Multi-Touch Attribution Tools: Guide to Top Attribution Platforms

Attribution in B2B marketing is broken. And most teams don't realize it until they're defending budget cuts in a quarterly review.

You're running LinkedIn ads, hosting webinars, sending email nurture sequences, and maybe direct mail. Your CRM shows a closed deal. But which touchpoint made the difference? Was it the whitepaper they downloaded six months ago, the demo request last Tuesday, or the retargeted ad they saw 35 times?

Last-click attribution says it was the demo form. Google Analytics credits the last tracked channel before the direct visit. Your sales team claims it was their stellar pitch. But the truth is, it was likely all of them, not any one alone.

That’s why multi-touch attribution tools exist. They track each step your buyer takes and credit different channels based on real impact, not just the last action before a sale.

This guide explains what multi-touch attribution tools do, which platforms are worth evaluating, and how to implement them without wasting months on setup.

TL;DR

• Multi-touch attribution is essential when deals involve long cycles, multiple stakeholders, and 6-15+ touchpoints tied to CRM revenue.
• Tool choice depends on your stack: GA4 covers basics, while platforms like Dreamdata, HubSpot, Rockerbox, LeadsRx, and factors.ai link attribution to pipeline and revenue.
• Accuracy depends on clean CRM data, consistent UTMs, defined lifecycle stages, and sales-marketing alignment.
• The future combines multi-touch attribution, marketing mix modeling, and incrementality testing to measure real revenue impact.

What are multi-touch attribution tools?

Multi-touch attribution (MTA) tools track every marketing touchpoint a buyer interacts with and assign credit to each based on its influence on the final conversion.

Here’s what that actually means: 

A prospect downloads your pricing guide on January 5th and attends a webinar on January 20th. They click a LinkedIn retargeting ad on February 3rd and open three nurture emails between February 10 and 25. They visit your case studies page on March 1st and book a demo on March 5th.

Multi-touch attribution splits credit across all six touchpoints. Depending on the attribution model, it assigns the following: pricing guide (20%), webinar (15%), LinkedIn ad (10%), email (15%), case study (10%), and demo form (30%).

The core purpose: To show which channels contribute to the pipeline, how touchpoints work together, and where the budget creates real impact instead of just capturing conversions.

Here’s how that same customer journey is interpreted under single-touch attribution:

Why B2B marketers need an advanced attribution platform

B2B buying cycles make traditional attribution tracking inadequate by design.

Buyers don’t move in a straight line from awareness to purchase. They research for months, revisit earlier content, involve multiple stakeholders, go quiet, re-engage, and interact across more than ten channels before deciding.

In fact, the typical B2B buying group involves 6-10 decision-makers, each doing 4-5 pieces of independent research.

Why standard attribution breaks in B2B

• Long sales cycles break last-click models: When deals take 90-180 days to close, the last touchpoint is usually a scheduled demo or contract signature. These activities deserve zero credit for pipeline generation. You need to see what happened in months 1-5, not just week 12.
• Multiple decision makers fragment the journey: Your CFO downloads an ROI calculator. Your VP of Marketing attends a webinar. Your Director of Ops reads case studies. Your CRO sees targeted ads. Last-click only captures one person's final action and ignores the rest of the buying committee.
• Cross-channel visibility is impossible without integration: You run paid social, organic content, email campaigns, webinars, and field events. Without MTA, you view channel performance in silos. LinkedIn reports 40 conversions, email 35, and organic 50, but they all claim credit for the same 25 deals.

What advanced attribution platforms give you

Advanced marketing attribution platforms are designed around how B2B buying actually happens. They provide:

• Accurate budget allocation: Stop guessing which channels work. If webinars consistently appear in high-value deal journeys but rarely get last-click credit, you know they're undervalued in traditional reporting.
• Campaign optimization based on real influence: You'll see your demand gen blog posts drive early pipeline entry, while product comparison pages appear right before demo requests. This changes what you write and when you promote it.
• Cross-channel insights: Maybe LinkedIn ads alone convert at 2%, but LinkedIn plus email nurture converts at 12%. MTA shows you which channel combinations actually drive results.
• Account-level tracking for ABM: B2B deals involve multiple contacts at the same account. MTA platforms aggregate touchpoints at the account level to show the complete buying committee's journey, not just individual behavior.

factors.ai handles this by mapping multi-stage buyer journeys across both anonymous and known interactions, then tying those journeys directly to pipeline stages and revenue in the CRM. The platform uses first-party data. It connects website behavior, paid engagement, form fills, and CRM activity at the account level, rather than relying on cookies or last-touch signals. 

That’s critical in B2B, where buyers move across devices, channels, and long research cycles that traditional tracking can’t reliably connect.

Core features of marketing attribution software

When evaluating multi-touch attribution vendors, here's what actually matters:

1. Cross-channel data integration

Your attribution tool is only as good as the data it can access. Look for native integrations with:

• CRM systems (Salesforce, HubSpot, Dynamics) for deal and revenue data
• Ad platforms (LinkedIn, Google Ads, Meta) for paid touchpoints
• Marketing automation (Marketo, Pardot, ActiveCampaign) for email and nurture tracking
• Analytics tools (Google Analytics, Mixpanel) for website behavior
• Event platforms (Zoom, ON24, Goldcast) for webinar attendance
• Conversational tools (Drift, Qualified) for chatbot interactions

The platform should automatically sync touchpoint data without the need for constant manual exports or API maintenance. If you spend more than 2 hours per week on data hygiene, your tool isn't integrated enough.

2. Flexible attribution models

Not every campaign needs the same model. Your platform should support:

• Linear attribution: Equal credit to all touchpoints. Useful for understanding total channel presence.
• Time decay: More credit to recent interactions. Makes sense when you know late-stage content drives urgency.
• Position-based (U-shaped, W-shaped): Higher credit to first touch, key middle conversions, and deal close. This reflects reality for most B2B funnels.
• Data-driven/algorithmic: Machine learning determines credit based on actual conversion patterns in your data. Requires significant volume but produces the most accurate results.

You should be able to switch between models to answer different questions: What drives awareness (first-touch), what closes deals (time decay), and what is the full story (data-driven).

3. Real-time dashboards and reporting

If you can't answer which campaigns drove pipeline this month in under 60 seconds, your dashboard isn't built right. Look for:

• Real-time dashboards with pipeline and revenue views
• Journey timelines showing how contacts or accounts interacted over time
• Drill-down reporting at campaign, channel, and asset levels
• Automated report delivery for recurring reviews

4. Account-level and contact-level tracking

B2B attribution must work at two levels:

• Contact-level: Track individual buyer behavior. Note what content they consumed, which ads they clicked, and when they engaged.
• Account-level: Combine all company contacts into a single view. For example, if three people from Acme Corp attend your webinar and two others download content, that is five touchpoints for one account, not five separate leads.

Your platform should automatically match contacts to accounts through domain-based identity resolution and CRM account hierarchies.

5. Privacy-compliant and cookieless tracking

Platforms still dependent on third-party cookies will break in the next 12-18 months. Make sure yours won't. Look for:

• First-party data collection using server-side tracking
• Cookieless identification using hashed emails, login states, or device fingerprinting
• Privacy-first architecture that complies with GDPR, CCPA, and regional data laws
• Consent management integration to respect user preferences

Top multi-touch attribution tools & vendors in 2026

Choosing the right multi-touch attribution vendors means finding one that fits your sales cycle, channel mix, reporting needs, and data maturity. Here's what's actually worth evaluating, with honest pros and cons:

1. HubSpot Marketing Hub: Best for integrated attribution reporting

HubSpot Marketing Hub offers multi-touch revenue attribution reporting in its Professional and Enterprise tiers. It supports first-touch, last-touch, linear, U-shaped, W-shaped, full path, and time decay models that you can switch between in reports.

Attribution lives inside the same platform as your marketing automation, CRM, and analytics, so you don’t need to sync data across multiple tools. 

Key features:

• Interaction tracking: Tracks emails sent and opened, pages visited, form fills, ad clicks, social posts, and CRM deal stages, tying them to closed revenue.
• Account-level attribution: Automatically aggregates touchpoints from multiple contacts at the same company into one unified account view.
• Full-funnel tracking: Attribute to multiple conversion points, such as contact creation, MQL, SQL, opportunity, and closed-won revenue.
• Pre-built dashboards: Attribution reports by channel, campaign, content asset, and time period load without custom configuration.

Pros:

• Users consistently praise its intuitive interface and unified dashboards. This makes campaign analysis accessible to non-technical users.
• Connects native CRM objects to marketing performance, giving visibility from first touch to revenue.

Cons:

• Setting up and interpreting multi-touch attribution reports requires training. 
• Full multi-touch attribution reporting is available only in the Enterprise edition. This increases costs as needs grow.

HubSpot Marketing Hub pricing:

HubSpot Marketing Hub starts at $890/month (Professional) for basic attribution or $3,600/month (Enterprise) for full multi-touch attribution features. Pricing scales with contact volume, which can get expensive fast as your database grows.

This steep jump makes it tough for mid-market teams who need advanced attribution but can't justify $3,600 per month. You either pay for features you don't fully use or miss capabilities you need.

2. Dreamdata: Deep B2B revenue attribution

Dreamdata is a B2B revenue attribution platform for account-based journeys and long sales cycles. When buying committees of 5-8 people conduct independent research, Dreamdata groups their activities into a single account view and shows which touchpoints influenced the deal.

Key features:

• Automatic revenue attribution: Pulls closed-won deal amounts directly from your CRM and distributes revenue credit across all influencing touchpoints.
• Visual journey timelines: Shows every interaction in chronological order with attribution percentages. Makes it easy to explain which channels drove specific deals.
• Anonymous-to-known visitor tracking: Connects pre-conversion website visits with post-conversion CRM data to capture the full account journey.
• Fast historical data import: Automatically builds attribution models from past CRM and marketing data. Delivers insights within days, not months.

Pros:

• Connects CRM, ad platforms, and marketing automation to create a “single source of truth” for revenue influence. 
• Journey maps show stakeholders which channels drove specific deals without digging through spreadsheets.

Cons:

• Creating highly custom reports requires workarounds or data exports.
• Teams may need training to interpret results and get the correct data flows.

Dreamdata pricing:

Dreamdata offers two tiers: Starter (free forever) and Advanced (custom pricing). The Starter plan includes B2B web analytics, cookie or cookieless tracking, engagement scoring, and an audience builder, with limits of 5 seats, 2-month user history, and self-serve onboarding. 

Advanced unlocks AI-based attribution and activation features and removes volume restrictions. Pricing is not publicly listed and requires contacting sales.

3. LeadsRx: Best for comprehensive omnichannel tracking

LeadsRx is designed for businesses running marketing campaigns across online and offline channels. It tracks digital touchpoints such as ad clicks, website visits, and email engagement, and attributes offline interactions, including phone calls, trade show attendance, direct mail responses, and in-person sales meetings. 

Key features:

• Universal call tracking: Attributes phone conversions to the marketing source (ad, email, organic search) that started the journey, even if the call occurs days later, after multiple touchpoints.
• Cross-device identity resolution: Tracks buyers across desktop, mobile, and tablet using device fingerprinting and probabilistic matching, even when they are not logged in.
• 100+ integration library: Connects with major ad platforms, CRMs, marketing automation tools, and call tracking systems without custom API development.
• Multi-channel deduplication: Prevents double-counting when the same person interacts across email, ads, and website within the same journey.

Pros:

• The intuitive, responsive interface simplifies campaign execution without technical complexity.
• Flexible pricing adapts to budgets without forcing customers to pay for unused features.

Cons:

• Initial setup is time-consuming and requires significant effort before attribution data becomes available.
•  Graphs are confusing, making it difficult to quickly interpret channel performance data.

LeadRx pricing:

LeadsRx offers three products with custom pricing: LeadsRx Attribution for multi-touch attribution, LeadsRx Journey for customer journey analytics with first-party data tracking, and Attribution for Agencies as a white-label solution. To get a quote, contact sales, as no public pricing tiers are listed.

4. ActiveCampaign: Best for automated channel attribution

ActiveCampaign is primarily a marketing automation and CRM platform. It also includes built-in multi-touch attribution reporting to track how email sequences, website visits, and basic ad platform data contribute to conversions.

Key features:

• Email sequence attribution: Shows which specific emails in automated sequences drive conversions (e.g., 12% of recipients converted after email 3 in a 5-email nurture flow).
• Source-based automation triggers: Automatically segments and tags contacts based on lead source, enabling personalized follow-up workflows.
• Campaign reporting dashboards: Tracks campaign value, ROI, and strategy gaps with custom reporting views.
• Filterable attribution reports: Filter by automation, campaign, tag, and time period to analyze specific segments.

Pros:

• A wide range of integrations makes it simple to connect with other marketing tools.
• Quick setup and onboarding help teams get up to speed fast.

Cons:

• Reporting lacks depth for multi-touch attribution and doesn't provide cohort-style views for advanced analysis.
• Pricing scales quickly as contact lists grow, and you need higher-tier features beyond basic plans.

ActiveCampaign pricing:

ActiveCampaign offers three main tiers: Plus (from $112/month for 1,000 contacts), Pro (from $142/month), and Enterprise (from $284/month). Pricing depends on contact count and increases as your list grows. 

Plus includes basic attribution and automation, Pro unlocks full cross-channel marketing orchestration with advanced attribution, and Enterprise adds AI-powered features and premium support.

5. Rockerbox: Best for unified marketing measurement with mix modeling

Rockerbox is an enterprise marketing measurement platform that combines three approaches in one system: multi-touch attribution (tracking individual buyer journeys), marketing mix modeling (analyzing aggregate channel performance and saturation points), and incrementality testing (running experiments to show which channels cause conversions).

Key features:

• Marketing data foundation: Centralizes and cleans data across all channels (online and offline) on SOC2-certified infrastructure.
• Scenario planning: Forecasts budget shifts and channel tradeoffs before committing spend.
• Open architecture: Push results to your data warehouse, ingest partner or internal models, and compare and reconcile in one platform.
• 100+ integrations: Supports complex marketing mixes across every major ad platform, CRM, analytics tool, and data warehouse.

Pros:

• Enables smarter budgeting decisions by identifying the most incremental channels.
• Easy to use and understand despite advanced features, allowing teams to get value quickly.

Cons:

• Initial setup is tedious and requires a full-time developer, as well as ongoing Rockerbox support. 
• Attribution accuracy is weak on view-based platforms such as TikTok and YouTube, where impressions matter more than clicks.

Rockerbox pricing:

Rockerbox uses custom enterprise pricing with no public tiers. Pricing depends on marketing spend, number of channels tracked, and the methodologies you use: MTA only, MMM only, or the full unified measurement suite.

The lack of transparent pricing leads to longer evaluation cycles. The platform's focus on enterprise clients suggests it is built for teams with large marketing budgets that need executive-level ROI justification.

6. Google Analytics 4: Best for baseline tracking

Google Analytics 4 (GA4) is Google’s free web and app analytics platform with built-in data-driven attribution. It uses machine learning to analyze conversion paths and assign credit to touchpoints based on their statistical impact.

It’s best suited for teams seeking baseline multi-touch visibility across digital channels without investing in a dedicated attribution platform.

Key features:

• Cross-platform tracking: Unifies web and app behavior, tracking journeys across devices to show complete conversion paths.
• Native Google Ads integration: Tracks Google Ads performance and attributes conversions to specific campaigns, ad groups, and keywords without manual UTM tagging.
• Customizable lookback windows: Set how far back GA4 looks to attribute touchpoints before a conversion.
• Key event attribution: Attribute to multiple conversion events you define as important, such as form submissions, purchases, demo requests, or account signups.

Pros:

• Dashboard provides instant visibility into user sources, page engagement, and drop-off points.
• Integrates with Google Search Console for deeper insights into organic search performance and user behavior patterns.

Cons:

• The interface can be complex and unintuitive, requiring training to use attribution effectively.
• Customer support relies on documentation, insufficient for urgent technical issues.

Google Analytics 4 pricing:

GA4 is free for data processing, attribution modeling, and reporting. A premium version, Google Analytics 360, is for enterprise clients with high data volumes and requires custom pricing and sales contact.

How to choose the right attribution tracking software

The right tool should fit your data environment, sales cycle, and decision-making needs. Use this decision framework:

Step 1: Map your actual customer journey complexity

Count the distinct channels buyers used in your last 10 closed deals. Pull this data from your CRM. The number shows if you are over- or under-engineering your attribution stack.

Step 2: Identify integration requirements

Open a spreadsheet. List every platform where buyer interactions happen:

Before demoing any attribution tool, send this list to their sales team and ask: "Which of these have native integrations, API-only, or are not supported?" If they can't integrate with your CRM or marketing automation platform, cross them off immediately.

Step 3: Determine model flexibility needs

Ask yourself: do you need different models for different questions, or just one consistent view?

You need flexible modeling if:

• You run distinct strategies (brand awareness content, ABM campaigns, demand gen ads) and need to see which touchpoints drive each separately
• You're testing new channels and want to compare first-touch impact vs. last-touch to understand their role
• Different stakeholders need different views (CMO wants revenue attribution, demand gen wants campaign attribution, content wants asset attribution)

On the contrary, single-model attribution works only with a simple, consistent funnel, 3 to 5 channels, and full team alignment on what “success” means.

Step 4: Define account-level vs. lead-level priority

Most deals involve multiple people in different roles, each consuming different content at different times. If attribution tracks only one contact, it will miss what truly moved the deal forward.

Here’s how to determine your required attribution level:

Non-negotiable check: Audit your last 20 closed-won deals. If over 50% of the involved contacts are from the same company, lead-level attribution undercounts influence. Account-level attribution is mandatory.

Step 5: Assess budget and team size

Match your spend tier to realistic tool costs

• Under $50K annual marketing spend: Use GA4 + HubSpot's built-in attribution or ActiveCampaign.
• $50K-$500K spend: Dreamdata, LeadsRx, or HubSpot Marketing Hub Enterprise.
• $500K-$5M spend: factors.ai, Dreamdata, Rockerbox, or Funnel, plus a custom data warehouse.
• $5M+ spend: Rockerbox, custom-built attribution infrastructure, or platforms like factors.ai that connect first-party intent signals with journey attribution.

Rule: Don't spend more than 5% of your marketing budget on attribution software. If you spend $100K on marketing, $10K on attribution is the limit.

Step 6: Evaluate reporting and stakeholder needs

List who will actually use attribution data and what questions they need answered:

Your attribution platform should answer these questions in under 60 seconds without a data analyst to build custom reports. 

Implementation best practices for B2B marketing teams

Getting attribution right goes beyond buying the right software. Here's how to actually make it work:

1. Clean your CRM data before implementing attribution

Attribution is only as accurate as the CRM data it connects to. Pull a report of your last 100 closed deals and check for:

• Duplicate accounts: Search for "Microsoft" in your CRM. If you see "Microsoft," "Microsoft Corporation," "MSFT," and "microsoft.com" as separate accounts, merge them. Use your CRM's deduplication tool.
• Missing contact-to-account associations: Run a report for "Contacts where Account Name is blank." These won't show up in account-level attribution. Manually assign them or use domain matching to auto-associate.
• Inconsistent stage naming: If your pipeline includes variations, like Demo Scheduled, Demo Completed, and Demo Qualified, attribution will fragment stage reporting. Standardize to 5–7 clear stages (for example: Lead → MQL → SQL → Opportunity → Negotiation → Closed-Won / Closed-Lost) and rename old deals before implementation.
• Incomplete deal close dates and revenue: Filter for Closed-Won deals where "Close Date" is blank or "Amount" is $0. Fill in actual dates and revenue. Without this, your attribution platform can't calculate ROI. 

2. Align CRM stages with attribution touchpoints

Your attribution platform must know which CRM stage each touchpoint drives:

• Lead: Content download, ad click, form fill
• MQL: Webinar attendance, pricing page visit, 3+ engaged sessions
• SQL: Demo request, free trial signup, "talk to sales" form
• Opportunity: Sales meeting held, proposal sent
• Closed-won: Contract signed

Also, different stages need different attribution windows:    

This prevents late-stage credit from leaking to unrelated early activity. Avoid changing stage definitions mid-quarter. Attribution needs consistency to remain comparable over time.

3. Avoid double-counting by setting clear touchpoint rules

If someone clicks a LinkedIn ad, visits your site, fills out a form, and receives an auto-reply email, is that four touchpoints or two?

Your attribution platform should deduplicate touchpoints that occur within minutes and represent the same action. Here’s how to define rules:

4. Get cross-functional buy-in from sales and marketing

Attribution fails when sales and marketing don't agree on what data means. Run alignment workshops to define:

• MQL: Fits ICP + visited pricing page + downloaded product guide (not just "filled out a form")
• SQL: Requested demo or responded to outreach asking for a meeting (not just "marketing sent it over")

Next, create shared accountability. Marketing commits to clean UTM tagging, accurate lead scoring, and weekly attribution reviews. Sales commits to updating CRM stages within 24 hours, logging all calls and meetings, and avoiding duplicate contacts.

Further, hold a 15-minute sync every Monday. Marketing presents top-attributed channels from last week. Sales flags deals with inaccurate or missing attribution data.

Attribution models explained: Beyond last-click

The attribution model you choose directly shapes budget decisions. It’s critical to understand what each model prioritizes and what it ignores.

1. Linear: Every touchpoint gets equal credit. If a buyer has 10 interactions before purchasing, each interaction earns 10% credit.

2. Time decay: Recent touchpoints get more credit. The closer to conversion, the higher the attribution percentage.

3. U-shaped attribution (position-based): First and last touchpoints get 40% credit each. Middle interactions share the remaining 20%.

4. W-shaped attribution: First touch, key middle conversion (usually MQL), and last touch each get 30% credit. Remaining 10% goes to other middle touchpoints.

5. Data-driven/algorithmic attribution: Machine learning analyzes thousands of conversion paths to identify which touchpoints statistically increase conversion likelihood. Credit is given based on actual influence, not arbitrary rules.

Most teams should run 2-3 models in parallel. If all models agree LinkedIn is your top channel, it's real. If only last-click says it, dig deeper.

Example: A buyer engages over four months before signing the contract. Here's how each model distributes credit:

The takeaway: If you optimize based on last-click, you'd cut blog posts and webinars because they don't drive conversions. Other models show they are critical to the pipeline.

How AI is changing attribution measurement

AI changes attribution from manual dashboard analysis to automated pattern detection inside your pipeline. 

Here’s how that shift shows up in practice:

1. Automated insight surfacing: Traditional attribution platforms show dashboards and expect you to interpret them. AI-powered platforms now surface insights automatically, such as: “LinkedIn ad spend increased by 15%, while pipeline contribution dropped by 8%. Investigate targeting changes.”

2. Predictive channel performance: AI uses historical CRM and campaign data to estimate which channels will generate pipeline next month. For example, if paid social generates leads in Q1 but rarely converts to Opportunity until Q3, the model identifies that pattern. This helps teams adjust the budget before stage-level performance drops. 

3. Anomaly detection: AI monitors attribution and revenue data for abnormal changes. A sudden drop in organic pipeline, an unusual spike in campaign-attributed revenue, or declining influenced revenue despite flat spend can indicate tracking errors or performance issues. 

4. Privacy-compliant identity resolution: AI links anonymous website activity to known contacts once it captures first-party data. It connects sessions across devices using hashed identifiers and probabilistic matching. At the account level, it aggregates activity from multiple stakeholders into one buying journey.

5. Natural language querying: AI eliminates the need for custom report building. Teams ask questions directly, such as “Which channels drove the pipeline for deals that closed under 60 days?” or “What’s the average number of touchpoints for deals over $100K?” The system translates these questions into queries and returns results instantly.

Challenges and the future of attribution platforms

Attribution has come a long way, but the rules are changing. Here’s where it still falls apart:

In a nutshell

Multi-touch attribution exists because last-click lies. When buyers spend months researching across 15-20 touchpoints, crediting only the demo form means you optimize for the wrong things.

Choose the right attribution platform based on whether you need account-level tracking, offline attribution, or just baseline digital measurement.

But tools alone don't fix attribution. Clean CRM data, consistent UTM tagging, and sales-marketing alignment matter more than the platform you choose. And run multiple attribution models to see what actually works.

FAQs for multi-touch attribution tools

1. What is an attribution platform?

An attribution platform tracks marketing touchpoints and assigns credit for pipeline or revenue. It connects ads, website activity, email, events, and CRM data to show what influenced deals.

2. How do multi-touch attribution tools improve marketing ROI?

They show which channels drive the pipeline, not just leads. This helps you shift budget toward revenue-generating activities and cut low-impact spend.

3. Which marketing attribution software works best for B2B?

B2B teams need account-level tracking and CRM integration. The right tool depends on deal length, stakeholder count, and channel complexity.

4. Can multi-touch attribution platforms integrate with CRMs?

B2B teams need account-level tracking and CRM integration. The right tool depends on deal length, stakeholder count, and channel complexity.

5. How do I evaluate attribution vendors for my business?

Map recent deals. Count touchpoints. Then compare vendors on integrations, model flexibility, data accuracy, and account-level visibility.