Why You Probably Don’t Need Media Mix Modelling (MMM)

Media Mix Modeling (MMM) has been the gold standard for measuring marketing effectiveness in B2C for decades. Walk into any FMCG company and you’ll find teams of data scientists crunching numbers to understand how TV ads, digital campaigns, and promotional activities drive sales.

But here’s the thing: what works brilliantly for selling cereal and smartphones often falls flat when applied to B2B marketing. Yet countless B2B companies are investing significant resources trying to force MMM into their measurement strategy, often with disappointing results.

Let’s examine why MMM struggles in B2B contexts and explore better alternatives for measuring your marketing impact.

What Makes B2B Marketing Fundamentally Different

Before diving into MMM’s limitations, it’s crucial to understand what sets B2B marketing apart from B2C:

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Why Media Mix Modeling Falls Short in B2B

Complex Sales Cycles Kill Attribution Accuracy

MMM works well when there’s a relatively direct path from exposure to purchase. See an advert for trainers on Tuesday, buy them on Thursday – that’s trackable.

B2B reality looks more like this: A prospect downloads a whitepaper in January, attends a webinar in March, visits your booth at a trade show in June, has multiple sales calls through August, and finally signs a contract in December.

Which marketing activity deserves credit for that sale? MMM struggles to untangle these complex, multi-touch journeys that span quarters or even years.

Data Limitations That Cripple Analysis

MMM requires extensive historical data to identify patterns and correlations. In B2C, you might have thousands of transactions per week to analyse. In B2B, you might have dozens of deals per quarter.

This data scarcity creates several problems:

• Statistical significance becomes nearly impossible to achieve

• Seasonal patterns are harder to identify with limited data points

• External factors (like economic conditions) can skew results dramatically

• Data quality issues become magnified when sample sizes are small

The Cost-Benefit Equation Doesn’t Add Up

Implementing MMM properly isn’t cheap. You need:

• Specialised data science expertise

• Robust data infrastructure

• Significant time investment (often 6-12 months to get meaningful results)

• Ongoing model maintenance and refinement

For many B2B companies, the cost of implementing MMM exceeds the potential value it can deliver. That budget might be better spent on additional marketing activities or other measurement approaches.

Better Alternatives for B2B Marketing Measurement

Rather than forcing a square peg into a round hole, B2B marketers should consider measurement approaches designed for their unique challenges:

Multi-Touch Attribution (MTA) Models

Unlike MMM’s top-down approach, MTA tracks individual customer journeys across touchpoints. This granular view works better for B2B’s complex sales cycles.

Modern MTA platforms can handle:

• Online and offline touchpoints

• Multiple decision-makers within the same account

• Long attribution windows (12+ months)

• Custom attribution models based on your sales process

Incrementality Testing

Instead of trying to model everything at once, incrementality testing isolates the impact of specific marketing activities through controlled experiments.

Examples include:

• Geo-based tests (running campaigns in some regions but not others)

• Audience holdout tests (excluding a control group from campaigns)

• Time-based tests (pausing activities to measure impact)

This approach provides clearer cause-and-effect relationships than MMM’s correlational analysis.

Marketing Qualified Account (MQA) Tracking

Rather than focusing solely on final conversions, track how marketing activities influence account-level engagement and progression through your sales funnel.

Key metrics might include:

• Accounts engaging with multiple content pieces

• Progression from marketing-qualified to sales-qualified accounts

• Time-to-conversion by initial marketing touchpoint

• Account engagement scores across different channels

Revenue Attribution with Sales Input

Combine quantitative data with qualitative insights from your sales team. Sales reps often have valuable context about which marketing activities influenced their deals.

Create structured processes for capturing this intelligence:

• Post-deal surveys asking sales reps about marketing influence

• Regular sales and marketing alignment meetings

• CRM fields tracking marketing touchpoints mentioned by prospects

Building a Practical B2B Measurement Framework

The most effective B2B measurement approaches combine multiple methodologies rather than relying on a single solution.

The Three-Layer Approach

Start Small and Scale

Don’t try to measure everything perfectly from day one. Begin with:

• Clear definitions of what constitutes marketing influence

• Consistent tracking of key touchpoints

• Regular analysis of high-value deal sources

• Simple incrementality tests on your largest channels

Build complexity gradually as your data quality and analytical capabilities improve.

When MMM Might Still Make Sense for B2B

To be fair, MMM isn’t completely useless in B2B contexts. It might work if you have:

• Very high transaction volumes (hundreds of deals per month)

• Shorter sales cycles (under 3 months)

• Significant investment in brand advertising

• Multiple product lines with different characteristics

But even then, MMM should complement, not replace, other measurement approaches.

The Bottom Line on B2B Marketing Measurement

Media Mix Modeling became popular in B2C because it solved real problems: understanding the impact of mass media channels on large-scale consumer behaviour. But B2B marketing operates under fundamentally different conditions.

Rather than forcing B2C solutions onto B2B problems, smart marketers are adopting measurement frameworks designed for their reality: longer sales cycles, smaller sample sizes, and relationship-driven sales processes.

The goal isn’t perfect measurement – it’s actionable insights that help you allocate resources more effectively. Sometimes a simpler approach that you can actually implement and act upon beats a sophisticated model that sits unused because it’s too complex or expensive to maintain.

Focus on measurement approaches that match your organisation’s analytical maturity, data availability, and resource constraints. Perfect measurement that you can’t afford or implement is worthless.

If you’re looking for a more integrated approach to managing your B2B marketing measurement and experimentation, Growth Method offers an AI-native project management tool specifically designed for marketing and growth teams. Our platform integrates ideation, experimentation, and analytics into one powerful solution. Book a call to speak with Stuart, our founder, to learn how we can help streamline your marketing measurement approach.