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Service AI Sales Profit

You can see where the money leaks.

Plan and actual in real time, broken down by channel, service and salesperson, plus a revenue forecast. Not an average across the department, but the exact stage of the pipeline where you lose profit.

What it is In plain words

A report you don't have to assemble.

What we do

We pull data from the CRM, telephony, ad accounts and payments, bring it into a single shape and show it on one dashboard: how much has been earned, how much is in progress, what the month will end with and where the gap against plan came from.

What we don't do

We don't build a pretty panel for the sake of it. If a number doesn't change any decision, it won't be on the dashboard: extra charts distract from the two or three figures the whole thing is built for.

The problem Sound familiar?

There is revenue, but no understanding.

The report is ready by Friday

While the numbers are collected by hand, the month is already over. Decisions get made on data that has aged, and it is too late to fix anything.

Everyone counts differently

Marketing has one revenue figure, accounting another, the head of sales a third. The meeting is spent arguing about whose numbers are right.

Average conversion says nothing

A single figure for the department hides both a collapsed channel and a strong salesperson. It doesn't tell you what to fix.

Advertising with no link to money

You can see the cost per lead, but not which leads ended in payment. The budget goes to a channel that brings enquiries and no revenue.

Drops are noticed after the fact

Enquiries fell two weeks ago, but it was only noticed when revenue dropped. You end up reacting to the consequence instead of the cause.

The forecast is a matter of faith

«I think we'll close around three million.» No calculation from the deals in progress, and no idea what has to happen this week for it.

Scope What's included

What the dashboard shows.

Plan and actual in real time

Revenue, number of deals and average deal value against plan — as of today, not at the end of the month. The pace is visible too: on track, or already behind.

Broken down by channel and service

How much money each source and each service brought in. Separately — cost per enquiry and cost per deal, so channels can be compared in money.

The pipeline stage by stage

Conversion at every stage and deal length. A drop is visible precisely: not «low conversion», but «we lose deals between the quote and the contract».

Salespeople

Workload, conversion, average deal value and response time for each. The comparison isn't for punishment — it is to see what the strong ones do and repeat it.

Revenue forecast

Calculated from the deals in progress, taking their stage and probability into account. You know in advance whether the current pipeline covers the plan or you need more enquiries.

Alerts and a weekly summary

A notification when a metric drops or a deal stalls. Once a week — a short summary for the owner: what changed and what to look at.

Result What changes

Three things you notice at once.

One number for everyone

Marketing, sales and the owner work from one source of data. The meeting starts with decisions instead of reconciling spreadsheets.

Problems show up within the week, not at month end

A dip is highlighted while there is still time to make it back. The month stops being the smallest unit of management.

Budget goes where the money is

Channels are compared by revenue, not by cost per lead. It becomes visible which advertising brings enquiries but no sales.

Foundation Where the numbers come from

Analytics is exactly as honest as the CRM behind it.

If the data is kept casually

Half the deals were entered after the fact, the source field is empty, the amounts are rounded. Any dashboard built on that data will be pretty and useless.

So we start with order

First we check that enquiries reach the CRM automatically and that fields get filled in without heroic effort. If they don't, we set up the CRM first and only then build the analytics.

How 4 steps

We set it up in four steps.

01

Questions to the numbers

We work out which decisions you make and which data is missing for them. The dashboard is built around the questions, not the other way round.

02

Data check

We look at what reaches the CRM and how, where the gaps and discrepancies are. We fix the sources, otherwise there is nothing to count.

03

Building the dashboard

We connect the CRM, telephony, advertising and payments, calculate the metrics and set up the alerts and the weekly summary.

04

Reconciliation and launch

We reconcile the figures against the actual money until they match. Then we show the team how to use this in their weekly work.

Money What it costs

First we count the losses, then the price.

The price depends on the number of data sources, how deep the calculations go and the current state of the CRM. The review and the estimate are free: first we look at the numbers you have today and say what can already be extracted from them and what you will first have to learn to collect.

If it turns out that three reports inside the CRM are enough for you, we'll say so rather than build a separate system.

Questions 6 answers

Frequently asked questions.

How is this different from the reports inside the CRM?

CRM reports only see what is in the CRM. Here the data is joined with advertising, telephony and actual payments, so you see not «how many deals», but how much money a channel brought in and how much it cost.

Our data is spread across different systems and spreadsheets

That is normal, and it is where we start. We reduce the sources to one set of metrics and agree what counts as revenue and what counts as a lead. Half the value appears at that step, before any charts.

What if the CRM data is kept badly?

Then we fix data collection first, otherwise the analytics will confidently show something untrue. Usually it is enough to create deals automatically from every channel and to keep three required fields instead of twenty.

How accurate is the revenue forecast?

It is built on your own deals in progress and their stages, not on wishes. For the first month or two the forecast is calibrated against actuals — after that the gap usually stops being a surprise.

Won't this turn into micromanagement?

The dashboard answers the question «what to fix in the process», not «who to punish». Comparing salespeople is there to find a working technique and hand it to everyone else.

Where is the data stored?

The calculations run on a server in Russia, the source data stays in your systems. What exactly is collected and why is set out in the personal data policy.

Open the policy

Let's look at your numbers together.

We'll review your current reporting free of charge and tell you which three metrics will give you the most — and what stops you calculating them today.

Review my numbers