CRM Analytics

Discovering potential trends, analyzing and predicting customer behavior, evaluating corporate performances are just a few of the valuable contributions of the application of statistical and mathematical models: a competitive benefit too important to be ignored!

Nowadays, every process to transform data in valuable information can be called Analytics. Conversely, we like to consider a less generic and more personalized concept, envisaging the use of statistical techniques, mathematical models, predictive models and machine learning algorithms.

We are specialized in the application of analytical methods of data analysis, based on the best technologies available on the market and on the know-how achieved from different CRM contexts for several industries.

Among these, considering Big Data Analytics, we can cite:

  • the analysis of unstructured data in the banking industry. For instance in bank transfer reasons for payment, through text mining and machine learning techniques;
  • the Market Basket Analysis used for the identification of breakpoints of the customer journey; like the next best offer in the Telecommunications and Retail industry
    the development of propensity and churn models.
DATA MINING

Consist of all analysis methods with the aim of find links between data, to forecast an event. It was born as a fusion of three fields of mathematical and statistical applications: machine learning, artificial intelligence and statistics itself.

The key tools in this field are algorithms and machine learning techniques, to conduct high benefit analysis for business needs. For instance, the customer segmentation, the identification of propensity indices for the purchase of products or of default risk. In addition, it makes possible to improve the decision-making process, optimizing the cost of marketing actions and providing information about the next best product for any single customer.

Forecasting

This method deals with predictive analysis of trend information, generally considering time series. A fundamental assumption in this context is the stationarity of the phenomena. In other words, the factors, which influence the series trend in the past, remain stable over time.

Understanding these kind of connections, identifying such factors and defining scenario analyses can be of great interest in contexts of forecasting sales, inventories, workloads, etc. The main benefit of this analyses is the consciousness in terms of future scenarios, suitable for who must manage corporate decision-making processes.

Optimisation

This is a mathematical analysis that, despite the definition of some constraints, allow to establish an objective function. For this reason, optimization techniques help to define the drivers for setting a budget, for choosing the customer base, with the highest propensity to purchase based on contact policy, etc.

Text Analytics

It is an analysis of unstructured data. For instance, in the world of web, mobile and social media these techniques play an important role in extracting information, usually not accessible and not analyzable. Knowing more information helps to classify, and to correctly segment customers. Moreover, some previously unknown phenomena are disclosed, and they are useful to adapt a company’s commercial strategies and enrich its database with new valuable information.