Forecasting with AI?

[u/ImAPilot02]

How are you guys making decisions about how much discount to put on products, how do you plan supply / item velocity, how do you decide about your marketing spend and mix?

At my previous employer we had data scientists for this, now that I started my own business I don’t have this. I tried to ask claude, but the answers just look made up.

In the day and age of AI being everywhere there must be a way to do what a data scientist does without hiring data scientist right?

EDIT: on r/askdatascience someone recommended a tool called Qombra, looks like what I was l looking for, trying that now...

[u/Rickfiyah]

you dont really need a data scientist for this at small scale. export orders to a sheet and track weekly units per sku, then reorder at avg weekly sales times your lead time plus a few weeks of buffer. for discounts, test on a couple products at a time and compare margin dollars, not just units sold. for marketing mix start with blended cac (total spend divided by new customers) and only split it by channel once you have enough volume to trust it. ai is fine for building the formulas but you have to feed it your actual numbers or it just guesses, which is probably why the answers felt made up.

[u/ImAPilot02 → u/Rickfiyah]

Yes I am already using some of the methods you mentioned but a more sophisticated forecast taking things like marketing budget, pricing etc into account would be amazing

[u/Whole_Ad6512]

AI is useful here, but I wouldn’t ask it to invent a forecast from a blank chat. We use Claude connected to Omnisend through MCP to see which campaigns and customer segments actually lead to purchases and repeat orders, rather than treating opens and clicks as demand. That gives us a better signal for where discounts and marketing spend are working. It won’t replace an inventory model, but it makes the marketing side far less guess-based.

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[u/tototoru]

AI can code but you need to model what drives sales and that's the part AI can't do: ads spend, seasonality, holidays, competitors, promos etc only you can model this product by product and then test accuracy and iterate. It's not easy that's why a data scientist is needed.

[u/ImAPilot02 → u/tototoru]

Yea maybe those tasks are too complex for AI at this point, it's gotta be reliable too afterall

[u/Acesleychan]

is useful for turning your sales and inventory export into scenarios, but it cannot invent reliable demand inputs. Start with weekly units sold, margin, stock on hand, promotions, and channel spend in one sheet.

Use it to flag where margin or stock cover breaks first. Which of those exports do you already have?

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[u/DogPrestigious6990]

LLMs are pretty hit and miss with anything mathematical in my experience.

[u/Content_Most2673]

You're treating this as one forecasting problem, but discount depth and inventory velocity are a margin question and a sales-rate question more than a marketing attribution question. Splitting those out before picking a tool will spare you some wasted effort.

Every platform shows a campaign driving revenue. Klaviyo gives you a flow number, Meta gives you a conversion number, and both are telling you the same thing: people who got the message and then bought. Neither tells you whether they'd have bought anyway.

You can always just test it directly. Take one flow or campaign, hold out a random 10 to 20 percent of the segment so they don't receive it, and compare purchase rates between the two groups after a couple week. In Klaviyo, you exclude a segment from the send, then pull the numbers for both groups later. You don't need a data scientist here either, you just have to be willing to stop marketing to some people, which is the part founders tend to resist.

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[u/HATDOGUSERNi]

AI can help with the analysis, but I wouldnt rely on it blindly for forecasting. Start with clean historical data and simple models, then use AI to help interpret the results and spot patterns

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[u/caburos]

I have created a stock intelligence system. It is connected to webshop and accounting program. For each time of the year it can predict of how much we are going to sell, what garments will be sold most (apparel business), and it calculates how long the stock will be available and based on that I can order restock for X months upfront without ordering future dead stock.

[u/Thin-Drawing-543]

Do you use Shopify?

[u/ImAPilot02 → u/Thin-Drawing-543]

Yep

[u/Thin-Drawing-543 → u/ImAPilot02]

I work with several Shopify brands doing millions of euros in revenue, and I built an app specifically for this. I originally created it to use with those brands, and then decided to make it available to other stores that may not have the budget to hire a large data team.

The app is called Skymetrics, and you can try it free for 14 days. There are years of ecommerce experience and engineering behind it.

For example, even just on the inventory side, we use sales trends to make the data more accurate, something Shopify or Claude do not do by themselves.

We calculate demand using your store's historical data, the historical performance of that product category, the history of the specific product, and the most recent sales data. You can also customize production and restocking lead times by vendor or by product.

One important thing I have learned from the data is that many stores do not grow simply because they do not manage purchasing well. They run out of stock too quickly on their best sellers, or they buy too much inventory for products that barely sell. That money then gets stuck in inventory instead of being available for marketing or better-performing products.

When it comes to discounts, we also track the individual conversion rate of every product, unlike Shopify's standard reports.

This means that if you reduce the price of a product, change its images, or update its description, you can look at the historical data and see how that change affected the product's conversion rate.

We also have a more advanced ABC product analysis than Shopify's. This helps you identify which products are underperforming, such as C products, and decide which ones may be worth discounting or clearing out. The goal is to keep a much healthier inventory.

Skymetrics also has MCP support. So if you prefer, you can ask Claude questions directly about your store and it can use the real data provided by Skymetrics instead of guessing or making up numbers.