Misinterpreting marketing measurement results can be costly. When you’re unclear on what impacted campaign performance or debating where to allocate next quarter's budget, surface-level dashboard scanning won't cut it. Getting it wrong could lead to difficult conversations about ROI later.
But getting it right changes everything. You can spot early warning signs of a struggling campaign, identify which channels drive growth despite biases and build a compelling case for budget changes.
This guide will help you accurately interpret marketing measurement reports using three key methods:
- Marketing mix modeling (MMM) for big-picture impact
- Multi-touch attribution for customer journey insights
- Incrementality testing to determine true lift
Together, these methods provide a complete view of marketing performance and help eliminate blind spots in decision-making.
What is typically in a marketing measurement report?
Marketing measurement reports show the impact of marketing activities across things like monthly recurring revenue, customer lifetime value (CLV), customer acquisition cost (CAC) spend, campaign results and market conditions. They’re the result of applying measurement methods to your data. Your measurement strategy might include methods like MMM, MTA and incrementality testing.
Generally, measurement reports share the following features:
- Data visualizations: Line and bar charts help illustrate trends over time.
For example, you might use these charts to visualize the cost of different ad channels compared to their impact on sales.
Line and bar charts break down how different channels or marketing campaigns affect results.
- Response curves: These show the relationship between spend and performance to predict diminishing returns.
If you're using an MMM report to determine ad spend allocation, a response curve could highlight where increasing spend leads to diminishing returns.
Use response curves to spot diminishing returns before overspending on saturated channels.
- Comparative tables: Standardized metrics displayed in table format make it easy to compare cross-channel performance.
Standardized marketing metrics make cross-channel performance easier to compare.
This next visualization allows us to track conversion trends across different channels over time. It helps identify which channels contribute the most to conversions, but additional data on cost and ROI is needed to determine true effectiveness.
Track conversion trends over time to spot channels gaining or losing effectiveness.
Each graph in a measurement report will correspond to some type of general response curve. Linear curves show marketing efforts increasing sales in a directly proportional way — they are straight diagonal lines that move upward or downward forever without changing. While neat in theory, linear curves rarely appear in real-world marketing.
More commonly, you'll see concave curves that represent diminishing returns. As you increase spending, the positive impact will gradually decrease until the curve flattens out completely.
Learn to spot different response patterns to predict marketing channel behavior.
Convex curves tell the opposite story — the more you invest in marketing, the faster sales grow. These exponential growth patterns are rare and usually signal that your marketing efforts haven't hit saturation.
Sometimes, you'll encounter S-shaped curves, which start convex before turning concave. Think of it as a natural progression: your initial investment drives exponential sales growth because you're far from saturation, but eventually, returns begin to diminish as you approach your market’s limits.
How to read an MMM report
Marketing mix modeling (MMM) reports show you how well different channels work based on statistical analysis. You get a clear picture of how things like TV spots, digital ads or even pricing strategies move revenue while accounting for market factors such as seasonal swings or what's happening in the economy.
Here’s a look at how the process for creating and reading an MMM report works:
- Define business objectives: Identify key questions (e.g., Which channels drive the most revenue?)
- Collect data: Gather historical data on marketing spend, sales and external factors.
- Apply statistical models: Use regression analysis to determine impact.
- Analyze response curves: Identify optimal spend levels for each channel.
- Make budget adjustments: Reallocate spend based on ROI insights.
MMM example
Let’s look at an example of an MMM report to see the power of this marketing measurement method in action.
A client of marketing intelligence agency Nepa used an MMM report to decide its next steps after spending some time juggling long-term brand-building and short-term performance campaigns. The report answered a critical question: should it increase its total budget or shuffle its channel mix?
The process started with nailing the questions they wanted to answer, then moving through data collection and visualization before hitting the modeling phase.
The gold was in the ROI breakdown. This company examined online, TV, out-of-home and print channels to determine which were actually delivering. They found some clear opportunities to change things.
Spend less on print and more on digital and TV to get better results from each dollar.
The columns reflect the company’s current strategy on the left and the adjusted strategy on the right. The sections stack up from online at the top, through TV and OOH, down to print at the bottom.
The shifts between columns tell the story: online's share of sales climbed from 37% to 40%, while TV rose to 45% when accounting for more spending. However, outdoor advertising stayed at 8% and print dropped from 9% to 6%.
The data suggests bumping TV investment by 4% or +4 percentage points (pp). It also suggests increasing online spend (web TV, banners and search) by 3% (+3 pp). Print wasn't pulling its weight, so it recommended a 3% (-3 pp) cut there, while out-of-home (OOH) was doing fine at its current levels. The +3 pp increase for online is a reallocation of resources from print advertising.
The key finding here was pretty striking — the company could see a 15% increase in sales just by shifting budget around.
The MMM analysis also showed how the company could maximize sales even further. If they chose to increase ad spend by 15% and reallocate the budget, they would see 25% more sales. As you can see in the graph below, this more aggressive approach would have brought their strategy to the point of diminishing returns, meaning, beyond that 15% budget increase, the returns would start to diminish and eventually flatten out.
Increase budget by 15% and adjust media mix to optimize channel performance.
When they compared the two scenarios: the conservative approach (keep the budget flat while optimizing the mix for 15% growth) and the aggressive (increase the budget by 15% while fine-tuning channels for 25% growth), the company decided to go with the conservative option.
Lastly, the report stacked predictions against actual results. In this case, their efforts brought a 17% incremental increase in in-store visits.
Actual results after these changes brought 17% more people to stores.
Common pitfalls of MMM analysis
When MMM works, the results can help you optimize your marketing budget. However, there are common pitfalls that can throw your analysis off track:
- Confusing correlation with causation: Just because TV ads correlate with higher sales doesn't mean they caused it.
- Ignoring external factors: Seasonality and economic shifts impact results. Make sure these background factors are considered in your analysis.
- Failing to refresh models: Markets change, so MMM models should be updated regularly.
How to apply MMM insights
Once you have an understanding from your MMM analysis, what should you do? Here are three ways to apply MMM insights to make the most out of your marketing mix.
- Shift budget toward high-ROI channels. Channels that offer a better bang for your buck should be given more support.
- Adjust for seasonality and market trends. For example, if your customers search online for your product in November and December more than the rest of the year, you might increase budget for online search during those months.
- Optimize spend before hitting diminishing returns. Always know where that sweet spot is to avoid going overboard on a high-performance channel and experiencing diminishing returns.
How to read an MTA report
Multi-touch attribution (MTA) reports show how different marketing touchpoints contribute to conversions. This measurement method assigns credit to multiple touchpoints (as opposed to single-touch attribution, which gives credit to just one touchpoint).
The insights you get are based on the specific attribution model you choose. Common models include linear, time-decay, position-based and data-driven. 
A linear model will reveal what’s happening throughout the entire customer journey. However, as it distributes credit evenly, it won’t show if certain touchpoints have a bigger impact.
Time-decay attribution hones in on the most recent touchpoints. It’s useful when you want to focus on actions that translate to immediate results. Time-decay models work well for B2C brands that have a short sales cycle, but they’re not a good fit for the typical lengthy B2B sales cycle.
A position-based MTA report would automatically assign credit based on a 40/20/40 split. The first and last interactions would each receive 40% of the credit, whereas all interactions in between would share the remaining 20%.
This example shows that Facebook and Google retargeting ads each get 40% of the credit for a sale, whereas interactions with a website or content in between share the remaining 20%.
A position-based attribution report tries to balance the importance of first and last interactions.
Data-driven models are useful if you have large datasets to work with. AI determines which touchpoints should be given the most credit for a conversion. Data-driven attribution is focused on real impact.
It’s important to note that an MTA report will vary depending on the model. For example, in an e-commerce report where a customer sees a Facebook ad, then watches a YouTube video, searches on Google and finally buys after clicking an email discount offer, you might see the following results depending on your MTA model:
- Linear: Each touchpoint gets 25%.
- Time decay: The email and Google search receive the most credit.
- Position-based: Facebook and email get 40%, while YouTube and Google split 20%.
Common pitfalls of MTA analysis
Multi-touch attribution has its flaws. It’s enticing to fall into the trap of putting too much responsibility on trackable clicks, but there could be other factors at play, like word-of-mouth marketing, brand reputation and offline advertising. Here are common pitfalls in MTA analysis you should be aware of:
- Only tracking digital interactions: Multi-touch attribution reports are limited to trackable interactions like clicks and don’t account for broader market trends or changes in target audience behavior. However, they can help support MMM reports, offering more nuanced insight.
- Misattributing credit: Digital attribution includes sales that would have happened anyway. The reality is that clicks don’t always mean causation.
- Underestimating long-term brand impact: Some touchpoints influence future purchases, even if they don't lead to immediate conversions.
How to apply MTA insights
The goal of an MTA report is to understand the impact of various interactions along the customer journey. A report might contain charts that show how credit gets divided between channels. The ROI calculations are based on that credit assignment. Here’s what you can do to apply MTA insights.
- Identify high-performing touchpoints. Look at which channels introduce customers to your brand, what content nurtures their journey and where they convert.
- Compare different attribution models for deeper insights. For example, you can use a linear model to see the sequence of your touchpoints and a data-driven model to get more precise information on the real impact.
- Use MTA alongside MMM for a full-funnel view. Combining marketing measurement tools can help you see both the big picture and the ROI impact of marketing activities on a granular level.
How to read an incrementality testing report
Incrementality reports compare what happened because of your activities to what would have happened without them. They display the results of two groups of people: one who saw your campaign (treatment group) and one who didn't (control group).
It’s important to use incrementality testing in your approach to marketing measurement because it offers a more precise understanding of the impact of marketing activities on consumer behavior. It answers questions like, “Where should we invest more budget to get the best return?”
You'll see the lift or the percentage difference your campaign achieved compared to the control group in the report. This matters because standard analytics platforms often overstate campaign success by attributing all conversions to your campaigns. They don’t account for conversions that would have happened naturally.
The first step in understanding marketing measurement reports with incrementality testing is mastering the basic formula that drives everything else:
Incremental conversions = treatment group conversions - control group conversions
This example compares a treatment group in France to a control group in the UK; France generated 982 conversions with €10,000 in spend, while the UK had 113 conversions with zero spend.
113 conversions would have happened anyway, making 869 conversions the true incremental lift.
This implies that 869 conversions were truly incremental — 113 would have happened regardless of marketing efforts. Now, this company can calculate the true cost per acquisition (CPA) for this campaign using 869 conversions as the base — €11.51 CPA.
Some incrementality reports display results as line graphs. Take this example channel incrementality test, which splits a graph into two sections.
Subtract control group performance from the treatment group to measure true campaign lift.
The left side shows nine weeks of normal performance before the new campaign, fluctuating between 4,000 and 6,000 registrations weekly. The right side reveals what happened after launch: a solid blue line shows actual registrations, while a dotted line indicates expected performance without ads. A shaded area represents normal variation ranges.
This report indicates a "significant lift " because actual performance clearly exceeded expectations. The gap between the solid and dotted lines matters more than the absolute numbers — if it's wider than the shaded area, the improvement is meaningful. In this case, the sustained 22% increase in registrations proves that the new ad channel delivered value beyond normal fluctuations.
How incrementality testing works
Incrementality testing is useful when you want to optimize your marketing mix or add a new marketing channel. It can help you understand the true performance of marketing efforts because it shows the additional causal impact. The basic process for incrementality testing looks like this:
- Select test and control groups. Randomly assign users to treatment (exposed) and control (unexposed) groups.
- Run the campaign. Ensure only the test group sees the marketing effort.
- Measure lift. Compare the conversion rates between groups.
You need all three marketing measurement methods to see the big picture
You need all three measurement methods to understand the impact of your various marketing activities. Context matters (a lot) when making strategy and budget decisions, and combining methods gives you that context.
Marketing measurement isn't a one-method job. Marketing teams that are good at measurement build reports using all three measurement methods — MMM, MTA and incrementality testing — because each technique accounts for the other’s blind spots.
Marketing mix modeling is like your satellite view. It shows which channels drive results in context with market conditions. Multi-touch attribution maps out how customers interact with campaigns before converting. Then, incrementality testing proves what moved the needle versus what looked good in your dashboard.
However, measurement alone isn't the goal — it's a tool to drive better decision-making. An overemphasis on numbers without strategic thinking can lead to short-term optimization at the expense of long-term brand growth. True effectiveness comes from balancing data-driven insights with a broader understanding of marketing’s role in shaping pricing power, brand equity and sustained business growth.
Relying on just one or two approaches is risky. You could miss important signals about what's working and what isn't. But even with all three, the real power lies in how you interpret and act on the insights, not just in collecting data. By using measurement as a guide rather than a constraint, you ensure that your marketing strategy is effective.
Want to learn more about using these three methods together? Check out our guide on marketing measurement triangulation.