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Custom KPI Dashboards & Automated Reporting

We build KPI dashboards and automated reporting around the metric definitions your team agrees on: ad spend, revenue, pipeline, and unit economics pulled from the sources you grant on a schedule, reconciled before deltas are computed, and delivered as a weekly draft a human edits rather than a spreadsheet everyone rebuilds. Stale or missing sources are flagged as unknown — never silently zero — and every headline figure traces back to a named source and timestamp.

  • One documented metric dictionary instead of five conflicting spreadsheets
  • Sources reconciled before any delta or KPI is computed
  • Freshness and discrepancy flags on every report
  • Weekly report drafted for review, not assembled by hand
ROAS dashboard, hover a daySample

Blended ROAS

3.4x+0.4x

Friday

  • Google Ads3.8x
  • Meta Ads1.6x

Meta dipped on Friday: tracking alert sent

Sample illustration with illustrative numbers, not a verified client result.

Who this is for

Small and mid-size teams that assemble the weekly marketing report by hand: one export per ad platform, a spreadsheet to reconcile them, and a doc that is stale before the meeting. The fit is strongest when you can name the KPIs leadership actually asks about and grant read access or scheduled exports to the systems that hold them.

What we need from you

We agree the exact sources up front — the ad platforms, analytics properties, CRM, and revenue systems that feed the report — and confirm which source actually exposes each KPI before defining it.

  • Read-only access or scheduled exports for each source in scope.
  • Your metric dictionary: how spend, conversions, revenue, and pipeline are defined.
  • Timezone, currency, attribution window, and data cutoff per report.
  • A named reviewer who owns the weekly narrative.

How reports are produced

On the agreed schedule the pipeline pulls each source, normalizes the agreed metrics, and reconciles them against the previous period and your revenue source before drafting. The weekly report and sync go out as a reviewable draft: the numbers and their provenance stay intact while your team edits the narrative. Dashboards cover the recurring questions you approve, and plain-language query access to approved data marts is available where leadership wants to ask questions directly.

Controls, limits, and acceptance

Platform-reported attribution and settled customer revenue stay in separate, labeled columns because they measure different things. If a source is unavailable or stale, the affected figures are marked unknown or incomplete rather than treated as zero, and attribution differences between networks are documented rather than merged into a number nobody can defend. Acceptance means reproducing one agreed reporting period from your raw exports, flagging a deliberately incomplete sample, and tracing any headline KPI back to its source.

How a pilot runs

We map sources and metrics on a call, build the pipeline against your exports, and reproduce one agreed reporting period end-to-end. You review the first dashboard and weekly draft, we tune definitions and cutoffs until the numbers reconcile, then the schedule runs with freshness flags and a documented owner for when a source changes or breaks.

Workflow examples

Example scopes and acceptance checks for custom builds. These are illustrative workflows, not verified client case studies.

Acquisition & Media01

Cross-Channel Ad Spend Reporting — One Media Buying Dashboard

Cross-channel spend and conversion reporting with reconciled revenue, freshness checks and traceable sources.

Seven ad networks, one numberSample

Spend by network, reconciled

4.1%

Media spend, last 30 days: $49,900, down 4.1% against the previous period

  • Google Ads$0
  • Meta Ads$0
  • TikTok Ads$0
  • Microsoft Ads$0
  • Taboola$0
  • Reddit Ads$0
  • Pinterest$0
Largest: Google Ads

A cross-channel ad reporting pipeline pulls spend, clicks, and conversions from the networks you scope into one reconciled view, checks revenue figures against your source exports, and flags discrepancies instead of hiding them. Metric definitions, timezones, and currency are agreed up front — one documented source of truth instead of a login per network and a spreadsheet.

Explore workflowDiscuss
Intelligence02

Automated Marketing Reporting

KPIs pulled on a schedule, deltas reconciled against source exports, and a weekly report drafted for human review.

Weekly report, drafted automaticallySample
Weekly marketing reportNov 3 to Nov 9, drafted Monday 8:00
Revenue
Up 6.1% to $48.2K
Blended CAC
Down 4% to $31.40
Meta ROAS
Down 0.4 to 1.9x
New customers
Flat at 1,412

An automated marketing reporting system connects your agreed ad, analytics, CRM, and revenue sources, computes deltas only after reconciliation, and drafts the weekly marketing report and sync for a human to edit. The team reviews the narrative instead of assembling the spreadsheet.

Explore workflowDiscuss
Intelligence03

Ask-Your-Data BI for Multi-Country Leadership

Leadership asks business questions in plain language — answered read-only from the data marts and metric dictionary you approve.

Plain-language question, guarded SQLSample
query_warehouse finished
Arguments
{
  "question": "Which channel grew revenue fastest in Q3?",
  "sql": "SELECT channel, SUM(revenue) FROM orders WHERE quarter = 'Q3' GROUP BY channel ORDER BY 2 DESC LIMIT 3",
  "guard": "read-only, approved tables"
}
Result
[
  {
    "channel": "Email & SMS",
    "revenue": 124180,
    "growth": "+31%"
  },
  {
    "channel": "Google Ads",
    "revenue": 98420,
    "growth": "+12%"
  },
  {
    "channel": "Meta Ads",
    "revenue": 61300,
    "growth": "-6%"
  }
]

An AI BI layer answers natural-language questions against the data marts and metric dictionary you approve, read-only, with every result traceable to its query and source totals. Cross-scope or unsafe requests are rejected rather than approximated, drill-down stays inside granted permissions, and questions the data cannot support come back as explicit unknowns.

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Related guides

Frequently asked questions

We build on the stack that fits your sources — including existing BI tools or a custom pipeline where your data needs more control. The choice is confirmed during scoping based on which systems you can grant access to.
Where the pilot includes approved data marts, leadership can ask questions in natural language and get read-only answers traceable to the query and source totals. Questions outside scope return an explicit unknown rather than a guess.
Source changes surface as freshness or discrepancy flags on the affected figures instead of silent zeros. The runbook names an owner and the steps to repair or re-map the source.
Not necessarily. The pipeline can feed the BI layer you already use, or stand alone where your sources need reconciliation and governance that a generic connector does not provide.
Attribution windows and definitions are agreed per network and documented in the metric dictionary. Differences are shown explicitly rather than blended into a single attributed-revenue figure.

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