You can read more about our use cases here to see how we have been able to help our customers.
We utilize a combination of external sources like competitor information, weather patterns and social media conversation and internal data sources like historical sales. Once we collect, clean, and feed the data into our machine learning algorithms, we can provide essential recommendations on how to improve your business.
We offer two types of predictions – a pre-season model to help buyers and merchandise planners buy smarter and more accurately and an in-season model that forecasts sales out for the next six month period.
With traditional forecasting methods, merchandise planners and buyers can take up to six weeks to make predictions for an upcoming season and a 55% average accuracy in identifying best to worst sellers. Alternatively, our algorithms can automate the process and reduce the forecasting calculation time to two hours. and improve the accuracy rate in the best-worst sellers to 85%.
We do not have any limitations on the number of SKU’s we can model.
Currently, we support any English-speaking country, as well as China.
On average, brands and retailers that implemented our solution were able to automate many tasks in the merchandise planning process and saw a 9x return on investment.
Yes, we work with online and offline retailers, as well as omni-channel retailers Our services are ideal for retailers, brands and wholesalers.
While we primarily focus on fashion retailers and brands, our services are open to all retailers, provided there is enough data.
We house our predictions on our cloud-based platform so you will need a website browser (Safari/Chrome/Firefox) and internet access.
When it comes to starting our services, you can begin immediately by first requesting a demo. In regards to the process of getting things set up, we initially go through a 3-month piloting period where we analyze and identify user’s key pain points. To find out more about the details of our process, click here.
We currently do not have a mobile or tablet version, however, our application is mobile responsive.
We do not restrict the number of users that can share a login as we do not operate on a per-use license model.
We start off with a 3-month pilot process where we analyze and identify user’s key pain points. From there, our machine learning algorithms learn and process the mass amounts of data, quickly discovering insights that may have been hidden to the average human eye.
Yes, multiple users can have their own individual logins as we do not charge based on the number of users.