基于评论行为的客户终身价值模型改进.docx

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Abstract
The customer lifetime value (CLV) model is a valuable tool for calculating the profitability of customers over their entire relationship with a company. However, traditional CLV models have limitations as they do not take into account customer feedback or online behavior. This paper provides a new and improved CLV model that considers the impact of customer comments and reviews on customer lifetime value.
Introduction
Customer feedback and reviews have become increasingly important in today’s economy. With the rise of social media and online shopping, customers have more power than ever to share their experiences with others. As a result, businesses must pay attention to customer feedback and respond accordingly. This also means that customer feedback can be used to predict customer behavior and overall lifetime value. Traditional CLV models do not take these factors into account, but this paper presents a new and improved model that does.
Background
Traditional CLV models calculate the net present value of a customer’s lifetime revenue, minus the cost of acquiring and servicing that customer. It assumes that profitability is directly proportional to revenue and inversely proportional to costs. While this is true to some extent, it does not take into account the impact of customer feedback on future purchasing decisions.
Customers who leave positive feedback and reviews are more likely to continue doing business with a company, as well as bring in new customers through word-of-mouth referrals. Conversely, customers who leave negative feedback are less likely to return, and may even deter potential customers from doing business with the company.
Proposal
The updated CLV model proposed in this paper takes into account customer feedback and reviews when calculating customer lifetime value. It considers the three following metrics:
1. Net promoter score (NPS)
The NPS is a simple metric that measures the likelihood of a customer recommending the company to others. It is calculated by subtracting the percentage of detractors (customers who would not recommend the company) from the percentage of promoters (customers who would recommend the company). Customers who are willing to recommend the company are more likely to be profitable in the long term, and thus have a higher CLV.
2. Sentiment analysis
Sentiment analysis is the process of using natural language processing (NLP) to determine the sentiment of customer feedback. Positive feedback increases the likelihood of repeat business and referrals, while negative feedback decreases it. By tracking sentiment over time, the CLV model can predict whether a customer is likely to continue doing business with the company or not.
3. Engagement metrics
Engagement metrics, such as the number of comments or reviews made by a customer, can also be used to predict future behavior. Customers who take the time to engage with a company are more likely to continue doing business with them, as well as spread positive word-of-mouth referrals.
Conclusion
The proposed updated CLV model takes into account the impact of customer feedback and reviews on customer lifetime value. By using metrics such as NPS, sentiment analysis, and engagement metrics, businesses can better predict future behavior and adjust their marketing strategies accordingly. This can lead to higher profitability and customer satisfaction in the long term.