Data mining solutions are used to model data, to establish trend analyses and forecasts by using mathematical algorithms and statistics. Our workings concerned with data mining which is the most academic part of the Business Intelligence have been continuing.
Thanks to data mining applications valuable behavioral patterns can be reached. Some areas in which we use data mining are those:
- Cross Sell Model
- Customer Segmentation
- Fraud Detection Model
- Customer Retention Model
- Customer Lifetime Valuation Model
- Social Network Analysis
Consulting services of Soglax in Data Mining covers various sectors and areas. Soglax embodies consultants having valuable experience in both academic and business sectors and training in the subject of data mining. Soglax consultants are experienced with prominent data mining tools like SAS, SPSS, Oracle/Inforsense. Some services of data mining in Soglax:
Customer Segmentation: The objective is to group your customers in terms of their common features and to design different actions for different groups. What is expected with segmentation is to come up with homogeneous customer groups. Three kinds of segmentation can be implemented according to the used variable:
Demographic/Firmographic Segmentation: While individual customers are grouped according to demographic factors like occupation, age, gender, location, etc. , commercial customers are grouped by using firmographic factors like sector, number of employees, age, market share, etc.
Value Segmentation: Customers are grouped according to the variables showing value of firms like sales revenue, costs, profitability and revenue per unit.
Behavioral Segmentation: Variables used here measure behaviours of customers like usage of operation channels, frequency of operations, the preferred location, product usage.
Hybrid Segmentation: It is done by using all variables stated above.
Cross Sell Model: It is also called as Inclination to Buy Model. The probability that customers buy certain products in a certain channel is forecasted. In that way, marketing activities can be directed to the customers for whom the probability to buy the product is high.
Fraud Detection Model: With this technique, it can be determined immediately whether an action is fraud or not. Credit card fraud, money laundering and fraud in insurance sector can be given as examples.
Customer Retention Model: All companies want to know which customers are loyal and which ones are about to leave. Forecasting models in data mining help to answer this question and therefore companies are able to take action to prevent high-valued customers from leaving.
Customer Lifetime Valuation Model: To analyze the past and current value of customers are beneficial but inadequate at the same time. For instance, which customer is more valuable? The customer with a high value who is about to leave or the customer with a relatively low value who is going to use products and services of the company for years? By taking all relationships between the customer and the company into consideration, techniques in Customer Lifetime Valuation Model enable us to make valuation.