# 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
The use of big data analytical tools in the field of social media contributes to the enhancement of the organization's marketing by creating instruments for personalizing recommendations [100]. This can be done through the pattern detections (which is one of the main aspects of data mining [131]) and may turn to be useful when generating positive insights. There are also broader attempts to analyze big data for customer intelligence [76]. The customer churn and customer retention and loyalty are amongst the most used big data analytics applications, together with market basket analysis, customer intelligence, retention modeling, and social media analytics [76]. As claimed in [132], analytics directed towards new data sources--e.g., social networks, clickstreams, CRM, emotions, opinions, audio and video conversations, images etc.--allows not only churn prediction but also insights on prevention manners and what incentives to use. It has also been proven that using big data analytical tools, such as Hadoop (with Datameer extension), with prior collection of consumer data (i.e., archive, network, mobile), can not only significantly contribute to creating a customer churn prediction model but also understanding the client retirement path [133]. More generally, contemporary big data analytics is successfully used to conduct research on customer loyalty and customer retention. Chiefly in industries under constant and dynamic development, as e.g., telecommunications, big data analytics is not only aimed at attracting new customers. It primarily focuses on retaining the \"old\" (current) ones through analyzing the behaviors characterized by the way telecommunications services are used [134]. In particular, big data techniques enable profound analysis of socio-demographic, psychographic, and behavioral features [107]. Insights on customer behaviors are nowadays strictly related with streaming analytics, social media analytics, and pattern mining. The aims of customer behaviors' analytics can be generally divided into detection of behavioral patterns and prediction of behaviors [135]. The former allows for a better understanding (description) of customers, while the latter, for an adjustment of enterprise's actions towards actual and potential customers. Obviously both aims are interrelated. Social media analytics arises as an important part of marketing insights because of an increasing role of sentiment and recommendations [95]. When used in various fields of marketing, business, or politics, it allows to constantly collect, monitor, and naturally analyze user's behavior or his/her sentiments while continuously inspecting user-generated content [136]. Sentiments show consumers' reactions to enterprises' offers and marketing activities. Moreover, social media analytics may reveal how information streams between consumers and how the network is formatted [120; 137]. Big data approach to social media may reveal consumers' communities and directions of social influence [60]. Insights on customers' social media activities, connections, interactions with the firm, and emotions are used to explore retention models [93; 132]. To prevent churn, firms may propose diversified offers to customers as well as diversified marketing content in various social media channels [108]. The summary of social media analytics to understand customers is presented in Figure 1, with various big data analytical activities on SM. Customer behavior may be observed and analyzed not only on social media, but also by tracing information from customer touchpoints with the firm. Broadly, data may come from service providers (e.g., business transaction, human resource, and financial data) or customers (e.g., demographic, behavioral, and purchase history data) [108]. Data may also originate from location services and interconnected technologies, like GPS [138]. With the use of big data analytics accompanied by these sources it is possible to detect customers' patterns.