Introduction
In today’s digital-first economy, customers rarely make purchase decisions after a single interaction. Instead, they move through a complex web of touchpoints—search engines, social media, review sites, emails, physical stores, and peer recommendations—before finally converting. This path from “think” to “buy” has become increasingly non-linear, especially in e-commerce and omnichannel environments.
For marketers, this creates a critical question: which marketing channels actually drive conversions? Traditionally, many organizations have relied on simplistic attribution rules, such as assigning all credit to the last channel a customer touched before purchasing. While easy to implement, such approaches often misrepresent reality and lead to inefficient marketing spend.
Channel attribution modelling addresses this challenge by distributing conversion credit across multiple touchpoints. Among the most robust and data-driven approaches to attribution is Markov Chain–based attribution modelling. This article explores the origins of channel attribution, explains how Markov Chains fit into marketing analytics, and demonstrates their real-world application through an e-commerce case study implemented in R.
Origins of Channel Attribution Modelling
The concept of attribution in marketing predates digital channels. In traditional advertising, marketers attempted to infer impact using media mix models, surveys, and controlled experiments. However, these methods were often expensive, slow, and limited in granularity.
With the rise of digital analytics platforms in the early 2000s, marketers gained the ability to track user-level interactions across channels. This led to rule-based attribution models such as:
First-touch attribution, where all credit is given to the first interaction
Last-touch attribution, where the final interaction receives full credit
Linear attribution, where credit is distributed equally across all touchpoints
While these heuristic models provided directional insights, they failed to capture the true interdependencies between channels. As customer journeys became longer and more fragmented, marketers began adopting probabilistic approaches inspired by operations research and stochastic processes. This shift laid the foundation for Markov Chain attribution models, which treat customer journeys as sequences of state transitions.
What Is Channel Attribution?
Channel attribution is the analytical process of assigning value to marketing touchpoints that influence a customer’s decision to convert. A conversion could be a purchase, sign-up, download, or any other business-defined outcome.
In practice, attribution answers questions such as:
Which channels initiate customer journeys?
Which channels assist conversions but rarely close them?
Which channels are essential for conversion to occur at all?
Rather than relying on assumptions, advanced attribution models aim to quantify the incremental contribution of each channel. Markov Chain models do this by evaluating how conversion probability changes when a channel is removed from the journey.
Understanding Markov Chains in Marketing
A Markov Chain is a mathematical process that describes transitions between states, where the probability of moving to the next state depends only on the current state. This property is known as memory lessness.
In a marketing context:
States represent marketing channels or touchpoints
Transitions represent customer movement from one channel to another
Transition probabilities represent the likelihood of moving between channels
A customer journey can be modelled as a directed graph, where nodes are channels and edges are transition probabilities. Because the next touchpoint depends only on the current one—and not the full history—the journey can be effectively modelled as a Markov process.
This approach is particularly powerful because it reflects how customers actually behave: sequentially, probabilistically, and across multiple channels.
The Removal Effect: Measuring True Channel Contribution
The key innovation of Markov Chain attribution is the removal effect. Instead of assigning arbitrary credit, this method measures how much overall conversion probability drops when a specific channel is removed from the model.
The logic is straightforward:
Calculate the probability of conversion with all channels present
Remove one channel and recalculate conversion probability
The reduction in conversion probability represents that channel’s contribution
Channels that are critical to the journey will show a large drop in conversions when removed, even if they are not the final touchpoint. This makes Markov attribution particularly effective at identifying assist channels—those that influence decisions indirectly.
Real-Life Applications of Markov Attribution
E-commerce and Retail
Online retailers use Markov attribution to balance spending across paid search, social media, display ads, and email marketing. For example, a channel like product reviews may rarely close sales but can be crucial early in the decision process.
Financial Services
Banks and fintech companies apply Markov models to understand how customers move between informational content, comparison tools, and advisory touchpoints before opening accounts or applying for loans.
Subscription Businesses
SaaS and streaming platforms use attribution modelling to evaluate free trials, retargeting ads, email nudges, and in-app prompts, optimizing retention as well as acquisition.
Omnichannel Marketing
Markov Chains are also effective in blending online and offline data—such as website visits, call centre interactions, and physical store visits—into a single, unified attribution framework.
Case Study: Channel Attribution for an E-Commerce Company
Consider an e-commerce company that surveyed customers about their pre-purchase journey. The dataset captures interactions across 19 marketing channels, followed by three terminal states:
Decision made
Purchase completed
Decision pending
The channels are grouped into categories such as company websites, research reports, online reviews, price comparison platforms, social recommendations, expert advice, retail stores, and miscellaneous promotional activities.
The business objective is to identify which channels deserve increased investment and which provide limited incremental value.
Each customer journey is represented as a sequence of channels leading to a conversion state. By aggregating identical paths and counting conversions, the data is prepared for attribution modelling.
Implementing Markov Attribution in R
Using R and specialized attribution libraries, the company builds both heuristic and Markov-based models. Heuristic models compute first-touch, last-touch, and linear attribution values, while the Markov model estimates channel contributions based on removal effects.
The Markov results reveal insights that are not obvious from heuristic approaches alone. For example:
Certain research and review channels show modest last-touch value but high removal effects
Decision-stage channels dominate last-touch attribution but are less influential earlier
Some channels consistently support conversions across multiple journey stages
By visualizing these results, stakeholders can clearly compare attribution methods and understand how channel importance changes depending on the model used.
Key Insights from the Analysis
The comparison between heuristic and Markov attribution highlights several important lessons:
Last-touch models tend to overvalue decision-stage channels
First-touch models overemphasize awareness channels
Linear models dilute the true impact of critical channels
Markov models provide a balanced, data-driven view of channel contribution
Most importantly, Markov attribution enables marketers to allocate budgets based on incremental impact rather than surface-level metrics.
End Notes
As marketing becomes increasingly consumer-driven and data-rich, simplistic attribution models are no longer sufficient. Markov Chain–based attribution offers a powerful framework for understanding complex customer journeys and uncovering the true value of each marketing channel.
By modelling customer behaviour as a probabilistic process and applying the removal effect, organizations can make confident, evidence-based decisions about where to invest their marketing resources. Whether in e-commerce, financial services, or subscription businesses, Markov attribution bridges the gap between analytics and strategy—turning customer journey data into actionable business insight.
This article was originally published on Perceptive Analytics.
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