Low Dashboards system

Lead Quality Tag Chart

A view that visualizes the distribution and trend of lead-quality tags — hot/warm/cold, qualified/unqualified, ICP-fit/not, junk/spam — and ties quality tags to downstream outcomes so 'quality' is validated against whether those leads actually book and complete. It reads the CRM quality/tag field and joins it to booking outcomes. Analytics over an existing tagging scheme; it does not auto-tag or score leads with AI.

Start a Project All Systems

3 to 6 days: timeline
Low: complexity
4: tools
4: steps

Built with real HMX dashboard tool paths

Supabase PostgresSQL (distribution + cross-tab)CRM quality/tag fieldNext.js 16 server componentsSupabase PostgresSQL (distribution + cross-tab)CRM quality/tag fieldNext.js 16 server components

01 // System facts

System facts

Lead Quality Tag Chart uses a reporting model and review layer for Dashboards. A view that visualizes the distribution and trend of lead-quality tags — hot/warm/cold, qualified/unqualified, ICP-fit/not, junk/spam — and ties qu... The architecture connects the quality taxonomy with, supabase postgres, sql, and owner review with an explicit control path.

Outcome

Owners see not just the quality mix but whether the quality labels are meaningful — confirming that 'hot' leads convert better, or exposing that the tagging is noise that needs fixing.

Main risk

Subjective, inconsistent tagging makes the mix reflect tagger behavior, not real quality, and the chart can lend false credibility to bad labels.

Prevention

Always validate tags against actual downstream conversion (the cross-tab), show untagged coverage, and treat a tag that doesn't separate outcomes as a flag that the taxonomy needs work.

Fallback

If tag coverage is too low to trust, report only the distribution with a prominent coverage caveat and recommend improving tagging discipline before drawing quality conclusions.

System architecture

Lead Quality Tag Chart Architecture

6 nodes

the quality taxonomy with

SQL for tag distribution

Supabase Postgres

SQL

Review Queue

Owner Review

01the quality taxonomy with
A view that visualizes the distribution and trend of lead-quality tags — hot/warm/cold, qualified/unqualified, ICP-fit/not, junk/spam — and ties qu...
02SQL for tag distribution
Write SQL for tag distribution over time plus a cross-tab of tag vs downstream outcome (booked rate, completed rate per tag) to test whether 'hot' actually converts better.
03Supabase Postgres
Supabase Postgres contributes the trusted model for Lead Quality Tag Chart so metrics are defined before they are visualized.
04SQL
Build a chart panel (server component) with the quality mix, its trend, and the tag-vs-conversion table that validates the tagging.
05Review Queue
If tag coverage is too low to trust, report only the distribution with a prominent coverage caveat and recommend improving tagging discipline befor...
06Owner Review
Owners see not just the quality mix but whether the quality labels are meaningful — confirming that 'hot' leads convert better, or exposing that th...

How it is built

Build steps

01Confirm the quality taxonomy with the owner (the allowed tag set and what each means) and ensure it's a controlled field, not freeform, so the chart is stable.
02Write SQL for tag distribution over time plus a cross-tab of tag vs downstream outcome (booked rate, completed rate per tag) to test whether 'hot' actually converts better.
03Build a chart panel (server component) with the quality mix, its trend, and the tag-vs-conversion table that validates the tagging.
04Add an 'untagged' slice and a coverage number so the share of leads never quality-tagged is visible rather than hidden.

Tools

Workflow surface

Supabase Postgres
SQL (distribution + cross-tab)
CRM quality/tag field
Next.js 16 server components

Inputs layer: Confirm the quality taxonomy with the owner (the allowed tag set and what each means) and ensure it's a controlled field, not freeform, so the chart is stable.
Transform layer: Write SQL for tag distribution over time plus a cross-tab of tag vs downstream outcome (booked rate, completed rate per tag) to test whether 'hot' actually converts better.
Metrics layer: Supabase Postgres contributes the trusted model for Lead Quality Tag Chart so metrics are defined before they are visualized.
Visualization layer: SQL (distribution + cross-tab) handles refresh, review, or reporting delivery while always validate tags against actual downstream conversion (the cross-tab), show untagged coverage, and treat a tag that doesn't separate outcomes a...
Action layer: Owners see not just the quality mix but whether the quality labels are meaningful — confirming that 'hot' leads convert better, or exposing that th...

Data flow

01Confirm the quality taxonomy with the owner (the allowed tag set and what each means) and ensure it's a controlled field, not freeform, so the chart is stable.
02Write SQL for tag distribution over time plus a cross-tab of tag vs downstream outcome (booked rate, completed rate per tag) to test whether 'hot' actually converts better.
03Build a chart panel (server component) with the quality mix, its trend, and the tag-vs-conversion table that validates the tagging.
04Add an 'untagged' slice and a coverage number so the share of leads never quality-tagged is visible rather than hidden.

Controls and fallbacks

Subjective, inconsistent tagging makes the mix reflect tagger behavior, not real quality, and the chart can lend false credibility to bad labels.
Always validate tags against actual downstream conversion (the cross-tab), show untagged coverage, and treat a tag that doesn't separate outcomes a...
If tag coverage is too low to trust, report only the distribution with a prominent coverage caveat and recommend improving tagging discipline befor...

Research basis