# On a Certain Research Gap in Big Data Mining for Customer Insights
## 3 Types of Customer Insights from Big Data and Their Role in Organizations  
Big data-enabled societies--particularly based on the foundations of the digital economy--are capable of opening new perspectives for organizations striving to get to know their customers better [86]. The enormous volumes of data deriving from a variety of sources allow to analyze greater number of dimensions depicting customers, than before. This is mostly due to the new sources of data origin, as e.g., social media. Thanks to the characteristics of big data, it offers gargantuan possibilities for gaining new insights [87,88]. These new insights are not narrowed to customer-centric decision-making processes but affect the whole operating space of an organization. In simplification, the insights from big data--if properly used--may contribute to value generation, as well as to innovations, and to the competitive advantage [89,90]. Specifically, big data analytics is considered together with data mining issues [91]. For example, [PERSON] et al. [92] propose a big data processing model from the data mining perspective. They point out that mining big data is data-driven and demand-driven. This context of data mining is present in many big data analytics definitions. In the paper by [PERSON] et al. [93], this type of analytics is described as the process of uncovering hidden patterns, unknown correlations, irregularities, and other data-driven intelligence. Data mining related to big data analytics' tasks also encompasses text mining (for sentiment analysis) and social media analytics (for community detection or social influence analysis) [60]. Especially the latter may be of paramount importance in modern dynamic operational environments, due to empowerment of organizations to perform the so-called situational data analytics instead of--or at least together with--classic static data analytics of transactional or enterprise data [94]. In the context of customer insights, there are several application areas of big data analytics which can be found in the literature. These include inter alia:  
* E-commerce [95; 96];
* Marketing [95; 97];
* Market basket analysis [76; 98];
* Customer intelligence [76];
* Social media analytics [76; 94; 95; 99; 100];
* Consumer banking [101];
* Public transportation management [102];
* Aviation [103];
* Telecommunications [78; 104];
* Tourism [105; 18];
* Policymaking, public governance, and administration performance [106];
* Customer relationships management [75; 107; 108]. In these application areas of big data analytics, several types of customer insights seem to dominate:  
1. Customer needs;
2. Customer satisfaction and experience, sentiment analysis and opinion mining;
3. Customer profiles;
4. Customer churn, retention, and loyalty;
5. Customer behavior. The use of big data analytics in acquiring customer insights is evidenced in many scientific studies confirmed by the successes of enterprises in the field of customer relationship management. Foremost, it should be noted that currently the largest corporations, such as Facebook, Amazon, Netflix, or Google, successfully use algorithms based on big data analysis in their business models in order to obtain \"digital advantage\" [109]. The case study conducted by [PERSON] and [PERSON] [110] showed that the use of BDA (along with the \"V\" axes of big data: volume, variety, velocity [111; 112]) in health sector organizations can obtain insightful business value, which led to their entrepreneurial triumph. The use of big data analytics is also used in the US Immigration and Customs Office to ensure the safety of citizens, immigrants, and travelers, as well as to counter terrorism [113].