Estimating Customer Lifetime Value Using Machine Learning Techniques

With the rapid development of the civil aviation industry, high-quality customer resources have become a significant way to measure the competitiveness of the participating airlines. It is well known that the competition for high-value customers has become the core of airline profits. The research of airline customer lifetime value can help airlines identify high-value, medium-value and low-value travelers. What is more, an airline can make resource allocation more rational, with the least resource investment for maximum profit return.


There is a shift in how marketers are reaching consumers. They are employing not only media celebrities but stars from the social media sphere to market to consumers online. Why are today's audiences more receptive to social media influencers than traditional celebrity spokespersons? How do influencers act as tastemakers? How do they create desire for products? You may find the answer in these articles! No matter what your industry is,the authors describe in the pieces abstracted below the opportunities for your brand to collaborate with influencers.

An Introduction to Predictive Customer Lifetime Value Modeling

This article provides a basic understanding of predictive customer lifetime value modeling. It defines the concept and explains why such models are also called customer equity models. Probabilistic models will help us define these parameters at the customer level and make inferences about future purchases and value. Machine learning and Markov models are also worthy approaches to CLV modeling, but they need to be tweaked and sometimes customized to fit the particulars of a business situation. The Pareto/NBD model focuses on modeling lifetime and purchase count.

Social TV: How Social Media Activity Interacts With TV Advertising

Social TV is the simultaneous consumption of television alongside social media chatter about the programming. This topic is highly relevant for marketers. Usually it is considered a bad thing for TV advertisers, and while there can be distraction from the ads, marketers can also benefit from Social TV's positive effects. Consumers' multiscreen activities can be used to attract more viewers, to leverage TV campaigns, and to increase sales. To take advantage of social TV, marketers need to develop a social media and ad design strategy for TV shows. Not every "social show‚" is good for them.

Case Study: Max Factor China Rejuvenates Customers' Loyalty With Social CRM

B2C marketers find it increasingly challenging to earn loyalty as empowered consumers become entitled customers with more options than ever before. This case study explores how Max Factor invested in social CRM to rejuvenate brand loyalty. B2C marketers can use this report to learn how to define an effective loyalty strategy that spans the entire customer life cycle, across channels. A marketer must start building loyalty with potential customers from the moment they first engage with her brand.

Advertising on Hulu, Everything You Need To Know

Advertising on Hulu can be a very effective means of getting a message out and building brand awareness. One 30-second ad can generate a 61% increase in top-of-mind awareness and a 22% increase in purchase intent, when compared to traditional TV ads. Branded title cards at the beginning of a show are 106% better at building top-of-mind awareness than traditional TV ads, with a 31% increase in purchasing intent. Hulu also allows the advertiser a variety of options and methods to target its audience and increase engagement with potential viewers.

12 CRM Features and Why You Need Them

"Only 33% of a sales rep's time is spent actively selling" (CSO insight). That leaves 67% of the rep's time for hidden tasks like prepping for the call. With more of the time spent on prepping for a sales call than on the actual sales pitch, it is important to consider a tool that flips these percentages around. In this article, the author talks about the most important features of a CRM system and dives into its functionality and application based on company size, industry, and sales cycle.

The Development of Innovative CRM E-Commerce: The Case of

This research discusses how e-commerce companies improve their competitiveness in the retail industry in Indonesia through innovative CRM. is one of the largest e-commerce companies in Indonesia , and stays competitive with other e-commerce companies by providing its clients with innovative services. This research also aims to explore the strategies carried out by in developing its CRM. It uses the 7s stages of the McKinsey analytical methodology.

Customer Behavior Mining Framework (CBMF) Using Clustering and Classification Techniques

The present study proposes a Customer Behavior Mining Framework on the basis of data-mining techniques in a telecom company. This framework takes into account the customers' behavior patterns and predicts the way they may act in the future. First, a clustering technique is used to implement portfolio analysis and previous customers are divided based on socio-demographic features through a k-means algorithm. The second phase is devoted to mining the future behavior of the customers.

Understanding Programmatic TV advertising

Television is undergoing tremendous technological developments, which will enable marketers to direct commercial messages to more specific audiences at the individual and/or household level. Traditional ways of buying TV advertising are being challenged by the programmatic approach, which originated with search-and-display ads, and uses data technology and real-time auctions to automate transactions between buyers and sellers.