Analytical CRM Management

SetXRM has created 3 different systems for its Analytical CRM solution. These systems work on internal data and offer different marketing approaches. The main purpose is to analyze customers well, segment them and determine actions accordingly.

In the results of this analysis; it is determined whether the activities carried out have reached the required target customer group and the accuracy of the applied sales and marketing strategies can be questioned. The following services work for SetXRM Analytical CRM.

Forecasting Service : Analytics is the most important CRM Service. The purpose of this service is to perform forecasting processes based on the classification algorithms developed in the previous period.
At this stage, it will be possible to predict which of the predetermined classes the new customers fit into by moving over the tree created using the decision tree algorithm within the forecasting service.
Decision Tree algorithm creates a decision tree by utilizing the available customer data. The root and intermediate nodes of the decision tree contain customer attributes and the leaf nodes contain classes.
By navigating the tree with the information of a new customer, it can be predicted which class this customer belongs to. The decision tree algorithm works on categorical data. For this reason, the continuous data we have should be categorized and sent to the decision tree generation service.

Customer Scoring: In analytical CRM applications, the RFM (Recency, Frequency, Monetary) scoring service scores customers based on their last shopping date, shopping frequency and quantity. This scoring can be used to measure customer loyalty.
Customers with the highest RFM scores are the most likely to respond to the firm’s promotions and marketing campaigns.

N Fold Validation Service In the analytical CRM application, the N-fold validation service was used to measure the success of the clustering algorithms developed. The n-fold validation technique is used in the service.
The number N is sent as a parameter to the methods. This number indicates how many times the data set will be divided and the validation process will be applied. From the service, the success percentage of the method is returned depending on the data set and the number n.