It is a way of doing business with customers in a manner that provides a customer experience in positive manner at after and even before the sale, which can help in doing repeat business, profits and customer loyalty (Kumar and Reinartz 2012). A customer centric business can be referred as much more than good service offers. For examples Zappos and Amazon are one of the best customer-centric, both of them have spent many years in creating culture among the customers and the needs of the customer. It does not simply means providing customers great services, rather than that it means offering customers better experience for the stage of awareness, through the purchase made and lastly through post-purchase of the goods (Nadeem 2012). Customer centric is a strategy that is based on giving most priority to the customers firstly and at the core of the business. It can be better understand by the following figure:
Figure 1: customer centricity
(Source: Google images)
Concentering customer at the core of the business helps in collecting mass information within the CRM software which can give access to a full 360 view of the customer, and in result these information can be used to enhance the experience between the customer and the business which in result will enhance the performance of the organization. There are many advantages of customer centric in business which can be listed as: information can be used to understand how a customer buys the product which can give opportunity to create new products and services that are best for the customers. Another advantage of customer centric is that data collected can be used to estimate customer lifetime value to highlight customers who spend more in that company or top spending customers (Tax, McCutcheon and Wilkinson 2013). It was found that companies that are customer centric make 60 % more profit than those who does not concentrate on the customers.
There can many practices to make it possible, among them best practices can be listed as:
A recommendation has several names like, recommender system, and sometimes system is replaced by engine or platform. It is in general subclass of the information filtering system, which is used to predict the preferences that a user will give to the product of an organization or company (Zhou et al. 2015). There are lots of application of this technology in various field including books, news, music, articles based on research, social posts or tags, and in general, products. It is basically a rating system that can be useful in reading the minds of the customer and produce products and services according the demand of the customers. This system is also used by experts such as jokes, collaborators, financial services, online dating, life insurance, restaurants, twitters and many more. There are two ways in which recommendation system work; firstly, content-based filtering which is a process based on personality identification approach. Technically, it recommends other items or products with similar features or identity along with the purchased goods by utilizing a series of discrete characteristics of the bought good. Another way is collaborative filtering which depicts the items which similar user had bought by depicting the interest of that buyer. Technically, it builds a model from the behavior of the past of the user by comparing the ratings, and the previously purchased items, which similar user had been doing in the past (Gomez-Uribe and Hunt 2016). Both the approaches are used to study the behavior and interest of the past buyer and recommend similar items or products by comparing the similar activities done by previous buyer. There are some hybrid recommendations also which involve both the technical process and in result recommend products or items to the user by predicting their interest and needs.
Companies and organization collects huge amount of information related to the behavior of the user and about the transactions that they made to buy items with the organization. This data and information can be stored systematically into the system as the profile of the user and which can be used to depict future interactions with the customers of same behavior (Lu et al. 2016).
This can be used in increasing profits of the company by involving following strategies:
Real world applications of this system are: Amazon at the top, Netflix, Spotify, Best Buy, youtube and many other websites. These all are using this technology to understand the behavior of the customers and present products and services according to their choices in order to enhance the business and make greater profits by fulfilling the needs and satisfaction of the customers.
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).
Gomez-Uribe, C.A. and Hunt, N., 2016. The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), p.13.
Kumar, V. and Reinartz, W., 2012. Customer relationship management: Concept, strategy, and tools. Springer Science & Business Media.
Lu, J., Wu, D., Mao, M., Wang, W. and Zhang, G., 2015. Recommender system application developments: a survey. Decision Support Systems, 74, pp.12-32.
Nadeem, M., 2012. Social customer relationship management (SCRM): how connecting social analytics to business analytics enhances customer care and loyalty?. Browser Download This Paper.
Tan, T.H., Chang, C.S. and Chen, Y.F., 2012. Developing an intelligent e-restaurant with a menu recommender for customer-centric service. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(5), pp.775-787.
Tax, S.S., McCutcheon, D. and Wilkinson, I.F., 2013. The service delivery network (SDN) a customer-centric perspective of the customer journey. Journal of Service Research, 16(4), pp.454-470.
ZHOU, J., TANG, M., TIAN, Y., AL-DHELAAN, A., AL-RODHAAN, M. and LEE, S., 2015. Social network and tag sources based augmenting collaborative recommender system. IEICE transactions on Information and Systems, 98(4), pp.902-910.
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