A Complete Guide to Marketing Attribution

A Complete Guide to Marketing Attribution

In our increasingly complex digital marketing landscape, the days of relying on simplistic “last-click” attribution are over. Today’s consumers interact with brands across a proliferating array of online and offline channels throughout their winding journey to purchase. To cut through this growing complexity and accurately measure marketing performance, there is a pressing need for sophisticated attribution modeling capabilities. This guide will walk marketers through the basics of marketing attribution, the model types and how to choose one, selecting software and partners, and the implementation tips needed to start and evolve a comprehensive attribution measurement strategy.

What is Multi-Touch Marketing Attribution?

At its core, multi-touch marketing attribution (MTA) refers to analyzing customer interactions with a brand across different online and offline media platforms. The goal is to quantify the influence that exposures across these multiple channels have on driving a desired action, typically a conversion like a purchase, form submission, or content download.

At its best, MTA uses statistical modeling and machine learning techniques to assign weighted credit to each marketing touchpoint involved at various stages of the customer journey. This illuminates which channels and messaging are most impactful at driving key performance indicators (KPIs). Marketers gain data-driven insights to optimize spending across channels and campaigns to maximize return on investment (ROI).

This approach provides a more accurate, holistic measurement than single-touch attribution models like first-click or last-click which assign all credit to one interaction. Multi-touch attribution takes the entire winding journey into account.

Why Multi-Touch Attribution Matters

In our increasingly fragmented media landscape, buyers now engage with brands across an ever-growing variety of digital and traditional marketing channels. Brands continue to develop omnichannel marketing strategies to capture customers, while consumer journeys continue to grow in variety and complexity. 

This complexity means that linear, predictable customer journeys are now the exception rather than the norm. With proliferating touchpoints across social media, email, mobile, web, in-store, events, and more, the path to purchase has grown highly nonlinear.

This environment makes measuring and optimizing marketing performance incredibly difficult. Marketers need deeper cross-channel intelligence in order to quantify business outcomes influenced by integrated, cross-channel campaigns.

Multi-touch cross-channel attribution provides the holistic visibility needed by revealing how different media platforms and marketing messages work together to drive conversions and revenue. Marketers can optimize spending across channels rather than relying on intuition or flawed single-touch models.

As consumers continue engaging brands across more digital and offline touchpoints, MTA grows increasingly essential. It enables marketers to adapt measurement and analytics capabilities to navigate the new complexities of an omnichannel world.

Types of Attribution

As omnichannel marketing and attribution measurement continue to evolve, different practitioners and brands often use different terms, sometimes conflating different measurement approaches. Below is a quick explanation of common attribution types and names.

Multi-Channel Attribution

Multi-Channel attribution can measure impact across multiple channels. However, a multi-channel attribution model could be multi-channel and not multi-touch. For example, one could attribute a sale to the most important channel (and not a specific touchpoint within the channel). In other cases, a business may use “cross-channel” or similar to refer to both, multi-channel and multi-touch attribution.

Multi-Touch Attribution

Multi-Touch attribution (MTA) usually refers to a model that captures all brand-customer interactions or touchpoints in a journey, regardless of channel, and is often contrasted with Single-Touch attribution models (e.g., first-click and last-click). The ease of digital tracking helped to accelerate MTA, and a background can be found in this “Introduction to Multi-touch Attribution.”

Single-Touch Attribution

Single-Touch attribution assigns all credit for a conversion or sale to one touchpoint, namely the first or last touchpoint in a channel or journey.

Full-Funnel Attribution

Full-Funnel attribution references all touchpoints during a consumer journey, ensuring touchpoints from all parts of the sales funnel are acknowledged in attribution calculations.

Full-Path Attribution

Full-Path attribution captures all touchpoints during a consumer journey, from first touch to conversion or purchase.

(not to be confused with “full-path attribution model,” which is a position-based model, also called Z-shaped attribution model. You can learn about this and other models here.)

Full-Funnel, Full-Path, and Multi-Touch are often used interchangeably. For the purpose of this paper, Multi-Touch Attribution captures and measures all touchpoints in a specific customer journey (full-path, full-funnel and cross-channel).

Revenue Attribution

While some approaches may purport to better plan adjustments in the marketing mix, or better understand the customer journey, the end goal of revenue attribution is better ROI–a mix of increased revenue and efficiencies. The key requirement of any revenue-focused attribution is the tying of both spend and return to every marketing asset (e.g., landing page, ad channel, ad, etc.) in order to determine the Return on Ad Spend (ROAS) or the Return on Marketing Investment (ROMI). While the former is focused exclusively on the ad spend cost, the latter is more inclusive and can include all marketing costs at work. In any case, all marketing attribution is revenue attribution; however, the degree of focus depends on a marketer’s intention as well as the complexity (and completeness) of the attribution solution.

Types of Attribution Models

There are a variety of modeling methodologies used for implementing marketing attribution. Each comes with its own approach to assigning credit weight to touchpoints along the customer journey. Common attribution models include:

Last-Click Attribution

This simplistic single-touch model assigns 100% of the credit for a conversion to the final touchpoint preceding the desired action. While easy to understand, last-click risks overlooking early funnel influence and optimization opportunities. Sometimes marketers refer to this model as last non-direct click because it measures the touchpoint before the final purchase touchpoint. 

First-Click Attribution

First-click attributes 100% of credit to the first marketing touchpoint in a customer’s sequence of interactions. This appropriately accounts for initial brand and product awareness but ignores influences closer to conversion. For example, the first contextual search for a product might generate a brand’s organic result on a Search Engine Results Page (SERP), or a Search Ad that a user clicks.

Linear Attribution

Linear attribution aims to simplify analysis by distributing credit evenly across all touchpoints involved in the customer path to conversion. Each interaction receives an equal share regardless of placement in the funnel. As a result, the Linear model captures the full path, but doesn’t delineate differing impacts of each interaction in the consumer journey.

Time Decay Attribution

Time decay attribution uses recency as a proxy for influence, with interactions closer to the conversion receiving greater credit and those further back decaying in value. In this case, an organic search 45 days before purchase would receive less credit for the sale than an ad clicked a few days before purchase. This acknowledges recency bias but still overlooks differences in actual impact.

Position-Based Attribution

Rather than an even distribution, position-based models allocate preset percentages of credit based on assumed impact at different stages of the marketing funnel. For example, 30% for early interactions, 50% middle, and 20% final. Common position-based models include the U-Shaped and W-Shaped models.

Data-Driven Attribution

Data-driven attribution leverages statistical modeling and machine learning to determine a dynamic, data-driven weighting for each marketing touchpoint based on its demonstrated ability to influence conversions historically. Data-driven attribution models are unique to each brand and are based on the volume of consumer interaction history. 

Choosing the Right Attribution Model

One of the first and most critical decisions is selecting the optimal attribution model for your analysis objectives, data constraints, and resources. Each approach carries distinct pros and cons. Selecting the right methodology requires trade-offs based on analysis objectives, available data, and resources.

• Last-click attribution provides simplicity but overlooks early funnel influence.
• First-click properly accounts for awareness but ignores late-funnel impact.
• Linear attribution is straightforward yet inaccurate in equal touchpoint weighting.
• Time decay acknowledges recency bias but still miscalculates true impact.
• Position-based relies on arbitrary percentages set by stage.
• Multi-touch data-driven attribution is most accurate but requires advanced capabilities.

Last-click and first-click models provide simplicity but lack accuracy. Linear attribution is easy to understand but flawed in equally weighting all touchpoints. Time decay and position-based improve on these simplistic models but still rely on arbitrary rules of thumb.

A robust attribution solution should be model-agnostic.

For maximum accuracy, data-driven multi-touch attribution is ideal. But it requires investments in statistical modeling and machine learning capabilities. Testing different models can determine which provides the greatest insight into your customer journey, including the influence points and optimization opportunities. Ideally, a marketing attribution solution should provide multiple models to allow easy comparison against customer journeys. 

Models are similar to reporting snapshots. They provide the ability to view journeys through multiple lenses, allowing a business to compare impacts at each point in the funnel, channel, or even landing pages and ads.

Getting Started with Multi-Touch Attribution

For marketers starting their attribution journey, these best practices help guide the process:

Identify Core Business Goals and KPIs

Confirm which metrics the model should optimize for based on core business objectives. For example, brands may be especially interested in lower funnel conversions, customer lifetime value, or other specific customer interaction or decision point.  While a first step is calculating ROMI or ROAS, leaders will want specific targeted initiative values.

Audit Existing Data Sources

Assess completeness of current tools and platforms for capturing cross-channel touchpoint data. Identify and fill gaps.

Collect Customer Interaction Data

Robust data collection provides the foundation. Relevant touchpoint data must be compiled across channels with unique identifiers to map interactions to conversions. Online and offline data requires integration.

Data-capture should be cookieless and API-independent.

Analyze Historical Conversion Paths

Analyze behavioral data to identify typical sequences, funnel stages, and influence of different touchpoint types. Provides baseline understanding of journey dynamics.

Select Attribution Model and Build Capabilities

Choose optimal methodology based on analysis objectives, available data, and resources. Prioritize accuracy. Leverage data science and technology partners as needed. For more insigfht into attribution models, see “A Complete Guide to Attribution Models.”

Integrate Insights into Tools and Reporting

Incorporate attribution data and insights into analytics platforms, campaign reports, and dashboards to inform decisions across teams, both marketing and sales.

Refine Tactics and Optimize Continuously

Iterate models with new data. Leverage attribution intelligence to continually shift budget across channels and campaigns to maximize performance based on influence modeled.

Attribution initiatives require extensive, ongoing data collection, model iteration, reporting integration, and analysis to adapt to changes in consumer behaviors and channels. While the upfront investment can be significant, the intelligence unlocked to optimize every marketing dollar is well worth the effort for most organizations.

Selecting Attribution Software

As attribution becomes more centralized to marketing measurement strategies, best-in-class software is essential. With many technology vendors now providing solutions, key selection criteria should map to the business specific needs:

• Depth of integrations with your marketing data sources – especially CRM and MAP systems.
• Flexible data connectivity and modeling capabilities to process both online and offline inputs.
• Access to advanced analytic methodologies like machine learning either natively or through partnerships.
• Ability to visualize insights across customer touchpoints and channels.
• Privacy and consent management functionalities.
• Ongoing customer success and analytics consulting to drive maximum value.

Look for breadth of data connectivity, customizable modeling techniques, robust reporting, and customer guidance. With the right technology software, attribution can scale across the organization.

Choosing Attribution Partners

Given the complexity of implementing attribution, partnering with experts is usually advisable:

• Analytics Consultancies: Can help audit existing data, build models leveraging proprietary methods, and train internal teams. Provide an unbiased, third-party assessment.
• Technology Providers: Leading attribution software vendors provide turnkey modeling capabilities and analytics visualization tools. Can get up and running quickly.
• Data Scientists: If looking to build fully custom in-house models, contracting data science specialists on a project basis brings specialized expertise.

Advisors provide an accelerated path to developing capabilities through knowledge transfer, while technology vendors enable scalable implementation and maintenance. Leverage partners strategically based on internal resource readiness.

Educating the Organization on Attribution

While the technical requirements often appear as the most formidable challenge, gaining organizational alignment and support are safeguards to successful launch and long-term adoption.

Gain Executive Sponsorship

Attribution efforts risk stalling without an executive champion to provide air cover. Identify and socialize a specific CXO sponsor early to drive support. For attribution insights to truly transform strategy, executive buy-in is crucial. 

• Connect attribution directly to revenue goals and business objectives. Model the financial impact of decisions informed by attribution.
• Focus reporting on big picture insights and strategic recommendations rather than granular analytics details.
• Provide regular updated reporting tied to core priorities. Encourage executives to share questions which attribution can help inform.
• Highlight specific examples where competitors are advancing through attribution-driven optimization.

Bridge Data Silos Between Teams

Channel-specific data trapped in organizational silos undermines modeling. Mandate open data sharing, with proper privacy protections, to enable single customer view across departments and channels.

Set Expectations on Immediate Wins

Resist pressure to demonstrate major early optimization wins until sufficient data volume and model maturity is achieved. Take an incremental “crawl, walk, run” approach.

Change Intuition Biases Against Data-Driven Findings

Overcome inherent skepticism of data-driven insights conflicting with conventional wisdom through change management and education. Focus education on:

• Attribution methodology: Explain the technology and logic behind your model in simple terms.
• Custom proofs of lift: Show examples where the model accurately attributed influence and credit.
• Optimization successes: Highlight business wins driven by attribution findings.
• Tailored reporting: Provide insights that address specific team goals and questions.

Address Compliance Concerns

Address legal and privacy concerns proactively through both technology controls and cross-team processes that integrate compliance stakeholders into efforts. Addressing these potential friction points head on ensures efforts maintain momentum and stay focused on business value delivery.

Gaining confidence in attribution requires education. But combined with a financial KPI focus, executives and team leaders are more likely to become advocates and invest in expanding capabilities.

Increasing Performance with Multi-Touch Attribution

Once a solution is in place, brands can ensure continued success by focusing on a few core best practices to drive ongoing value from attribution:

Tie Insights Directly to Business Goals

Connect attribution data to revenue, customer acquisition cost, lifetime value, and other financial KPIs. Relevant insights to the bottom line are understood where interim metrics are not.

Employ Both Rule-Based and Machine Learning Models

Leverage business logic where solid theories exist on attribution, and machine learning where influencing factors are less clear. A robust solution should be model-agnostic, allowing marketers and business leaders to see different modeling views and different focal points within the sales funnel.

Validate Model Performance with Statistical Testing

Use significance testing to ensure models accurately assess channel influence and prevent “overfitting” on limited data.

Watch for Shifts in Consumer Behavior

Continually assess model performance and recalibrate based on changes in channel preferences, economic conditions, and other factors.

Customize Reporting to Stakeholder Questions

Tailor attribution analysis and reporting to address the specific goals and questions of each internal stakeholder group.

Build Capabilities Incrementally

Start with simpler models before advancing to more sophisticated capabilities as resources allow. Phased approach helps build confidence.

Managing Privacy Considerations

As attribution relies heavily on collecting and analyzing customer data, privacy regulations like GDPR must be considered thoroughly:

• Conduct a data privacy audit to validate marketing data practices comply with all applicable regulations.
• Anonymize or aggregate any customer data used for modeling–never leverage direct PII data.
• Provide clear opt-in consent to customers for collecting and using data for attribution purposes.
• Allow consumers access to view and delete any data held on them per data transparency expectations.
• Minimize collection of personal data only to what is required for modeling needs.

Any brand capturing customer data should understand the primary regulations driving compliance (e.g., CCPA, GDPR, etc.). With consumer privacy concerns rising, following local data regulations and best practices around consent, transparency, anonymity, and minimization is crucial for any attribution initiative. Involve legal/compliance partners.

Evolving Attribution Governance

As attribution expands, more coordinated governance is required. Data processes must have clear ownership to ensure insights reach action, and thus, increased ROI:

• Document clear data access policies balancing openness with privacy.
• Institute cross-functional data governance committees to oversee model impacts.
• Build reliability measures into reporting to surface insights requiring additional validation.
• Create feedback loops for continuously improving methodology.
• Centralize tool ownership under a leader who coordinates cross-team efforts.

Formal data governance gives structure to what will be a dynamic modeling ecosystem within your marketing technology stack.

Focus first on revenue metrics–don’t chase vanity metrics.

Driving Action on Attribution Insights

The true measure of attribution success is driving better decisions and results. Teams that stay pragmatic and business focused are likely to maintain continuous improvement. To spur action:

• Focus first on revenue metrics. Don’t chase vanity metrics like impression volumes rather than business impact. Focus on measurement of statistical significance.
• Create standardized reports and dashboards tailored to each stakeholder group.
• Build workflows for rapidly communicating insights to relevant teams.
• Train stakeholders how to interpret findings and consult on optimizations.
• Develop processes for quickly testing and learning from new strategies.
• Continually improve by refining KPIs based on new insights to keep the model(s) relevant.
• Focus on large-scale optimizations with A/B testing to limit risk. Don’t chase small or short-term optimization at the expense of larger brand equity.

Tight coupling between reporting and activation ensures insights get translated into decisions leading to business impact. This engagement feedback loop is essential.

Integrating Attribution Data with CRM and MAP

To maximize value, marketing attribution data should be integrated across core business platforms like CRM and marketing analytics:

• CRM: Connecting attribution data to customer records provides insights for sales on campaign influence for their accounts and opportunities. Powers smarter hand-offs.
• Marketing Analytics: Feeding attribution data into analytics platforms enables holistic reporting and optimization across online and offline channels.

Integrations between attribution systems and CRM and marketing analytics platforms help unify data into a single consistent view of the customer journey. This breaks down channel-specific silos enabling true cross-channel optimization.

Layering Attribution with Marketing Mix Modeling

While providing detailed insights into the digital customer journey, MTA has a unique footprint just as other methodologies. In recent years, advanced leaders have begun using MTA combined with other methodologies in a larger measurement framework and experimentation. For example, the specific focus areas below can benefit from marketing mix modeling (MMM).

• Reliance on clickstream data means some upper-funnel brand interactions are excluded from analysis and modeling. While broadcast awareness can be input into attribution models, the process creates unique additional challenges.
• Short analysis window focusing on recent conversions and interactions. Longer-term effect of ads and content is often lost. However, this can be reduced with unlimited conversion windows of more sophisticated solutions.
• Susceptibility to data loss from privacy changes like cookie blocking and mobile identifier restrictions. Again, while data loss is common with traditional tracking mechanisms, more contemporary solutions avoid cookies and mobile identifiers and extract first-party data throughout the journey. Additionally, ML-driven modeling can match and predict to overcome data gaps.

To reinforce these areas, some analytics teams complement attribution with marketing mix modeling (MMM). MMM is a statistical technique that leverages aggregate marketing and sales data to model the ROI of different media channels, including offline platforms like TV, radio, and out-of-home.

MMM quantifies marketing influence based on historical channel performance rather than individual-level click analysis. This expands visibility into impression-based and offline touchpoints missed by attribution.

By combining multi-touch attribution and marketing mix modeling, modern digital marketers benefit from the best of both disciplines:  detailed digital journey analysis and holistic measurement across online and offline interactions. Together, the techniques provide comprehensive insights to guide cross-channel budget and strategy optimization.

Evolving Your Attribution Strategy

For companies with advanced measurement needs and growing data science capabilities, consider extending impact in these areas:

Drive Methodology Standards

Don’t wait for the industry to develop standards to follow. Instead, build the standards internally and with analytics partners around optimal methodology, metrics, and reporting. Clean unclear taxonomies for channel types and names. Monitor evolving capabilities and anticipate trends. Leader brands will capture future opportunities.

Expect and Address Increased Journey Complexity

Proliferating customer journey touchpoints across devices makes accurate modeling increasingly demanding at a larger scale. Manage the expected rise in complexity and lengthening journeys. Ensure your capture tech and methodology are neutral and universal to capture customer touchpoints and data from any channel, without reliance on biased APIs or privacy-averse cookies. Use long range or unlimited conversion windows to capture both ends of long sales cycles.

Incorporate Offline and Impression Data

Add impression and audience exposure data from upper-funnel media like TV, radio, OOH advertising to quantify impact beyond digital interactions.

Improve Model Accuracy

Shorten data latency through real-time pipelines. Remove outliers and false positives through statistical checks. Continuous refinement of inputs and statistical validation of outputs is imperative for reliable modeling

Build Custom Algorithms

Design proprietary algorithms tailored to your unique customer journeys, data sets, and business needs rather than rely on pre-built models.

As attribution capabilities grow more centralized to your measurement stack, marketers can customize approaches and expand impact metrics to address wider business objectives beyond bottom funnel clickstreams.

Expanding Attribution Across the Business

While attribution initially focuses on optimizing channel spending based on sales or conversions, over time marketers should look to expand success metrics modeled:

• Brand awareness, consideration and preference lift
• Website and app engagement
• Customer retention and loyalty
• Share of wallet gains
• Customer lifetime value

Evolving attribution beyond the last touchpoint conversion provides intelligence to guide brand building, engagement, retention and loyalty initiatives holistically.

While an initial focus is on marketing elements, attribution insights should expand across the customer-facing organization, maximizing relevance of attribution to leadership teams. Opportunities include:

• Product: Quantify product and feature impact on growth. Guide development. Identify winning features driving conversions to inform development.
• Sales: Inform sales and account planning through marketing insights. Provide attribution data on marketing influence for accounts to optimize coordination.
• Service: Understand the role of customer acquisition source on satisfaction. Tailor onboarding. Inform support teams on key customer acquisition sources to tailor engagement.
• Finance: Connect marketing spend to revenue and profit contribution.Quantify marketing’s financial contribution by tying attribution to revenue data.
• Executive: Track performance against executive growth and market share goals. Attribution trends against growth goals will maximize visibility.

With attribution powering key decisions across product, sales, service and finance, its business value compounds. Sharing impactful data ensures executive commitment to a shared measurement framework. Practices such as self-serve data access, building common KPIs, and sharing insights through regular cross-team meetings naturally breaks down data silos and aligns the organization.

The Bottom Line

Marketing attribution has quickly become essential in tackling the omnichannel complexity faced by modern brands. By taking an incremental approach focused on business value, marketers can build capabilities that evolve in sophistication over time. Single-source models are no longer sufficient in a proliferating digital and offline environment. There is too much complexity–and too much opportunity left unaddressed.

Today’s Martec offers full-funnel omni channel attribution that aligns with almost any business. For example, Arcalea’s Galileo captures the complete customer journey, multiplying insights and revenue for any marketing mix. 

In our fragmented world, winning customers requires truly understanding each journey. Multi-touch attribution provides that intelligence. Brands leveraging this resource gain an enduring competitive advantage.