Explaining Markov Chain With a Car Analogy
Let’s say you’re driving a car from Point A to Point B, and you have to make a few stops along the way for gas, food, and rest. Markov chain attribution is a way of tracing which stop had the biggest impact on getting you to your final destination. Think of each stop as a state in the chain, and the process of driving from one stop to the next as a transition.
The probability of making a transition from one state to another depends on the factors that influenced your decision to stop there in the first place, such as the distance to the next stop or how hungry you were. By looking at the sequence of stops you made and the probabilities of making each transition, we can determine which stop had the biggest impact on your final destination (Point B). In other words, we can attribute a portion of the total “journey effect” to each stop along the way.
So, in a sense, Markov chain attribution helps you understand which factors had the biggest impact on reaching your goal, by tracing the steps you took to get there.
How Markov Chain Attribution Would Look Like in a Digital Context
Let’s say you’re a digital marketer, and you’re running a marketing campaign to drive website traffic and conversions. You want to know the secret behind doing more with attribution modeling, but you are still not certain how they work.
Here’s a quick analogy to help you get started with the Markov chain.
The Markov chain attribution model can help you understand the impact of different marketing touchpoints on website visitors’ journey to becoming a customer.
Here’s an example:
A visitor sees a Facebook ad for your product and clicks through to your website. This is State 1.
The visitor browses your product page but doesn’t make a purchase. However, they sign up for your email newsletter. This is State 2.
The visitor receives an email from your company with a discount code and clicks through to your website. This is State 3.
The visitor uses the discount code to make a purchase. This is State 4.
By using Markov chain attribution, you can determine the probability of transitioning from one state to another, and the contribution of each touchpoint to the final conversion.
For example, you might find that the Facebook ad had a 40% probability of driving a visit to the website (State 1), the email newsletter had a 30% probability of leading to a visit (State 2), and the email with a discount code had a 50% probability of leading to a purchase (State 4).
Using these probabilities, you can then attribute a portion of the conversion to each touchpoint. In this example, you might attribute 40% of the conversion to the Facebook ad, 30% to the email newsletter, and 30% to the email with the discount code.
This information can be very valuable in optimizing your marketing campaigns, by allowing you to understand which channels and tactics are most effective in driving conversions and adjust your strategy accordingly.
What You Need to Get Started with Markov Chain Attribution
To use Markov chain for attributing the value of specific ad channels in digital marketing, you need the following information:
Marketing touchpoints: A complete list of all the marketing touchpoints that a customer has interacted with, such as social media ads, email campaigns, search ads, display ads, etc.
Conversion events: A clear definition of what constitutes a conversion event, such as a sale, sign-up, download, etc.
Timestamps: Timestamps for each marketing touchpoint and conversion event, so that you can understand the order of interactions and the time taken between touchpoints and conversions.
User information: User information such as their demographics, location, device type, etc. to help you understand the customer journey and segment the data.
Transition probabilities: Data on the probabilities of moving from one state (touchpoint) to another, which can be calculated using historical data and machine learning algorithms.
Conversion probabilities: Data on the probabilities of a customer converting after interacting with each touchpoint, which can be calculated using historical data and machine learning algorithms.
By having this information, you can perform Markov chain attribution to determine the contribution of each marketing touchpoint to the final conversion, and optimize your marketing campaigns accordingly.
Resources to Help You Get Started
Ready to dive into Markov Chain Attribution model for your digital marketing projects? The resources listed below will help you get started and become confident in using Markov chain for attributing the value of your ad channels. From Google Analytics to MOZ, you’ll find a wealth of information to get you on your way.
Google Analytics: Google Analytics provides a Multi-Channel Funnel report that can help you understand how different marketing channels contribute to conversions. You can use this data to calculate the probabilities of transition between states and perform Markov chain attribution.
Marketing Evolution: Marketing Evolution provides a Markov chain attribution platform that helps digital marketers optimize their campaigns by providing actionable insights into which channels and tactics are most effective.
Analytics Vidhya: Analytics Vidhya is a website that provides in-depth tutorials and articles on data science, machine learning, and artificial intelligence. They have several articles on Markov chain attribution and its applications in digital marketing.
MOZ: MOZ is a website that provides SEO and digital marketing insights and tips. They have several articles on Markov chain attribution and how it can be used to optimize your digital marketing campaigns.
Quora: Quora is a website where you can ask questions and get answers from experts in various fields. You can search for questions and answers related to Markov chain attribution and digital marketing to get started.
These resources can help you understand the basics of Markov chain attribution and how to navigate attribution models in the SaaS industry.
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