How to optimize ad spend: an econometrics-driven guide for marketers

Research shows that 56% of ad buyers will focus more on marketing mix modeling in 2025. Most marketers waste up to 40% of their advertising budgets because they're measuring the wrong things.

Last-click attribution captures only 1.3% of Pinterest's true impact on sales. When your measurement is off by 98.7%, you're not optimizing spend. You're redistributing waste.

The solution isn't more budget. It's econometric analysis that reveals the actual ROI of every marketing activity, enabling you to reallocate existing resources for dramatically better results.

Understanding the econometric approach to ad spend optimization

Traditional attribution models track user journeys across touchpoints. Econometric modeling analyzes aggregate data over time to isolate the incremental impact of each marketing activity.

Econometrics captures what attribution misses. When you run TV ads that boost branded search volume, attribution credits the search click. Econometrics credits the TV ad that created demand. When display advertising builds awareness that shortens consideration time three months later, attribution sees nothing. Econometrics quantifies the lasting effect.

Marketing mix modeling separates your sales into two components: base sales driven by long-term brand strength, market conditions, pricing, and distribution; and incremental sales directly attributable to your marketing activities.

This distinction matters for optimization. If you cut a channel that builds base sales, you might not see the impact for quarters. Econometric models prevent these costly mistakes by revealing both immediate returns and sustained effects.

The approach uses multivariate regression to quantify how much each marketing variable contributes to business outcomes. You get not just which channels work, but how much each contributes and how those contributions change with increased investment. These response curves show exactly where you hit diminishing returns, where you're underinvesting, and where you're burning money.

Building an MMM-based budgeting framework

Effective econometric budgeting starts with comprehensive data collection. You need at least two years of historical data including granular channel spend, weekly sales or revenue data, external factors like weather and major events, and competitor activity where available.

Only 23% of European marketers holistically measure digital and traditional media spending together, according to Nielsen's 2025 marketing survey. This fragmented approach explains why optimization efforts fail. You can't optimize what you don't measure consistently.

Quantify adstock and carryover effects. Not all marketing impact happens immediately. TV advertising creates lasting brand awareness well after campaigns end. Econometric models apply adstock transformations to capture both immediate response and delayed effects persisting for weeks or months.

Consider a retail brand's summer campaign. Without adstock modeling, you see a sales spike during the campaign and assume impact ended when ads stopped. With proper econometric analysis, you discover that 30% of the campaign's total impact occurred in the eight weeks after it ended, as word-of-mouth and consideration cycles played out. Attribution would have missed this entirely, leading you to undervalue the campaign and potentially cut effective spend.

Model diminishing returns accurately. Every channel hits a saturation point where additional spend produces minimal incremental return. MMM reveals these curves channel by channel. A B2C software company might discover their paid search ROI drops from 4:1 to 1.5:1 after €50,000 weekly spend, while their display advertising maintains steady returns up to €80,000 weekly.

These insights enable precise optimization. You're not guessing where to cut or where to invest. You're following mathematical evidence of marginal returns across your entire portfolio.

Run scenario simulations to identify optimal allocation. Modern MMM platforms execute millions of simulations testing different budget scenarios across channels, timing, and creative treatments. The output shows you exactly how to reallocate current spend for maximum incremental lift.

Channel mix optimization using econometric insights

The most valuable MMM output is understanding how channels work together. Display advertising research demonstrates that effectiveness amplifies significantly when combined with TV campaigns. Boots UK observed substantial improvements in paid search performance when running TV simultaneously.

Identify true incrementality by channel. An e-commerce fashion retailer might discover through MMM that TV advertising delivers 3.2:1 ROI with 60% of impact within four weeks; paid search shows 5.1:1 ROI but 70% represents brand search that would have happened anyway (low incrementality); display advertising produces 2.8:1 ROI with strong carryover effects lasting 12 weeks; and influencer partnerships generate 4.5:1 ROI concentrated in the first two weeks.

Traditional attribution would overweight paid search dramatically. Econometric analysis reveals that reallocating 20% of paid search budget to TV and display would increase total incremental sales by 15% without spending an additional euro.

Map cross-channel synergies quantitatively. MMM reveals interaction effects that attribution cannot capture. When a telecommunications provider analyzed their media mix, they discovered that running digital display alongside TV didn't just add individual effects; the combination produced a 30% synergy bonus. This insight led them to synchronize campaigns for maximum impact rather than staggering them.

Balance immediate response with long-term brand building. According to Nielsen's 2025 marketing survey, 59% of European marketers prioritize revenue growth as their primary objective. But overindexing on short-term performance channels erodes brand strength over time. Econometric models quantify both immediate conversion effects and sustained base sales contribution, enabling you to balance acquisition with brand building.

A telecommunications company using econometric analysis might discover that their performance marketing drives 80% of attributed conversions but only 40% of incremental sales, while their brand campaigns drive 20% of attributed conversions but 60% of incremental sales. This reversal happens because brand campaigns create demand that converts through performance channels. Attribution sees the conversion. Econometrics sees the cause.

Measuring true incrementality beyond attribution

Attribution tells you what happened. Incrementality measurement tells you what happened because of your marketing.

Implement geo-based testing for ground truth. Divide your markets into test and control groups, vary marketing pressure, and measure the sales difference. A grocery chain might run TV advertising in 60% of markets while holding out 40% as controls. The sales lift in test markets, adjusted for baseline trends, reveals true incrementality.

Layer this ground truth data into your MMM. The combination provides both strategic direction from modeling and tactical validation from testing. When your model predicts a 12% lift from a channel increase and your geo-test measures 11%, you have high confidence. When there's a large discrepancy, you investigate model assumptions and refine.

Use MMM to identify hidden cannibalization. A retailer running aggressive promotional campaigns might see strong attributed sales but miss that promotions were reducing full-price sales by 12% while maintaining flat revenue. Econometric analysis reveals these trade-offs by modeling impact on both promoted and non-promoted SKUs.

You're not just measuring what you gained. You're measuring what you lost elsewhere to understand net impact. This prevents you from scaling tactics that appear successful in isolation but destroy value holistically.

Quantify the halo effect of brand campaigns. Brand advertising rarely receives credit in attribution because it doesn't drive immediate clicks. MMM captures how brand campaigns lift organic search, direct traffic, and conversion rates across all channels. When Mercedes-Benz analyzes campaign effectiveness econometrically, they forecast long-term brand equity impacts alongside short-term conversion metrics.

The result is a complete picture: immediate sales lift, sustained base sales contribution, cross-channel amplification, and long-term brand strength. You can finally answer whether brand investment delivers returns, not just assert that it must.

Advanced attribution through econometric lenses

Modern MMM implementations now deliver weekly or bi-weekly insights through AI automation, enabling more agile decision-making than the quarterly refreshes of the past.

Combine MMM with multi-touch attribution strategically. Use attribution models for tactical optimization within channels: which keywords, which placements, which audiences. Use econometric modeling for strategic decisions across channels and for measuring anything attribution can't track (TV, radio, outdoor, PR) plus the long-term effects of all channels.

Attribution excels at answering "which ad delivered this conversion?" Econometrics excels at answering "which channels should receive more budget and which should receive less?" The questions are complementary. The methods should be too.

Integrate privacy-compliant measurement from the start. GDPR compliance limitations significantly impact Google Analytics accuracy for European traffic. MMM provides measurement that works regardless of cookie deprecation or privacy regulations because it analyzes aggregate data rather than individual user behavior.

As privacy regulations tighten globally, econometric approaches become more valuable. You're not fighting against consumer privacy. You're measuring in a way that never required violating it.

Leverage Bayesian methods for faster insights. Traditional regression MMM required extensive data and long development cycles. Bayesian approaches incorporate prior knowledge and uncertainty explicitly, enabling robust models with less data and providing probability distributions around predictions rather than point estimates. Modern tools have made these methods accessible to enterprises.

Bayesian MMM tells you not just "this channel delivers 3.5:1 ROI" but "we're 90% confident this channel delivers between 3.1:1 and 3.9:1 ROI." This probabilistic framing enables smarter risk management when making budget decisions.

Implementing continuous optimization processes

Building the model is step one. Extracting value requires turning insights into action systematically.

Establish rapid testing and learning cycles. Modern mAI-driven approaches combine AI computing power with human insight to continuously test and refine. Run weekly model updates, identify underperforming allocations, test reallocation hypotheses in-market, measure incremental impact, and feed results back into the model.

A travel company following this approach discovered through MMM that weekend email campaigns showed declining returns. They tested shifting 30% of email frequency to Tuesday mornings. Two weeks of data confirmed a 12% lift in incremental bookings. The insight got incorporated into the model and scaled across markets. Small optimizations compound into significant performance improvements.

Integrate external factors dynamically. Your marketing doesn't operate in a vacuum. Economic conditions, weather, competitor activity, and seasonal patterns all affect sales. Comprehensive econometric models incorporate these macro variables to isolate your marketing impact accurately.

Consider a beverage brand whose MMM revealed that digital advertising ROI appeared to drop in August. Deeper analysis showed that hot weather drove significant base sales increases that attribution credited to whatever ads were running. Adjusting for weather patterns revealed consistent digital performance year-round and prevented a costly budget cut.

Without controlling for external factors, you risk cutting effective marketing during favorable conditions or doubling down on ineffective marketing during unfavorable ones. Econometric models separate correlation from causation.

Create feedback loops between measurement and execution. The value of MMM compounds when insights flow directly into media planning. Organizations achieving the highest returns establish clear processes: modeling teams deliver optimized budget allocations, media buyers implement changes within agreed parameters, performance is measured against predictions, and discrepancies trigger model refinement.

This closed loop accelerates learning. Your model gets smarter with each cycle. Your team develops intuition for what drives performance. Your organization builds a sustainable competitive advantage in capital allocation.

Practical implementation roadmap

Getting started with econometric optimization requires both technical infrastructure and organizational alignment.

Assess your data readiness. You need clean, consistent data with sufficient history and granularity. Most enterprise implementations require four to eight weeks just for data collection and validation. Audit your current data sources, identify gaps (particularly for offline channels), and establish processes to maintain data quality going forward.

Data quality determines model quality. If your TV spend data is quarterly aggregates while your digital data is daily, you can't accurately compare channel performance. If you're missing competitive activity data, you'll misattribute sales fluctuations to your own marketing.

Choose between build and buy carefully. Open-source tools like Meta's Robyn and Google's Meridian have made MMM more accessible, but they still require significant data science expertise. Managed services combine software with analyst teams who understand both the mathematics and the marketing strategy.

For most marketing organizations, partnered approaches deliver faster time-to-value. The analyst team handles technical complexity while your marketers focus on turning insights into optimized strategies. You don't need to hire econometricians. You need to interpret findings and act on them.

Set realistic expectations for ROI. Enterprises using MMM typically see 20% to 30% improvements in overall marketing efficiency and 15% to 25% reductions in wasted spend. But these gains take time to realize. Budget three to six months for full implementation: four to eight weeks for setup, two to three weeks for model training, two to four weeks for validation, and ongoing optimization cycles.

The financial returns justify the investment. Organizations report profit gains up to 95 times their initial modeling investment through smarter budget allocation. But securing that return requires patience during implementation and discipline during optimization.

Secure cross-functional buy-in early. MMM insights often challenge existing assumptions. When econometric analysis reveals that your highest-performing channel by attribution actually delivers low incrementality, expect resistance. Successful implementations involve finance, executive leadership, and media teams from the start, establishing shared KPIs and decision-making frameworks before the first model runs.

Politics kills more optimization initiatives than bad mathematics. Align stakeholders on what you're measuring, why it matters, and how you'll act on findings before you generate findings that threaten someone's preferred channel or vendor relationship.

Squeezing more from your current budget

You don't need to increase spending to improve results dramatically. The real opportunity lies in eliminating systematic inefficiencies hiding in every marketing mix.

European marketers tell Nielsen they prioritize cost efficiency and transparency over accuracy when selecting measurement technologies. That's backwards. Inaccurate measurement guarantees inefficiency. Econometric methods provide the accuracy needed to identify where money is wasted and reallocate it to higher-performing activities.

Start with the channels and tactics you're already running. Map their true incremental contribution. Model how they interact. Test strategic reallocations based on data rather than hunches.

Consider what this looks like in practice. A retailer discovers through MMM that their Facebook advertising delivers 2.8:1 ROI up to €40,000 weekly spend, then drops to 1.2:1 ROI beyond that threshold. They're currently spending €70,000 weekly. They reallocate €30,000 to display advertising, which their model shows has unused capacity with steady 2.5:1 returns. The reallocation increases total incremental sales by 18% with zero budget increase.

These opportunities exist in every marketing mix because most organizations optimize within channels using attribution but never optimize across channels using incrementality. You're leaving money on the table by not moving it between channels.

The combination of econometric rigor and practical marketing expertise helps European organizations eliminate ad waste and achieve business goals faster through calculated, data-driven decisions. Discover how mAI-driven media strategy can transform your marketing efficiency without increasing your budget.