Recommendations as personalized marketing: insights from customer experiences

Abstract

Purpose – The purpose of this paper is an exploratory study of customers’ “lived” experiences of commercial recommendation services to better understand customer expectations for personalization with recommendation agents. Recommendation agents programmed to “learn” customer preferences and make personalized recommendations of products and services are considered a useful tool for targeting customers individually. Some leading service firms have developed proprietary recommender systems in the hope that personalized recommendations could engage customers, increase satisfaction and sharpen their competitive edge. However, personalized recommendations do not always deliver customer satisfaction. More often, they lead to dissatisfaction, annoyance or irritation. Design/methodology/approach – The critical incident technique is used to analyze customer satisfactory or dissatisfactory incidents collected from online group discussion participants and bloggers to develop a classification scheme. Findings – A classification scheme with 15 categories is developed, each illustrated with satisfactory incidents and dissatisfactory incidents, defined in terms of an underlying customer expectation, typical instances of satisfaction and dissatisfaction and, when possible, conditions under which customers are likely to have such an expectation. Three pairs of themes emerged from the classification scheme. Six tentative research propositions were introduced. Research limitations/implications – Findings from this exploratory research should be regarded as preliminary. Besides, content validity of the categories and generalizability of the findings should be subject to future research. Practical implications – Research findings have implications for identifying priorities in developing algorithms and for managing personalization more strategically. Originality/value – This research explores response to personalization from a customer’s perspective.

Analysis of Demography, Psychograph and Behavioral Aspects of Telecom Customers Using Predictive Analytics to Increase Voice Package Sales

“…It can be inferred that buying it or not, the calculation generated a higher value compared to previous research and the accuracy in this research shows the precision in predicting customers who would purchase voice packages. This is contrary to research by Shen (2014) that argues that preferences are constructed when decisions are being made, which means that customers often do not have stable preferences to be retrieved and applied to decisions. For instance, Ma et al (2017) believed that when preferences often ill-defined, consumers will evaluate personalized recommendations based on how easily they can identify their stated preferences…”

“…According to Vesanen (2005), personalized marketing uses technology and customer information to individually adjust electronic commerce interactions between businesses and customers. Furthermore, with customers of ever-increasing technology savvy, personalized products and services become a business necessity (Shen, 2014). Using the information previously obtained in real-time about the customer profile is then used to offer products or services according to customer needs.…”

Application of Mining Algorithm in Personalized Internet Marketing Strategy in Massive Data Environment

“…Through mining algorithms, e-commerce companies can turn useful information into knowledge and serve marketing to make the right decisions and create value. It can be found that data is the basis for personalized marketing, and then formulating personalized strategies through data mining can give full play to the potential value of each user, help improve the competitive advantage of enterprises [15], and maximize the interests of enterprises. This study focused on the marketing strategy of telecommunication enterprises.…”

Artificial Intelligence for marketing plan: the case for e-marketing companies

“…Besides, the organization could provide the promotional message according to customers' characteristics, identified through collecting and analyzing customer information (Khodabandehlou and Rahman, 2017). It could also improve promotional messages and send them again according to the changes that may occur to the customer (Shen, 2014). Using AI, e-mail could be used to promote the product based on identifying the customer's characteristics previously identified and then sending an e-mail to each customer according to his characteristics and desires in the products presented (Raunaque et al, 2016).…”