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Demand forecasting is an analytical process that is used to predict the demand for a product over time. Ecommerce stores have a lot of products belonging to different categories

So predicting demand for each product is a challenging task for storekeepers.

Demand forecasting involves predicting trends in sales and customer demand, there are a lot of ways available to predict the product demand.

Here we compare the demand forecasting methods using vizB and Shopify Stocky.

Why is demand forecasting needed in Ecommerce?

To take better decisions in Inventory planning:

When you have the predicted demand for the products in your store, it helps to make better decisions to prevent out of stocks of products and also avoids product overstocking.

Price optimization:

Using the predicted demand price elasticity is computed using that we can get optimal price suggestions of the products which help to generate better revenue than before.

Traditional demand forecasting techniques:

It involves rule-based techniques like moving average-based forecasting or based on the previous sales data it will compute the average quantity expected to be sold in a time period.

Artificial Intelligence(AI) based forecasting techniques:

AI-based demand forecasting involves collecting the entire historical data of a product and based on that it identifies parameters like trends, seasonality, Cyclicity using that data Machine Learning (ML) algorithms are trained and used to predict the demand for future time periods.  

How does demand prediction work in Shopify Stocky?

Shopify Stocky is an Inventory management app that has a demand forecasting feature, but it is based on Rule-based techniques like getting average sales of products and predicting for future time periods.

Example forecasting in Shopify Stocky:

Last X days: Takes average orders per day in-sample period and multiplies it by the number of days you enter to predict

Custom date range: If you have a previous sales pattern and you want to run the same sale again, it takes the average on the past pattern and multiplies it by the number of days you enter to predict.

Same period last year: It uses the previous sales data and provides the exact quantity made last year as prediction, like if you want to know the demand for month July provides the exact quantity sold last year July as the prediction.

This is how Shopify Stocky works to predict demand, it only uses the average and gives predictions. but there are better ways to predict demand beyond average values.

AI demand prediction using vizB:

vizB has a demand forecasting future that uses modern AI techniques to predict the demand for products in your store.

vizB demand forecasting involves collecting the overall historical data of a product and preprocess in such a way that can be used to train the Machine Learning (ML) model from which it predicts the expected quantity to be sold in the next 30 days.

The internal algorithm works in such a way that it identifies the trend, seasonality patterns in the historical data, and based on that the ML model predicts the quantity for the next 30 days.

Along with the predicted demand vizB also has AI techniques that compute the price elasticity of the products and using that it suggests the optimal price through which maximum revenue can be generated.


vizB demand forecasting feature with modern AI and ML techniques helps to make better decisions for the store inventory management and also suggests optimal prices so that one can generate maximum revenue out of it.

Make Smarter Decisions with vizB

Have questions on how to connect your eCommerce Store with vizB

Gandhi Arumugam

Gandhi Arumugam

Gandhiarumugam, an AI Engineer at DCKAP, keenly looks at ways to innovate new solutions using Data Science and Artificial Intelligence technologies. I enjoyed participating in various tech hackathons and coding contests.