When I first started measuring marketing performance years ago, ROAS (Return on Ad Spend) seemed like the holy grail. Spend £1,000 on ads, generate £5,000 in revenue, celebrate a 5:1 return. Simple maths, clear results. But here’s the uncomfortable truth I’ve learnt: basic ROAS calculations can be dangerously misleading. They tell you what happened, not why it happened, or whether your marketing actually caused those sales.
Today’s marketers need advanced marketing ROI measurement techniques that go far beyond surface-level metrics. In this post, I’ll walk you through the sophisticated approaches that separate genuinely effective marketing from expensive mistakes.
>>> see also B2B Marketing Attribution for UK SMEs: A Practical Guide
Why Basic ROAS Falls Short
ROAS has a fundamental flaw: it measures correlation, not causation[1][2]. When someone clicks your Facebook ad and purchases an hour later, did the ad cause the sale? Or was that person already planning to buy, and the ad simply appeared along their journey?
This distinction matters enormously. Research shows that traditional attribution models can overestimate channel effectiveness by up to 30%, leading to massive budget misallocations[3]. That £100,000 you’re pouring into a channel showing strong ROAS might be generating far less incremental revenue than you think.
The marketing landscape has also changed dramatically. By 2025, 30% of businesses are using AI-driven analytics tools to measure ROI across multiple channels[4]. Privacy regulations have made browser-based tracking unreliable, with ad blockers and cookie restrictions creating blind spots in your data[5]. Meanwhile, customer journeys have become impossibly complex, spanning multiple devices, channels and touchpoints before conversion[6].
Incrementality Testing: Measuring True Causal Impact
Incrementality testing has become the gold standard for understanding advertising’s true impact[7][8]. Unlike attribution models that assign credit based on touchpoints, incrementality testing asks a simple but powerful question: what would have happened without this marketing activity?
The methodology is straightforward but rigorous. You create two statistically similar groups – one exposed to your marketing (test group) and one not exposed (control group)[9]. By comparing outcomes between these groups, you can quantify the incremental revenue your marketing actually generated, not just the revenue it touched.
A beauty brand running an incrementality test for Performance Max campaigns discovered an incremental ROAS of £6, meaning every £1 invested generated £6 in truly incremental revenue[8]. Conversely, a financial institution tested YouTube campaigns and found an incremental ROAS of only £1.10, revealing the channel was barely profitable[7][8]. This heads-up allowed them to reassess their strategy before wasting more budget.
There are several approaches to incrementality testing. Geo holdout testing pauses marketing activity in specific geographic regions whilst continuing it elsewhere, allowing you to measure the impact by comparing performance metrics between regions[10][11]. Audience split testing randomly assigns users to test and control groups, then exposes only the test group to your campaign[9]. Time-based toggle tests turn campaigns on and off in controlled sequences to measure the lift generated during active periods[9].
The key output is incremental ROAS (iROAS), calculated by dividing incremental revenue by media spend[7][8]. This metric cuts through vanity numbers to show the true value of your investment. When you combine incrementality testing with Marketing Mix Modelling, you achieve both continuous ROI measurement and experimental validation[12].
Marketing Mix Modelling: The Strategic View
Whilst incrementality testing provides tactical insights, Marketing Mix Modelling (MMM) offers a strategic, bird’s-eye view of marketing effectiveness[13][14]. MMM uses statistical analysis to quantify how various marketing activities drive sales, accounting for external factors that basic attribution ignores.
The beauty of MMM is its comprehensiveness. It analyses the impact of both online and offline media – TV, radio, print, digital display, paid search and social[14]. More importantly, it accounts for external factors like seasonality, weather, pricing changes, promotional activities and competitor actions[13][14]. This holistic approach reveals the true incremental value of each marketing activity.
MMM excels at identifying diminishing returns and optimal spend levels[13][15]. Every channel has a saturation point beyond which additional investment delivers progressively smaller returns[16][17]. A company spending £10 million on marketing might see £32 in revenue per pound spent, but increasing spend to £100 million could drop returns to just £3.25 per pound[16]. MMM’s response curves show you exactly where these inflection points occur.
By 2025, Marketing Mix Modelling is experiencing a revival, with more than 61% of marketing professionals adopting it as privacy regulations make digital attribution less reliable[13][18]. Modern MMM platforms incorporate AI and machine learning, enabling real-time insights and automated budget recommendations[19]. Tools like Meta’s Robyn now offer real-time incremental measurement, allowing marketers to adjust strategies on the fly[19].
The strategic value is immense. MMM helps you optimise budget allocation across channels, understand synergies between marketing activities, and plan for long-term brand building rather than just short-term conversions[13][15].
Advanced Attribution Models Beyond Last-Click
Traditional last-click attribution is woefully inadequate for today’s multi-touch customer journeys[5][6]. Fortunately, several sophisticated attribution approaches have emerged.
Data-driven attribution uses machine learning to analyse conversion paths and assign credit based on actual performance data rather than predetermined rules[20][21]. Google’s algorithms examine your specific customer behaviour patterns, adapting the model to reflect your unique business rather than applying universal assumptions[20]. This approach has gained significant validity because it accounts for the individual characteristics of each business[20].
Multi-touch attribution distributes credit across every interaction that led to conversion[5][22]. Linear attribution gives even credit to all touchpoints, useful for understanding the full customer journey[6]. Time decay attribution assigns increasing credit to touchpoints closer to conversion, recognising that recent interactions often carry more weight[6]. Position-based (U-shaped) models assign 40% credit each to first and last touches, with the remaining 20% distributed among middle touchpoints[22][6][23].
For complex B2B sales cycles, W-shaped attribution gives equal weight to three key moments: first touch, middle touch and last touch[6][23]. Z-shaped attribution extends this further by adding a fourth key moment – the sales-qualified lead stage[6].
The Shapley value approach, based on game theory, provides a mathematically fair way to distribute credit among marketing touchpoints[24][25][26]. It measures the marginal contribution each touchpoint makes by examining all possible combinations of channels[25][27]. The formula considers how much value each channel adds when joining different subsets of other channels, then averages these contributions[27][28].
Shapley values are particularly powerful because they reveal how channels work together[27][29]. A channel might show modest performance in isolation but significantly amplify other channels’ effectiveness when combined.
Customer Lifetime Value: The Long-Term Perspective
Advanced marketing ROI measurement must extend beyond immediate transactions to Customer Lifetime Value (CLV)[30][31]. CLV represents the total economic value a customer generates over their entire relationship with your company[30][32].
The components of CLV include customer acquisition cost, retention rate and average revenue per customer[30][31]. By focusing on high-value customer segments and cultivating long-term relationships, businesses optimise marketing investments and maximise lifetime profitability[30][33].
CLV fundamentally changes how you evaluate marketing effectiveness. A campaign that appears expensive based on acquisition cost might be incredibly profitable when you factor in repeat purchases and referrals over multiple years[30]. Research shows that companies linking first-party data sources can generate double the incremental revenue from a single marketing outreach by focusing on CLV[34].
Personalised marketing approaches play a pivotal role in CLV optimisation[30]. Amazon’s personalised recommendations based on browsing and purchase history effectively drive cross-selling and increase CLV[30]. Customer relationship management systems enable organisations to deliver targeted messages that resonate personally, enhancing engagement, satisfaction and loyalty[30].
When evaluating marketing investment decisions, CLV allows you to assess the profitability of different initiatives and channels[30]. By measuring CLV generated from different campaigns, you can identify the most effective strategies and ensure resources are allocated to activities that generate the greatest long-term value[30].
Cohort Analysis for Retention Intelligence
Cohort analysis segments customers by shared characteristics or time periods, then tracks their behaviour over time[35][36][37]. This approach reveals retention patterns and helps evaluate the long-term quality of marketing campaigns.
Acquisition cohorts group users by when they were first acquired[35][37]. You can spot churn patterns early – for instance, users acquired in April might drop off faster than those acquired in March[35]. This helps you evaluate which marketing campaigns bring in customers who actually stick around[35].
Behavioural cohorts group users based on shared behaviours and interactions[37][38]. This analysis reveals which actions drive retention and which lead to churn[39]. For subscription businesses, behavioural cohorts might identify that customers who complete onboarding are 80% more likely to remain active after six months[37].
Predictive cohorts use behavioural data to forecast future actions[38]. This helps you identify which users to target with retention campaigns or upsell opportunities[38].
When assessing cohort data, focus on identifying trends and patterns[36]. Which cohort sizes prove most lucrative? Was there a specific promotion or increased ad spend that month? Did you run a special retention campaign for that cohort?[36] The numbers back you up when doubling down on successes like lower churn, more purchases and increased average order value[36].
Marginal ROI and Diminishing Returns
Understanding marginal ROI – the return on each additional pound invested – is crucial for optimising spend levels[16][40][41]. The law of diminishing marginal returns explains why efficiency improvement slows as marketing spending increases[16].
At first, you might see incredible gains from a campaign. But over time, each additional pound delivers progressively less value[42][16]. The audience becomes saturated, engagement drops, and returns flatten[42][16]. A campaign performing well early on starts underperforming as people become overexposed[16].
Marginal ROAS is calculated by dividing incremental revenue by incremental cost[40]. If your initial campaign generated £10,000 in revenue from £2,000 spend (ROAS of 5), and investing an additional £1,000 generates £2,000 more revenue, your marginal ROAS is 2[40]. This tells you each additional pound generates £2 in revenue, compared to £5 for your initial spend.
Marketing Mix Modelling helps identify diminishing return curves for each channel[17][41]. If investments are at point A on the curve, each additional pound drives significant sales. But as you move to point B with increased investment, marginal ROI decreases substantially[41].
The practical application is powerful. Model projected outcomes using MMM or incrementality testing before scaling budgets[16]. Watch for flattening ROAS or engagement before increasing spend. Set performance thresholds and only scale when marginal efficiency holds steady[16].
The Unified Measurement Framework
Leading organisations are adopting Unified Marketing Measurement (UMM) frameworks that integrate multiple methodologies[43][44][45]. Rather than relying on separate, disconnected approaches, UMM creates a single, consistent measurement framework.
The triangulated approach combines three core methodologies[43]. Marketing Mix Modelling provides strategic, long-term insights. Incrementality testing offers causal validation through controlled experiments. Causal attribution or multi-touch attribution delivers granular, real-time tactical guidance[43][45].
The process begins with MMM analysing historical data to quantify the impact of every marketing and non-marketing driver on sales[46]. It accounts for seasonality, promotions, pricing, competitor activity and economic trends[46]. MMM also identifies adstock effects (how long advertising impact persists) and saturation curves (points of diminishing returns)[46].
Incrementality tests then validate these findings through rigorous experiments[43][46]. Geo-lift tests, holdout testing and synthetic control groups provide indisputable proof of causal impact[43].
Finally, insights from MMM and incrementality tests calibrate real-time attribution data[46]. The formula is simple but powerful: Incremental ROI equals Attributed ROAS multiplied by Incrementality Factor[46]. If a platform reports ROAS of 5, but incrementality testing shows only 60% of conversions were truly incremental, the real causal ROI is 3[46].
This calibrated data – causal attribution – provides a granular, real-time, causally informed view of performance[46]. Research from Google shows that applying a unified measurement approach can lead to a 40% increase in expected uplift in sales contribution[47].
Privacy-First Measurement Approaches
As privacy regulations tighten and third-party cookies disappear, marketers must adapt their measurement strategies[48][49]. Server-side tracking has emerged as the gold standard for maintaining accurate conversion measurement in a privacy-first world[50][51].
Traditional client-side tracking relies on users’ browsers to send data to analytics platforms[50]. Browser restrictions, ad blockers and privacy tools create massive data gaps[50][51]. Server-side tracking fundamentally changes this by routing data through your own server before sending it to third-party platforms[50][52].
The benefits are substantial. Server-side tracking resists ad blockers, providing 10-30% more recorded conversions on average[51]. It improves data accuracy by eliminating browser-related data loss[50][53]. Implementation gives you more control over data collection, processing and enrichment before sharing with advertising platforms[50][52].
Google Tag Manager Server-Side and Meta’s Conversions API are the primary platforms supporting this approach[50]. Square implemented server-side tagging and improved conversion tracking ability by 46%[52].
First-party data collection has become critical for accurate measurement[34][54]. Companies that link all first-party data sources can generate double the incremental revenue and 1.5 times the cost efficiency improvement over companies with limited data integration[34]. Building first-party data through newsletters, account registrations, loyalty programmes and customer surveys provides the foundation for privacy-compliant measurement[5].
Brand Lift Studies for Upper-Funnel Impact
Not all marketing impact shows up in immediate conversions. Brand lift studies measure how advertising affects brand awareness, recall, consideration and favourability[55][56][57].
The methodology compares consumers exposed to your campaign with those who haven’t seen it[55]. Pre- and post-campaign surveys measure shifts in brand metrics, demonstrating whether changes in consumer attitudes result directly from advertising[55].
Key metrics include brand awareness (consumer recognition after campaign exposure), ad recall (ability to remember seeing your advertisement), brand consideration (increased willingness to engage or purchase) and favourability (improved attitudes and positive associations)[55].
A fashion retailer used brand lift studies to measure their new ad campaign’s impact[57]. Results showed significant increases in both brand awareness and purchase intent among those exposed to ads, allowing the retailer to refine future campaigns and allocate budget more effectively[57].
GWI AdFX and similar platforms measure brand lift by comparing groups exposed to digital campaigns with matched control groups[55]. The methodology provides robust insights beyond sales metrics alone[55][58].
Marketing Efficiency Ratio as a North Star
Marketing Efficiency Ratio (MER) has emerged as a crucial metric for evaluating overall marketing effectiveness[59][60][61]. Unlike ROAS, which focuses on advertising spend alone, MER considers total marketing investment against total revenue.
The calculation is straightforward: divide total revenue by total marketing spend[59][60][62]. If Q3 revenue is £200,000 and total marketing spend is £50,000, your MER is 4, meaning you earned £4 for every £1 spent on marketing[60].
Advertising professionals often suggest targeting a MER between 3 and 5[60]. For e-commerce businesses, where production costs are higher, a MER of 5 or above is considered good[61].
The strategic advantage of MER is that it provides a blended view of all marketing activities[61][62]. It removes attribution complexity and shows the simple relationship between total marketing investment and total revenue generated[59].
In 2025, marketing efficiency has become a top priority for CMOs and CFOs[13]. MER provides the necessary framework to achieve this efficiency by improving overall marketing effectiveness rather than just optimising individual channel ROAS[13].
Practical Implementation Recommendations
Moving beyond basic ROAS requires a structured approach. Start by establishing your current baseline with comprehensive data collection from all marketing channels, both online and offline[44][63]. Implement first-party data capture through your website, CRM and customer interactions[34][54].
Set up server-side tracking to improve data accuracy and prepare for the cookieless future[50][51]. Deploy Google Tag Manager Server-Side or Meta’s Conversions API to recover lost conversions[50].
Choose measurement methodologies based on your business needs and maturity[45]. Small to medium businesses should start with Marketing Efficiency Ratio for overall tracking and basic multi-touch attribution for channel insights[60][61]. As you mature, add incrementality testing for key channels and quarterly MMM analysis[12][13].
Larger enterprises should implement the full unified measurement framework, integrating MMM, incrementality testing and causal attribution[43][45]. Use MMM for strategic planning and budget allocation, incrementality experiments for validating channel effectiveness, and calibrated attribution for tactical optimisation[43][46].
Build a testing roadmap by prioritising business questions you want answered[1]. Start by testing incrementality on channels comprising the largest share of marketing spend, then run scale tests to determine how much more to invest in the most profitable strategies[1].
Monitor marginal ROI continuously to avoid overspending on saturated channels[16][40]. Watch for warning signs like higher cost per click, more impressions with fewer conversions, or dropping ROAS[42][16].
Calculate Customer Lifetime Value for different cohorts and channels[30][31]. Focus marketing investment on acquiring customers with the highest predicted CLV[30][33].
Run regular brand lift studies to measure upper-funnel impact[55][56]. Not every marketing pound should be judged on immediate conversions. Brand awareness and consideration drive long-term sustainable growth[55][58].
Moving Beyond ROAS Thinking
The shift from basic ROAS to advanced marketing ROI measurement represents a fundamental evolution in how we think about marketing effectiveness. ROAS measures correlation. Incrementality measures causation[1][2]. ROAS looks at immediate returns. CLV examines lifetime value[30]. ROAS treats channels in isolation. MMM reveals synergies and interactions[13][15].
By 2025, the most sophisticated marketers are combining multiple measurement methodologies within unified frameworks[43][45]. They’re using incrementality testing to validate what truly works, MMM to optimise strategic allocation, and causal attribution for tactical execution[43][46].
The privacy-first future demands these advanced approaches[48][49]. As browser-based tracking becomes less reliable, server-side measurement and first-party data provide the foundation for accurate ROI calculation[50][51].
Most importantly, advanced marketing ROI measurement transforms marketing from a perceived cost centre into a demonstrable profit centre[64]. When you can prove causal impact, quantify incremental revenue, and optimise based on marginal returns, marketing becomes a strategic investment rather than an expense.
The uncomfortable truth is that basic ROAS calculations have been leading many marketers astray for years. The comfortable truth is that better measurement techniques are now accessible to businesses of all sizes. The question is whether you’ll continue relying on correlation-based metrics, or embrace the advanced approaches that reveal marketing’s true impact.
References and Further Reading
[1] Unlock Revenue with Incrementality Measurement & Scale … https://www.measured.com/blog/unlock-revenue-with-incrementality-measurement-and-scale-testing/
[2] The Future of Marketing Measurement: Beyond ROAS in 2025 https://www.adriel.com/blog/the-future-of-marketing-measurement-beyond-roas-in-2025
[3] Causal Inference in Marketing: A Machine Learning … https://ijcaonline.org/archives/volume187/number22/causal-inference-in-marketing-a-machine-learning-approach-to-identifying-high-impact-channels/
[4] Marketing ROI Statistics: 30+ Stats to Boost Your Strategy … https://firework.com/blog/marketing-roi-statistics
[5] 10 Effective Strategies To Measure Marketing ROI in 2025 https://notyouridea.com/blog/strategies-to-measure-marketing-roi
[6] The complete guide to marketing attribution models in 2025 https://www.mediahawk.co.uk/blog/marketing-attribution-models/
[7] Use incrementality testing for effective marketing … https://business.google.com/in/think/marketing-strategies/incrementality-testing/
[8] Use incrementality testing for effective marketing … https://www.thinkwithgoogle.com/intl/en-emea/marketing-strategies/data-and-measurement/incrementality-testing/
[9] What is Incrementality Testing? A Guide for Marketers in 2025 https://lifesight.io/blog/incrementality-testing/
[10] All you need to know about geo holdout testing https://funnel.io/blog/geo-holdout-testing
[11] Geo Holdout Testing: What It Is and How to Use It in Your … https://www.rockerbox.com/blog/geo-holdout-testing
[12] What is Incrementality Testing? Guide for Marketers https://sellforte.com/blog/what-is-incrementality-testing
[13] Why marketing mix modeling is crucial in 2025 and beyond https://searchengineland.com/marketing-mix-modeling-crucial-448348
[14] The Revival of Marketing Mix Modelling https://www.dentsu.com/nl/en/the-revival-of-marketing-mix-modelling
[15] Marketing Mix Modelling in 2024: Complete Guide https://kleene.ai/marketing-mix-modelling/
[16] Law of Diminishing Marginal Returns in Marketing Explained https://www.eliya.io/blog/marketing-spend-optimization/law-of-diminishing-marginal-returns
[17] Diminishing Returns of marketing https://support.sellforte.com/en/articles/6960624-diminishing-returns-of-marketing
[18] FAQ: What marketers need to know about marketing mix … https://www.emarketer.com/content/why-mmm-making-comeback
[19] Marketing measurement lessons learned and what to … https://funnel.io/blog/marketing-measurement
[20] The Complex Reality of Marketing Attribution | MMM https://www.marketmymarket.com/the-complex-reality-of-marketing-attribution-moving-beyond-simple-models/
[21] Tips to nail your marketing attribution model https://www.thinkwithgoogle.com/intl/en-emea/marketing-strategies/data-and-measurement/overhaul-marketing-attribution-model/
[22] Marketing Attribution Guide: Models, Tools & ROI https://mcgaw.io/blog/marketing-attribution-multi-touch-models-tools-best-practice-alternatives/
[23] The definitive guide to marketing attribution: giving credit … https://www.appsflyer.com/resources/guides/marketing-attribution/
[24] Attribution 2.0: Is it the solution to ROAS measurement? https://www.revenuemarketingalliance.com/chasing-roas-marketing-attribution-pipe-dream/
[25] Shapley Value Methods for Attribution Modeling in Online … https://arxiv.org/pdf/1804.05327.pdf
[26] Marketing Mix Modeling and Shapley Value Attribution https://www.linkedin.com/pulse/marketing-mix-modeling-shapley-value-attribution-data-chakraborty-7wzdc
[27] Shapley Values in Marketing: Know What Really Works https://lebesgue.io/marketing-attribution/understanding-shapley-values-in-marketing
[28] A Beginner’s Guide to Shapley Value https://www.thedatacherry.com/post/understanding-attribution-model-a-beginner-s-guide-to-shapley-value
[29] A Comparison Of Attribution Models – Corvidae.ai https://corvidae.ai/blog/corvidae-vs-shapley-and-markov/
[30] CLV Insights for Strategic Marketing & Financial Performance https://www.tandfonline.com/doi/full/10.1080/23311975.2024.2361321
[31] What Customer Lifetime Value (CLV) Is & How to Calculate It https://www.netsuite.com/portal/resource/articles/ecommerce/customer-lifetime-value-clv.shtml
[32] What Is Customer Lifetime Value (CLV) and How to … https://www.salesforce.com/blog/customer-lifetime-value/
[33] How to Calculate Customer Lifetime Value (CLV) https://www.optimove.com/resources/learning-center/how-to-measure-customer-lifetime-value
[34] Responsible Marketing with First-Party Data https://www.bcg.com/publications/2020/responsible-marketing-with-first-party-data
[35] Cohort analysis for businesses: Here’s what to know https://stripe.com/gb/resources/more/cohort-analysis-for-businesses
[36] Cohort Analysis Guide: Improve Customer Retention https://www.peelinsights.com/post/your-guide-to-cohort-analysis
[37] Cohort Retention Analysis 101: How to Measure User … https://userpilot.com/blog/cohort-retention-analysis/
[38] Cohort Retention Analysis: Reduce Churn Using Customer … https://amplitude.com/blog/cohorts-to-improve-your-retention
[39] Churn Rate Cohort Analysis: Guide To Boost Retention https://www.chargebee.com/blog/chargebee-churn-rate-cohort-analysis-retention-strategies/
[40] Guide to Marginal ROI and Maximizing Ad Spend https://influencermarketinghub.com/marginal-roi/
[41] Marginal Return On Investment (Marginal ROI) https://support.sellforte.com/en/articles/8320556-marginal-return-on-investment-marginal-roi
[42] How Diminishing Returns Affect Ad Spend https://mailchimp.com/resources/diminishing-returns/
[43] Unified Marketing Measurement (UMM) : A Guide for Modern … https://lifesight.io/blog/unified-marketing-measurement-guide/
[44] Unified Marketing Measurement https://www.measured.com/faq/what-is-unified-marketing-measurement/
[45] What is Unified Marketing Measurement https://mma.com/resources/what-is-unified-marketing-measurement/
[46] Causal Attribution in Marketing: What Is It & How Does It Work? https://lifesight.io/blog/causal-attribution-in-marketing/
[47] Unified online marketing measurement https://www.thinkwithgoogle.com/intl/en-emea/marketing-strategies/data-and-measurement/unified-online-marketing-measurement/
[48] Privacy-First Marketing: Key Trends & Tools for Success https://www.go-globe.com/privacy-first-marketing-changing-the-rules/
[49] Unlocking the Future of Data with Privacy-First Measurement https://cassandra.app/resources/unlocking-the-future-of-data-with-privacy-first-measurement
[50] How to use server-side tracking for better conversion tracking https://www.linkedin.com/pulse/how-use-server-side-tracking-better-conversion-easyinsights-dw8ac
[51] The 8 benefits of Server-side Tracking for more conversions https://taggrs.io/server-side-tracking/benefits-for-more-conversions/
[52] Square improves conversion measurement securely with … https://marketingplatform.google.com/about/resources/square-improves-conversion-measurement-securely-with-server-side-tagging/
[53] How to set up server-side conversion tracking for ads … https://usercentrics.com/guides/server-side-tagging/server-side-conversion-tracking/
[54] The marketer’s guide to first-party data https://www.appsflyer.com/resources/guides/marketers-first-party-data/
[55] Brand lift study: How to measure the true impact of your … https://www.gwi.com/blog/brand-lift-study
[56] What Is a Brand Lift Study | How to Measure Brand … https://www.cint.com/blog/what-is-a-brand-lift-study/
[57] What is a Brand Lift Study and Why You Should Run One? https://www.nexd.com/blog/what-is-a-brand-lift-study-and-why-should-you-run-one/
[58] Brands need incrementality measurement for a privacy-first … https://www.criteo.com/blog/brands-need-incrementality-measurement-for-a-privacy-first-world/
[59] Marketing Efficiency Ratio (MER) – Vision Labs https://visionlabs.com/metrics/mer/
[60] Marketing Efficiency Ratio: How To Calculate + Improve MER https://www.shopify.com/uk/blog/marketing-efficiency-ratio
[61] Marketing efficiency ratio – explaining MER in marketing https://funnel.io/blog/marketing-efficiency-ratio
[62] What Is A Good Marketing Efficiency Ratio? Definition, … https://thecmo.com/marketing-operations/marketing-efficiency-ratio/
[63] Guide to Unified Marketing Measurement https://keends.com/blog/unified-marketing-measurement/
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