How much do you know your competitors?
How much your competitors have expanded their markets?
What specific/unique features do your competitors’ products have?
What is the most realistic possible segmentation in the market?
What do your customers have in common? Where are their differentiation points?
Are your customers loyal to you? To what extent?
Regarding your previous sales what model forecast your future sales with fairly accurate approximation?
What kind of issues influence your sale the most based on previous data?
Data mining technique, discovers hidden and meaningful patterns and rules within the data through examining and analyzing large amounts of data. Data mining programs identify and analyze relationships and patterns among raw data based on user’s requests, Patterns that cannot be identified without using advanced statistical and mathematical modeling techniques available to these software applications, even for experts of sales and marketing departments. Identifying Customer’s purchasing behavior Patterns, assessing customer’s loyalty, and customer churn analysis are some of data mining services.
Customer profiling is a way to create a customer image and can help a great deal with the management decisions related to the service. Customers are divided into similar groups based on common goals and features and each group has an agent, with a photo, a name, and a description of the group. Thus a small group of customers is used for key decisions.
Having a precise profile of competitors can be the first step in identifying a competitive market. It helps to clarify the competition market and to understand competitive dynamics which form the industry and defensive or offensive strategies. An effective program to evaluate competitors in order to compete successfully in projects or to evaluate company personal information.
Dividing customers according to usage of product, product loyalty rate and behavioral division/distribution of customers can be done in order to improve overall strategies of a company or to be used in forecast models in data analysis methods to predict the possibility of customer response to marketing messages.
Conjoint Analysis is one of the well-known methods in marketing research that is used by marketers to determine new product features and to set optimized prices.
Time Series- based Modeling
Time Series are referred to a set of quantitative observations that are measured consecutively and in time intervals. Time-series analysis methods fit the best mathematical and statistical models on data so they are able to forecast data behavior in near future. Such analysis are applicable in forecasting the sale trends, demand trends, market share changes and so on.
Regression is a statistical technique for investigation and modeling the relationship between variables. The purpose of regression is to discover the linear or non-linear relationships between data and the impact of independent variables on dependent ones. This kind of analysis can be usable and efficient in forecasting the behavioral patterns of vast range of dependent variables.