based on their perceived influence on the customer’s journey.
• After selecting an attribution model, businesses can assign credit to the various touchpoints based on the model’s rules. For example, in a first-click attribution model, credit would be assigned to the first touchpoint the customer interacted with on their journey. MTA tends to look at all touchpoints, so credit is fairly and impartially identified.
• Once a credit is assigned, businesses can analyze the results to determine which touchpoints are most effective in driving conversions. They can then adjust their marketing strategies accordingly to focus on the channels that are most effective.

Mind the Gaps with Multi-Channel Attribution

Identifying what is NOT working is also important. Multi-channel attribution helps businesses to identify any gaps or weaknesses in their marketing strategies and to make data-driven decisions that are informed by customer behavior. This can ultimately help businesses to provide better customer experiences, build stronger relationships with their customers, and increase customer loyalty.

Here are some ways that multi-channel attribution/MTA can help identify gaps in marketing:

• Multi-channel attribution can help identify channels that are not performing. By analyzing the data, businesses can determine which channels are not contributing significantly to conversions and may need to be reevaluated or discontinued.
• Multi-channel attribution can help businesses understand how customers are engaging with their marketing channels. By analyzing the data, businesses can identify areas where customers are dropping off or not engaging with the channels, which can help identify gaps in the marketing strategy.
• Multi-channel attribution allows businesses to test different marketing strategies and see which ones are most effective in driving conversions. By testing different channels or campaigns, businesses can identify areas where their marketing strategy may be falling short and adjust accordingly.
• Multi-channel attribution can help businesses focus on the customer experience and identify areas where they can improve. By understanding how customers are interacting with the various channels and touchpoints, businesses can identify areas where they can improve the customer experience and better meet their needs.

Make Informed Decisions About Budgeting with Multi-Channel Attribution

Multi-channel attribution can help with budgeting by providing businesses with data-driven insights into the effectiveness of each marketing channel and how they contribute to conversions. Here are some ways that multi-channel attribution can help with budgeting:

• Multi-channel attribution allows businesses to optimize their marketing spend by identifying the channels that are most effective in driving conversions. By allocating more budget to these channels, businesses can maximize their ROAS and achieve better results.
• It can also help businesses reduce wasted spend by identifying channels that are not contributing significantly to conversions. By reallocating the budget away from these channels, businesses can avoid wasting money on ineffective marketing efforts.
• Multi-channel attribution allows businesses to test and experiment with different marketing channels and strategies to see what works best. By testing different approaches and analyzing the results, businesses can identify the most effective channels and allocate budgets accordingly.
• It can also help businesses plan for the future by providing insights into how customer behavior is likely to change over time. By anticipating changes in customer behavior, businesses can adjust their marketing strategies and budget accordingly.

Studies Show the Value of Multi-Channel Attribution

Study after study shows the value of multi-channel attribution and MTA. Here are a few:

• In this study, titled “Attribution modeling (sic) in digital advertising for e-commerce,” “the authors extend the multi-channel attribution theory and showed that statistics-based multi-channel attribution model (Markov chains) assigns credit for all touchpoints in a customer journey, not just one like in default heuristics models (last-click and first-click). This study shed light on how to apply the statistics-based multi-channel attribution model to measure advertising effectiveness in a more accurate way.”
• Here are a couple of relevant points from this study, “Deep Neural Net with Attention for Multi-channel Multi-touch Attribution”:
  • “In a sequence of observations of touch points, the same touchpoints may be differentially important at different time locations and at different frequencies (sic) of occurrence. Our model introduces a (sic) attention mechanism that lets the model pay (sic) more or less attention to individual touchpoints when constructing the representation of the customer path.”
  • “Attention serves two benefits: it not only provides us reasonably (sic) better performance but also gives insight on how touchpoint contributes to the conversion decision at any specific time which is the most valuable part of an attribution conversion problem.”
• This study, titled “Bayesian Modeling of Marketing Attribution,” has a great abstract of what to expect from the study: “In a multi-channel marketing world, the purchase decision journey encounters many interactions (e.g., email, mobile notifications, display advertising, social media, and so on). These impressions have direct (main effects), as well as interactive influence on the final decision of the customer. To maximize conversions, a marketer needs to understand how each of these marketing efforts individually and collectively affects the customer’s final decision. This insight will help her optimize the advertising budget over interacting marketing channels. This problem of interpreting the influence of various marketing channels on the customer’s decision process is called marketing attribution. We propose a Bayesian model of marketing attribution that captures established modes of action of advertisements, including the direct effect of the ad, decay of the ad effect, the interaction between ads, and customer heterogeneity. Our model allows us to incorporate information from customers’ features and provides usable error bounds for parameters of interest, like the ad effect or the half-life of an ad. We apply our model on a real-world dataset and evaluate its performance against alternatives in simulations.”

Overall, multi-channel attribution is essential for marketing because it provides businesses with the insights they need to create effective and efficient marketing strategies that drive growth and deliver results.

To learn more about multi-channel attribution, ask for a brief, no-obligation demo.