SDI Lab / BI cockpit Demo on a made-up Swiss insurance-lead business, data as of 28.10.2026 18:00. Names, brokers and numbers are invented.

Leads (Marketing)

1'102

▲ +58% vs August (698) · by creation date

Trend * October 2026 so far: the data runs to 28.10.2026 18:00, so it is not a full month.

As a table
CantonLeadsRows
ZH272Rows →
BE141Rows →
VD126Rows →
FR120Rows →
AG98Rows →
SG82Rows →
GE76Rows →
LU73Rows →
BS60Rows →
TI54Rows →
How it's calculated

Every lead, counted on the day it arrived. Owner: Marketing.

Why other departments count differently →

Definition in metrics/leads.yml

name: leads_marketing
label: Leads
noun: leads
description: Every lead, counted on the day it arrived.
measure: lead_count
time_basis: created_at
owner: marketing
certified: true

The number

select count(distinct lead_id) as value
from leads
where created_at >= timestamp '2026-09-01 00:00:00' at time zone 'Europe/Zurich'
  and created_at < timestamp '2026-10-01 00:00:00' at time zone 'Europe/Zurich'

The trend

select to_char(created_at at time zone 'Europe/Zurich', 'YYYY-MM') as period,
       count(distinct lead_id) as value
from leads
where created_at >= timestamp '2026-04-01 00:00:00' at time zone 'Europe/Zurich'
  and created_at < timestamp '2026-11-01 00:00:00' at time zone 'Europe/Zurich'
group by 1
order by 1

The breakdown by canton

select canton as label,
       count(distinct lead_id) as value
from leads
where created_at >= timestamp '2026-09-01 00:00:00' at time zone 'Europe/Zurich'
  and created_at < timestamp '2026-10-01 00:00:00' at time zone 'Europe/Zurich'
group by 1
order by 2 desc, 1
limit 50

The app runs these queries with parameters; here the values are written in, so they can be read or pasted into psql.