Demand forecasting
Per-SKU, per-location forecasts that account for seasonality, promotions and trend.
- Weekly and monthly horizons
- Confidence intervals, not single numbers
- Promotion and event uplift
Stop guessing. Start knowing what happens next.
Most businesses have the data to predict next month and use it to describe last month. Forecasting turns the numbers you already collect into decisions you can act on before the money is committed.
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/ THE LEAK
What it costs you today.
Two columns, same business. The difference is whether the repetitive part is done by a person or by a system.
Without AI
Decisions made on a feeling
- ✕You buy stock on last year's number and a hunch about this year
- ✕Customers leave and you find out from the revenue report, months later
- ✕Prices were set once and have never been tested against demand
- ✕Every report describes what already happened and changes nothing
- ✕Cash sits in slow stock while the fast lines run out on a Friday
With Hagamart AI
Decisions made on evidence
- ✓Demand forecast by SKU, week by week, with a confidence range you can plan against
- ✓Churn risk scored before the customer leaves, with a reason attached
- ✓Price sensitivity tested per segment so margin is chosen, not inherited
- ✓Dashboards that tell you what to do, not just what occurred
- ✓Reorder points that move with real sell-through instead of a spreadsheet from 2023
/ WHAT WE BUILD
Six ways we build it.
Churn prediction
Which customers are about to leave, how confident we are, and what usually saves them.
- Risk scoring by account
- Driver analysis
- Automated save campaigns
Pricing & margin analysis
Where you are leaving margin on the table and where a rise would cost you volume.
- Elasticity by segment
- Discount leakage
- Competitor-aware repricing
Live KPI dashboards
One screen your team actually opens, wired to the source systems rather than a monthly export.
- Role-specific views
- Mobile-readable
- Scheduled snapshots
Anomaly detection
The system notices the number that moved before you would have, and says why it thinks so.
- Revenue and traffic dips
- Stock and fulfilment breaks
- Alert fatigue controls
Data plumbing
The unglamorous part that makes the rest possible — one place where the numbers agree.
- Source consolidation
- Deduplication and cleaning
- Definitions everyone shares
/ HOW IT WORKS
How the build actually runs.
Step one
We find out which number matters
Not every forecast is worth building. We start from the decision you make repeatedly and expensively, then work backwards to the data that would change it.
Step two
We test against your history
Before it forecasts anything forward, the model has to predict the last twelve months it never saw. If it cannot, we say so rather than shipping a confident-looking chart.
Step three
We put it where the decision happens
A forecast in a dashboard nobody opens is worth nothing. It goes into the reorder screen, the weekly email, the buying meeting — wherever the call is actually made.
Step four
We watch it drift
Models decay as your business changes. We monitor accuracy against reality and retrain, so it is still right in month twelve, not just month one.
/ WHO IT IS FOR
Built for your industry, not in general.
The engine is the same; the build is yours. Here is where this service earns hardest.
/ FAQ
About forecasting & analytics.
How much data do we need before forecasting is useful?
Roughly twelve to eighteen months of transaction history gives a model enough seasonality to work with. Below that we can still do useful trend and anomaly work, and we will tell you plainly which questions your data cannot yet answer.
Our data is messy and spread across systems. Is that a blocker?
No — it is the normal starting point. Consolidating and cleaning the sources is part of the build, and it usually delivers value on its own because it is the first time the numbers agree.
How accurate will the forecast be?
We report accuracy honestly, as an error range measured against periods the model never saw. A forecast without a stated error range is marketing, not analysis. What matters is whether it beats your current method, and we measure that explicitly.
Can it forecast for a seasonal business?
Yes — seasonality is one of the easier patterns to model, provided you have enough history to show the cycle repeating. Promotions, weather and one-off events can be included as separate inputs.
Will this replace our analyst?
No. It removes the report-building that consumes their week so they can spend it on the questions that need judgement. Teams that adopt this usually get more out of the analyst, not fewer analysts.
What tools do you build it in?
Whatever fits your stack and your team's ability to maintain it — often your existing warehouse or spreadsheet layer plus a hosted model and a dashboard. We do not sell a platform you have to keep renting from us.
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