we have the possibility to offer two personalized analytics platforms.
the first one gives resellers a visual representation of the data linked to stores, brands and the market.
this information is segmented based on the market and financial data. in turn, this enables a better informed decision-making process, and a deeper understanding of the efficiency pertaining category management efforts.
the other platform gives an overview focused on brand performance, allowing resellers to access a detailed and immediate snapshot of different KPIs relative to brand performance (total or segmented) across all points of sales.
planning the placement of your products in the retail space is a key factor for a good sale. this proprietary SaaS allows category managers to easily and intuitively create, manage and share planograms used by virtually any stakeholder.
the first step is to choose the type of retailer, point of sale and structure for which the planogram will be created. afterwards, the initial assortment is filtered, and one is able to build the planogram through a simple drag&drop interface.
in addition, one can access metrics and predictive analysis both for specific products and for the entire planogram, enabling one to have a comprehensive understanding of the consequences behind individual shelf displays.
download here the user's guide of planogram builder.
tracking systems give you an accurate account of everything that has been happening to a specific product.
this system is based on the concept of market governance that allows to have a better understanding about all the steps of the product; in this way, it will be possible to know the origin, the itinerary and the destination in order to take more informed decisions.
by doing this, you will experience less overstock and reduce the likelihood of running out. furthermore, it will help you make your purchasing habits more efficient and assist in the management of stock across multiple locations.
machine learning is a technique of data analysis which makes computers able to learn without being explicitly programmed.
the major benefit of machine learning is that models use algorithms to learn from past outcomes, and continually improve their predictions based on new and different data. once a model is forged from multiple data sources, it has the ability to identify relevant variables. the speed at which machine learning consumes data allows it to tap into raising trends and produce real-time data and predictions.
by using this system, we developed a solution able to predict the time needed for executive’s warehouse workers to process an order.