# On a Certain Research Gap in Big Data Mining for Customer Insights
## 6 Discussion  
Consumer insights have been analyzed also before the big data era--by applying the data mining techniques to huge customer databases. Obviously, this concerned the structured data. These databases have been mined for various purposes and with various machine learning (ML) techniques. [PERSON] et al. [204] mined customer data for new product development and for customer relationship management in the tourism industry. [PERSON] [205] applied database clustering to identify the high-value, and low-risk customers. Clustering has been also exploited, together with market basket analysis, in [206] with the ultimate goal to discover customer groups defined by lifestyle. As these examples show, the temporal aspect of analysis has been rarely addressed. It is touched in [207] where classification and clustering of time-stamped banking transaction data is performed for customer profiling. In [208] historical electricity consumption, data are mined to automatically estimate the absence probability of a customer. The input data are temporally ordered as time series. Hence, the temporal dimension is present only in the simple form of timestamping. The advent of big data, with its heterogeneity, velocity, volume, and other features, offered quite new possibilities of mining customer insights. However, the big data mining approaches found in the literature seldom address the temporality. e.g., [209; 210; 211] propose various social media mining approaches, to analyze and visualize Twitter clusters, to extract customers' opinions on product features, to analyze customer requirements. All these approaches do not touch any time aspects of data nor analytics. Similarly, in the e-commerce area, Ref. [213] combine machine learning methods: association rule mining, clustering, and classification to enhance precision marketing and personal referral services; Ref. [214] mine customer data to predict remanufactured product demand. These two approaches do not address the temporal dimension of data as well. Textual data of consumer complaints is mined with the Outcome-Driven Innovation method in [215] where the problem of time-dependent opinion changeability is omitted by introducing the concept of a job. The job is assumed to be stable in a certain period of time. [PERSON] et al. [216; 217] offer two exhaustive literature reviews on big data mining in banking, and on textual big data mining, respectively. As for the banking area, no research with explicit time aspect is detected, only some publications on real-time analytics are pointed out. For textual big data analytics, the problem of changes over time, typical to social media data, has been noticed. Hence, Ref. [217] point out the research gap linked to temporal big data analytics, but it is limited to textual social media content analysis. Temporal big data mining for consumer insights has been addressed in a limited number of research works. Ref. [218] propose the sentiment intelligence tool to analyze consumer complaints expressed via social media. Although the analysis is performed in real time, it is not fully temporal. It does not offer analysis of changes over time, of time structure, of causal relationships and other temporal characteristics. Only timestamping of complaints is used. Timestamping of events depicted by big data is used by several other researchers too. Ref. [219] use clustering of time series of smart meter data to discover time and intensity of water use by end customers. Refs. [220; 221] mine processes in event logs and location data, respectively. In both cases, data is simply time-stamped which means that the simplest approach to time is used, namely linear calendar time. A more complete view of time is presented by [222], who address the problem of temporal clustering of sentiments in emails. Sentiment features are represented as a trajectory which enables the discovery of sentiment flow in emails with regard to topic and time.