How to Use No-Code AI For Demand Prediction

Our guide to using no-code AI for demand prediction.

If you’re running an eCommerce store, it’s critical to manage inventory to meet the needs of your customers.

If you lack inventory, the consequences are obvious: Money is left on the table and revenue is lower than it could be. Customers who don’t find the products they’re looking for will likely immediately turn to competitors not only now, but in the future as well.

On the flipside, if you have too much inventory, you’ll be needlessly increasing storage costs and organizational complexity, while limiting your cash flow.

Meeting the Goldilocks-level of supply for each product is all about demand prediction. Let’s dive into how you can use no-code AI to do this effortlessly.

The Hidden Complexities of Demand Prediction

Demand prediction isn’t as easy as it may seem at first glance. There are innumerable variables at play, which is why machine learning comes into play: Machine learning finds patterns in huge amounts of data that a human would simply gloss over.

For example, different products might sell differently location-by-location, sales will change over holiday seasons (not to mention weekdays versus weekends, seasons of the year, and so on), product demand will be impacted by marketing campaigns, variables in your product listings will impact demand, and so on.

Demand Prediction Made Effortless

All this information serves as valuable data to fuel no-code demand forecasting models in Obviously.AI. As with any tabular machine learning problem, it’s a simple three-step process:

First, start a free trial with Obviously.AI. Then, connect your data — from wherever it’s sitting. Next, select the column you want to predict, such as “sales.” Then, simply hit predict and Obviously.AI will do the heavy lifting in the background!


You’ve probably noticed that data is the core of the process above. Data is how Obviously.AI learns about the patterns of supply and demand in your eCommerce operation.

The availability of high-quality data is the biggest challenge for eCommerce stores in demand prediction, so you might want to think about methods for data acquisition and cleaning if you don’t yet have a high-quality, central source for your eCommerce data.

Deploying Demand Prediction Models

Building models isn’t enough, you’ll ultimately want to deploy these models and act on the insights you’ve found.

Creating demand prediction models as explained earlier will help you answer questions such as:

  • What should your marketing plan be?
  • What markets should you enter, which products should you sell (and how many)?
  • How should you allocate R&D resources over time?

Supply and demand is at the heart of every eCommerce store, so taking a data-driven approach to demand prediction will give you a killer edge.

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