Neti Forecast uses machine learning for highly accurate forecasts.
Until recently, companies used classic tools for forecasting demand and financial planning: Excel spreadsheets, individual modules of ERP systems, specialized BI systems, for example, Cognos or QlikView.
These tools make forecasts based on historical data that is accumulated in ERP systems. For example, such tools can predict future sales of a shoe by analyzing only its previous sales data, assuming that the future is determined by the past. This approach does not allow making accurate forecasts if the list of items is large and the demand for products is irregular. In addition, these tools cannot combine different types of data that change over time (such as competitor prices, discounts, web traffic, and headcounts) with corresponding explanatory variables such as product characteristics and store location.
Using Machine Learning technologies, Neti Forecast combines historical data stored in an ERP system with additional variables to make forecasts.
In addition to financial data, machine learning can take into account:
- the weather
- competitors’ prices
- various events and activities
- news background
- social media activity
To get started with Neti Forecast technology, you just need to provide historical data, as well as any additional data that, in your opinion, may affect the forecasts. For example, the demand for a particular color of a dress may vary depending on the season and location of the store. These non-obvious relationships are difficult to determine on your own, machine learning is ideal for identifying them.
Once you submit your data, Neti Forecast will automatically examine it, determine what is relevant, and create a model capable of making 50% more accurate predictions than using historical data alone.