Text analysis

Beat off your competitors ... thanks to text analytics! Get to know your customers better, find out what they are like, what they lack, what they say about you or your competitors...

View through data

Data science recognizes two basic types of data. Structured data and non-structured data. The majority of models and analytical tools use structured data. Most often they are transactional data (purchase data, utilization data, etc.) or data from website traffic, etc. Analyzing such structured data, be it customer, financial or other, is a common practice today.

Unstructured data, often ignored, bring new and vital information that usually remain hidden.

Non-structured data are especially useful in the following situations:

Unstructured data sources, i.e., data for text analytics, include, for example:

Unstructured data analysis is more demanding than structured data analysis. However, the benefits can be fundamental.

Data Science - Data Mind - Text analysis

 

A real-life example:
A customer files in a complaint. The system analyzes the email via text analytics to understand the severity of the issue and combines it with data from the CRM system about the customer’s importance. Instantly, the complaint is assigned priority based on the severity of the issue and the given customer’s importance.Customers file different types of queries or complaints to their service or product providers. Through text analytics, we are able to identify the content and sentiment of such communications and determine their significance. By analyzing customer data (structured), we are able to determine the importance of individual customers.Once these two inputs are linked, it is possible to identify the priority of individual queries with regard to both the importance of the customer and the importance of his query. Thus we can prevent a significant customer from churning.