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Removal effect with a Markov chain model
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Remove a BigQuery User within CaliberMind
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Campaign Best Practices Across Systems
Add BigQuery Users within CaliberMind
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Which of my leads are the most engaged?
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How to Create a Filter Bookmark
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Insights - Attribution Overview Dashboard 2.0
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Insights - Engagement Overview
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Insights - ROAS Terminology & Key Concepts
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Funnel Trigger Events Dashboard
Funnel Company Inclusion Configuration
What is a Marketing Funnel?
Event Explore for Funnels
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Funnel History Event Configuration
Funnels - Start Here!
What channels move journeys through a given stage?
Funnel Stages Configuration and Stage Definitions
Funnel Trend Dashboard
How can I see the number of journeys in or passing through a funnel stage during a period of time?
Sales Funnel Metrics
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How do I see the first event (or "tipping point") in each stage of my funnel?
Funnel Events Technical Documentation
Marketing & Sales Funnel Foundations
Funnel Static Event Configuration
What is a Starter Funnel?
Funnel Person Inclusion Configuration
Funnel Cohort Analysis Dashboard
Sales Funnel Glossary
Where are my customers stuck in journeys?
Funnel Cohort Analysis Use Cases
Funnel Data Explore Dashboard
Use Cases for Funnels
Sales Funnel Journey Questions and Answers?
What is the difference between an Active Journey and a Journey?
How do I know the last event that happened before a journey stage change?
How do I identify stage conversion rates?
Funnel Person Status Exits
Introducing CaliberMind's Funnels
Funnels - Lead, Contact, Account, Opportunity Statuses and Stages
Pressure Event Dashboard 101
Funnel Stage Definitions
Demand Generation Funnel Dashboard
Table of Contents
- All Categories
- Removal effect with a Markov chain model
Removal effect with a Markov chain model
Updated by Nolan Garrido
Removal Effect with Markov Chain Model
This article assumes you are familiar with the Markov chain model as it relates to attribution. If you'd like a brief overview, check out our knowledge base article on the topic.
In order to determine the contribution of a channel in a customer’s journey from first touch to last touch, you can leverage the principle of removal effect. Removal effect principle demonstrates that the contribution of each channel in the customer journey is determined by removing each channel and seeing how many conversions occur without that channel being in place. In other words, it answers these question:
- how much does a given channel affect the probability of conversion?
- which touch points are the most important in the customer journey?
Removal affect can take into account either conversion or revenue, depending on the outcome you want to track.
Here's an example removal effect report:
In the above graph, the removal effect of the "Visit Monthly Newsletter Email" event is 7% - meaning, if the company were to stop sending this email, they can expect to have a 7% reduction in conversions. This is powerful, actionable marketing intelligence that other attribution models cannot provide.
There are limitations, of course. For example, the model groups inaccurate or generically labeled events as "Event" (the first bar on the left). These events have a 58% removal effect, but cannot provide actionable insight because of the way the events are labeled. We would recommend that this company correctly classify these events a bit more granularly. As there are enough events in each classification that they are statistically significant, this report will provide even more insight.
To see how you can use the removal effect report in CaliberMind, contact your Customer Success Manager.