Proving ROI with Revenue Attribution
In today's data-rich marketing landscape, marketers must quantify campaign performance to optimize budgets. With many touchpoints across channels, determining which efforts convert leads is difficult. Marketers can no longer rely on gut feelings; data validation is required. Revenue attribution models offer a solution.
By analyzing customer journeys across touchpoints, these tools identify high-performing channels for acquiring and converting customers. Marketers gain visibility into how campaigns and channels work together to drive revenue. Rather than spreading budgets thinly, marketers can double down on proven drivers of growth.
With clear attribution, executives can hold marketing accountable. Revenue impact can be quantified and optimized. Attribution transforms guesswork into actionable data, enabling strategic budget allocation and ultimately revenue growth. For modern marketers, revenue attribution is crucial to compete and thrive.
From Tracking Conversions to Attribution Revenue
Many marketers use conversion tracking to monitor form submissions, email signups, and other micro-actions on their websites. This provides helpful data on engagement. However, conversion tracking has limitations in linking efforts to revenue.
Revenue attribution delivers deeper insights. It analyzes the entire customer journey across channels to determine each touchpoint's impact on driving sales. Unlike conversion tracking, attribution focuses on the macro view--how marketing drives customers through the funnel to purchase.
Revenue attribution transforms marketing by connecting activities to real business impact.
For today's accountable marketers, conversion tracking is table stakes. To compete, marketers must level up to revenue attribution. With attribution providing clear ROI proof, marketing earns its seat at the strategy table.
Example: Conversion Attribution VS Revenue Attribution
Large volume marketers still using conversion attribution can miss significant value by focusing on the interactions that generate leads. As the examples below reveal, the results of revenue attribution can be enlightening and counterfactual to conversion attribution.
Conversion Attribution Report
In the Conversion Attribution report above Google Paid Search, Google Display, and Programmatic lead the way with conversions, represented here as leads. However, adding in revenue attribution below provides a completely different value equation.
Revenue Attribution Report
Once revenue is added to channel attribution, the real value becomes clear. The conversion attribution originally revealed Google Paid Search, Google Display, and Programmatic advertising excelling in lead generation. With revenue attribution added, we see that organic’s value is tremendously higher (a CAC of $580 vs a CAC of $11,833).
How Revenue Attribution Transforms Campaign Analysis
Legacy marketing metrics paint an incomplete picture. They lack visibility into how touchpoints work together to drive sales. Revenue attribution provides a solution.
Attribution analyzes every customer interaction across channels. It maps the entire journey from first website visit to final purchase. Each engagement is tied back to underlying marketing efforts. Algorithms can then proportionately attribute revenue generated to each touchpoint.
This full-funnel analysis reveals marketing's true revenue impact. With clear visibility, marketers can shift budgets to high-performing channels. Weak performers can be refined or removed.
Armed with accurate attribution, CMOs can optimize spending for ROI. Campaigns can be adjusted quickly based on revenue data instead of hunches.
For today's accountable marketers, revenue attribution is essential. It provides the robust analysis required to grow revenue in a measurable way. Marketing graduates from cost center to profit driver.
Getting Started with Revenue Attribution
Marketers getting started with full-funnel, cross-channel revenue attribution do not need to create a roadmap from scratch. While businesses may differ in attribution deployments, the basic requirements are the same:
- Tracking and Centralizing Data
- Conversion Analysis
- Application for ROAS
Each step varies by business context, or software components selected; nevertheless, these guidelines will ensure effective revenue attribution.
1. Tracking and Centralizing Data
Regardless of the technology for tracking and storage, revenue attribution requires the capture of complete journeys. Many solutions use global tracking pixels that are independent of third-party cookies or ad platform APIs.
Regardless of the structure of a key conversion, whether forms, pages, links, etc, the global pixel automatically learns and captures conversions across brand marketing elements. Critical at this stage is the capture of metadata and financial data so that marketing elements, such as channels, campaigns, and landing pages are tied to revenue.
Diverse captured data is consolidated and connected with other customer data, often in cloud-based storage, CRMs, or both.
2. Conversion Analysis
The next requirement are the data transformations and views required to conduct conversion analysis. Marketers need the capability to query the data, using sorts, filters, and views to isolate high- and low-performing marketing elements. Only then can marketers find the accurate revenue value of any marketing element or KPI dimension, predict future outcomes, and identify specific optimization opportunities.
Modeling should be agnostic, allowing a business to view aggregate and individual customer journeys through any lens, including rules-based models such as linear, or data-driven or AI-enabled. For complex journeys, machine learning can identify the value-maximizing combinations faster than rules-based models.
3. Application for ROAS
While many marketers assume that applying learnings from analysis is accepted as given, often the greatest challenge is ensuring learnings result in optimizations and increased return on ad spend (ROAS). Optimizations need not turn marketing strategy upside-down. For leadership new to conversion analysis, start small. Identify lowest-performing tactics, and reallocate those resources to higher performing ones.
Choosing Models for Revenue Attribution
Selecting the optimal attribution model is critical yet challenging. The "best" approach depends on marketing strategy, sales cycle, and data infrastructure. For large organizations with complex sales cycles and high-volume media spends, data-driven or algorithmic models are a necessity. Smaller organizations, or those with short cycles and simple journeys, can be effective with less sophisticated models.
However, revenue attribution can be effective for businesses of any type or size. As the conversion attribution example above shows, even channel-level attribution can pay huge dividends when connected to revenue.
The Tradeoffs of Single-Touch Attribution
Single-touch attribution models assign 100% revenue credit to one touchpoint, typically first or last click. This simplifies analysis for linear customer journeys.
Pros:
- Easy implementation
- Isolates specific campaign performance
Cons:
- Incomplete picture
- Misses cross-channel influence
- Risks incorrect optimization
While appealing for their simplicity, single-touch models rarely reflect the complexity of modern buyer journeys. Key channels often get undervalued or overlooked.
For most businesses, single-touch attribution provides insufficient insights to inform strategy and budgeting. Multi-touch models are better suited to quantify marketing's full revenue impact.
The optimal attribution approach balances simplicity and completeness. For strategic growth, marketers need models that capture synergies across touchpoints while remaining focused on the revenue end goal.
First-Touch Attribution
First-touch or first-click attribution assigns 100% credit to the first marketing touchpoint in a buyer's journey.
Pros:
- Easy to implement
- Quantifies new customer acquisition by channel
Cons:
- Inaccurate picture of revenue influence
- Undervalues mid and late funnel efforts
- Lookback window can skew data
First touch attribution suits awareness-focused initiatives like lead generation. But for businesses focused on conversion, it provides an incomplete view. Key drivers of closing deals may be overlooked.
While easy to deploy, first-touch models rarely reflect the complexity of modern revenue attribution. Marketers risk suboptimal budget and strategy decisions without a multi-touch perspective.
For strategic insight, first-touch attribution should be supplemented with models that quantify mid and late funnel influence. Revenue impact requires a complete funnel view.
Lead Creation Attribution
Lead creation attribution assigns 100% credit to the touchpoint where a lead was generated, typically the first known interaction.
Pros:
- Quantifies lead generation by channel
- Useful B2B proxy before revenue conversion
Cons:
- Ignores anonymous pre-lead interactions
- Discounts non-lead generation efforts
- Incomplete buyer journey view
While helpful for optimizing top-funnel activities, lead creation attribution lacks insight into broader funnel influence. Customer journeys and conversions depend on more than lead generation alone.
For comprehensive impact analysis, lead attribution should complement multi-touch models. Full cross-channel data is required to optimize strategy and budgets for revenue growth. No single model provides the complete picture.
Strategic marketers must balance model simplicity and completeness. Lead attribution delivers partial insights, but integrated attribution is needed to maximize marketing's business impact.
Last-Touch Attribution
Last-touch or last-click attribution assigns 100% credit to the final touchpoint before conversion. Focuses on lower-funnel influence.
Pros:
- Simple implementation
- Quantifies final conversion drivers
Cons:
- Ignores early and mid-funnel touchpoints
- Unsuited for long sales cycles
- Incomplete customer journey view
Last-touch models work for short sales cycles with minimal touchpoints. But for longer journeys, they miss cross-channel synergies that influence buying decisions. Also called "Last Click Attribution," the model can be effective despite shortcomings.
While easy to deploy, last-touch attribution rarely provides sufficient insights to optimize strategy and budgets. It risks undervaluing activities that build relationships across the customer lifecycle.
For businesses focused on conversion, multi-touch attribution better quantifies marketing's complete revenue impact. Last-touch insights should complement fuller funnel analysis.
Last Non-Direct Touch Attribution
Last non-direct touch attribution assigns full credit to the final touchpoint before conversion, excluding direct traffic.
Pros:
- Removes inaccurate direct traffic
- Highlights final conversion drivers
Cons:
- Still discounts early/mid-funnel
- Incomplete customer journey view
By excluding unreliable direct traffic, this model provides slightly clearer conversion insights than standard last-touch attribution.
However, like all single-touch models, it fails to capture cross-channel influence and synergies across the buyer's journey.
For businesses focused on conversion, multi-touch attribution is required to quantify marketing's full revenue impact. Last non-direct models are insufficient to inform strategy and optimization.
While easy to implement, single-touch attribution's simplicity comes at the cost of actionable insights. Strategic marketers need models that reflect the complexity of modern revenue attribution.
Last Channel Attribution
Last channel attribution assigns full credit to the final touchpoint in a specific marketing channel before conversion. Common for specific platforms like AdWords.
Pros:
- Easy plug-and-play setup
- Quantifies channel-specific conversion impact
Cons:
- Biased to overvalue assigned channel
- Ignores other channel contributions
- Incomplete customer journey view
Platform-specific models can help assess that channel's role in the buyer journey. But in isolation, they provide an inaccurate picture of overall attribution.
While easy to implement, last channel models fail to capture cross-channel influence essential for optimization. They risk incorrect budget allocation skewed by channel bias.
For an accurate view of marketing's revenue impact, multi-touch attribution is required. Last channel insights should complement full funnel analysis, not replace it. Partial data leads to partial results.
The Benefits of Multi-Touch Attribution
Multi-touch attribution models distribute credit across multiple touchpoints influencing a conversion. This reflects the complexity of modern buyer journeys. Moreover, multi-touch attribution (MTA) offers complex models that naturally outperform single-touch.
Pros:
- Complete view of customer journey
- Identifies influence of all activities
- Aligns cross-functional teams on revenue
Cons:
- More complex implementation
- Requires time for sufficient data
While more involved, multi-touch attribution provides a comprehensive picture of marketing's revenue impact. Marketers quantify synergies across channels rather than siloed efforts.
For businesses focused on conversion, multi-touch attribution is essential. Full cross-channel insights enable optimal budget allocation and strategy refinement.
Single-touch models provide fragmented data for fragmented results. Multi-touch attribution delivers the integrated analysis today's strategic marketers need to drive growth as profit centers.
Linear Attribution
Linear attribution evenly distributes conversion credit across all touchpoints in the customer journey.
Pros:
- Captures entire funnel
- Simple to implement
Cons:
- Treats all touches equally
- Fails to quantify differential impact
Linear attribution provides a complete view of the buyer's path compared to single-touch models. However, it lacks granularity into the influence of specific activities.
With even credit distribution, linear attribution risks incorrect optimization and budget allocation. High-impact activities get no extra weight.
For strategic insight, linear attribution should be supplemented with models that quantify touchpoint effectiveness. Revenue growth requires understanding the drivers of conversion, not just the path.
While easy to implement, linear attribution's simplicity comes at the cost of actionable data. Strategic marketers need models that reflect marketing's uneven impact across the funnel.
Time Decay Attribution
Time decay attribution distributes credit across touchpoints but weights recent interactions more heavily. Favored for long sales cycles.
Pros:
- Still has cross-channel view
- Emphasizes late-funnel influence
- Suited for long B2B journeys
Cons:
- Undervalues early touchpoints
- Inaccurate if early content drives late decisions
Time decay models attempt to capture recency bias. But they risk undervaluing early relationship-building touchpoints that influence late-funnel choices.
While useful for quantifying late-stage impact, time decay risks incorrect optimization without a balanced view. High-value early content may get reduced budget.
For an accurate picture, time decay attribution should complement models that also account for early and mid-funnel influence. No single model provides complete insights.
Strategic marketers must balance model simplicity and completeness. Time decay delivers partial insights, but integrated attribution is best for maximizing marketing's business impact.
U-Shaped Attribution
U-shaped attribution assigns 40% credit each to first touch and lead creation, splitting the remaining 20% among other interactions.
Pros:
- Quantifies early and late funnel impact
- Identifies lead generation and conversion drivers
Cons:
- Undervalues mid-funnel efforts
- Incomplete post-lead view
- Still lacks full journey insights
U-shaped models attempt to value early awareness and final conversion. However, the dispersed 20% allocation likely underrepresents mid-funnel influence.
While an improvement on single-touch, U-shaped attribution still provides an incomplete picture. Full customer journey insights are needed to optimize strategy and budgets.
For businesses focused on conversion, multi-touch models like algorithmic attribution better quantify marketing's revenue impact throughout the buyer's path. No one model tells the full story.
W-Shaped Attribution
W-shaped attribution assigns 30% credit each to first touch, lead creation, and opportunity creation. Remaining touchpoints share 10%.
Pros:
- Captures early, mid, and late funnel
- Quantifies key funnel transitions
Cons:
- More complex implementation
- Still lacks complete picture
- Risks overvaluing lead generation
By incorporating the opportunity stage, W-shaped models provide fuller funnel insights than U-shaped attribution. However, the dispersed 10% allocation likely underrepresents touchpoint influence.
While an improvement, W-shaped attribution still discounts impact between its set allocation points. Full journey data is required to optimize strategy and budgets.
For complete insights, W-shaped should complement algorithmic models quantifying incremental influence of each interaction. There is no one-size-fits-all attribution approach--strategic marketers must integrate multiple models.
Full Path Attribution
Full path (or Z-shaped) attribution assigns 22.5% credit each to first touch, lead creation, opportunity creation, and close. Remaining touchpoints share 10%.
Pros:
- Truly end-to-end attribution
- Values post-opportunity marketing
- Identifies full journey drivers
Cons:
- Very sales-dependent
- Still an incomplete picture
- Complex implementation
Full path models provide the most complete view of traditional attribution models. However, the dispersed 10% likely underweights touchpoint impact between key milestones.
While an improvement, full path lacks the precision of algorithmic models in quantifying each interaction's influence. Significant drivers may still be overlooked.
For comprehensive insights, full path should integrate algorithmic attribution data. Even full path provides only a partial view alone. Strategic marketing requires cross-model analysis for optimal budget and strategy decisions.
Custom Attribution Models
Custom attribution enables businesses to define their own model based on channels, industry, and customer journeys. Credit percentages are fully customizable.
Pros:
- Tailored to company needs
- Tuned over time
Cons:
- Complex implementation
- Risks incorrect assumptions
- Ongoing maintenance
While customizable, custom models require extensive data science expertise. They risk baking in the same biases as previous ineffective efforts.
Off-the-shelf algorithmic attribution leverages aggregated cross-industry data into models that continuously improve. Far more scalable than building in-house.
Still, algorithmic models should allow for some customization based on business needs. The optimal balance provides tailored insights without over-customization.
For most, third-party multi-touch attribution delivers strategic insights out-of-the-box. But some customization options help focus algorithms on unique customer journeys.
Algorithmic Attribution
Algorithmic attribution leverages machine learning to analyze customer behavior and assign revenue credit based on data--not preset rules.
Pros:
- Identifies highest-value touchpoints
- Continuously optimizes model
- Scalable SaaS-based option
Cons:
- Requires sufficient data volume
- Still benefits from customization
Powered by aggregated cross-industry data, algorithmic models provide unmatched precision in quantifying marketing's revenue impact. They surface the optimal attribution insights for each business.
While robust, out-of-the-box, algorithmic attribution works best when supplemented with some customization based on unique customer journeys. The right balance of automation and configuration provides strategic visibility.
For most B2B marketers, algorithmic attribution delivers actionable insights faster than building in-house models. And with continuous refinement, it only gets better over time.
Finally, with so many options to refine models and optimize the marketing mix, a healthy reporting engine provides marketers a window into the customer journey, what works, what doesn't, and where greatest revenue opportunity resides.
A Few Guidelines for Revenue Attribution Models
Optimizing Attribution for B2B vs B2C
B2B and B2C demand tailored attribution approaches due to different customer journeys.
B2B buyers interact with multiple touchpoints, teams, and stakeholders. Journeys are complex. B2B marketers often benefit from multi-touch models like algorithmic attribution. This captures cross-channel synergies and distributed influence.
B2C paths are more direct. Single touch models like first-click or last-click attribution suffice for many B2C marketers. These simplify analysis while still connecting actions to revenue.
However, the lines between B2B and B2C blur as buyers demand seamless experiences. Sophisticated attribution is increasingly important for both. The optimal model balances simplicity and completeness based on the nuances of the business.
With the right attribution approach, B2B and B2C marketers gain clarity into revenue drivers. They can shift budgets to high-performing channels and campaigns. Attribution transforms marketing into a profit center, not a cost center.
Simplifying Attribution Model Selection
The number of marketing channels dictates the optimal attribution approach:
- 4+ channels: Adopt multi-touch attribution. This captures complex customer journeys across diverse touchpoints. Provides complete analysis for optimal budget allocation.
- <4 channels: Single-touch attribution often suffices. First-click or last-click models still link actions to revenue. Simplifies analysis for straightforward environments.
Of course, the lines blur as customers demand integrated experiences. Sophisticated attribution helps all marketers quantify ROI and optimize spending, whether B2B or B2C.
With the right model, attribution provides the actionable data today's marketing leaders need. It elevates marketing as a profit driver.
Attribution for Long Sales Cycles
Lengthy sales cycles create complex customer journeys. Prospects engage with multiple touchpoints across channels over time.
To analyze performance, marketers need attribution models that capture nuance. Multi-touch attribution tracks each interaction and assigns proportional credit. Provides visibility into how touchpoints work together to drive conversion.
With long sales cycles, no single touchpoint converts customers alone. Multi-touch attribution reveals synergies and cross-channel influence. It enables optimal budget allocation based on complete data.
For complex sales with long runways, sophisticated attribution is essential. Marketers must quantify touchpoint ROI across the entire customer journey. Multi-touch attribution provides the revenue insights needed to drive growth.
When Does Multi-Touch Attribution Deliver ROI?
Sophisticated attribution has costs. The traditional ROI breakpoint is around $10,000 monthly ad spend. Below this, single-touch models may suffice.
With multiple channels, long sales cycles, and over $10K in monthly spend, multi-touch attribution pays off. It provides comprehensive analysis of complex journeys, and enables optimal budget allocation and touchpoint optimization. Finally, testing an MTA platform against your marketing mix will quickly reveal a fit or not. For example, Arcalea's MTA platform, Galileo, consistently multiplies revenue and reduces waste.
Of course, every business is unique. Rigorous testing of attribution approaches is advised. But for most modern marketing organizations, multi-touch attribution is becoming table stakes.
For today's CMOs, proof of marketing's revenue impact is mandatory. Multi-touch attribution provides the visibility required into how touchpoints work synergistically to drive sales. With clear attribution, marketing earns its seat at the strategy table.
