Revenue Intelligence

Analyze revenue by channel, campaign, and touchpoint with multi-touch attribution modeling.

Revenue Intelligence in SalesOS provides a comprehensive view of where your revenue comes from, which channels and campaigns drive the most value, and how different touchpoints contribute to closed deals. By combining multi-touch attribution modeling with revenue analytics, you gain the insight needed to allocate marketing spend effectively, optimize your go-to-market strategy, and forecast revenue with greater accuracy.

Unlike basic reporting that shows revenue totals by period, Revenue Intelligence connects the dots between marketing activities, sales engagement, and closed revenue -- giving you a complete picture of the buyer journey and its financial outcomes.

Revenue Intelligence Dashboard

The Revenue Intelligence dashboard is accessible from the main navigation under Analytics > Revenue Intelligence. The dashboard is organized into several sections that provide progressively deeper analysis.

Dashboard Header

At the top of the page, four summary cards provide a high-level snapshot:

Total Revenue -- Closed-won revenue for the selected time period.
Attributed Revenue -- Revenue that has been successfully attributed to at least one marketing or sales touchpoint.
Average Deal Size -- Mean closed-won deal value for the period.
Attribution Coverage -- The percentage of revenue that has complete attribution data (touchpoints recorded from first interaction through close).

Time Period Selector

Use the date range picker in the top-right corner to select the analysis period. Options include:

Preset ranges: This Month, This Quarter, Last Quarter, Year to Date, Last 12 Months.
Custom date range for ad-hoc analysis.

Revenue Intelligence uses the deal close date as the anchor for time-based filtering. A deal closed in Q1 will appear in Q1 reports regardless of when the first touchpoint occurred.

Channel Performance

The Channel Performance section breaks down revenue by the source, medium, and campaign that influenced each deal. This view answers the question: which channels are generating the most revenue?

Revenue by Source

A horizontal bar chart displays attributed revenue by traffic source (organic search, paid search, direct, referral, social, email, partner, events, and others). Each bar shows:

Total attributed revenue from that source.
Number of deals influenced.
Average deal size for deals attributed to that source.

Revenue by Medium

Drill deeper into medium-level performance to distinguish between, for example, paid social versus organic social, or display ads versus search ads. The table includes:

Medium	Revenue	Deals	Avg Deal Size	Conv Rate
CPC	--	--	--	--
Organic	--	--	--	--
Email	--	--	--	--
Referral	--	--	--	--
Event	--	--	--	--

Click any row to expand and see the specific campaigns within that medium.

Revenue by Campaign

The campaign view provides the most granular breakdown, showing revenue attributed to individual marketing campaigns. This is where you evaluate ROI for specific initiatives:

Campaign name and associated source/medium.
Total revenue attributed to the campaign.
Number of deals the campaign influenced.
Number of touchpoints (total interactions from that campaign across all deals).
Cost per acquisition (if campaign spend data is connected via integration).
Return on ad spend (ROAS) when cost data is available.

Use the search and filter controls to find specific campaigns or filter by source/medium.

Multi-Touch Attribution Models

Real buyer journeys involve multiple touchpoints -- a prospect might discover you through a blog post, attend a webinar, receive a nurture email, and then close after a sales demo. Multi-touch attribution distributes credit for the resulting revenue across these touchpoints according to a model you select.

SalesOS supports four attribution models, and you can switch between them at any time to see how the distribution changes.

First-Touch Attribution

All revenue credit goes to the first recorded touchpoint in the buyer's journey. This model answers: Which channels are best at generating new pipeline?

Use case: Evaluating top-of-funnel effectiveness and awareness campaigns.
Strength: Simple, clear, and highlights channels that create initial demand.
Limitation: Ignores the contributions of nurture, sales engagement, and closing activities.

Example: A prospect first visited your site from a Google Ad, then attended two webinars and received five emails before closing. Under first-touch, 100% of revenue credit goes to the Google Ad.

Last-Touch Attribution

All revenue credit goes to the final touchpoint before the deal was created or closed (configurable). This model answers: Which channels are best at converting pipeline to revenue?

Use case: Evaluating bottom-of-funnel effectiveness and conversion drivers.
Strength: Highlights what directly triggers a purchase decision.
Limitation: Ignores the demand generation and nurturing that made the final touchpoint possible.

Example: Using the same journey above, if the last touchpoint before deal creation was a webinar, 100% of credit goes to the webinar.

Linear Attribution

Revenue credit is distributed equally across all touchpoints in the buyer's journey. This model answers: Which channels contribute consistently across the full journey?

Use case: Getting a balanced view when you believe every interaction matters equally.
Strength: No single touchpoint is over- or under-valued. Gives a holistic view.
Limitation: Treats a casual blog visit the same as a high-intent demo request.

Example: If there were 8 touchpoints in the journey, each receives 12.5% of the revenue credit.

W-Shaped Attribution

Revenue credit is concentrated on three key moments: first touch (30%), lead creation (30%), and opportunity creation (30%), with the remaining 10% distributed across all other touchpoints. This model answers: Which channels drive the key conversion moments?

Use case: B2B sales cycles where specific milestones (awareness, MQL, SQL) are well-defined.
Strength: Balances recognition of demand generation, lead capture, and pipeline creation.
Limitation: Requires clear definitions of "lead creation" and "opportunity creation" events to assign the milestone touchpoints correctly.

Example: First touch (Google Ad) gets 30%, the touchpoint that converted the visitor to a lead (ebook download) gets 30%, the touchpoint that triggered opportunity creation (demo request) gets 30%, and the remaining 5 touchpoints split 10%.

Switching Between Models

Use the Attribution Model selector at the top of the channel performance section to switch views. All charts, tables, and metrics update immediately to reflect the selected model. This allows you to compare how different models value your channels and identify which are consistently strong regardless of model choice.

Revenue Trends Over Time

The Revenue Trends section displays how your revenue and attribution patterns change over weeks, months, or quarters.

Revenue Timeline

A line chart shows total closed revenue by period (weekly, monthly, or quarterly depending on your date range). Overlay options include:

By channel -- Stacked area chart showing revenue contribution from each source over time.
By model -- Compare attributed revenue across models side-by-side for the same period.
Growth rate -- Month-over-month or quarter-over-quarter revenue growth percentage.

Seasonality Detection

SalesOS automatically identifies seasonal patterns in your revenue data and highlights them on the timeline. This helps you distinguish between genuine growth/decline and cyclical fluctuations when evaluating channel performance.

Cohort Analysis

Cohort analysis groups deals by the time period in which the first touchpoint occurred (the cohort creation date) and tracks how those cohorts progress through the funnel to revenue.

How Cohorts Work

A cohort is defined by the month (or quarter) in which the buyer's first interaction with your brand was recorded.
For each cohort, SalesOS tracks: total leads generated, pipeline created, deals closed, and revenue won.
This reveals the true time-to-revenue for each acquisition period and helps you evaluate channel investments with appropriate lag.

Cohort Table

The cohort view displays a matrix:

Rows represent cohorts (e.g., Jan 2026 cohort, Feb 2026 cohort).
Columns represent months since first touch (Month 0, Month 1, Month 2, etc.).
Cells show cumulative revenue closed from that cohort by that month.

This visualization makes it clear how long different channels or campaigns take to generate revenue. A paid search cohort might show faster revenue realization (Month 1-2) while a content marketing cohort might take 4-6 months but ultimately deliver higher lifetime value.

Filtering Cohorts

Filter cohorts by:

Source/Medium -- Compare cohort performance across channels.
Campaign -- Evaluate specific campaign cohorts.
Segment -- Analyze by customer segment (SMB, mid-market, enterprise).
Deal size -- Filter by revenue tier to see if larger deals come from different cohorts.

Revenue by Product and Segment

This section breaks down attributed revenue by product line and customer segment, answering questions about what you sell and to whom.

Product Revenue

A breakdown of revenue by product or product category:

Revenue per product -- Bar chart showing which products generate the most revenue.
Product mix trends -- How the percentage of revenue from each product changes over time.
Cross-sell and upsell attribution -- For expansion revenue, which touchpoints influenced the additional purchase.

Segment Revenue

Revenue segmented by customer characteristics:

By company size -- SMB, Mid-Market, Enterprise revenue distribution.
By industry -- Which verticals generate the most revenue.
By geography -- Regional revenue breakdown.
By new vs expansion -- First-time customer revenue versus upsell/cross-sell/renewal revenue.

Each segment can be further broken down by channel to understand which marketing strategies work best for different audience segments.

Custom Attribution Rules

For organizations with unique buyer journeys or specific business logic, SalesOS allows you to create custom attribution rules that modify how touchpoints are credited.

Creating Custom Rules

Navigate to Settings > Revenue Intelligence > Attribution Rules to manage custom rules.

Available rule types:

Touchpoint weighting -- Assign higher or lower weight to specific touchpoint types. For example, give demo requests 2x the weight of blog visits in a linear model.
Decay rules -- Reduce the credit for touchpoints that occurred far in the past. A touchpoint from 12 months ago receives less credit than one from last week.
Exclusion rules -- Remove certain touchpoints from attribution entirely. For example, exclude internal traffic or automated system-generated emails.
Milestone mapping -- Define which events constitute "lead creation" and "opportunity creation" for the W-shaped model.

CSV Export -- Click the Export button in the top-right corner of any section to download the visible data as a CSV file. Exports respect your current filters and date range.
Full Attribution Export -- Under Settings > Revenue Intelligence > Export, generate a complete attribution dataset that includes every deal, every touchpoint, and the credit assigned under each model.
Scheduled Exports -- Configure weekly or monthly automated exports delivered to email or pushed to a connected data warehouse (available on Growth and Enterprise plans).

Export Contents

The full attribution export includes:

Deal ID, name, amount, close date, and owner.
All touchpoints with timestamp, source, medium, campaign, and content.
Attribution credit under each model (first-touch, last-touch, linear, W-shaped).
Customer segment, product, and account information.

Best Practices

Start with linear attribution, then refine. Linear attribution gives you a balanced baseline view. Once you understand the general patterns, switch to W-shaped for a more nuanced perspective that highlights key conversion moments without completely ignoring mid-funnel touches.
Compare multiple models before making budget decisions. A channel that looks weak under first-touch might be essential under last-touch (or vice versa). If a channel performs strongly under multiple models, you can be more confident in its value. If it only appears valuable under one model, investigate further before increasing investment.
Use cohort analysis for channel ROI. Do not judge a campaign's performance in the same month it launched. B2B sales cycles can span 3-9 months. Cohort analysis reveals the true revenue impact of a campaign with appropriate time lag accounted for.
Ensure touchpoint coverage. Attribution quality depends on data completeness. If significant buyer interactions are not being tracked (e.g., offline events, partner referrals, direct sales outreach), your attribution will systematically under-credit those channels. Audit your touchpoint capture regularly.
Connect campaign spend data. Attribution becomes significantly more powerful when combined with cost data. Integrate your ad platforms and marketing tools so you can calculate true ROI and cost-per-acquisition alongside attributed revenue.
Segment before drawing conclusions. Channel performance varies dramatically by segment. Paid search might be highly effective for SMB deals but irrelevant for enterprise. Always check segment-level attribution before making blanket channel investment decisions.
Review attribution monthly. Set a recurring cadence (monthly for operational decisions, quarterly for strategic budget allocation) to review Revenue Intelligence data and adjust your go-to-market accordingly. Buyer behavior shifts over time, and what worked last year may not work this year.
Document your attribution model choice. Ensure your marketing and sales leadership agree on which model is the "source of truth" for reporting and compensation purposes. Having different teams reference different models creates confusion and misalignment.