E-commerce and retail have a high potential for using analytics and machine learning to increase revenue and obtain more satisfied customers.
Typically data comes from the web-shop system backend, like Magento or WooCommerce, or from cash registers for example. Depending on the application, new sources of data might be needed and we can assist you with collecting data from a variety of sources.
Churn Prediction Model
Predict if and when a customer or subscriber wants to stop doing business with you and take actions before a customer wants to leave.
Sales forecasting for the upcoming months
Predict sales for the upcoming months in order to plan your stock. We develop deep learning models by taking multiple factors into account. Internal factors like sales history and external factors such as seasonality, holidays, product data and much more. Ultimately the model is integrated into your IT-infrastructure, where it delivers automated sales predictions for specific products.
A classic use case of machine learning, which nowadays is well established at the big players such as Amazon and Netflix. Now also smaller e-commerce sites can highly benefit from a custom designed recommendation system.
We can help with inventory management with custom machine learning algorithms and analytics to detect patterns and correlations among elements. The algorithms are then applied to define optimal stock and inventory strategies.
Dashboard of the most important information
Get an overview of what is happening in your business with custom and up-to-date dashboards and alerts of key-metrics. Usually web-shop systems come with some basic analytics, we can assist you to get the most out of your data from the web-shop backend and combine it with data from other sources.
These are only a few examples of what can be done with data and advanced analytics in e-commerce and retail. If you want to know more, please contact us.