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JetStack AI Documentation

Sales Hub Data Points

The Sales Hub section evaluates the health and effectiveness of a portal’s sales operations across 5 analysis blocks. These blocks require Sales Hub Starter or above for basic data points and Sales Hub Professional or above for advanced metrics like sequences and lead scoring.

Deal Pipeline

Section titled “Deal Pipeline”

The Deal Pipeline block analyzes the structure and flow of deal stages, measuring how efficiently deals move through the sales process.

Data points captured:

  • Pipeline count — Number of deal pipelines configured. Multiple pipelines may indicate different sales processes (new business, upsell, partner) or may signal unnecessary complexity.
  • Stage count per pipeline — Number of stages in each pipeline. Too few stages obscure the sales process; too many create friction for reps.
  • Stage progression — Whether deals generally move forward through stages or frequently skip stages or move backward, indicating process issues.
  • Stage velocity — Average time deals spend in each stage. Identifies bottlenecks where deals stall.
  • Stage conversion rates — The percentage of deals that advance from each stage to the next. Drop-offs reveal where deals are lost.
  • Pipeline age distribution — How long deals have been open, broken down by stage. Old deals in early stages may need cleanup.
  • Win probability settings — Whether deal stages have win probability percentages configured, which enables accurate forecasting.

What good looks like: 3-8 stages per pipeline with clear progression, no single stage holding more than 30% of active deals, stage conversion rates that do not drop more than 50% between consecutive stages, and deal velocity metrics that align with the expected sales cycle length.

Deal Performance

Section titled “Deal Performance”

The Deal Performance block measures the outcomes and financial health of the sales pipeline.

Data points captured:

  • Average deal size — The mean and median closed-won deal value. Median is often more informative as it is less affected by outlier deals.
  • Win rate — The percentage of deals that close as won versus total deals that reach a closed state (won or lost). Calculated overall and by pipeline.
  • Sales cycle length — Average time from deal creation to close (won). Broken down by pipeline and deal size ranges.
  • Deal value distribution — How deal values are distributed across ranges (e.g., under $5K, $5K-$25K, $25K-$100K, over $100K). Identifies revenue concentration risks.
  • Revenue trends — Monthly and quarterly closed revenue over the past 12 months (Full mode only). Shows growth trajectory and seasonality.
  • Lost deal reasons — The most common close-lost reasons recorded. Missing or generic reasons indicate poor loss analysis practices.
  • Forecast accuracy — Comparison of weighted pipeline value against actual closed revenue for past periods (Full mode only).

What good looks like: Consistent or growing win rates, sales cycle lengths that match industry benchmarks, diverse deal value distribution without over-reliance on a few large deals, and documented close-lost reasons for at least 80% of lost deals.

Sales Activity

Section titled “Sales Activity”

The Sales Activity block evaluates the volume and patterns of sales team engagement with prospects and customers.

Data points captured:

  • Activities logged — Total logged activities (calls, emails, meetings, notes, tasks) over the past 30 and 90 days. Broken down by type.
  • Activity per rep — Average activities logged per sales user, identifying high performers and underperformers relative to the team average.
  • Engagement rate — The ratio of deals with recent activity (past 14 days) versus deals with no recent activity. Unengaged deals risk going stale.
  • Call volume — Number of calls logged, average call duration, and calls per rep. Requires calling tools integration or manual logging.
  • Email engagement — One-to-one sales email sends, open rates, and reply rates from the Sales Hub email tools (not marketing emails).
  • Meeting bookings — Meetings scheduled through HubSpot meeting links, including booking rate and no-show rate.
  • Task completion — Task creation and completion rates. A high ratio of overdue tasks indicates workflow or capacity issues.

What good looks like: Consistent daily activity logging across the team, engagement rates above 70% for active deals, email reply rates above 10%, and task overdue rates below 15%.

Lead Management

Section titled “Lead Management”

The Lead Management block assesses how leads are qualified, scored, and routed through the sales process.

Data points captured:

  • Lead status usage — Whether lead status values are actively used and what the distribution looks like across contacts. Unused or poorly distributed lead statuses indicate weak lead management.
  • Lead status definitions — The custom lead status values configured (e.g., New, Contacted, Qualified, Unqualified). Evaluated for completeness and logical progression.
  • Lead scoring adoption — Whether HubSpot lead scoring is configured and actively used. Includes the number of scoring models and the properties used in scoring criteria.
  • MQL to SQL conversion — The rate at which Marketing Qualified Leads convert to Sales Qualified Leads. This handoff metric is critical for sales-marketing alignment.
  • Lead response time — Average time between a lead being assigned to a sales rep and the first recorded activity. Slower response times correlate with lower conversion rates.
  • Lead source tracking — Whether original source and latest source data is reliably populated. Missing source data prevents accurate attribution.
  • Lifecycle stage distribution — How contacts are distributed across lifecycle stages (subscriber, lead, MQL, SQL, opportunity, customer). Bottlenecks at any stage warrant investigation.

What good looks like: Clearly defined lead statuses with logical progression, lead scoring actively used with at least one model, MQL-to-SQL conversion rate tracked and above 20%, and average lead response time under 1 hour.

Quotes and Products

Section titled “Quotes and Products”

The Quotes and Products block examines the usage and configuration of HubSpot’s CPQ (Configure, Price, Quote) features.

Data points captured:

  • Quote usage rate — Percentage of closed-won deals that have an associated quote. Low usage may indicate reps are quoting outside of HubSpot.
  • Product catalog size — Number of products and line items defined in the product library. An empty catalog suggests the product features are not being used.
  • Product usage in deals — How often products/line items are attached to deals. Deals without products lack revenue attribution at the product level.
  • Quote template adoption — Whether custom quote templates are configured and used versus the default template.
  • Discount patterns — Average discount percentage applied to quotes and the frequency of discounting. High or inconsistent discounting may indicate pricing issues.
  • Revenue per product — Revenue attribution by product line (Full mode only). Identifies top-performing products and underperformers.
  • Quote approval workflows — Whether quote approval processes are configured for deals above certain thresholds.

What good looks like: Quotes attached to at least 70% of closed-won deals, a product catalog that reflects the actual product/service offering, consistent use of line items for revenue attribution, and discount policies enforced through approval workflows.

Next Steps

Section titled “Next Steps”