Why Marketing Measurement Is Broken

If you’ve ever tried to justify your marketing spend to your CFO, you know how hard it is to answer: “What’s really driving revenue, and where should we invest the next dollar?”

Most teams rely on dashboards, attribution tools, and endless reports that show activity but not true impact. As guest Pranav Piyush, Co-Founder and CEO at Paramark, explains the real problem isn’t the lack of data, it’s the lack of a scientific framework to interpret it.

That’s where Marketing Mix Modeling (MMM) and Incrementality Testing come in.

What Is Marketing Mix Modeling (MMM)?

MMM is a top-down statistical approach that uses correlations in historical data to estimate how each marketing channel contributes to revenue outcomes.

Think of it as running a scientific A/B test across your entire marketing mix, not just your website. MMM applies mathematical and statistical methods to understand the real impact of marketing.

Unlike traditional multi-touch attribution (MTA), which tries to stitch together every click and touchpoint, MMM looks at the bigger picture.

As Pranav put it: “If it was possible to perfectly stitch together every user journey across devices, who wouldn’t want that? The problem is, it’s not possible. MMM accepts that incompleteness and deals with it.”

What Is Incrementality Testing?

Incrementality testing measures the actual causal impact of your marketing campaigns.

Most marketers don’t realize they can launch self-serve conversion lift studies directly in platforms like Meta and LinkedIn. These studies split your audience into test and control groups to determine how much incremental lift your ads drive, just like a website A/B test.

You don’t need external tools. You just need to be comfortable with Conversion API setup and know what success signals to track.

This kind of testing gives you real answers to questions like: Did that ad campaign actually cause more conversions? Or would those have happened anyway?

When Are You Ready for MMM and Incrementality Testing?

Not every company is ready to invest in MMM. According to Pranav and Jason Hart, Principal Consultant at Domain Methods, these are the key readiness indicators:

• You’re spending at least a few million dollars per year across multiple channels
• You have 18-24 months of historical marketing data
• You’ve built a marketing data warehouse and reporting infrastructure
• You have a dedicated analytics or marketing ops function
• Your mix includes a variety of upper- and lower-funnel channels
  

If you’re earlier-stage or only spending across 1-2 channels, focus first on running incrementality tests using native platform tools.

As Jason emphasized: “Your results are going to vary depending on how clean and consistent your data is. MMM only works if your data house is in order.”

Why Most Companies Fail at Experimentation

The biggest obstacle isn’t technical, it’s cultural.

Top tech companies like Amazon, Uber, and Booking.com publish their experimentation stats. They only see statistically significant lift in about 10–20% of their tests.

That doesn’t mean experimentation doesn’t work. It means most ideas don’t win—which is exactly why you need experimentation in the first place.

If leadership (CEO, CMO, CFO) expects 100% win rates, they’re not ready to support a real test-and-learn culture.

Pranav’s advice:

• Start with top-down buy-in
• Allocate at least 10% of budget to experimentation
• Get comfortable with uncomfortable truths, like what would happen if you stopped marketing altogether
  

The Data Foundation That Makes It All Possible

Before you can implement MMM or reliable incrementality testing, your data needs to be centralized, clean, and complete.

Most marketing teams still operate with data siloed across platforms like LinkedIn, Google Ads, Salesforce, and spreadsheets.

What you need instead:

• Centralized data in a warehouse
• Consistent definitions for key metrics (leads, pipeline, revenue)
• Historical depth (at least 6–8 quarters of reliable data)
• Internal alignment between marketing and finance teams
  

If you try to implement MMM or AI-driven analytics without that foundation, the results will be misleading at best, and damaging at worst.

Takeaways for B2B Marketing Teams

• Marketing Mix Modeling offers a scalable way to understand what’s actually working across your full mix
• Incrementality testing lets you prove causality, without relying on black-box attribution
• Both require clean data and internal alignment
• True measurement starts with cultural commitment to experimentation and truth-seeking
• You don’t need expensive tools to get started, LinkedIn and Meta now offer built-in lift testing options
  

Next Steps: Build Smarter Measurement Systems

• Run a native lift test on LinkedIn or Meta to see how incrementality testing works
• Centralize your marketing data in a warehouse and clean up your key metrics
• Align your marketing, analytics, and finance stakeholders on what success really looks like
• Start building a roadmap of channel-level experiments based on measurable business outcomes

Download the Lead Conversions Playbook: Reform’s Lead Conversions Playbook gives you the exact experiments we’ve used to consistently drive high-quality, sales-accepted leads for B2B companies.

Watch the Episode here.

Free resource: Website Optimization Quick Wins Playbook
Get 24 tactical website experiments designed to drive more pipeline, without touching your backend.

For B2B marketers, demand gen leads, and GTM teams, getting the lead isn’t enough, you need context. 

And the old playbook of relying on enrichment providers or one-size-fits-all forms is just not cutting it anymore.

In this episode of the Revenue Funnel Optimization podcast, Arun Sivashankaran and David Janczyn share how one of FunnelEnvy’s clients struggled to make sense of inbound leads, because they didn’t know who they were actually capturing.

The Challenge: Leads Without Context

Like many B2B companies, the client targeted multiple audiences but captured minimal segmentation data through their form. Without knowing which segment a lead belonged to and with enrichment providers falling short, they couldn’t:

• Run relevant nurture campaigns
• Deliver tailored SDR outreach
• Optimize retargeting or paid media offers

Generic messaging. Poor response rates. Frustrated sales teams.

The Fix: Self-Segmentation Through Smart Form Design

Here’s how the FunnelEnvy team solved it, without tanking conversion rates.

1. Multi-Step Forms with Embedded Context

Instead of long, impersonal forms, they used a multi-step experience:

• Step 1: Soft segmentation questions like role, company type, or team size
• Step 2: PII capture (name, email, etc.)

This structure avoids overwhelming the user, while capturing high-value self-reported data that enrichment tools simply can’t provide.

2. Relevance Drives Completion

When questions are framed as relevant to the user’s experience, completion rates increase. Prospects are more willing to fill out a form if they believe:

• They’ll get a more tailored post-form experience
• Their answers influence the value they receive

This principle: “Relevance always wins,” helps balance lead volume with lead quality.

3. Testing, Not Guessing

The team didn’t assume this new structure would work. They tested:

• Conversion rate impact
• Down-funnel performance (MQL to SQL to revenue)
• Data usability by sales and marketing teams

Even if form fills decreased slightly, lead quality and conversion to pipeline went up.

What Happens After the Form Fill Matters More

Capturing better data unlocked revenue-driving opportunities:

• More relevant nurture sequences tailored to buyer type
• Higher-performing SDR outreach with custom messaging per segment
• Segmented retargeting campaigns with aligned offers and positioning

Plus, with clean, self-reported data, the marketing team could build more accurate audiences for LinkedIn and display advertising, without waiting on data enrichment vendors.

Think of Your Form as Growth Infrastructure

As Arun says:

“The form isn’t just a frontend thing. If you treat it like a growth engine, it connects to everything downstream, nurture paths, paid campaigns, even attribution.”

Key Takeaways:

• Don’t rely on enrichment tools for segmentation.
• Use self-identification to let prospects tell you who they are.
• Structure forms in multi-step flows that prioritize relevance.
• Test for conversion and quality.
• Use the data to power campaigns, not just reports.

Free resource: Website Optimization Quick Wins Playbook
Get 24 tactical website experiments designed to drive more pipeline, without touching your backend.

This blog just scratches the surface. For the full breakdown, including real examples and frameworks 👉 Watch Episode 6 on YouTube

If you’re done with junk leads, enrichment noise, and low pipeline conversion, book a Full Funnel Audit with FunnelEnvy.

Let’s get real about A/B testing.

You ran a test. Got a 28% lift. Extrapolated it over 12 months. Share the number with your VP.

Congrats. You just lied to your CFO.

It’s not your fault. The industry has clung to linear ROI projections for years because they’re simple and look great in decks.

But they’re also wildly inaccurate.

In this episode of the FunnelEnvy podcast, Arun Sivashankaran and David Janczyn go deep into a smarter, more truthful model: Intent Decay.

Why Linear ROI Models Fail

When you apply a constant uplift over time based on your test results, you’re making two big assumptions:

1. That your test lift is permanent
2. That all site visitors behave the same way

Neither of these is true.

Most A/B tests, especially those that rely on urgency, gimmicks, or motivational tweaks, start to decay in performance within weeks.

The deeper truth? You’re mostly capturing low-intent traffic that wasn’t going to convert anyway. That behavior is temporary.

Introducing Intent Decay: A Smarter ROI Model

Arun, David and the team at FunnelEnvy built an approach that accounts for visitor intent and test type to calculate long-term ROI more accurately.

Here’s how it works:

There are two types of visitors:

• High intent: Already motivated, further down the funnel
• Low intent: Uncertain, exploratory, need persuasion

Most CRO experiments lift low intent visitors. But those conversions decay faster.

By identifying the type of experiment you ran (e.g. friction reduction vs. urgency play), you can apply a decay multiplier to estimate more realistic ROI.

Test Types That Decay (and Those That Don’t)

Not all experiments are created equal.

Here’s how test types break down in terms of decay risk:

This framework lets you prioritize high-leverage experiments that will stick.

How to Apply This to Your CRO Program

David lays out a simple approach:

1. Estimate Intent Split (use 70% high intent / 30% low intent as baseline)
2. Identify Test Type and corresponding decay factor
3. Apply Monthly Decay to initial lift
4. Model ROI Over Time, not just Week 1

“Your ROI projections should reflect real-world behavior, not vanity metrics.”

Bonus: Use This Model to Prioritize Experiments

Most teams use ICE (Impact, Confidence, Ease) for prioritization. But it ignores long-term decay.

By layering in a decay factor, you stop prioritizing flashy, fast-fading tests, and start investing in sustainable wins.

This is how you:

• Defend your roadmap
• Earn budget credibility
• Build a testing culture based on truth, not just temporary spikes

Final Takeaways

• Linear ROI is easy. But it’s wrong.
• Intent Decay gives you a credible, repeatable model
• Use it for reporting, prioritization, and strategic alignment

You don’t need to overhaul your stack. Just start using better math.

Watch episode 5 (Part 2) of the FunnelEnvy Podcast to go deeper.

Want to see how this applies to your funnel? Book a Full Funnel Conversion Audit.

You got a 31% lift from that A/B test? Cool.
Now ask yourself: “will that number still hold in 6 months?”

If you’re in marketing or growth, you’ve probably seen this happen:

– You run a successful experiment
– You see a conversion lift, let’s say 25-40%
– You slap that % on top of your average revenue and forecast the annual impact
– Victory slides in the QBR

…except there’s one major problem:
That lift probably won’t last.

In this episode of the Revenue Funnel Optimization Podcast, Arun Sivashankaran and David Janczyn dig into one of the biggest lies in CRO:

– Most ROI projections are inflated.
– Linear extrapolations of test wins are misleading.
– And the majority of uplift comes from low-intent visitors who are least likely to stick.

Let’s break it down.

The Problem with Linear ROI Projections

The way most CRO programs calculate ROI is dead simple: “We got a 31% lift from this test. Let’s apply that across all future traffic for the next 12 months and show a $$$ win.”

This sounds great on paper. But in the real world, it’s fundamentally flawed.

Here’s why:

• Results decay over time, especially when you’re optimizing for low-intent traffic (more on that below)
• Not all tests have the same lasting impact, friction removal ≠ motivational copy tweak
• Stakeholders get misled by inflated wins → misallocate budget → question CRO credibility later
  

Enter: The Conservation of Intent Framework

At the core of FunnelEnvy’s new model is a powerful concept: The Conservation of Intent.

It means:

• You can’t manufacture intent out of thin air
• Your website visitors fall into 2 buckets:
  • High-intent: ready to convert regardless of tweaks
  • Low-intent: need persuasion, UX help, motivation
• Most A/B test lifts come from that group (low-intent)
• But those low-intent gains fade faster over time
  

What This Means for Your Experiments

Let’s say you run a test and get a 31% lift. Here’s what happens next:

• The high-intent users you win will likely keep converting
• The low-intent users are more volatile, some convert due to urgency or friction removal, but many won’t stick long term
• Over the next few months, your results decay, eventually hitting a stable point

Instead of pretending that your lift holds steady, FunnelEnvy now uses a decay-based model that factors in:

1. Initial lift
2. % of high vs. low intent visitors
3. Decay rate by experiment type (e.g. friction reduction vs. motivation boost)

You get a much more credible, defensible ROI number, even if it’s smaller.

Why Smaller ROI Is Actually a Win

We get it. Telling clients or execs that your 31% win is actually worth 10% long-term isn’t sexy.

But here’s the truth:

– It builds trust and credibility
– It lets you prioritize smarter
– It sets realistic expectations for testing ROI
– And it avoids misallocating budget based on inflated forecasts

As Arun puts it in the episode: “Do you want ROI that looks good in the QBR or ROI that’s actually real?”

Want to Report Your Test Results More Accurately?

We are building simple models you can plug your own data into, but in the meantime, here’s how to level up your reporting:

• Segment your test impact by intent (use GA4 or session replay to get insight)
• Estimate decay based on test type (we’ll cover this in part 2 of the episode)
• Stop using annual linear projections (it’s a credibility killer)
• Educate your stakeholders on why smaller wins = long-term trust

Watch the Full Episode Now

This is one of the most insightful, behind-the-curtain episodes we’ve released to date. If you’re in CRO, RevOps, or growth strategy, you need to watch it.

Watch Episode 5 Part 1 on YouTube »

And don’t forget to subscribe, Part 2 goes even deeper into test types, decay rates, and the actual model we use.

Bonus Resource: Website Optimization Quick Wins

If you’re still optimizing with gut feel or best guesses, this free guide will change how you approach form UX, friction, and full-funnel optimization.

Get the Website Optimization Quick Wins guide here.