# Data Science for Finance: Best-Suited Methods and Enterprise Architectures
## 2 Background
### Domains of Application of Data Science in Financial Companies  
In this section, we outline the domains in which data science is used currently in financial companies [17; 18; 19]:  
_Customer lifecycle value analysis_: This approach uses data analysis methods that try to assess the profitability of individual customers (or certain groups of customers) over the lifetime of the relationship and analyze transactions between the company and the customer. The sophisticated models that were developed can provide an accurate estimate of the costs to the company when a customer either purchases or uses a particular product. The model includes the cost of the sales channel, the likelihood of return, the likelihood of the consumer returning for repurchase, the cost of customer service calls, and so on.  
_Market testing_: By using direct mail, direct marketing, mailshot, through changes made on the homepage of the company, promotions, and the application of other techniques, marketing staff can test different sales parameters to determine the customer response to a particular offer. In general, different methods are used for different (ideally randomly selected) (consumer) groups, based on dependent and independent variables that reflect a hypothetical causal relationship. This market testing experiment is evaluated by an appropriate measurement, and the results are compared to gauge the effectiveness of the change.  
_Price optimization_. Also known as revenue management, this technique assumes that the primary explanatory variable of the customer's purchasing behavior is the price (causality). The most important issue is usually price elasticity or the customer's reaction (change in demand) to the increase or decrease in the price of the product. Price optimization analyses typically create price elasticity curves to describe the impact of price changes, based on various adjustments in business and sales conditions.  
_Understanding advert effects_: One way to analyze advertisement effects on customers is to use time series analysis. Time-series is a type of statistical data analysis in which samples have been appraised at consecutive times (periods), so this timeline is an important feature of the data. These observations are used to determine whether the condition applied at a given point in time results in a change in the variables examined.  
_An assessment of the preferences of customers and consumers_ regarding the combination of certain products and service characteristics by conjoint analysis and the strength and direction of the attraction of certain combinations of attributes: For example, in the case of a car purchase, a consolidated analysis of the characteristics of the car means which factors, such as price, quality, car dealership, etc.--the most important thing for a customer when purchasing a new car.  
The recent technological, organizational, and external environment of financial services companies implicates that knowledge management is a technology-intensive branch of business process management. Service engineering and innovation cannot be realized without knowledge that is gained by the application of data science and machine learning. One of the important factors of service innovation is the application of components of the enterprise and information architecture in a novel way [20; 21; 22; 23].