Manoj Babu

Technical Architect, Data Engineering, 🌥️solutions

[Manoj Babu @manojbabud]

@noahzweben @bcherny @noahzweben @bcherny for which plans it is supported? Initially it was working for me in terminal at least but shows now unsupported for me as max user?

[Manoj Babu @manojbabud]

Traditional MMMs treat intercept as constant. But real businesses grow or shrink over time.
Modern tools like Meridian allow a time-varying intercept baseline that shifts with your brand's health.
Small detail, big impact.

[Manoj Babu @manojbabud]

Day 15 of learning Marketing Mix Modeling
Back after a break! Today's concept: The Intercept 📉
It's the unsung hero of your MMM equation.
🧵 #LearningMMM

[Manoj Babu @manojbabud]

The intercept = your baseline sales when all marketing spend is zero.
It captures:
• Brand loyalty
• Organic demand
• Repeat customers
Basically, what your business earns without ads running.

[Manoj Babu @manojbabud]

Advanced MMMs try to capture these interaction effects.
Ignoring synergy means you might cut a channel that's quietly helping others perform.
The whole is greater than the sum.

[Manoj Babu @manojbabud]

Day 14 of learning Marketing Mix Modeling
Today's concept: Channel Synergy 🔗
Channels don't work in isolation. They amplify each other.
🧵 #LearningMMM

[Manoj Babu @manojbabud]

Example: Someone sees your TV ad, then searches your brand on Google.
TV drove awareness. Search captured intent.
Together they convert. Alone? Maybe not.

[Manoj Babu @manojbabud]

MMM gives you the big picture. Incrementality tests give you ground truth.
Use both together:
• MMM for planning
• Tests for validation
Trust, but verify.

[Manoj Babu @manojbabud]

Day 12 of learning Marketing Mix Modeling
How do you know your MMM is actually right? 🧪
Answer: Incrementality testing.
🧵 #LearningMMM

[Manoj Babu @manojbabud]

Incrementality tests = real-world experiments.
Turn off ads in one region, keep them on in another. Measure the difference.
If your MMM predicted a 10% lift and the test shows 9% you're on track.

[Manoj Babu @manojbabud]

As a Python person, I'm starting with PyMC-Marketing.
But the concepts - adstock, saturation, priors - transfer across all of them.
Pick one. Start experimenting. Learn by doing.

[Manoj Babu @manojbabud]

Day 11 of learning Marketing Mix Modeling
Time to get hands-on 🛠️
The best part? Top MMM tools are free and open-source.
No expensive consultants needed.
🧵 #LearningMMM

[Manoj Babu @manojbabud]

The big three:
• PyMC-Marketing - Python, Bayesian, flexible
• Google Meridian - open-sourced Jan 2025
• Meta Robyn - R-based, automated
Each has trade-offs in customization vs ease of use.

[Manoj Babu @manojbabud]

MMM:
• Uses aggregate data
• Works across online + offline
• Privacy-friendly
Better for strategic budget allocation. No user tracking needed. Different tools, different jobs. Use both if you can.

[Manoj Babu @manojbabud]

Day 10 of learning MMM
MMM vs MTA — what's the difference? 🤔
Both measure marketing. But they solve different problems.
🧵 #LearningMMM

[Manoj Babu @manojbabud]

MTA (Multi-Touch Attribution):
• Tracks individual user journeys
• Digital-only
• Needs cookies/tracking
Great for tactical, real-time optimization. But blind to offline.

[Manoj Babu @manojbabud]

Why it matters:
If 70% of sales are base, your marketing isn't driving as much as you think.
If incremental is high, you know your spend is working.
MMM gives you the truth, not vanity metrics.

[Manoj Babu @manojbabud]

Day 9 of learning Marketing Mix Modelling
Today's concept: Base vs Incremental Sales 📊
Not all sales come from marketing. Some would happen anyway.
MMM helps you separate the two.
🧵 #LearningMMM

[Manoj Babu @manojbabud]

Base sales = what you'd sell with zero marketing. Brand loyalty, repeat customers, organic demand.
Incremental sales = the lift your marketing actually created.
This split is the whole point of MMM.

[Manoj Babu @manojbabud]

As a data engineer, this feels familiar.
MMM is only as good as the data feeding it. Garbage in, garbage out.
Spend time on data prep. Your model will thank you later.

[Manoj Babu @manojbabud]

Day 8 of learning MMM
Real talk: your data won't be perfect 🧹
Missing spend values. Inconsistent channel names. Gaps in history.
So how do you handle messy marketing data?
🧵 #LearningMMM"

[Manoj Babu @manojbabud]

Common fixes:
• Missing values → interpolate or use zero (know the trade-off)
• Inconsistent naming → standardize early
• Short history → Bayesian priors help fill gaps
The goal: clean enough, not perfect.