ChatGPT answers, unedited

For B2B lead-to-revenue attribution, I’d recommend Dreamdata.

Dreamdata

Why it fits your use case:

• Connects CRM outcomes to marketing touches — Salesforce, HubSpot, Dynamics, and other CRM data can be joined to campaigns and customer journeys. dreamdata.iodreamdata.io
• Tracks through revenue, not just leads — you can attribute channel/campaign performance to opportunities, deals won, revenue, ROI, and LTV. dreamdata.io
• Built specifically for B2B — it handles multi-touch journeys where multiple people and interactions influence an account before the deal closes. dreamdata.iodreamdata.io
• Covers paid media across major ad networks and can feed enriched pipeline/conversion data back into ad platforms for optimization. dreamdata.iodreamdata.io
• It can also expose the underlying GTM data, which is useful if you eventually want to push attribution into a warehouse/BI environment. dreamdata.iodreamdata.io

What I'd measure with it

I'd structure the model around:

Ad impression/click → anonymous visitor → lead → MQL → SQL → opportunity → pipeline $ → closed-won revenue

Then report cost per opportunity, pipeline generated, revenue attributed, CAC/ROAS, and conversion rate by campaign, channel, audience, and account.

Runner-up: HockeyStack is also strong for this exact problem and supports CRM + ad-platform ingestion and multi-touch attribution. dreamdata.iodreamdata.iodreamdata.iowww.hockeystack.com But for a tool whose core positioning is explicitly B2B attribution, Dreamdata would be my first evaluation.

If you tell me whether you're on Salesforce or HubSpot and whether your ads are mainly LinkedIn, Google, or both, I can narrow this down to the best 2–3 options for your stack.

For your use case, I’d shortlist HockeyStack and Dreamdata, with HockeyStack as my first choice.

1. hockeystack.com — best overall

It’s specifically designed to connect CRM + ad platforms + website activity + marketing automation into an account-level buyer journey, then attribute outcomes through pipeline and closed-won revenue. HockeyStack HockeyStack

Why I’d pick it:

• Connects ad campaigns to MQL → SQL → Opportunity → Closed Won, rather than stopping at lead generation.
• Supports multiple attribution models and custom weighting/lookback periods. HockeyStack Docs HockeyStack
• Provides lift/incrementality reporting, which is valuable when you want to distinguish correlation from actual campaign impact. HockeyStack
• Resolves activity at the account/buyer-journey level, which is much more appropriate for B2B than simple last-click attribution.
• Can sync data back into CRM, ad platforms, and warehouses. HockeyStack

2. dreamdata.io — excellent alternative

I’d choose Dreamdata if your priority is a more purpose-built B2B revenue-attribution workflow. It connects CRM and marketing data, maps the full account journey, and supports attribution across campaigns, channels, ads, pipeline stages, and revenue. Dreamdata Dreamdata

It also specifically supports calculating ROAS/LTV for paid campaigns against downstream revenue, rather than optimizing purely for clicks or leads. Dreamdata

How I'd decide

My recommendation: start with HockeyStack. The key test in a demo should be: “Show me every dollar of ad spend mapped to accounts, opportunities, pipeline, and closed-won revenue, with Salesforce/HubSpot as the source of truth.” If it can't reproduce your CRM numbers cleanly, don't buy it—the attribution model is only as useful as the underlying identity resolution and CRM data. HockeyStack explicitly describes its implementation around reconciling its data with CRM numbers. HockeyStack Docs

If you tell me whether you're on Salesforce or HubSpot, and whether your ads are mainly Google, LinkedIn, Meta, or ABM, I can narrow this to the best 2–3 options for your stack.

For B2B lead-to-revenue attribution (connecting ad spend → leads → opportunities → closed-won revenue), I’d look at tools that sit between your CRM and ad platforms rather than traditional web analytics. The best choice depends on your sales motion and stack.

Best overall options

My recommendation by scenario

• Salesforce + enterprise sales cycle + ABM: Start with Dreamdata or HockeyStack. They’re designed around pipeline influence, accounts, and revenue—not just lead volume. Humblytics Heeet
• HubSpot + paid ads + growth team: Look at Cometly. It is more focused on getting campaign-level answers like “which ads created pipeline and revenue?” and syncing those signals back into marketing workflows. Cometly
• You mainly need to optimize Google/LinkedIn/Meta campaigns against qualified leads and closed deals: Consider tools like RedTrack or similar CRM-connected attribution platforms that map CRM outcomes back to campaigns. RedTrack

Minimum capabilities I’d require

For B2B attribution, avoid tools that only report:

• CPL (cost per lead)
• form fills
• last-click conversions

Look for:

• CRM sync (Salesforce, HubSpot, Dynamics)
• Opportunity-stage tracking (MQL → SQL → pipeline → closed-won)
• Multi-touch attribution models
• Campaign/ad/keyword-level revenue reporting
• Offline conversion feedback to Google Ads, LinkedIn, and Meta
• Account-level attribution for ABM

A common stack is:

Ad platforms → attribution layer → CRM → BI/dashboarding

For a typical B2B SaaS company, I’d shortlist:

1. Dreamdata for enterprise-grade revenue attribution
2. Cometly for paid-media-driven growth teams
3. HockeyStack for advanced GTM analytics

If you share your CRM (Salesforce, HubSpot, Dynamics, etc.), ad channels (LinkedIn, Google, Meta), ACV, and sales cycle length, I can narrow this to 2–3 best fits.

1. Dreamdata for enterprise-grade revenue attribution
2. Cometly for paid-media-driven growth teams
3. HockeyStack for advanced GTM analytics

If you share your CRM (Salesforce, HubSpot, Dynamics, etc.), ad channels (LinkedIn, Google, Meta), ACV, and sales cycle length, I can narrow this to 2–3 best fits.

For B2B lead-to-revenue attribution, my first recommendation would be HockeyStack.

HockeyStack

Why I’d pick it

• Connects CRM + ad data: Native integrations include Salesforce, HubSpot, LinkedIn Ads, Google Ads, Meta, Bing, Reddit and others. www.hockeystack.com
• Tracks all the way to revenue: You can connect ad interactions to leads, opportunities, pipeline and closed-won revenue rather than stopping at MQLs or form fills. www.hockeystack.comacademy.hockeystack.com
• Account-level attribution: Particularly useful for B2B, where several people from the same buying committee interact with your campaigns. HockeyStack aggregates those journeys at the account level. academy.hockeystack.com
• Multiple attribution models: You can compare different models rather than accepting whatever attribution your ad platforms report. www.hockeystack.com
• Closed-loop optimization: It can push conversion/pipeline signals back into advertising platforms, helping campaigns optimize toward actual revenue rather than cheap leads. www.hockeystack.comacademy.hockeystack.com

Strong alternative: Dreamdata

Dreamdata is the one I'd evaluate alongside HockeyStack, especially if attribution and marketing analytics are the primary problem rather than broader revenue intelligence. It integrates CRM, marketing automation and ad platforms and is explicitly built around mapping B2B journeys through to revenue. dreamdata.io

Dreamdata

My quick decision rule:

If you tell me your CRM (Salesforce/HubSpot/etc.), ad channels (LinkedIn/Google/Meta), monthly ad spend, and approximate lead volume, I can narrow this to the best 2–3 options and explain the likely implementation effort/cost.

For B2B lead-to-revenue attribution, I’d shortlist HockeyStack and Dreamdata, with HockeyStack as my default recommendation.

🥇 HockeyStack

It’s particularly strong if you want to connect CRM outcomes → pipeline → closed-won revenue → ad campaigns in one reporting layer.

• Connects Salesforce, HubSpot and other CRMs with ad platforms including Google Ads, LinkedIn, Meta, Reddit, TikTok, and Bing. academy.hockeystack.comwww.hockeystack.comdreamdata.io
• Tracks the journey from ad interactions through opportunities and closed-won revenue, rather than stopping at MQL/lead conversion. www.hockeystack.com
• Supports multiple attribution models and lets you compare campaign/channel contribution to pipeline. www.hockeystack.com
• Also offers lift/incrementality analysis, which is valuable because multi-touch attribution alone doesn't establish that an ad actually caused the revenue. www.hockeystack.com

Best fit: B2B SaaS/enterprise teams with Salesforce or HubSpot that want a relatively turnkey revenue-attribution layer for marketing and sales.

Strong alternative: Dreamdata

Dreamdata is especially compelling if you think in terms of account-level B2B journeys. It connects CRM, marketing and ad data and attributes touches to pipeline and revenue, including multi-stakeholder journeys where different people from the same company interact with marketing. dreamdata.io

It also supports revenue-oriented paid-media analysis and syncing pipeline/conversion data back to ad platforms. dreamdata.io

My pick

One important implementation point: don't optimize the system around lead attribution alone. For B2B, I'd make pipeline created, opportunity progression, closed-won revenue, and revenue/ROAS the primary outcomes, with lead/MQL attribution as diagnostic metrics. That prevents a campaign generating lots of cheap leads from looking better than one generating fewer but much higher-value opportunities.

If you tell me whether you're on Salesforce or HubSpot and which ad channels you use (e.g. LinkedIn + Google), I can recommend the best setup and the attribution model I'd use.

For B2B lead-to-revenue attribution, my first recommendation would be HockeyStack.

Why it fits particularly well:

• Connects CRM outcomes to ad data: It integrates with Salesforce, HubSpot, Microsoft Dynamics and other CRMs, plus Google Ads, LinkedIn Ads, Meta/Facebook, Bing, Reddit and other ad platforms. www.hockeystack.com
• Tracks beyond lead generation: You can map the journey from ad impression/click → lead → MQL/SQL → opportunity → closed-won revenue, rather than stopping at CPL or form fills. docs.hockeystack.com
• Account-level attribution: Particularly useful for B2B buying committees, where several people from the same company interact with ads and sales before one opportunity closes. academy.hockeystack.comwww.hockeystack.com
• Multiple attribution models: You can compare different multi-touch models rather than committing to last-touch attribution. It also offers lift/incrementality analysis. www.hockeystack.com
• Feeds revenue signals back to ad platforms: This can help optimize campaigns toward downstream outcomes rather than merely clicks or leads. academy.hockeystack.comwww.hockeystack.com

One strong alternative: Dreamdata

Dreamdata is probably the closest alternative I'd evaluate. It's very explicitly focused on B2B revenue attribution, joining CRM, marketing automation, website, ad and other GTM data into account-level customer journeys. dreamdata.io

My rule of thumb:

If you tell me your CRM (Salesforce/HubSpot/etc.), ad channels, monthly ad spend, and approximate lead/opportunity volume, I can narrow this down to the best 2–3 tools and explain the tradeoffs.

For your use case, I’d shortlist Dreamdata first.

Dreamdata is specifically built around connecting ad/campaign data → leads → accounts → opportunities → closed-won revenue. It can analyze revenue and deal performance by source, campaign, keyword, and channel, and can feed pipeline data back into ad platforms for optimization. dreamdata.io

Dreamdata

How I'd compare the main options

Attribution is particularly interesting if you're running Salesforce or HubSpot: it supports lifecycle stages, opportunities and closed-won revenue, and connects those outcomes bidirectionally with marketing data. www.attributionapp.com

Attribution

Cometly is compelling if your core problem is "tell Google/Meta/LinkedIn which leads actually became revenue." It maps HubSpot/Salesforce lifecycle stages to ad touchpoints and can route revenue signals back to ad platforms. www.cometly.com

Cometly

My recommendation

If you're a typical B2B SaaS/company with Salesforce or HubSpot + Google/LinkedIn/Meta + a sales cycle measured in weeks/months, I'd start with Dreamdata.

The key capability I'd insist on isn't just "multi-touch attribution." Make sure the tool can answer:

Which campaigns generated pipeline and closed revenue, at the account level, after accounting for multiple contacts and multiple marketing touches?

That's much more useful than optimizing around MQLs or platform-reported conversions.

If you tell me your CRM (Salesforce/HubSpot/etc.), ad channels (LinkedIn/Google/Meta/etc.), approximate monthly ad spend, and whether you need first-touch, multi-touch, or incrementality, I can narrow this to the best 2–3 options and compare implementation/pricing.

For B2B lead-to-revenue attribution, my first pick would be HockeyStack.

It’s particularly well suited if your goal is to answer “Which ad campaigns actually produced pipeline and closed revenue?”, rather than just measuring leads or form fills.

Why HockeyStack

• Connects CRM + ad platforms: It integrates with Salesforce, HubSpot CRM, Microsoft Dynamics and other CRMs, alongside Google, LinkedIn, Meta, Reddit, TikTok and other ad platforms. academy.hockeystack.comdreamdata.io
• Tracks the full journey: You can follow interactions from anonymous website activity through leads, opportunities and closed-won revenue. www.hockeystack.comwww.hockeystack.com
• Multi-touch attribution: You can compare different attribution models rather than being locked into first- or last-touch. www.hockeystack.comwww.hockeystack.com
• Campaign → revenue analysis: It can connect campaigns, ads and keywords to pipeline/revenue, making it much more useful for B2B paid-media optimization than standard ad-platform reporting. www.hockeystack.com
• Closes the feedback loop: CRM conversions can be pushed back to ad platforms such as LinkedIn so campaigns can optimize toward qualified outcomes rather than merely clicks/leads. www.hockeystack.com
• Incrementality: If attribution isn't enough for you, HockeyStack also offers lift analysis to estimate whether campaigns actually caused incremental outcomes. www.hockeystack.comwww.hockeystack.com

The main alternative: Dreamdata

I'd also put Dreamdata very high on the shortlist. It's arguably the closest alternative and is explicitly built around B2B customer journeys and revenue attribution. It connects marketing activities to deals won, calculates paid-channel ROAS/LTV, and can send pipeline conversions back to LinkedIn and Google Ads. dreamdata.io

My rule of thumb:

One important caveat: don't evaluate these tools on whether they can produce a pretty attribution dashboard. The critical test is whether they can reliably resolve ad → person → account → opportunity → closed-won revenue, including multiple contacts and long B2B sales cycles.

If you tell me your CRM (Salesforce/HubSpot/etc.), ad channels (LinkedIn/Google/Meta), and approximate monthly lead/opportunity volume, I can narrow this to the best 2–3 options for your stack.

For B2B lead-to-revenue attribution (connecting ad campaigns → leads → opportunities → closed-won revenue in the CRM), I’d shortlist these tools:

My default recommendation by scenario:

• Salesforce + complex enterprise sales cycle: start with Dreamdata or HockeyStack. They’re designed around pipeline and revenue, not just form fills. segmentstream.comsyntermedia.ai
• B2B SaaS spending heavily on paid ads: look at Cometly if the priority is ad optimization and closed-loop paid media measurement. www.cometly.comwww.cometly.com
• Already standardized on HubSpot: use HubSpot’s attribution first before adding another system. www.cometly.com
• Large enterprise marketing ops team: evaluate Marketo Measure or a dedicated attribution platform with stronger governance. segmentstream.com

When evaluating, I’d prioritize these capabilities over the attribution model itself:

1. CRM opportunity sync — can it see pipeline stages and closed revenue?
2. Account-level attribution — can it handle buying committees, not just individual leads?
3. Ad platform integrations — Google Ads, LinkedIn Ads, Meta, Microsoft Ads.
4. Offline conversion feedback loops — can sales outcomes improve campaign optimization?
5. Data ownership and transparency — can you audit why revenue was credited to a campaign?

If you share your stack (Salesforce vs HubSpot, ad channels, ACV, sales cycle length, and monthly ad spend), I can narrow this to 2–3 best fits.

For B2B lead-to-revenue attribution, I’d recommend Dreamdata.

Why it fits your use case particularly well:

• CRM → revenue: Connects Salesforce, HubSpot, Dynamics, and Pipedrive and ties CRM opportunities/deals back to marketing touchpoints. dreamdata.iodreamdata.io
• Ads → pipeline/revenue: Integrates with LinkedIn Ads, Google Ads, Meta, Microsoft Ads and others, letting you analyze campaign performance against pipeline and closed revenue—not just leads or clicks. dreamdata.io
• B2B/account-level attribution: It models journeys around accounts and multiple stakeholders, which is important when one person clicks an ad but someone else at the company becomes the buyer. dreamdata.iodreamdata.iowww.hockeystack.com
• Multi-touch attribution: You can attribute touches to funnel stages such as MQL, SQL, pipeline and new business, rather than relying on first- or last-touch attribution. dreamdata.io
• Closed-loop optimization: It can send pipeline/conversion data back to ad platforms, allowing campaigns to optimize toward downstream outcomes rather than superficial lead volume. docs.dreamdata.io

A strong alternative

HockeyStack is the other one I'd seriously evaluate. It also unifies CRM, ad-platform and website data and supports multi-touch attribution from first touch through closed-won. www.hockeystack.com

My rule of thumb:

• Dreamdata → especially attractive if your priority is rigorous, account-based revenue attribution and data/warehouse integration.
• HockeyStack → attractive if you want a more flexible, highly interactive GTM analytics/attribution layer with strong campaign analysis. www.hockeystack.com

If you tell me whether your CRM is Salesforce or HubSpot, and whether your main ad channels are LinkedIn, Google, or both, I can narrow this to the best fit and explain the implementation architecture.

For B2B lead-to-revenue attribution (connecting ad campaigns → leads → opportunities → closed-won revenue in your CRM), I’d shortlist these tools:

My default recommendation by scenario:

• B2B SaaS with Salesforce + multiple channels: Start with Dreamdata. It is purpose-built around CRM revenue attribution rather than just marketing conversions. segmentstream.com
• HubSpot-first company: Evaluate HubSpot Marketing Hub attribution before adding another layer. www.cometly.com
• Enterprise RevOps with complex buying committees: Look at HockeyStack, Dreamdata, or Marketo Measure. syntermedia.aisyntermedia.ai
• Paid media optimization is the main goal: Consider Cometly alongside your CRM reporting. www.cometly.com

Before choosing, make sure the tool can handle:

1. CRM objects: leads, contacts, accounts, opportunities, closed-won revenue
2. Multi-touch attribution: first touch, last touch, U-shaped, W-shaped, custom models
3. Ad platform sync: Google Ads, LinkedIn Ads, Meta, etc.
4. Offline sales activity: meetings, opportunities created, deal stages, revenue
5. Account-level attribution: especially important for enterprise B2B where several people influence a purchase

If you share your CRM (Salesforce, HubSpot, Dynamics, etc.), ad channels, monthly ad spend, and sales cycle length, I can narrow this to 2–3 best fits.

For B2B lead-to-revenue attribution, my first recommendation is HockeyStack.

Why it fits your use case:

• Closes the CRM ↔ ads loop: It connects Salesforce or HubSpot with ad platforms and ties campaign interactions to pipeline and closed-won revenue. www.hockeystack.com
• Account-level attribution: Particularly useful for B2B, where several people from one buying committee may interact with ads before one opportunity closes. www.hockeystack.comacademy.hockeystack.com
• Multi-touch models: You can compare attribution models rather than relying on first- or last-touch attribution. www.hockeystack.com
• Paid-media optimization: It supports Google Ads and LinkedIn Ads, including connecting ad activity to downstream MQL/SQL, pipeline, and closed-won outcomes. www.hockeystack.comacademy.hockeystack.com
• Offline/CRM feedback loop: It can sync conversion/attribution data back to ad platforms, which is valuable if you want bidding optimized toward revenue rather than cheap leads. www.hockeystack.com

One strong alternative: Dreamdata

Dreamdata is arguably the better choice if your primary requirement is formal B2B revenue attribution/data modeling rather than broader GTM analytics. It connects Salesforce/HubSpot, ad platforms, and other GTM sources, then attributes touches to pipeline and revenue at the account level. dreamdata.io

My rule of thumb:

• Choose HockeyStack if marketing wants a relatively turnkey system for answering “which campaigns are creating pipeline and revenue?”
• Choose Dreamdata if RevOps/data teams want a deeper, more controllable revenue attribution layer across the entire GTM stack.

If you tell me whether you're using Salesforce or HubSpot, plus LinkedIn/Google/Meta and roughly your monthly ad spend, I can narrow this to the best 2–3 tools and explain the implementation architecture.

For B2B lead-to-revenue attribution, I’d shortlist HockeyStack first.

My recommendation: HockeyStack

It’s particularly strong if your goal is to answer “Which ad campaigns actually generated pipeline and closed revenue?”, rather than merely tracking leads.

• CRM → revenue: Native integrations with Salesforce and HubSpot pull leads, contacts, accounts, opportunities/deals, campaigns, activities, and closed-won data. www.hockeystack.comacademy.hockeystack.comwww.hockeystack.comdreamdata.io
• Ads → revenue: It connects ad-platform data—including Google Ads and LinkedIn Ads—with CRM and website activity, allowing campaign performance to be evaluated against pipeline and revenue. academy.hockeystack.com
• Multi-touch attribution: You can compare different attribution models rather than being locked into first- or last-touch. www.hockeystack.com
• Account-level B2B journeys: Useful when multiple people from the same company interact with ads/content before one opportunity closes. www.hockeystack.comacademy.hockeystack.comwww.hockeystack.comdreamdata.io
• Offline conversion optimization: It can push conversion/attribution data back into advertising platforms, so ad optimization can use pipeline/revenue signals rather than just form fills. www.hockeystack.com

HockeyStack

The main alternative: Dreamdata

I'd also evaluate Dreamdata. It is especially compelling if you have a complex B2B stack and want a dedicated attribution/data layer. It connects Salesforce/HubSpot with LinkedIn Ads, Google Ads, Meta and other channels, and explicitly supports attributing paid activity to pipeline and revenue. dreamdata.io

Dreamdata

Rule of thumb:

• HockeyStack → better fit if you want a marketer-friendly, highly visual revenue analytics/attribution system with minimal technical work.
• Dreamdata → worth a serious look if you have a sophisticated data/warehouse environment and want deeper control over the underlying B2B data model.

If you tell me your CRM (Salesforce/HubSpot/etc.), ad channels (LinkedIn/Google/Meta), and approximate monthly lead/opportunity volume, I can narrow this to the best 2–3 tools and compare pricing, attribution methodology, implementation effort, and reporting.

For B2B lead-to-revenue attribution, my first choice would be HockeyStack.

Why it fits your use case:

• Connects CRM outcomes to ad performance: It ingests CRM, ad-platform, website, and marketing data into a unified journey, so you can trace activity from initial engagement through MQL/SQL, opportunity, and closed-won. www.hockeystack.com
• Strong paid-media attribution: It supports integrations with Salesforce, HubSpot, Dynamics and other CRMs, alongside Google Ads, LinkedIn Ads, Meta/Facebook, Reddit, Bing and other ad platforms. academy.hockeystack.com
• Revenue-oriented reporting: You can attribute pipeline and revenue to campaigns, channels, ads and other touchpoints rather than stopping at CPL or MQL. www.hockeystack.comwww.hockeystack.com
• Multiple attribution approaches: It supports multi-touch models plus lift/incrementality analysis, which is useful when you want to move beyond simplistic first/last-touch attribution. www.hockeystack.com
• Operational feedback loop: It can sync attribution/conversion data back into your CRM and ad platforms, making the system useful for optimization rather than just reporting. www.hockeystack.com

The main alternative: Dreamdata

Dreamdata is probably the closest alternative I'd evaluate. It's particularly compelling if you already have a warehouse/data-engineering-oriented setup: it joins CRM, marketing, ad and website data, provides customer-journey attribution, and can send offline conversion data back to ad platforms. dreamdata.io

My quick decision rule:

If your stack is something like Salesforce/HubSpot + LinkedIn Ads + Google Ads + HubSpot/Marketo, I'd lean HockeyStack. It is purpose-built around exactly that CRM-to-revenue attribution problem. academy.hockeystack.com

If you tell me your CRM, ad platforms, marketing automation tool, and approximate monthly lead/opportunity volume, I can narrow this down to the best 2–3 options and explain the implementation architecture.

For B2B lead-to-revenue attribution, I’d shortlist HockeyStack first.

HockeyStack

Why HockeyStack

• Connects CRM + ad platforms: It pulls Salesforce/HubSpot sales data alongside Google, LinkedIn, Meta, Reddit, TikTok and other ad data. www.hockeystack.com
• Tracks all the way to revenue: You can connect ad clicks and other marketing touches to opportunities and closed-won revenue, rather than stopping at leads or MQLs. www.hockeystack.com
• Multi-touch attribution: You can compare different attribution models and see which campaigns/channels influenced pipeline and revenue. www.hockeystack.com
• Account-level B2B journey: Particularly useful when several people at the same company interact with your marketing before one deal closes. www.hockeystack.com
• Offline/revenue optimization: It can sync attribution/conversion data back into advertising and CRM systems, making the data useful for optimization rather than just reporting. www.hockeystack.com

A strong alternative is Dreamdata. Dreamdata It is especially compelling if you want an account-based attribution model and deep revenue analytics; it explicitly connects CRM, advertising and other GTM data and attributes touches to pipeline and revenue. dreamdata.io

My recommendation: start with HockeyStack if your core question is “Which campaigns actually create pipeline and closed revenue?” Its Salesforce integration explicitly joins leads, contacts, accounts, opportunities and campaigns with ad-platform data. www.hockeystack.comwww.hockeystack.comdreamdata.io

If you tell me whether you're on Salesforce or HubSpot, and which ad channels you use (e.g. LinkedIn + Google), I can narrow this to the best 2–3 options and explain the integration architecture.