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 invoiced (Finance)

192

▲ +3% vs August (187) · by invoice 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
ChannelLeads invoicedRows
Google Ads71Rows →
Comparison portal49Rows →
Meta Ads36Rows →
Referral36Rows →
How it's calculated

Every lead billed to a broker, counted on the invoice day. Owner: Finance.

Why other departments count differently →

Definition in metrics/leads.yml

name: leads_invoiced_finance
label: Leads invoiced
noun: invoiced leads
description: Every lead billed to a broker, counted on the invoice day.
measure: lead_count
time_basis: invoiced_at
owner: finance
certified: true

The number

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

The trend

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

The breakdown by channel

select channel as label,
       count(distinct lead_id) as value
from leads
where invoiced_at >= timestamp '2026-09-01 00:00:00' at time zone 'Europe/Zurich'
  and invoiced_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.