Marketing attribution software has garnered more and more attention in the martech world since advertising channels have diversified. Marketers today use, on average, 14 tools to cater to their marketing efforts, increase sales, and scale their online businesses.

Knowing precisely which channel to scale and where to invest more time, effort, and budget is crucial to any marketing department or agency.

When choosing between the vast amount of channels, businesses often disregard one or the other option, making decisions based on the industry's best practices or inaccurate data. Ideally, identifying the best-performing channel is based on accurate tracking and savvy reporting capabilities.

What is marketing attribution?

Marketing attribution is an analytical technique used to determine which touchpoints are driving sales or any other type of conversion. Marketing professionals leverage attribution models to evaluate which channel, tactic, or message contributes to conversions.

What is marketing attribution modeling?

Marketing attribution modeling refers to correlating a conversion to a specific touchpoint in the customer journey. Once attributed, marketing efforts can be valued and scaled accordingly. Attribution models differ widely, and today we differentiate between single- and multi-touch attribution, lift or incrementality studies, and others.

Ideally, marketing attribution modeling helps to determine, on the customer journey map, the most successful marketing tactics and channels at each step with the ultimate goal of increasing ROAS while scaling ad spend.

Types of marketing attribution models

Several marketing attribution models have been successfully used in the past years, ranging from ML-based attribution models to those deployed by advertisers, like Meta’s or Google Analytics’s model.

As described by Alistair Rennie, Research Lead, Market Insights UK at Google, the messy middle is a concept referring to the buyer decision process and the current stage of marketing exposure that consumers face daily. From the first interaction to the actual sale, a person would be exposed to a complicated web of touchpoints across various channels and mediums - all of these model individuals' behavior while the ultimate factors influencing the purchase decision often remain unknown.

Marketing professionals can use various types of attribution models to navigate the web of advertising channels and understand the value-driving touchpoints.

Single-touch attribution or single-source attribution model

Single-touch models assign credit to only one event or touchpoint during the entire customer journey. The most important single-touch attribution models are

First touch attribution model

Last touch attribution model

Such models have declined in popularity in the past years, especially since customer interaction with a brand is not singular, meaning a purchase is no longer occurring due to a single touchpoint. On the contrary, the current marketing efforts of any brand consist of a multitude of channels and efforts that influence highly complicated purchase decision processes.

Multi-touch attribution models

Multi-touch attribution models, also known as fractional attribution models, assign a value to each touchpoint leading to a sale or conversion, thus helping marketers understand where the most value lies in the marketing mix. Some examples of multi-touch models are linear, time decay, position-based, or custom rule-based models.

Multi-touch attribution models can rely on either a predetermined mathematical equation that assigns a certain percentage to each channel in the customer journey or can be custom, meaning personalized based on previous data.

A noncomplete list of multi-touch attribution models

Most of these models can be differentiated based on how they distribute credit to different touchpoints of a customer’s journey. Following is an explanation of some popular multi-touch models.

Linear attribution distributes the credit for a purchase equally among the various touchpoints a customer interacted with.

Like the linear model, time decay attribution looks at all touchpoints leading to conversion but attributes the highest value to the most recent engagement, considering them of a more significant impact.


Position-based attribution
, also known as U-shaped attribution, has a predefined logic assigning 40% credit to the first touch and the last touchpoint before the conversion. The remaining 20% are divided among the touchpoints between the first and the last interaction, thus creating a U-shaped graphical representation.

Data-driven attribution

Besides the single touch and multi-touch attribution, data-driven, algorithmic or probabilistic attribution uses machine learning and statistical modeling to determine the most probable conversion points across the marketing funnel, thus attributing value to these conversion-driving touchpoints. Data-driven attribution models require manual customization and large amounts of customer journey and conversion data to training and refinement.

Benefits of marketing attribution

Marketing attribution helps marketers understand a vast series of dimensions and characteristics of their marketing efforts. As shown by researchers, more than 92% of customers don't have any purchasing intention when visiting a website for the first time. Customers converting after seeing just one advertisement are rare, so exposing potential buyers to a multitude of online and offline channels can add difficulties in measuring the effectiveness of these marketing activities and choosing a winning strategy.

Different attribution models can indicate which copy, creative, channel, or other dimension performs best. Savvy marketers use omnichannel attribution to scale their ads, improve ROAS, and increase demand and CLV.

How to choose the best marketing attribution software?

When looking for the best marketing attribution software, DTC store owners and performance marketing professionals should search for a tool that brings visibility and helps to reliably scale their advertising efforts.

In an increasingly competitive online landscape, still recovering from a surge in demand brought by the pandemic curfews, most marketers have turned to online advertising, spending more dollars on advertisements than ever before. The bids have gone up, but consumers also show fatigued behavior towards ads.

Therefore, crafting unique strategies and understanding which channels perform best is crucial for scaling online businesses.

Marketing attribution software tools for Shopify should

  • Operate with first-party data
  • Offer an accurate overview of the customer journeys
  • Provide capabilities to compare the outputs of different attribution models
  • Provide reporting capabilities on mentioned dimensions
  • Show the best performing creatives among all channels
  • Show the most impactful brand messages that convert
  • Measure the impact of influencers among other digital campaigns
  • Consider non-paid channels like organic search or social


How Admetrics Data Studio for Shopify can help you scale your business with marketing attribution

Admetrics Data Studio for Shopify offers marketers various attribution models and incredible reporting capabilities. Brands can measure a unique blend of dimensions and metrics across all of their paid and unpaid channels, This includes dimensions like traffic sources, campaign types, ad texts, and metrics such as ROAS, CPO, repurchase, retention rate, and many more.

With Admetrics, DTC brands can choose between a selection of single- and multi-touch models. These models help brands establish a marketing strategy with the winning channels, while understanding which campaigns and creatives perform best for the different audiences in each environment.

Before jumping on the exemplification of various dimensions and metrics based on different attribution models, we provide a glimpse into the customer journeys of a brand using an omnichannel marketing approach, including influencers, email, and much more.

Customer journey vizualisation of an actual client


Given the multitude of channels, the client would have to navigate a sea of data to gain clarity on the best-performing ones. With Admetrics, as shown in the example below, choosing a multi-touch attribution model brings visibility and helps marketers see the sales-driving channels.

It becomes clear that certain channels are driving significantly more first touchpoints than last ones - by optimizing using a single-touch model some channels would be disadvantaged and potentially removed from the marketing mix, although they contribute to the purchasing decision-making.

This chart visualizes the differences in revenue attributed to each channel by the different attribution models

As shown in the example above, by using Admetrics Data Studio for Shopify, brands gain access to more than just simplified reporting. Marketers can leverage a powerful toolbox to analyze all of their channels and extract actionable insights.


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