Effective Attribution at Scale
As growth-focused enterprises strive to optimize marketing ROI, attribution analytics have become essential for quantifying channel and campaign performance. Yet for brands with lengthy sales cycles and complex buyer journeys, many turnkey attribution platforms fail to provide accurate measurement.
Today sophisticated attribution analytics enable B2B brands with even the most complex sales cycles to quantify true marketing impact on revenue. Capabilities like comprehensive cross-channel tracking, flexible conversion windows, channel-agnostic modeling, and natural language query processing empower 360-degree visibility into campaign and activity performance. Such robust B2B attribution not only delineates marketing influence at each step, but its actionable insights compound revenue growth and efficiencies across the enterprise.
Read More:
- The Failure of Limited Attribution
- What True B2B Attribution Looks Like
- Maximizing ROI with Revenue Attribution
- Is Full-Path Revenue Attribution just for B2B?
- Build or Buy?
- Optimizing for Continuous Growth
The Failure of Limited Attribution
Experiences with limited attribution tools may lead sophisticated brands to give up on attribution or fall back to legacy measurement approaches. The data bears this out: In 2016 nearly 40% of marketers were using some form of multi-touch attribution (MTA), and 30% were planning near-term implementation. But in 2021, the numbers were nearly the same.
While preparation and expectation factor into the successful adoption of marketing attribution, choosing misaligned platforms is more likely the cause of poor results. Although a fully B2B-aligned solution will provide accurate analysis and optimization, partial attribution solutions remain the first choice for many marketers.
Ad Platform Attribution
While ad platforms provide attribution data, relying solely on their limited scope often misguides marketers. The walled gardens of Google Ads and Facebook Ads only showcase activity within their own ecosystems. Yet the customer journey extends far beyond those channels before and after an ad click or conversion. Without end-to-end visibility, marketers see only fragments of multi-touch journeys. Platform-centric models also frequently overweight the impact of media spend due to last-click bias. Marketers who take platform attributions at face value risk suboptimal spending and missed growth opportunities across channels.
Google Analytics
While Google Analytics serves as the default web analytics tool for many B2B marketers, its shortcomings for full-path attribution are well-documented. Reliance on last-click models, lack of cross-channel visibility, and limited data retention periods paint an incomplete picture. Google Analytics tracks individuals in silos, not unified customer accounts over time. Without stitching together disparate interactions into full journey maps, marketers risk inaccurate model assumptions and conclusions. The tool also lacks flexibility for custom attribution approaches. Marketers who rely solely on Google Analytics attribution data often miss influencing interactions both early and late in the buying journey. For confident optimization, marketers need comprehensive attribution that looks beyond the sampling of website behavior provided by Google Analytics alone.
Bolt-on Attribution Utilities
Seeking the path of least resistance, many marketers adopt packaged attribution tools within existing martech stacks. However, bolt-on modules within broader platforms rarely provide robust modeling. These tools focus more on core functionality than analytics depth. Data access and flexibility to address attribution's complexity are limited. For example, HubSpot's built-in CRM attribution only analyzes a subset of interactions within its orbit. While convenient, plug-and-play attribution rarely achieves the depth required for ongoing optimization. Marketers relying solely on add-on analytics modules fail to realize the full benefits. Comprehensive B2B attribution requires purpose-built or custom platforms, not basic add-ons.
Off-the-Shelf B2C Platforms
B2C solutions are often optimized for simple linear ecommerce journeys rather than complex decision cycles. Lookback windows are limited, and many commonly focus on ad-centric metrics rather than revenue-related metrics that matter most. Finally, B2C-structured solutions struggle to adapt rapidly to shifts in consumer behavior, channel mix, and market conditions.
The end goal of full-path B2B revenue attribution is not merely to calculate discrete values of marketing elements, but to understand how value is created so that brands can maximize revenue, efficiency, and customer satisfaction.
Galileo CXO Dashboard
What True B2B Revenue Attribution Looks Like
Effective revenue attribution can multiply ROI for brands by capturing the full buyer journey and removing uncertainties. A responsive B2B revenue attribution solution can accommodate a brand's idiosyncratic paths and parameters using the features below.
Independent Tracking Mechanism
A universal cookieless tracking approach frees attribution from ad platform bias or cookie deprecation. With some browsers already blocking third-party cookies, increased privacy awareness and regulations limit cookie tracking’s future. Ad platform APIs limit data to their respective walled gardens, misrepresent user activity, and lead to poor optimizations.
Open Omnichannel Tracking
B2B attribution covers an exhaustive range of channels and touchpoints. B2B buyer journeys can cross dozens of channels, including channels unique to an industry or even region. Open omnichannel tracking captures customer-brand interaction in any channel. Arcalea’s Galileo uses the cookieless Quark PixelTM to capture event data in real time, but is able to commit the data asynchronously to limit any performance issues and enable the capture of offline touchpoints.
Unlimited Conversion Windows
B2B attribution is tailored for lengthy buying cycles. Many limited attribution systems, such as those in ad platforms or Google Analytics, limit conversion windows (e.g., 90 days). For B2B or long-cycle B2C, shortened windows eliminate key touchpoints at top funnel. By opening up conversion windows, revenue attribution can consider all touchpoints of long funnels.
Account-based Stakeholder Journeys
B2B attribution accommodates multiple stakeholders. When a business purchase represents a large investment, chances are that multiple stakeholders are involved at various parts of consideration and decisioning. Account-based tracking captures and groups these brand interactions.
Interconnected Customer, Brand, Metadata and Revenue
Data transformation ensures that all data elements that constitute brand KPIs are captured and connected for later queries and analysis. While single-channel platforms or bolt-on attribution tools connect subsets, full-path attribution connects metadata across the journey. Without perfectly aligned data matching, B2B brands are left with analysis blindspots.
Platform Connectivity
To leverage attribution insights, businesses need customer behavior history accessible to sales, operations, and customer service. A versatile revenue attribution solution adds key data to CRM records that ensure all customer-focused teams are working from the same data. The result is a better aligned sales and marketing team, shared understanding of MQL and SQL criteria, and a more nuanced and focused customer experience.
Model-Agnostic Attribution
B2B Attribution Modeling is open, free from specific rules-based or black-box models. Attribution modeling was driven by the shift from traditional offline advertising to digital media. The massive increase in data required a short-hand approach to analyze tens of thousands of touchpoints. While attribution models have expanded far beyond last-click, even custom algorithms can be overfitted when processing volume and variety.
Specific models can help show insights when data is hidden or inaccessible to different toolsets. For example, in Google Analytics (below) and ad platforms, full individual-level journey data is unavailable. Instead, the model choices provide a way of interpreting obscure data.
GA4 DDA Aggregate Customer Journey View
However, full-path attribution with business-centered visualization supports agnostic modeling that cuts through the shorthand of weighted models. Unfiltered individual and aggregate customer journeys create transparency that can isolate high-performing marketing elements and the elements that waste resources. No longer must marketers force unique brand journeys into specific models to analyze value.
Transformative UI for Data Query
The final piece of a B2B attribution solution is the user-configurable dashboard. Since all brand-customer interactions, metadata, and revenue are connected, marketers can query by any KPI dimensions to find counterintuitive optimizations that multiply revenue and efficiency. As the examples below illustrate, a transformative dashboard can calculate the efficiency and ROI of each channel, path, ad, landing page, or content. The surfaced optimizations multiply return otherwise impossible with traditional, partial attribution solutions.
Maximizing ROI with Revenue Attribution
With a B2B solution in place, brands capture and connect customer data, all customer-brand interactions, revenue, metadata. With a dashboard for queries, marketers gain granular and aggregated journey visibility. So how do marketers multiply return?
With end-to-end buyer journey visibility, businesses can:
- See the exact relationship between spend, lead, and revenue
- Quantify the return in leads and revenue
- Compare revenue sources, customer acquisition costs (CAC), ROAS
- Identify paths that create leads and paths that create revenue
- Identify the specific paths, ads, landing pages, and messages that drive revenue (and those that don’t)
- Reallocate spends and resources, optimize paths, and architect journeys for maximum return
View Channel ROI at a Glance
In the below example, leads follow spends closely. However, comparing the same spend with revenue-producing closed deals tells a different story. Google Paid Search returns far less than Google Organic, even though Paid Search spends are much higher.
Galileo CXO Dashboard
Compare Lead and Revenue Conversions
By comparing total lead conversions and revenue conversions, the precise efficiency and return of each channel is revealed. The top three lead conversion sources produce nearly 10k leads: Google Ads (52%), Facebook Ads (33%), and Google Organic (15%).
Galileo Channel Attribution Dashboard: Leads
However, filtering by revenue-producing conversions only, the true attribution value is clear. These same channels produce only 170 revenue transactions: Google Ads (52%), Facebook Ads (9%), Google Organic (39%).
Galileo Channel Attribution Dashboard: Revenue
Calculate CAC and ROAS
By comparing spend and return across channels, marketers can calculate ROAS and CAC.
In the B2B attribution data above, the brand spends $2M annually in Google Ads, $750k on Facebook Ads, but only $60k for Google Organic. The average transaction value is $50k. With the attribution data above, current channel inefficiencies are known:
- Google Ads CAC: $22k (ROAS of 2:1)
- Facebook Ads CAC: $50k (ROAS of 1:1)
- Google Organic CAC: $900 (ROAS of over 50:1)
In this case, Facebook is unlikely to be a revenue source for the brand. Google Ads offers significant optimization and reallocation. But Google Organic offers an untapped opportunity.
The low efficiency rate can be further resolved by examining the specific value of each ad and landing page in lead and revenue conversion paths.
Examine Path Performance
To identify ROI opportunities, start by looking at the page paths for leads (conversions w/o revenue) and revenue-producing paths. Google Ads has 743 converting paths, but only 46 revenue-producing paths.
Galileo Content Frequency Dashboard: Unique Page Paths
Examine Content Performance
Analyzing paid media and organic paths, marketers can isolate ads, landing pages, and content that consistently produces revenue. By removing the inefficiencies and reallocating their spend to performing marketing elements, brands can multiply returns.
Multiply ROI
In the example above, Google Ads efficiency rate (number of leads vs number of revenue transactions) is currently less than 2%. In other words, 98% of Google Ad spends are wasted.
Google Ads Efficiency
$2M (.98) = $1.9M without producing revenue
If the wasted spend were eliminated, the brand gains nearly $2M/year while retaining current channel revenue. However, if it is reinvested in higher-performing channels, the return is exponential.
Google Organic Reallocation
$60k creates 66 transactions @ $50k, or a return of $3.3M
If just part of the wasted Google Ads spends were reallocated to organic,
$600k creates 660 transactions @ $50k, or a return of $33M.
Finally, while simple reallocation can multiple returns, brands can gain by optimizing and even architecting buyer paths across every channel. With discrete end-to-end attribution, brands can identify the specific marketing elements that drive performance at each phase of the journey.
Is Full-Path Revenue Attribution just for B2B?
Full-path revenue attribution is a match for any business with needs that go far beyond the limitations of partial attribution from ad platforms, bolt-on attribution tools, or even Google Analytics. While an industrial water systems manufacturer might have little in common with a typical B2C brand, the imperative to understand buyer journey, lead and revenue paths, and optimization points are universal for growth-focused brands. For example, higher education institutions have extremely long sales windows, large transaction values, and multiple stakeholders.
Build or Buy?
For brands needing only to understand GAds performance, a well-configured GA4 install connected with GAds with Max CAC smart bidding may be sufficient.
If, however, brands have a long-cycle, big-ticket offerings, and complex channel mix, they must determine whether their needs can be met by an existing platform or solution, or if it makes sense to build.
Cost Factors
While both buy and build require investments, a good starting point is estimating the Expected Value of Perfect Information (EVPI). Each business can review their marketing mix and associated spends, and identify the current information gaps that preclude efficiency and optimization.
For example, if one paid media channel spend is $100K/month, and only 20% of paid clicks generate revenue, what would the EVPI be for knowing specifically which paths and ads were effective, and which were not? As in the example above, the delta can be transformative.
Once an EVPI range is determined, a business knows the annual return value of an attribution solution. Can the business design, build, and maintain a full-path attribution solution for much less than an existing aligned solution?
Planning and Design
KPIs and Data Elements
Before building, a business must define all the data elements, connections, and parameters for KPIs and conversions. An effective attribution solution must meet the needs of all teams and requires input from all stakeholders.
Data Schema
For every KPI or expected query, a web of data and metadata must be captured and connected, beginning with an anonymous user-event, and connecting with dozens of metadata elements and a converting identity. The user interface value depends on connecting data so that it is accessible for queries, sorts, filters, and visualizations responsive to all stakeholders.
Flexibility
While some parameters can be defined once, others must be defined in ranges to allow for on-the-fly adjustments. For example, MQL and SQL definitions must be modifiable at any time, as these could change as more insights are uncovered and business segments and practices evolve.
Development
Technology Choices
For every component in the life-cycle, feasible and maintainable technology must be selected and developed: from universal capture, data cleaning and transformation, data storage, to the user interface for queries and visualization.
Resources
While design demands internal stakeholder commitment, development requires skilled data scientists, DBAs, and software engineers. While development can be outsourced to partners, the lack of internal resources can increase the cost of long-term maintenance.
Maintenance
Expect Change
While thorough planning can design an effective attribution platform, invariably new needs will arise, and unforeseen issues can derail operations. For example, the solution may need to add channels, KPIs, or any parameters that were missed or a result of new business models. Maintenance costs–whether internal or outsourced–should factor into a build decision.
Optimizing for Continuous Growth
Choosing an existing full-path B2B attribution solution will save most businesses time and money. Because off-the-shelf B2B solutions must meet an array of functional demands, most brands will come out far ahead: less cost to build and maintain, advanced functionality for future growth, and faster time to optimization.
Whether brands buy or build, a well-designed B2B attribution platform delivers unparalleled buyer journey visibility and insights. Our full-path multi-touch revenue attribution platform, Galileo, can help you begin to multiply insights and revenue. Once marketers adopt a full-path attribution designed for complex businesses, they can measure the discrete value of each marketing element, identify optimization multipliers, and begin to grow revenue and market share.
