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Fix SaaS Demo: adjust ai hints #42

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79b7ff2
adding back roi
torimwhaley Dec 2, 2025
af0a8b5
restricting verbosity and special characters in ai hints
torimwhaley Dec 3, 2025
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35 changes: 15 additions & 20 deletions models/demo/deals.yml
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,7 @@ models:
description: All sales deals of any status, pulled from our CRM
primary_key: deal_id
ai_hint: |
CloseRate Kevin: This is your primary deals model for analyzing pipeline progression and win/loss performance.
Use this to measure stage-to-stage conversion (New → Qualified → PoC → Negotiation → Won/Lost), calculate win rates, and identify stalled deals.
Key dimensions: `stage` (with stages: New, Qualified, PoC, Negotiation, Won, Lost), `created_date` (for cohort analysis).
Core metrics: `win_rate` (won deals / total deals), stage-specific counts (`new_deals`, `qualified_deals`, `poc_deals`, `negotiation_deals`, `won_deals`, `lost_deals`), `total_amount`, `total_won_amount`.
Critical for AE performance analysis. Focus on demo-to-close rates (PoC → Negotiation → Won conversion) and identifying aging deals at each stage.
Default to line charts for trend analysis, with period-over-period comparisons using `created_date` dimensions.
CloseRate Kevin: Primary deals model for analyzing pipeline progression and win/loss performance. Use for stage-to-stage conversion analysis and calculating win rates.

explores:
deals_accounts:
Expand Down Expand Up @@ -59,9 +54,9 @@ models:
categories:
- kpi
ai_hint: |
CloseRate Kevin: Use `win_rate` to measure overall deal closure effectiveness (won deals ÷ total deals).
This is a core KPI for AE performance. Break down by time period (week, month, quarter) to identify trends.
Compare period-over-period to spot declining win rates early. Pair with `loss_rate` to understand win/loss balance.
CloseRate Kevin: Use win_rate to measure overall deal closure effectiveness. This is won deals divided by total deals.
Core KPI for AE performance. Break down by time period like week, month, or quarter to identify trends.
Compare period-over-period to spot declining win rates early. Pair with loss_rate to understand win/loss balance.
loss_rate:
type: number
format: percent
Expand All @@ -88,7 +83,7 @@ models:
- kpi
- sales
ai_hint: |
CloseRate Kevin: Use `unique_deals` as the denominator for win rate and stage conversion calculations.
CloseRate Kevin: Use unique_deals as the denominator for win rate and stage conversion calculations.
This represents total deal volume. Break down by time period to track pipeline growth or contraction.
new_deals:
type: count_distinct
Expand All @@ -114,8 +109,8 @@ models:
filters:
- stage: 'PoC'
ai_hint: |
CloseRate Kevin: Use `poc_deals` to track the "demo" or proof-of-concept stage—critical for demo-to-close analysis.
Calculate PoC → Won conversion by dividing `won_deals` by `poc_deals`. Monitor aging PoC deals to identify stalled demos.
CloseRate Kevin: Use poc_deals to track the demo or proof-of-concept stage which is critical for demo-to-close analysis.
Calculate PoC to Won conversion by dividing won_deals by poc_deals. Monitor aging PoC deals to identify stalled demos.
negotiation_deals:
type: count_distinct
groups: ['Deal Counts']
Expand All @@ -134,8 +129,8 @@ models:
- kpi
- sales
ai_hint: |
CloseRate Kevin: Use `won_deals` as the numerator for win rate calculations and stage conversion analysis.
Track over time to measure sales momentum. Compare against `poc_deals` for demo-to-close performance.
CloseRate Kevin: Use won_deals as the numerator for win rate calculations and stage conversion analysis.
Track over time to measure sales momentum. Compare against poc_deals for demo-to-close performance.
lost_deals:
type: count_distinct
groups: ['Deal Counts']
Expand Down Expand Up @@ -163,9 +158,9 @@ models:
dimension:
type: string
ai_hint: |
CloseRate Kevin: Use `stage` to track deals through the sales funnel: New → Qualified → PoC → Negotiation → Won/Lost.
CloseRate Kevin: Use stage to track deals through the sales funnel from New to Qualified to PoC to Negotiation to Won or Lost.
Critical for calculating stage-to-stage conversion rates and identifying where deals stall or drop off.
Filter by specific stages to analyze aging deals (e.g., deals stuck in Negotiation). Use `stage_order` for proper funnel sequencing.
Filter by specific stages to analyze aging deals like deals stuck in Negotiation. Use stage_order for proper funnel sequencing.
additional_dimensions:
stage_order:
type: number
Expand Down Expand Up @@ -216,8 +211,8 @@ models:
- kpi
- sales
ai_hint: |
CloseRate Kevin: Use `total_amount` to measure total pipeline value across all deal stages.
Track over time to monitor pipeline health and growth. Break down by `stage` to see value distribution across funnel stages.
CloseRate Kevin: Use total_amount to measure total pipeline value across all deal stages.
Track over time to monitor pipeline health and growth. Break down by stage to see value distribution across funnel stages.
total_won_amount:
groups: ['Deal Amounts']
type: sum
Expand All @@ -226,8 +221,8 @@ models:
filters:
- stage: 'Won'
ai_hint: |
CloseRate Kevin: Use `total_won_amount` to measure actual closed revenue—your core revenue KPI.
Track by time period (week, month, quarter) to monitor revenue trends. Compare period-over-period for growth analysis.
CloseRate Kevin: Use total_won_amount to measure actual closed revenue which is your core revenue KPI.
Track by time period like week, month, or quarter to monitor revenue trends. Compare period-over-period for growth analysis.
spotlight:
visibility: show
categories:
Expand Down
52 changes: 29 additions & 23 deletions models/demo/leads.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -6,12 +6,7 @@ models:
is_admin_saas_demo: 'true'
primary_key: lead_id
ai_hint: |
CloseRate Kevin: This is your primary leads model for analyzing top-of-funnel performance and SDR effectiveness.
Use this to measure lead-to-opportunity conversion, identify stalled leads, and diagnose drop-off patterns by stage.
Key dimensions: `sdr` (for individual rep performance), `sdr_team` (for country-level team analysis), `lead_status` (stages: new → contacted → qualified → converted).
Core metrics: `conversion_rate`, `avg_days_to_convert` (funnel velocity), `unique_lead_count`, and stage-specific counts (`open_leads`, `contacted_leads`, `qualified_leads`, `converted_leads`).
Join to `deals` via `deal_id` to analyze full funnel from lead creation through deal closure.
Focus on period-over-period trends using `created_at` or `converted_at` time dimensions. Default to line charts for trend analysis.
CloseRate Kevin: Primary leads model for analyzing top-of-funnel performance and SDR effectiveness. Use for lead-to-opportunity conversion analysis and identifying stalled leads.

joins:
- join: users
Expand Down Expand Up @@ -46,10 +41,10 @@ models:
categories:
- leads
ai_hint: |
CloseRate Kevin: Use `conversion_rate` to measure SDR and team effectiveness at converting leads to opportunities.
Break down by `sdr` for individual performance or `sdr_team` for country-level comparisons.
Trend over time using `created_at` (by week or month) to identify declining conversion patterns.
Compare period-over-period (e.g., this month vs. last month) to spot performance changes early.
CloseRate Kevin: Use conversion_rate to measure SDR and team effectiveness at converting leads to opportunities.
Break down by sdr for individual performance or sdr_team for country-level comparisons.
Trend over time using created_at by week or month to identify declining conversion patterns.
Compare period-over-period to spot performance changes early.
avg_days_to_convert:
type: average
description: "Average number of days it takes for a lead to convert"
Expand All @@ -61,9 +56,9 @@ models:
categories:
- leads
ai_hint: |
CloseRate Kevin: Use `avg_days_to_convert` to measure funnel velocity—how quickly SDRs move leads through qualification.
Lower values indicate faster pipeline movement. Compare by `sdr` to identify top performers and slower reps.
Group by `sdr_team` to compare country-level velocity. Rising values may signal pipeline bottlenecks or stalled leads.
CloseRate Kevin: Use avg_days_to_convert to measure funnel velocity and how quickly SDRs move leads through qualification.
Lower values indicate faster pipeline movement. Compare by sdr to identify top performers and slower reps.
Group by sdr_team to compare country-level velocity. Rising values may signal pipeline bottlenecks or stalled leads.
cost_per_conversion:
type: number
description: "Average cost per converted lead"
Expand All @@ -74,6 +69,17 @@ models:
visibility: show
categories:
- leads
roi:
type: number
description: "Return on Investment: (Total Won Amount - Lead Cost) / Lead Cost"
sql: SAFE_DIVIDE((${deals.total_won_amount} - ${total_lead_cost}), NULLIF(${total_lead_cost}, 0))
format: '0.00'
groups: ['Lead Performance']
spotlight:
visibility: show
categories:
- leads
- marketing

columns:
- name: lead_id
Expand All @@ -92,9 +98,9 @@ models:
- leads
- marketing
ai_hint: |
CloseRate Kevin: Use `unique_lead_count` as your base metric for total lead volume.
Break down by `sdr` to measure individual workload or by `sdr_team` to compare country teams.
Combine with stage-specific metrics (`contacted_leads`, `qualified_leads`, `converted_leads`) to calculate stage-to-stage conversion rates.
CloseRate Kevin: Use unique_lead_count as your base metric for total lead volume.
Break down by sdr to measure individual workload or by sdr_team to compare country teams.
Combine with stage-specific metrics like contacted_leads, qualified_leads, converted_leads to calculate stage-to-stage conversion rates.
open_leads:
type: count_distinct
groups: ['Lead Counts']
Expand Down Expand Up @@ -161,9 +167,9 @@ models:
dimension:
type: string
ai_hint: |
CloseRate Kevin: Use `sdr` to analyze individual SDR performance. Critical for identifying top performers vs. underperformers.
Combine with `conversion_rate`, `avg_days_to_convert`, or stage counts to measure rep effectiveness.
Compare individual SDRs within the same `sdr_team` for fair benchmarking.
CloseRate Kevin: Use sdr to analyze individual SDR performance. Critical for identifying top performers vs. underperformers.
Combine with conversion_rate, avg_days_to_convert, or stage counts to measure rep effectiveness.
Compare individual SDRs within the same sdr_team for fair benchmarking.
- name: industry
description: "The industry associated with the lead's company"
meta:
Expand Down Expand Up @@ -231,7 +237,7 @@ models:
format: '$#,##0'
groups: ['Lead Costs']
ai_hint: |
Sum of attributed lead acquisition costs. Useful for calculating CAC and evaluating marketing ROI when joined to `deals.total_amount`.
Sum of attributed lead acquisition costs. Useful for calculating CAC and evaluating marketing ROI when joined to deals.total_amount.
spotlight:
visibility: show
categories:
Expand All @@ -253,7 +259,7 @@ models:
dimension:
type: string
ai_hint: |
CloseRate Kevin: Use `lead_status` to track progression through the funnel (new → contacted → qualified → converted).
CloseRate Kevin: Use lead_status to track progression through the funnel from new to contacted to qualified to converted.
Filter by status to identify stalled leads or analyze drop-off rates at each stage.
Combine with time dimensions to spot aging leads that need attention.
- name: created_at
Expand Down Expand Up @@ -287,6 +293,6 @@ models:
type: string
label: "SDR Team"
ai_hint: |
CloseRate Kevin: Use `sdr_team` to compare performance across country-based SDR teams.
CloseRate Kevin: Use sdr_team to compare performance across country-based SDR teams.
Essential for team-level analysis and understanding regional performance differences.
Group by `sdr_team` when measuring aggregate conversion rates, velocity, or lead volume by geography.
Group by sdr_team when measuring aggregate conversion rates, velocity, or lead volume by geography.
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