Marketing agencies
Answer "why did our CPL move?" without three exports
The problem
The report goes out Monday and the problem happened on Wednesday. A client asks why their cost per lead moved and someone loses a morning exporting from Google Ads, Meta and GA4 to build an answer that already arrives late. Multiply that by every account the team runs and more time goes into explaining numbers than adjusting campaigns. And when the explanation finally lands, sometimes the number had moved within its normal range anyway.
What we do
Neuro Performance pulls each account's sources into one view, spots variation against the previous period and against the target, and answers by naming the campaign, the amount and the evidence. Neuro Intelligence covers the questions the dashboard was never built for, the ones that currently wait for whoever has time to build a pivot table. Neuro CRM handles the agency's own pipeline, not your clients': it keeps deals current from what was actually said and leaves the next email drafted for you to review and send.
- One view per client account covering spend, leads, conversions, CPL and ROAS
- Anomaly detection by variance, baseline and rules you configure per account
- Every figure traceable to its source and period, so you can defend it in front of the client
- Ad hoc questions in plain language, with the query run against the data rather than estimated by the model
- The agency's new business pipeline updated from emails and calls, with no manual entry
The modules behind it
Nothing is built from zero. Each solution is a combination of modules that already exist, fitted to your operation.
Neuro Performance
It is literally a marketing and sales KPI console, which is the agency's daily job: what moved, in which campaign, with what evidence.
Neuro Intelligence
It covers the odd client question, the one no dashboard anticipated, without an analyst dropping everything to cross two files.
Neuro CRM
The agency sells too: it keeps new business deals current and flags when a follow-up has gone cold for too long.
Neuro Performance
Marketing and sales KPI console
Indicators
Spend
$47,500
+7.5% vs. previous period
Leads
224
-10.8% vs. previous period
CPL
$212
+20.4% vs. previous period
ROAS
5.05x
-5.4% vs. previous period
Trend
Campaigns
Prospecting MX
Meta Ads · 41% share
Display Remarketing
Google Ads · 21.9% share
Brand Search
Google Ads · 20.2% share
Lookalike 3%
Meta Ads · 16.8% share
What is only true in this industry
Many accounts under one roof
An agency does not have one dataset, it has one per client, and some of those clients compete with each other. Per account separation and access control are set at the start rather than patched in later: who sees which account is part of the design, not a permission adjusted after somebody complains.
Every platform counts differently
GA4, Google Ads and Meta Ads do not agree on what a conversion is or which window it gets attributed to, and the client's CRM usually holds a third version of the truth. We connect the sources with each metric definition spelled out, and if a connector fails the module says so instead of filling the gap.
The report is the deliverable
What the client sees cannot depend on somebody tidying the view before the meeting. The view shown is the one the team works from, with the KPI definition and the period next to the figure. We also keep a metric moving separate from a metric mattering: the alert carries the variation and its context, and the call stays with the account team.
What to expect
What to expect is no longer assembling the report by hand, and walking into the meeting with the variation already explained and its source on show. What still depends on you is access to the accounts, a campaign naming convention that survives more than a quarter, and an agreement with the client on what counts as a lead. The module points at what moved and suggests an action; who moves the budget is still your team.
Further reading
How to calculate AI ROI on a real project
How to calculate AI ROI: freeze the baseline, count the full cost, value the benefit, and run a worked example step by step. Every figure has a cited source.
AI for business: 15 use cases with real impact on the numbers
AI for business: 15 use cases by function, with the expected impact of each one and how solid the evidence behind it really is. Sources cited throughout.
AI agents for sales: automate prospecting, follow-up and quoting
A practical guide to AI agents for sales: prospecting, speed-to-lead, qualification, follow-up and quoting. With sources cited from McKinsey, NBER and more.
Does this look like your case?
Tell us how your operation runs today and we will tell you which modules apply, what would need adjusting and how long until it runs. If your industry is not on this list, the conversation is still worth having.
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