Reconcile Sales: GA, Platforms, & True Attribution

How do I reconcile Google Analytics sales with platform attribution reports? For modern marketers and business owners, the ability to accurately measure marketing performance is paramount. Yet, the persistent challenge of reconciling sales data between Google Analytics (GA) and various advertising platforms like Meta Ads, Google Ads, or TikTok often leads to profound confusion about true marketing Return on Investment (ROI) and where to allocate valuable budget. These discrepancies aren't mere statistical noise; they stem from fundamental differences in tracking methodologies, attribution models, and the ever-evolving landscape of data privacy. Understanding these variances, embracing advanced strategies, and adopting a unified approach are crucial steps toward achieving accurate sales attribution. This comprehensive guide will equip you with the knowledge and actionable insights to navigate these complexities, transform data discrepancies into strategic advantages, and ultimately maximize your sales.

Executive Summary

* Discrepancies are Inherent: Expect 10-15% conversion tracking differences across platforms, and 20-30% between GA4 and Google Ads, due to distinct tracking methods, lookback windows, and attribution models. * Privacy Reshapes Attribution: iOS 14.5+ and third-party cookie deprecation have significantly reduced data visibility, requiring a shift to first-party data, server-side tracking, and consent management. * Unified Measurement is Key: Relying on siloed platform reports distorts ROI. Implement consistent UTM tagging, align attribution models, and consider Customer Data Platforms (CDPs) or dedicated attribution software for a holistic view. * Data Fuels AI Bidding: High-quality, clean conversion data is critical for Google and Meta's AI-driven bidding algorithms. Leverage Enhanced Conversions and Conversion APIs to feed these systems effectively. * Multi-Touch is Essential: Move beyond last-click attribution. Data-Driven Attribution (DDA) and custom multi-touch models provide a more accurate picture of complex customer journeys, proven to improve ROI and budget justification. * Focus on Trends, Not Perfection: While reducing discrepancies is important, accept that minor variances are normal. Prioritize long-term performance trends and directional insights for strategic decision-making.

Background Context: The Pervasive Attribution Challenge

The digital marketing landscape is a complex web of touchpoints, channels, and devices. A customer's journey from initial awareness to final purchase rarely follows a linear path, often involving numerous interactions with ads, social media, organic search, and direct visits. This intricate dance creates a significant challenge for businesses striving to pinpoint which marketing efforts truly drive sales. Industry statistics paint a clear picture of the scale of this problem: * It's common for conversion tracking data to differ by at least 10-15% across various platforms, with discrepancies between GA4 and Google Ads conversion tracking often ranging from 20-30%, which is considered normal. This is not an anomaly, but the standard, as highlighted by Accuracast. * One advertiser observed Facebook claiming 375 purchases, but GA showed only 147 transactions from Facebook Ads over the same period, meaning GA reported just 39% of transactions and 35% of revenue claimed by Facebook, according to Accuracast research. * Salesforce estimates it takes 6-8 touchpoints to generate a lead, with other studies suggesting it could be 50 or more, highlighting the complexity of customer journeys and data fragmentation (Altior & Co.). These figures underscore that reconciliation issues are a widespread, significant problem impacting marketing ROI, budget justification, and strategic planning. Without proper attribution, businesses risk making flawed decisions, wasting budgets, and misallocating resources. As expert Ben Heath, founder at Heath Media, notes, "There's always going to be a discrepancy between what Facebook tells you and other data sources... Total accuracy is unrealistic — but you shouldn't settle for a 60% difference in reporting data."

Why High-Quality Data Feeds AI: Powering Your Bidding Algorithms

In today's advertising ecosystem, your conversion data isn't just for reporting; it's the lifeblood of sophisticated AI-driven bidding algorithms on platforms like Google Ads, Meta (Facebook) Ads, and TikTok. These algorithms, designed to optimize for maximum performance, rely on accurate, comprehensive conversion data to make real-time decisions about who to show your ads to, when, and at what cost. * Google's Enhanced Conversions: Google Ads' Enhanced Conversions utilize a one-way hashed algorithm (SHA256) on first-party customer data (like email addresses) to model and fill gaps in observable conversion data caused by privacy restrictions. By sending this securely hashed data, you provide Google's AI with more signals, leading to better optimization for bids and targeting, ultimately driving higher Quality Scores and lower Cost Per Acquisitions (CPAs). * Meta's Conversion API (CAPI): Meta's Conversion API enables direct, server-to-server communication of audience behavior signals from your website or CRM to Meta's systems. This significantly reduces reliance on browser-based cookies, which are increasingly blocked by privacy settings and ad blockers. CAPI improves data accuracy, mitigates the impact of iOS 14.5+ changes, and provides Meta's AI with a more robust dataset for ad delivery optimization, especially critical given Meta has confirmed at least 15% of total sales are being underreported due to these privacy shifts. * TikTok Events API: Similarly, TikTok's Events API allows advertisers to send conversion data directly from their servers to TikTok, improving the accuracy of campaign measurement and optimization. Without clean, consistent, and comprehensive conversion data, these powerful AI systems operate with a handicap. They struggle to learn, leading to suboptimal bidding, wasted ad spend, and missed sales opportunities. Investing in robust attribution and data reconciliation isn't just about reporting accuracy; it's about directly fueling the intelligence that drives your digital advertising success and maximizes your sales.

Core Analysis: Unpacking Sales Discrepancies

Understanding the Discrepancy Divide: Why Your Numbers Don't Match

The journey to accurate sales reconciliation begins with acknowledging and understanding the fundamental reasons why Google Analytics and ad platforms will never perfectly align. These are not flaws in your tracking, but inherent differences in how these systems operate: * Clicks vs. Sessions: Ad platforms (e.g., Facebook) count any interaction (click, view) with an ad. GA4, however, registers a `session` only when a user successfully lands on your website and the GA4 tracking code executes. Multiple clicks on the same ad by one user within a short timeframe might be several clicks on the platform but only one session in GA4. * Different Tracking Mechanisms: GA4 primarily relies on first-party cookies and Google Signals (when enabled). Ad platforms use their own pixels, login data, and proprietary tracking methods. These distinct systems process and attribute data differently. * Unfired Tracking Code: If a user clicks an ad but quickly abandons the landing page before GA4's code loads, the ad platform registers a click, but GA4 records nothing. * Varying Lookback Windows: Each platform defines a "lookback window"—the period after an ad interaction during which a conversion can be attributed. Facebook Ads might default to a 7-day click or 1-day view, Google Ads to 30 days, while GA4's default for acquisition events is 30 days (up to 90 days for other conversions). A conversion might fall within one platform's window but outside another's. View-Through Conversions (VTCs): Platforms like Meta frequently attribute conversions even if a user viewed* an ad without clicking it (VTCs). GA4, by default, focuses on direct clicks and engagement, leading to an underestimation of the ad platform's influence in GA4 reports. * Attribution Model Differences: Ad platforms typically employ last-touch models (e.g., 7-day click in Meta) or give full credit to their own ad if it's in the path (Google Ads). GA4's cross-channel Data-Driven Attribution (DDA) model, however, distributes credit across multiple touchpoints, leading to vastly different figures. * Conversion Counting & Definition Differences: GA4 counts every instance of an event marked as a conversion (e.g., three form submissions in one session are three conversions). Google Ads can be configured to count only "one" unique conversion per user per interaction. * Cross-Device Tracking Challenges: Stitching user journeys across mobile, desktop, and other devices is complex. While GA4 with Google Signals attempts this, neither system is perfect. * Privacy Settings & Ad Blockers: User privacy settings, ad blockers, and consent choices (e.g., declining analytical cookies) can prevent one tracking system from collecting data while another might still record interactions or use modeled data. Time Lag in Conversions: Google Ads often attributes conversions to the date of the initial ad click (conversion backfill), meaning a conversion from today might be reported for a click a week ago. GA4, conversely, reports conversions based on the actual date and time they occur*. This can create significant discrepancies over specific date ranges. * "Walled Gardens" and Duplication: Each ad platform operates as a "walled garden," only tracking interactions within its own ecosystem. This "platform attribution myopia" causes platforms to inherently over-credit themselves. Summing conversions from multiple platforms will result in significant duplication, far exceeding actual business outcomes. This challenge is why "No single tool offers the full truth," as experts attest, emphasizing the need for a unified view.

Strategies for Bridging the Attribution Gap: Reclaiming Your Data Accuracy

While perfect alignment is unattainable, a strategic approach can significantly reduce discrepancies, enhance your understanding, and provide more accurate insights for decision-making and maximizing sales. 1. Consistent UTM Tagging: This is foundational. Implement a standardized UTM (Urchin Tracking Module) tagging strategy across all ad campaigns. Use tools like Google's Campaign URL Builder to create consistent `utm_source`, `utm_medium`, and `utm_campaign` parameters. This ensures GA4 accurately attributes traffic and conversions from specific campaigns, allowing you to slice and dice data effectively. 2. Compare Equivalent Metrics: Educate yourself and your stakeholders. "Clicks" on an ad platform are not "sessions" in GA4. Instead, compare similar metrics (e.g., Facebook's "Landing Page Views" with GA4 "sessions" or "engaged sessions") to get a more apples-to-apples comparison. 3. Align Attribution Models and Windows: While full alignment is often impossible, understand the defaults. Utilize GA4's `Attribution Model Comparison` tool to evaluate how different models allocate credit and choose one that aligns with your business goals for more consistent reporting. Where possible, configure lookback windows on platforms to be more consistent with GA4's approach. 4. Implement Server-Side Tracking (SST): This is rapidly becoming essential. Move tracking from the client-side (browser) to the server-side. SST offers more control over data, improves accuracy, enhances data privacy compliance, and significantly reduces data loss from ad blockers and browser restrictions. Solutions like TrueROAS provide robust server-side tracking capabilities. 5. Unified Measurement Solutions & CDPs: For a truly holistic view, adopt advanced marketing attribution software or Customer Data Platforms (CDPs) like Segment or Amplitude. These solutions ingest and unify data from various sources (ad platforms, CRM, GA, offline interactions), creating a single source of truth for a comprehensive customer journey view, moving beyond siloed platform reporting. 6. Leverage Google Signals and User ID: For businesses with authenticated user experiences (e.g., login), implement User ID tracking in GA4 to connect sessions across devices. Activate Google Signals to track cross-device activity for logged-in Google users, enhancing cross-device attribution capabilities. 7. Manual Conversion Setup in Google Ads: Instead of relying solely on importing GA4 conversions, manually define conversion goals in Google Ads using Google Tag Manager (GTM). This provides more precise data collection and greater control over how conversions are measured and reported for ad optimization, directly fueling Google's bidding algorithms. 8. Ensure Consistent Data Filtering: Apply consistent filters and exclusions across all platforms (e.g., filtering out internal IP addresses, bot traffic, or test transactions) to ensure you are comparing truly comparable data sets. 9. Focus on Trends and Directional Insights: Accept that minor discrepancies (e.g., 10-15% or even 20-30% between GA4 and Google Ads) are normal and expected. Instead of chasing perfect matches, focus on long-term performance trends and directional insights to make strategic decisions. This helps you maximize sales by understanding which channels are generally performing better. 10. Implement Consent Management Platforms (CMPs) & Google Consent Mode: Ensure compliance with privacy regulations (GDPR, CCPA) and prepare for third-party cookie deprecation by using CMPs and Google Consent Mode. This allows websites to adjust data collection based on user consent, ensuring legal compliance while providing data to Google tags effectively, often via modeled conversions for non-consenting users.

The Technical Underpinnings of Attribution: How It Works and Where It Fails

Understanding the mechanics of attribution models and their inherent flaws is critical for making informed decisions about your marketing spend and truly maximizing sales. How Marketing Attribution Models Work: Attribution models are frameworks that assign credit to different touchpoints in a customer's journey leading to a conversion. * Single-Touch Models: Assign 100% of the credit to a single touchpoint. * First-Touch: Credits the initial interaction. Good for measuring brand awareness but ignores all subsequent nurturing efforts. * Last-Touch: Credits the final interaction immediately before conversion. Simple to implement but notoriously overlooks all prior engagements that influenced the purchase, making it unreliable for complex journeys. * Multi-Touch Models: Distribute credit across multiple touchpoints. * Linear: Divides credit equally among all interactions. Provides a holistic view but doesn't differentiate the importance of each touchpoint. * Time Decay: Assigns more credit to touchpoints closer to the conversion. Useful for longer sales cycles where recent interactions might be more influential. * Position-Based (U-shaped/W-shaped): Allocates significant credit to the first and last interactions (e.g., 40% each), with the remaining credit distributed among middle touchpoints. This balances initial awareness and final conversion drivers. * Data-Driven Attribution (DDA): Uses machine learning algorithms to dynamically allocate credit based on the actual contribution of each touchpoint to conversions. This model analyzes all available data to provide a more precise perspective but requires substantial data volume to be effective. GA4 utilizes a cross-channel DDA model. * Custom Models: Tailored frameworks using advanced analytics to reflect unique customer journeys specific to a business or industry. Common Problems in Digital Attribution Tracking: * Data Fragmentation: Customer journeys span numerous channels and devices, leading to data being scattered across different systems (ad platforms, GA, CRM), making a unified view challenging. * Untracked Touchpoints: Digital tracking often misses offline interactions (e.g., phone calls, in-store visits, word-of-mouth), providing an incomplete picture of the customer journey. This leads to lost sales attribution. * Privacy and Signal Loss: The deprecation of third-party cookies, stricter browser controls, and app tracking transparency (iOS 14.5+) reduce the precision and granularity of attribution data, directly impacting sales measurement. * Codependent Touchpoints: Attribution models often oversimplify the customer journey by treating channels independently. In reality, marketing channels often influence and depend on each other, affecting their roles in driving conversions and sales. * Model Bias: Built-in attribution models within ad platforms (e.g., Google Ads, Meta Ads) naturally over-credit their own channels, making objective cross-channel comparisons difficult. * Cross-Device Identification: Accurately identifying and tracking users across different devices is challenging without robust identity resolution mechanisms like User IDs or Google Signals. * Insufficient Data for Advanced Models: Data-Driven Attribution models require a substantial volume of conversion data to function correctly. Without enough data, these models can produce inconsistent or unreliable results, leading to flawed optimization. * "Black Box" Technologies: Some attribution technologies lack transparency regarding how data is processed or how attribution decisions are made, leading to distrust in their results. * Correlation vs. Causation: Attribution models can sometimes mistakenly attribute sales spikes to marketing campaigns based on correlation, without accounting for other factors like seasonal trends, economic conditions, or concurrent marketing efforts. This can lead to misallocated budgets and lost sales potential.

Evidence & Proof: Why This Matters to Your Bottom Line

Industry Statistics Unmasking the Challenge

The numbers don't lie: * Post-iOS 14.5, only 4% of U.S. users chose to allow apps to track them, according to Flurry Analytics. This severe data signal loss impacts ad platforms' ability to accurately report conversions and optimize. * Meta (Facebook) has confirmed at least 15% of total sales are being underreported due to iOS 14.5+ changes, with an estimated $4 billion per year in lost ad revenue for Facebook and increased Cost Per Clicks (CPCs). This directly affects advertisers' ability to prove ROI. * 95% of data and advertising decision-makers in the U.S. expect continued legislation and signal loss in 2024 and beyond, with two-thirds foreseeing further state privacy laws that will decrease personalization capabilities (Marketing Attribution.org). These statistics underscore that reconciliation issues are a widespread, significant problem impacting marketing ROI, budget justification, and strategic planning.

Expert Voices on Unified Measurement

Experts universally agree on the need for sophisticated solutions: * "No single tool offers the full truth. Ad platforms report only on what happens in their own ecosystem... Relying on these platforms in isolation leads to blind spots and distorted decisions about where to spend. To cut through the noise, you need a unified view. That starts with first-party tracking and multi-touch attribution," emphasizes industry analysis. * "You can't optimize what you can't measure. And you can't measure what you don't track properly," a clear call to action for businesses to prioritize data accuracy. * "Privacy is no longer just a buzzword but a foundational principle shaping our operational strategies," highlighting the unavoidable shift towards privacy-centric solutions (Marketing Attribution.org).

Real-World Success Stories

Companies that tackle attribution head-on see tangible results: * SaaS Company Improves Attribution Accuracy and ROI: A $5M ARR SaaS company faced significant attribution challenges, with only 40% of deals traced to marketing. By implementing proper UTM tracking and a custom multi-touch attribution model (20% first touch, 30% mid-funnel content, 30% demo request, 20% last touch) within their existing CRM, they saw: * Attribution accuracy increased by 38%, tracing 94% of deals back to marketing (up from 40%). * Conversion rates improved by 5% by optimizing for revenue, not just leads. * Marketing ROI visibility increased by 85%. * The CEO subsequently increased the marketing budget by 40%, directly linking to the improved data. (Altior & Co.) * E-commerce Business Increases Sales with Multi-Touch Attribution: A local e-commerce business successfully improved its sales by 35% after transitioning from a last-click to a multi-touch attribution model (Digital Marketing Institute). This demonstrates a direct correlation between sophisticated attribution and increased revenue. * Client Gains Enhanced Visibility and Secures Budget: After implementing a multi-touch attribution model, a client gained enhanced visibility into their marketing impact, allowing them to see a clear breakdown of which channels drove conversions. This directly helped them secure their 2024 marketing budget, highlighting the strategic importance of accurate data for financial planning (UniFida).

The Competitive Landscape: Navigating Attribution Solutions

The market offers a diverse array of solutions to address attribution challenges and help you reconcile sales data, from general analytics platforms to specialized software and data integration tools. * General Analytics Platforms with Attribution Features: * Google Analytics 4 (GA4): A powerful, versatile tool offering basic to advanced attribution models (last-click, first-click, linear, time decay, position-based, data-driven). GA4 is designed for a multi-device, multi-interaction landscape and integrates well with Google's ecosystem. * Adobe Analytics: Part of the Adobe Experience Cloud, providing deep insights into customer journeys and robust attribution modeling, particularly suited for large enterprises with complex data analysis needs. * Mixpanel: Focuses on user behavior analytics, offering insights into how users interact with websites or apps and the impact of campaigns on user behavior and conversion rates. * Dedicated Attribution & MarTech Solutions: These tools specialize in comprehensive attribution, often integrating with various data sources. * Ruler Analytics: Provides closed-loop multi-channel marketing attribution, linking online and offline data (including phone calls) to specific marketing activities. * WhatConverts: Specializes in call tracking and analytics, offering attribution for phone call conversions. * HubSpot Marketing Hub: An integrated CRM with attribution features for tracking and analysis, including multi-touch attribution and offline conversion tracking, often ideal for larger B2B companies. * Salesforce Data Cloud for Marketing: A robust CRM with attribution capabilities that leverage prospect and sales data from the CRM to attribute revenue. * Hyros: An AI-based attribution software targeting high-revenue online businesses and digital marketers, offering advanced data tracking across multiple platforms. * Northbeam: Provides marketing analytics tools for understanding marketing performance across various channels, specializing in multi-touch attribution. * TrueROAS: Offers comprehensive server-side tracking and advanced attribution modeling to unify your data, providing a single source of truth for your ROAS and customer journey. It's designed to overcome data signal loss and platform attribution biases. Check out the TrueROAS Shopify app or the TrueROAS WordPress Plugin for WooCommerce for easy integration. * Emerging Data Integration & Identity Solutions: * Customer Data Platforms (CDPs): Tools like Segment or Amplitude unify customer data from various touchpoints, providing a holistic view essential for multi-touch attribution. * Identity Resolution Tools: Solutions such as LiveRamp or Neustar help connect disparate user identities across different devices and channels in a privacy-compliant manner. * Data Clean Rooms: These secure environments are becoming increasingly sophisticated for multi-touch attribution, media mix modeling, and consumer journey analysis, especially in a privacy-first world.

Recent Trends Reshaping Digital Advertising Attribution

The digital advertising landscape is undergoing significant shifts driven by increasing privacy regulations and technological advancements, profoundly impacting attribution capabilities and making sales reconciliation more complex. * iOS 14.5+ and App Tracking Transparency (ATT): * Impact: Apple's ATT framework (April 2021) dramatically reduced user opt-in rates (as low as 4-5% in the U.S.), disabling the Identifier For Advertisers (IDFA). This severely limits advertisers' ability to target personalized ads and measure campaign effectiveness for opted-out users, leading to diminished visibility of the user journey and underreported sales. * Adaptation: Major platforms updated SDKs to support ATT. Google introduced parameters like "ltd=" to specify user consent. * Third-Party Cookie Deprecation: * Impact: Browsers like Safari and Firefox already block third-party cookies, and Google Chrome plans to phase them out entirely by 2025. This eliminates a long-standing foundation for cross-site tracking, behavioral targeting, and attribution, significantly reducing cross-site visibility and threatening the accuracy of traditional attribution modeling. This directly impacts your ability to track and attribute sales accurately across different platforms. * Adaptation: Increased reliance on first-party data (collected directly from consumers). Adoption of alternative IDs and renewed focus on contextual advertising. Google's Privacy Sandbox aims for privacy-by-design targeting. Server-Side Tracking (SST) and Facebook Conversion API (CAPI) are becoming essential for maintaining data flow. * Evolving Privacy Regulations (GDPR, CCPA, GPC, Google Consent Mode): * Impact: Stricter global data privacy laws enforce explicit consent and transparency, limiting personalization and increasing compliance risks. 95% of U.S. advertising decision-makers expect continued legislation and signal loss (Marketing Attribution.org). * Google Consent Mode: Becoming mandatory for all websites using GA4, Google Ads, or Floodlight from March 2024, enabling websites to adjust data collection based on user consent preferences while maintaining some data flow for modeling. * Rise of AI in Measurement and Attribution: * Trend: Deep learning and AI are increasingly adopted to enhance attribution accuracy and predictive capabilities. AI models can forecast conversion probabilities and optimize campaigns in real-time, helping to fill data gaps created by privacy changes. The industry is shifting from deterministic to AI-based, probabilistic techniques for accurate sales attribution. * Growing Importance of Data Clean Rooms: * Trend: Data clean rooms are gaining sophistication and importance for secure, privacy-preserving multi-touch attribution, media mix modeling, and consumer journey analysis. These trends represent the "new normal" in digital advertising. They explain why traditional reconciliation methods are becoming insufficient and highlight the urgent need for proactive adaptation to new technologies and privacy-centric strategies to maintain effective attribution and avoid losing sales.

Practical Implications for Your Business

For businesses navigating these complexities, the implications are profound: * Don't Lose Sales, Maximize Them: Without accurate attribution, you’re flying blind. Misattributing sales means misallocating budget, underinvesting in high-performing channels, and overspending on underperforming ones. Implementing robust attribution ensures you know which campaigns truly drive revenue, allowing you to optimize for maximum sales. * Prove and Justify ROI: Accurate reconciliation provides the clear data needed to prove the ROI of your marketing efforts, justify budget increases, and make informed strategic decisions. This visibility allows you to stop losing sales to inefficient spending and start maximizing them by doubling down on what works. * Understand Your Customer Journey: Moving beyond last-click attribution to multi-touch or data-driven models offers a far richer understanding of how customers interact with your brand across various touchpoints. This insight is invaluable for optimizing the entire customer journey, not just the final click. * Future-Proof Your Marketing: Adapting to server-side tracking, first-party data strategies, and consent management isn't optional; it's essential for future compliance and data collection efficacy in a privacy-first world. Businesses that embrace these changes will maintain a competitive edge and continue to grow their sales. * Better Data, Smarter AI: The quality of your conversion data directly impacts the effectiveness of platform bidding algorithms. Clean, comprehensive data fuels AI, leading to more efficient ad spend, lower CPAs, and ultimately, more sales.

Achieving True Attribution with TrueROAS

The challenges of reconciling sales data are real, but they are not insurmountable. Tools like TrueROAS are designed specifically to address these modern attribution dilemmas. By offering robust server-side tracking, advanced attribution models, and a unified view of your marketing performance, TrueROAS helps businesses cut through the noise of conflicting reports. We help you move beyond last-click biases and platform-centric views, providing the true ROAS that empowers smarter budget allocation and maximizes your sales potential. With features like comprehensive data unification and simplified integration for Shopify and WooCommerce, TrueROAS enables you to gain clarity, optimize your spend, and reclaim confidence in your marketing data.

Conclusion

Reconciling sales data between Google Analytics and ad platforms is one of the most pressing challenges in digital marketing today. Discrepancies are an inherent part of the landscape, driven by differing tracking methodologies, attribution models, and the pervasive impact of privacy regulations. However, accepting these differences doesn't mean resigning yourself to confusion. By adopting a proactive approach that includes consistent UTM tagging, embracing server-side tracking, aligning attribution models, leveraging unified measurement solutions, and continuously adapting to privacy changes, you can transform data noise into actionable intelligence. The goal is not perfect numerical alignment, but rather a robust, comprehensive understanding of your customer journey that allows you to confidently prove ROI, justify budgets, and strategically maximize your sales in a data-driven world. Don't let attribution discrepancies lead to lost sales; take control of your data and unlock your true marketing potential.

Fact Sheet

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Sources

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