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.
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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.
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.
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 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 (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 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 references all touchpoints during a consumer journey, ensuring touchpoints from all parts of the sales funnel are acknowledged in attribution calculations.
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).
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.
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:
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 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 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 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.
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 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.
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 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.
For marketers starting their attribution journey, these best practices help guide the process:
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.
Assess completeness of current tools and platforms for capturing cross-channel touchpoint data. Identify and fill gaps.
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 behavioral data to identify typical sequences, funnel stages, and influence of different touchpoint types. Provides baseline understanding of journey dynamics.
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."
Incorporate attribution data and insights into analytics platforms, campaign reports, and dashboards to inform decisions across teams, both marketing and sales.
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.
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:
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.
Given the complexity of implementing attribution, partnering with experts is usually advisable:
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.
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.
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.
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.
Resist pressure to demonstrate major early optimization wins until sufficient data volume and model maturity is achieved. Take an incremental “crawl, walk, run” approach.
Overcome inherent skepticism of data-driven insights conflicting with conventional wisdom through change management and education. Focus education on:
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.
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:
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.
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.
Use significance testing to ensure models accurately assess channel influence and prevent “overfitting” on limited data.
Continually assess model performance and recalibrate based on changes in channel preferences, economic conditions, and other factors.
Tailor attribution analysis and reporting to address the specific goals and questions of each internal stakeholder group.
Start with simpler models before advancing to more sophisticated capabilities as resources allow. Phased approach helps build confidence.
As attribution relies heavily on collecting and analyzing customer data, privacy regulations like GDPR must be considered thoroughly:
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.
As attribution expands, more coordinated governance is required. Data processes must have clear ownership to ensure insights reach action, and thus, increased ROI:
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.
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:
Tight coupling between reporting and activation ensures insights get translated into decisions leading to business impact. This engagement feedback loop is essential.
To maximize value, marketing attribution data should be integrated across core business platforms like CRM and marketing analytics:
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.
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).
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.
For companies with advanced measurement needs and growing data science capabilities, consider extending impact in these areas:
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.
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.
Add impression and audience exposure data from upper-funnel media like TV, radio, OOH advertising to quantify impact beyond digital interactions.
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
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.
While attribution initially focuses on optimizing channel spending based on sales or conversions, over time marketers should look to expand success metrics modeled:
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:
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.
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.