Budget Allocation: A Markov Chain Approach to Cross-Channel Spending
If you’re still allocating budget based only on what Meta, Google, or any ad platform tells you, there’s a good chance you’re overspending on channels that just grab the last click, and underspending on the ones that actually start the customer journey.
And in a world where customer paths are messy and multi-touch, last-click attribution is basically guessing.
For sophisticated performance marketers, the Markov Chain Model offers a powerful, data-driven approach to budget allocation that assigns true value to every channel interaction.
This guide will explain the Markov Chain concept, its application in marketing, and how to use it to optimize your budget for maximum efficiency.
What Exactly Is a Markov Chain?
A Markov Chain is a stochastic (random) model describing a sequence of possible events where the probability of each event occurring depends only on the state attained in the previous event. In simpler terms, the future depends only on the present, not on the sequence of events that preceded it.
Now, in marketing terms
A Markov Chain is a fancy way of saying... “We can predict what happens next based only on what’s happening right now, not the whole past.” In marketing attribution, each “state” represents a channel through which someone interacts: Google, Meta, Email, Direct, etc. Users jump from one channel to the next until they either convert or drop off.
This model helps you understand which channels actually move people forward and which ones just appear at the end.
Why Last-Click Attribution Doesn’t Cut It Anymore
This is the problem with last-click vs. Markov:
- Last-click
- Gives 100% credit to the final touchpoint
- Ignores all top and mid-funnel work
- Leads to under-investing in awareness and discovery channels
- Linear attribution
- Gives equal credit to every touch
- Treats a blog view the same as a demo request
- Oversimplifies everything
- Markov Chain attribution
- Measures real impact
- Shows how much conversions drop if you remove a channel
- Reveals true incremental value
- This is why high-performing marketers are replacing guesswork with Markov models.
The Mechanics: How the Markov Chain Calculates Value
The power of the Markov Chain Model lies in its ability to calculate the "Removal Effect" (or Shapley Value), which determines the value a channel contributes to a conversion by calculating how much the overall conversion probability would drop if that channel were removed from all customer paths.
Map how users move between channels
The model looks at every recorded path and calculates the probability of moving from one channel to the next.
- Example: If 60% of users who open an email convert immediately, then the probability from Email → Conversion is 0.6.
Calculate the probability of conversion
Using all these transitions, the model figures out the likelihood that someone will eventually convert from any channel.
Remove each channel and measure the change
- Run the model normally
- Remove a channel and run it again
- See how conversions change
That difference shows how important the channel is in driving conversions, not just showing up at the end.
The 3-Step Budget Allocation Strategy
Applying Markov Chain results to your budget is a three-step process that ensures you are funding channels based on their true contribution.
Step 1: Calculate the Marketing Mix Index (MMI)
The MMI tells you whether a channel is under-funded, over-funded, or just right.
You calculate it by dividing:
- The % value the Markov model assigns to the channel
by - The % of your budget currently going to that channel
How to read MMI:
- MMI > 1 → This channel is under-funded. Increase the budget.
- MMI < 1 → You’re overspending relative to its impact. Reduce or reallocate.
- MMI ≈ 1 → You’re spending the right amount.
This instantly shows where your budget is misaligned.
Step 2: Calculate the True Cost Per Conversion
Once you know how many conversions each channel truly drives, you can calculate its real CPC/CPA.
Formula:
True CPC = Total Spend / Markov Conversions
This gives you a fair comparison across channels, something last-click can’t do.
Step 3: Validate With Incrementality Testing
Markov tells you how valuable a channel is within your current ecosystem. Incrementality testing tells you whether the channel actually causes growth.
Here’s how to use them together:
- Identify high-value, under-funded channels
- Run incrementality tests (geo splits, holdouts, ghost bidding, etc.)
- If the channel shows incremental lift, confidently scale it
This prevents you from accidentally scaling channels that look good but don’t actually drive new customers.
The Bottom Line: Smarter Budgets, Not Bigger Budgets
A Markov Chain model gives you a clearer, more accurate view of how your marketing channels truly work together. Instead of guessing based on last-click reports—or being influenced by platform bias—you make decisions grounded in:
- actual probability
- real contribution
- cross-channel dependency
- and long-term customer value
When you invest based on true impact, every dollar goes where it genuinely moves customers closer to buying.
