Common Misconception: Cutting budgets based on short-term metrics while ignoring the role of each channel
A brand launches a marketing campaign by evenly allocating budget across channels such as Google Search, Social Ads, Display, and Trade Marketing. After just two days of testing, the channels with higher CTR and lower CPL are retained, while the rest see their budgets cut or are paused entirely. This may sound reasonable at first glance. However, evaluating channel performance solely based on surface-level metrics—without considering each channel’s role in the customer journey—can easily lead to an imbalanced marketing campaign and inefficient budget allocation. In reality, channels with weaker short-term metrics are not necessarily ineffective; they may play a critical role as indispensable touchpoints within the conversion funnel.
Below are several approaches that help reassess how marketing campaign performance is measured—taking a more strategic perspective that reflects the true role of each channel, instead of a short-sighted “test and cut” mindset.
1. Attribution Model – Allocating value across customer touchpoints
Attribution models are among the most widely used approaches for measuring marketing campaign performance, helping marketers understand how each activity within a digital marketing campaign contributes to conversion outcomes—especially as the customer journey becomes increasingly complex and non-linear. Attribution models are commonly categorized into five main types based on touchpoint positioning:
1.1. First Touch – The initial customer touchpoint
First Touch Attribution assigns 100% of the conversion value to the very first interaction between a customer and a brand. This model assumes that the initial touchpoint plays the most critical role, as it kickstarts the customer journey toward conversion.
First Touch is particularly useful when businesses want to identify which channels, content formats, or campaigns are most effective at generating initial awareness—especially in top-of-funnel marketing campaigns.
Advantages
This model is simple, easy to implement, and enables businesses to quickly identify channels or content that excel at attracting new audiences—laying the foundation for scaling potential customer acquisition.
Limitations
First Touch can lead to over-investment in awareness channels while underestimating the importance of nurturing and conversion activities in later stages of the customer journey. It may also distort marketers’ understanding of user behavior by implying that conversions happen immediately after the first interaction, without the need for subsequent touchpoints.
1.2. Last Touch – The final conversion touchpoint
In contrast to First Touch, Last Touch Attribution credits only the final interaction immediately before a customer completes a conversion action (often becoming an MQL). This model treats the last touchpoint as the decisive factor that triggers customer action.
Last Touch is commonly used to track direct-response channels and is suitable for evaluating content or channels designed to “close the deal,” such as landing pages, sign-up forms, or blog posts with strong CTAs.
Advantages
This model is straightforward, easy to deploy, and particularly effective for identifying channels or content that directly drive conversions. It allows marketers to quickly pinpoint the final touchpoint that motivates customers to take action.
Limitations
Last Touch tends to overemphasize the importance of the final interaction while ignoring the entire nurturing process that builds awareness and trust beforehand. This creates a narrow perspective and undervalues early- and mid-funnel marketing activities within the customer journey.
1.3. Linear Attribution – Equal credit across all touchpoints
Linear Attribution distributes conversion value evenly across all touchpoints a customer experiences—from initial awareness to final conversion. Each interaction is considered equally important, regardless of its actual influence.
This model is well-suited for campaigns that require a holistic view of the customer journey and aim to avoid bias toward any single touchpoint, offering a balanced perspective across multi-channel and multi-touch journeys.
Advantages
Linear Attribution provides a more comprehensive and fair view compared to simple models like First Touch or Last Touch, as it recognizes the contribution of every interaction. This helps businesses avoid overlooking the value of mid-funnel activities that support nurturing and guidance toward conversion.
Limitations
The main drawback of Linear Attribution is its inability to reflect the true impact of individual touchpoints. In practice, not all interactions carry the same weight—some have a stronger influence on conversion, while others play only a supporting role.
1.4. Time Decay Attribution – Prioritizing touchpoints closer to conversion
Time Decay Attribution assumes that touchpoints occurring closer to the conversion moment have greater influence. As a result, later-stage interactions receive higher weighting than earlier ones.
This model is especially suitable for bottom-of-funnel marketing campaigns such as remarketing, lead nurturing, or email automation, where the primary goal is to push customers toward the final action.
Advantages
Time Decay Attribution helps businesses optimize conversion-focused activities by highlighting the value of touchpoints that directly influence decision-making in the final stage of the customer journey.
Limitations
This model tends to undervalue early-stage touchpoints that play a critical role in building awareness and nurturing demand. As a result, businesses may risk cutting top-of-funnel budgets, weakening their long-term ability to attract new customers.
1.5. U-Shaped Attribution – Emphasizing the first and activation touchpoints
U-Shaped Attribution allocates value across the customer journey while emphasizing two critical touchpoints: the first interaction where customers initially discover the brand, and the “activation” point where they take their first meaningful action (such as submitting a form, signing up, or becoming a marketing qualified lead – MQL). A common distribution assigns 40% of the value to the first touch, 40% to the activation touch, and the remaining 20% evenly across intermediate interactions.
U-Shaped Attribution is particularly effective for B2B or long sales-cycle journeys, where early awareness and initial engagement are crucial in guiding customers toward eventual conversion.
Advantages
This model helps businesses focus optimization efforts on the most critical stages of the customer journey—initial awareness and first conversion-related action—thereby improving lead quality and early-stage commitment.
Limitations
U-Shaped Attribution may undervalue the subtle persuasive role of intermediate touchpoints. This becomes a limitation in industries or products that require extensive customer education through complex content sequences, where mid-journey interactions are essential for nurturing and conversion.
2. Experimentation – Testing-based measurement methods
Experimentation—most commonly A/B Testing—is a measurement approach that quantifies the specific impact of individual variables on customer behavior. This method is especially effective for optimizing UI elements, content, and user experience within marketing campaigns.
The process involves randomly splitting the target audience into two groups: Group A interacts with the original version (e.g., the current website, email, or live ad), while Group B is exposed to a test variant (such as an email with a new subject line or a landing page with a different CTA color). Performance data from both groups is then compared to determine which variation delivers better results. This approach is particularly useful for tactical optimization in areas like email marketing campaigns, improving ad CTR, or increasing website conversion rates.
Advantages
A/B Testing enables precise measurement of how individual elements influence marketing performance, empowering marketers to make data-driven decisions rather than relying on intuition. Variables such as email subject lines, CTA button colors, or landing page layouts can be tested to identify the most effective version—improving key metrics like open rate, CTR, and conversion rate.
Limitations
At scale, A/B Testing can become complex and costly—especially when multiple variables are tested simultaneously. It requires sufficiently large sample sizes, longer test durations, and substantial technical resources. Without rigorous design and control, experiments may yield inconclusive or misleading results, leading to wasted effort without delivering real value.
3. Marketing Mix Modeling (MMM)
Marketing Mix Modeling (MMM) is a measurement approach that leverages aggregated data combined with statistical or machine learning techniques to quantify the impact of marketing activities—such as advertising, promotions, and pricing—on business outcomes like sales, app installs, or brand awareness.
Formula: y = β0 + β1x + ε
Where:
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y – Sales: The dependent variable representing the outcome predicted by the model.
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x – Advertising Spend: The independent variable representing investment in advertising (e.g., budget, impressions, number of campaigns), used to explain and predict sales.
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β0 – Base Sales: Expected sales when there is no advertising spend (x = 0).
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β1 – Advertising Impact: The incremental change in sales driven by advertising spend, indicating how much sales increase per unit of investment.
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ε – Random Error: The unexplained variation due to external factors, unmeasured variables, or randomness.
Advantages
Marketing Mix Modeling provides a holistic view of ROI by quantifying how each marketing lever—advertising, promotions, pricing—contributes to overall business performance. By relying on aggregated data rather than individual-level data, MMM helps organizations comply with privacy regulations such as GDPR and App Tracking Transparency. Additionally, it supports strategic-level budget optimization and investment forecasting, enabling more accurate and effective resource allocation.
Limitations
Despite its strengths, MMM has limitations that businesses should consider. It struggles with granular, micro-level analysis—such as evaluating individual ad sets or specific audience segments. When input variables are too numerous or data quality is insufficient, model accuracy can decline significantly. Moreover, MMM requires high-quality historical data and a relatively long setup and calibration period to ensure reliable and stable results.
Conclusion
Businesses can combine MMM with other measurement approaches such as attribution models, causal experiments, or campaign-level metrics (e.g., brand lift) to offset the strengths and weaknesses of each method—building a more comprehensive, data-driven decision framework.
To fully unlock the value of these methods, selecting the right tools aligned with business objectives is critical for optimizing resources and maximizing ROI. Addressing this need, SmartAds has developed its Brandformance solution—combining brand building with performance optimization. Leveraging audience data from trusted media platforms, the solution aims to help businesses reach the right customers and improve conversion rates at an optimized cost.
