Companies are experimenting with various techniques to increase the footprint of the stores. The in-store retail analytics plays a major role in this matter. The digitally savvy customers are changing the retail environment every day with their various needs and demands. Thus assessing the customers in today’s world and increasing the footprint is quite a tough job being faced by the Retailers. This should be done quite analytically to get a profitable result. From retaining a Customer for a particular Brand or for launching a new Product that might attract Customers, need to be planned properly.
Methods to Be Followed by In-store Retail Analytics to Increase the Footprint:
The dictionary meaning of Regression Analysis is to find a relationship between different variables. This process is widely used for forecasting or for predicting something. Path tracing is a model that uses this regression analytic model, these in-store retail analytics models can widely be used for knowing various aspect of human behavior.
The Human Behavior and its aspects can easily be Predicted or evaluated by joining the nodes of the graph or various points of the graph. This again demands the understanding of the topological structure of the human behavior. This model is used to enlighten various important part of a human behavior like this depicts what is the most shortest path that a human being opt for while looking at the entire graph starting from the source of the graph to its nodal point. There is always a path or a way that lies in between this two that is the shortest path which is being opted by the people or the human being at a much local level and the path that lies at global level or at a much wider level.
The job of this process is to predict the graphs or the path that are being widely used by the customers of different group or people of same status. Unlike this modern method, the earlier methods focused on the layout of the graph or in improving the visualization of the graph.
Graph readability is another technique being used by the company to know their customer’s behavior. In this process the companies pay attention to how their customers are behaving after reading or understanding the graph. This mainly depicts the graph reading behavior of a customer. This graph reading behavior of the customers varies with the tasks the in-store retail analytics deals with.
If the result of this methods are studied analytically it can be concluded why a customer is sticking to the previous graph or brand, or what is the reason for a changing the decision, these also depicts what are the common path being used by the customers, etc. These are all complexities of a human behavior and are hard to find. There must be various trial and error methods to be followed before concluding something. The modern day methods have somewhat made the job easy for the analyst to conclude something about the customer’s behavior and increasing the footprints at the retail stores. Thus the in-store retail analytics plays an important role to increase the footprint at the stores.