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Performance reporting and data analysis

Your performance can be measured when the figures are read, not merely collected

We build performance reporting and data analysis from your own sources: an agreed definition for every indicator, a report per function (sales, operations, inventory, finance, purchasing, people), targets with a measured gap, and analysis of cause, trend and concentration — reconciled with accounting, with the differences published rather than hidden.

Request a data-readiness assessmentWhat we deliver
  • Agreed definitions for every figure
  • Reporting by branch, rep and item
  • Margin analysis, not only revenue
  • Reconciliation with accounting
  • Automatically scheduled reports
Sales analytics
Temporary placeholder — slot for the executive dashboard

Direct answers

The questions asked first

Where does sales analytics actually start?

It does not start with dashboards but with two things: an agreed definition for each figure, and the quality of what was entered. We begin with a definitions session: what does “a sale” mean — the order, the delivery or the invoice? Is a return deducted in the month it happened or in the original sale’s month? Is a discount charged to the rep or to the branch? Then we review the data: duplicated items, unclassified customers, branches without addresses. Without those two steps you get a handsome report nobody can rely on.

Do we need a new system to get good reporting?

Usually not. We work with what you have: Odoo, another system, or exported files where no API exists. If your system does not capture enough — it does not store a discount per line, or no item cost — we say so plainly: that gap needs a system change or extra entry, and it cannot be solved in the reporting layer, because reports do not create data that was never captured.

Do you build interactive dashboards or fixed reports?

Both, each with a purpose: a dashboard for management to see the picture, the trend and where the anomaly is; a report for the person who must act, with figures traceable to their documents. And every number on the dashboard is linked to its source — which document it came from and how it was calculated — because a dashboard whose numbers cannot be explained gets abandoned after two weeks however good it looks.

What decides whether analytics succeeds after delivery?

Three things outside the reports themselves: consistent entry in the source system, an owner for each figure who signs off its definition and corrects it when it changes, and a decision actually taken from the report. A report nobody decides on becomes a monthly file opened once and forgotten. So we measure use rather than delivery: did anything change after the report?

Deliverables

What we deliver

Eight tangible outputs, each with a report or a dashboard you can inspect — not recommendations left to interpretation.

Executive sales dashboard

One screen for management: revenue, margin, trend and variance against target, with alerts for what is out of the ordinary rather than every figure.

Output: a dashboard where each figure has one meaning

Sales by branch

Branches compared on a fair basis — same period, same definition — with the effect of new branches and seasonality made explicit before any judgement on performance.

Output: a fair branch comparison

Rep performance

What each rep sold, the margin on it, their discounts and their returns — not the sales value alone, because selling at a deep discount is not an achievement.

Output: performance by margin, not volume

Margin and discount analysis

How much revenue survives as profit after item cost, discount, freight and returns — the report that exposes what sells a lot and earns little.

Output: profit per line, not per total

Item and category analysis

Fastest, slowest and most profitable items per category and period, including the ones sold at no margin or returned at a high rate.

Output: decision lists for purchasing

Customer and retention analysis

Who returns, who stops and who holds a large share of revenue — because dependence on a single customer is a risk a conventional sales report does not show.

Output: concentration risk made visible

Returns and cancellations

The return rate and its reasons per item, branch and rep — because a high return rate is a catalogue, description or selling problem, not a system one.

Output: why it was returned, not just how many

Collections and ageing

What was sold against what was actually collected, with ageing per customer and rep — because a sale not yet collected is not revenue yet.

Output: sales against collections

Method

The analytics method

Six stages that start with definitions and data quality rather than with drawing a dashboard, because the reverse order produces a report nobody believes.

  1. 01

    Source inventory and quality check

    Where the data genuinely comes from, which source is the truth, and what is missing: duplicated items, unclassified customers, empty fields, branches without addresses.

    Output: a data-readiness report

  2. 02

    Agree the definitions

    A session where definitions are written down formally — what sale, revenue, margin, return and collection mean — and who owns and approves each figure.

    Output: an approved metric dictionary

  3. 03

    Build the data layer

    Consolidating and cleaning the sources and joining them on unified identifiers, so a figure is calculated once and then read from one place rather than five files.

    Output: one source for the figures

  4. 04

    Reports and dashboards

    Building the agreed reports and dashboards, with every figure linked to its source and the ability to drill down to the document that created it.

    Output: traceable reports

  5. 05

    Verify and reconcile

    Reconciling the report figures against accounting and inventory, documenting any difference and its cause instead of patching it by hand in the file.

    Output: a documented reconciliation with differences

  6. 06

    Training and the reporting cycle

    Training the people who read and use the report, scheduling its issue, and reviewing the definitions periodically as the business or the policy changes.

    Output: reporting that runs without us

Measurement

What we measure

Indicators for the analytics work itself — because a report nobody uses is a cost, not an advantage.

Reconciliation with accounting

The share of lines that reconcile with no difference, and the differences published with their causes — the truest measure that the figures can be relied on.

Time to produce the report

How long the report took by hand and how long it takes now — the first thing the team notices in practice.

Share of automated reports

What is issued automatically on schedule versus what is still assembled by hand, because a manual report stops during the first busy week.

Source coverage

Which sales channels and sources are inside the figures and which are still outside, so an incomplete number is never read as a complete one.

Use of the report in decisions

Whether the report is opened and acted on, or issued monthly and archived — the real test of whether the project succeeded.

These are indicators read from your reports and your system, not promised outcomes. We announce no accuracy percentage and guarantee no sales improvement: the accuracy of a figure follows the quality of entry, and sales follow the market, the product and the price — and we are explicit about what reporting can and cannot fix.

Who for

Where this fits

Operations whose decisions rest on scattered numbers: several branches, several reps, many items, or multiple sales channels.

Branch chains

Comparing branches on one definition, and separating a genuinely strong branch from one that only looks strong because of its location.

Distribution and field reps

Rep performance by margin and collection rather than volume, and routes that need a quick decision on price or item.

Manufacturing

Item and customer profitability after actual cost rather than selling price alone, to point production at what genuinely earns.

Contracting and projects

Profitability, claims and arrears per project, because a large project with high revenue can be a loss that only shows up late.

E-commerce

Visit-to-order conversion, basket value, shipping cost and returns per channel — not just total sales.

Restaurants

Item profitability after recipe cost and waste, peak hours, and delivery-channel performance against dine-in.

Multi-entity groups

Unified figures across entities in different currencies, with a fair comparison and the exchange-rate effect made explicit.

Services and maintenance

Contract profitability, hours consumed and renewal rate, because a contract that looks profitable on paper may consume more hours than expected.

Performance

Sales performance

Revenue and margin against target by branch, rep and channel, separating the price effect from the volume effect because each one calls for a different decision.

Indicator: gap against target

Operations and production

Output against plan, delay, waste and rework, and downtime with its causes — not one opaque efficiency figure.

Indicator: plan against actual

Inventory and warehouse

Turnover, count accuracy, slow-moving items and stockouts — the first thing that explains a sales blockage.

Indicator: turnover and count accuracy

Financial performance and collections

Revenue against cost, collections against sales, receivables ageing, and the difference between accounting profit and cash flow.

Indicator: profit and cash flow

Purchasing and suppliers

On-time delivery, price movement, the rejection rate on receipt, and the purchase cycle from request to receipt.

Indicator: reliability and quality

People and productivity

Productivity per role against plan, and activity against outcomes, within statutory limits and without using the data for any other purpose.

Indicator: productivity per role

Analysis

Descriptive analysis

What happened: the actual figures for the period, at the level of detail and grouping you need — the base that cannot be skipped.

Question: what happened?

Comparative and trend analysis

Against the previous period, against target, against another branch or rep — stating plainly what makes the comparison fair or unfair.

Question: how do we compare?

Diagnostic analysis

Why it happened: splitting the variance into its causes (price, volume, discount, returns, timing) so the cause is treated rather than the symptom.

Question: why?

Target gap analysis

Where we fell short and by how much, and whether the gap is in revenue, in margin or both — because chasing revenue alone can reduce profit.

Question: how far short?

Exception and alert analysis

What fell outside the normal range: a cash difference, an unusual return, a negative margin, or repeated delay — so the exception is read rather than every figure.

Question: what is out of line?

Concentration and distribution

How far revenue depends on one customer, item or branch, and how sales spread across channels — analysis that exposes risk invisible in the totals.

Question: where is the risk?

Sources

Data sources

We read from the systems you actually run — through the system, an API or a file — and state plainly which source is the source of truth.

Odoo sales and point of sale

Sales orders, invoices and till sales read from the same database, so channels are compared on one definition.

The sales source of truth

Accounting

Reconciling revenue, collections and receivables with the ledger, and publishing the difference and its cause instead of patching the report.

The reconciliation reference

Inventory and cost

Item cost and waste, so margin is calculated on a real cost rather than a remembered estimate.

The basis for margin

Exported files (Excel / CSV)

Where no API exists we work from exported files under the same definitions, documenting what the file does not provide.

A practical interim route

External systems

Other systems read through their API or a scheduled file, with the sync interval stated exactly — because the figures follow it, not the moment.

A stated sync interval

Scope

Engagement scopes

Described scope with no published prices: cost follows the number of sources, reports, data volume, branches and users, and is quoted in a proposal.

مدخل محدود

One deep report

Definitions plus one report chosen for its impact (usually margins or rep performance), reconciliation with accounting, and training for whoever uses it.

The right start with one decision

النطاق الكامل

Sales analytics package

The full definitions set, the data layer, branch, rep, margin, item, returns and collection reporting, a management dashboard, and a scheduled issue.

The common scope

نطاق ممتد

Multi-system reporting platform

Everything above with more than one source system and several entities, an enterprise metric dictionary, periodic definition reviews, and training for the figure owners.

Quoted after the assessment

We publish no price before assessing the sources: cost follows the number of sources, reports, data volume, branches and users.

FAQ

Questions asked before starting

Direct answers on accuracy, sources, responsibility and what we do not guarantee — without inflation.

Where does sales analytics actually start?

It does not start with dashboards but with two things: an agreed definition for each figure, and the quality of what was entered. We begin with a definitions session: what does “a sale” mean — the order, the delivery or the invoice? Is a return deducted in the month it happened or in the original sale’s month? Is a discount charged to the rep or to the branch? Then we review the data: duplicated items, unclassified customers, branches without addresses. Without those two steps you get a handsome report nobody can rely on.

Do we need a new system to get good reporting?

Usually not. We work with what you have: Odoo, another system, or exported files where no API exists. If your system does not capture enough — it does not store a discount per line, or no item cost — we say so plainly: that gap needs a system change or extra entry, and it cannot be solved in the reporting layer, because reports do not create data that was never captured.

Do you build interactive dashboards or fixed reports?

Both, each with a purpose: a dashboard for management to see the picture, the trend and where the anomaly is; a report for the person who must act, with figures traceable to their documents. And every number on the dashboard is linked to its source — which document it came from and how it was calculated — because a dashboard whose numbers cannot be explained gets abandoned after two weeks however good it looks.

What decides whether analytics succeeds after delivery?

Three things outside the reports themselves: consistent entry in the source system, an owner for each figure who signs off its definition and corrects it when it changes, and a decision actually taken from the report. A report nobody decides on becomes a monthly file opened once and forgotten. So we measure use rather than delivery: did anything change after the report?

Do you guarantee the accuracy of the figures?

We do not guarantee accuracy whose source we do not control: a figure’s accuracy follows the accuracy of what was entered in the source system. What we do commit to is method: we reconcile the reports against accounting and inventory, document every difference and its cause, and flag any figure resting on an estimate or an incomplete field. The official definitions are the client’s decision; we write them with you, get them approved, and then hold to them until they are formally changed.

Do you use artificial intelligence for analysis or forecasting?

We do not claim it and we do not sell it as a promise. What we do offer for forecasting are stated statistical methods (a moving average, a seasonal trend) when you have enough clean history, with the forecast error measured and shown next to the figure so you know how much to trust it. Where history is insufficient we say so and offer no forecast — because a forecast without a measured error is worse than none.

What is out of scope?

Audit and any opinion on the financial statements, tax advice, manually correcting very large historical datasets, modifying the source system itself (we estimate and deliver that as separate scope), buying or hosting visualisation tool licences if you need an external tool, and advanced statistical consulting beyond management reporting. We say all of this at assessment stage, before any commitment.

Request a data-readiness assessment

Four details are enough: the systems you sell and account in, how many branches and reps, the three reports you need most, and whether someone owns the numbers and signs off the definitions.

  • A written data-readiness assessment
  • Written scope before any commitment
  • Your data is not shared or used for anything else
Services needed

By submitting you agree that we may contact you about your request. We do not share your data with third parties.

Ready to know where your profit actually comes from?

Tell us your systems, how many branches and reps you have and which reports you need, and we will come back with a data-readiness assessment and a clear scope.

Request a proposal

Related topics

We are a systems implementation, integration and analytics company — not an audit firm and not a certification body. We build the definitions, reports and dashboards and reconcile them with your accounting; the accuracy of what is entered in the source system and the approval of the official definitions remain the client’s responsibility. We announce no accuracy percentage and guarantee no improvement in sales or profit before measuring it against an agreed baseline, and we neither use your data for any other purpose nor share it with any third party.

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