Solution

I SAMM neural module helps managers of footwear chains and retail stores effectively control sales. The system detects “unpopular” shoe models and will ensure warehouse stock balance for those models decreases by 35―40% by the end of the season.

Over a period of two years our team of marketing consultants, psychologists and mathematicians conducted research in various retail chains and discovered a correlation between a customer’s purchasing behavior and their thoughts and emotions. This has allowed us to determine what emotions a person feels towards certain goods and gauge their interest within the accuracy of 95 %. What’s more, we can digitalize these emotions allowing for adjustments to be made that improve our customers’ profitability.

 

How Does it Work

We install data transmitters on shoes that will read all of the necessary information. The system will then process the data and form a report that we can use to draw conclusions or train I SAMM to give recommendations and commands. You can either let the management make the decisions or let the system run fully automated.

Заголовок

relevant analytics as soon as 2 weeks after implementation

Заголовок

a neural module teaches itself and increases accuracy of its prognosis

Заголовок

the prognosis is based on over 100 variables

Заголовок

making changes to orders of future collections and set parameters

+ 30%

profit

- 40%

stock balance

+ 60%

forecast accuracy




With I SAMM you can introduce effective discounts for unpopular models (during regular sales, stores often lose money by offering a 70 % discount for goods that would just as well sell at 56 % off), return excess inventory to the factory, spot defective goods and install interactive shelves.

 Finally, based on analytics from several stores in different regions you can organize a rotation of unpopular goods within the chain.





If you still have questions about the technology or want to discuss a partnership, leave your details, we will call you back