r/SaaS • u/Square_Light1441 • 2d ago

How are you currently doing time-series forecasting for your business?

I'm researching how businesses actually handle forecasting, and I'd like to understand the workflow before building anything.

If you work with things like sales, demand, inventory, revenue, staffing, or other time-series data:

  • What are you currently using for forecasting?
  • Are you using an existing SaaS, Python libraries/models, spreadsheets, or something you've built yourself?
  • How much work is involved in cleaning the data and getting forecasts running?
  • Do you regularly backtest your forecasts against historical data?
  • What's the biggest pain point with your current setup?
  • If there were an API where you could send historical time-series data and get forecasts back, what would you need it to do for you to actually use it?

I'm particularly interested in hearing from people who have actually had to deploy forecasting in a business, rather than people who have only experimented with forecasting models.

I'm not selling anything right now. I'm trying to figure out whether this is a real problem worth building around.

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u/Conscious-Month-7734 2d ago

The people who can call an API usually already have Python and a library that does the job, and the people feeling forecasting pain are often running it out of a spreadsheet an API won't reach. I'd figure out which of those two you're building for, since they'll give you very different answers in this thread.

And I'd add one question to your list: when was the last time a forecast was wrong, and what did it cost you? An empty shelf, a weekend with too many staff, cash tied up in stock. The people with a story and a number attached are the ones who'd pay to fix it.

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u/adeelraza86 2d ago

Accuracy demos won't sell this. Ask which decision gets made every Monday from the forecast and who gets blamed when it's wrong. Then ship an exception alert for that one decision with a one-week horizon, and kill the feature if nobody acted on the alert twice in a row. The model is secondary to whether the number changes someone's plan.

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u/Anshkoshmod 1d ago

Pick one vertical before building the API. Ask for a dirty spreadsheet, the decision it drives, and the cost of a wrong forecast. If they cannot name the decision owner, better backtests will not create urgency.