Twice weekly articles to help SMB companies optimize business performance with data analytics and to improve their analytics expertise.
While most businesses today can easily tell how many products they sold to their customers, they cannot easily tell which customer bought what product or which customer was more profitable and which customer was less profitable. Does this pose a problem? In today's world of highly knowledgeable consumers and social networks which have begun to impact purchase behaviors (think reviews, facebook likes, etc), the future success of many companies will depend on how they can retain customers by means of individual propositions and not simply rely on volumes or mass-market appeal alone.
In a previous article we provided an overview of the different types of analytics one can run for problems related to customer acquisition, customer retention and customer churn. We mentioned how most of these questions either fall into strategic or tactical categories and can be addressed by either descriptive analytics or predictive analytics. In this article we will explore in a little more detail some of the tactical problems that can be addressed by predictive analytics.
The concept of Customer Lifetime Value (CLV) essentially treats customers as long term investments where you invest in them systematically over a period of time in the expectation that you recoup the customer acquisition and servicing costs plus some profits. In a sense they are similar to financial instruments such as bonds. As anyone with a background in finance theory knows, bonds can be priced, and so we can apply a similar logic to "price" the value a given customer brings to the business.
In business schools they take great pains to emphasize that the ultimate objective of a business is maximing the so-called "shareholder value". On a less abstract level, experts like Goldratt simply state that the ultimate purpose of any business is to make profits. A very intriguing idea is to make the connection between the market capitalization of a company and the profitability of each individual customer of that company.
This is the fourth in a series of articles on the predictive metric, Customer Lifetime Value (CLV). Customer lifetime value is defined as a measure of the present value of future cash flows attributed to the customer relationship.
This is the third in a series of articles on the predictive metric, Customer Lifetime Value (CLV). Customer lifetime value (CLV) is defined as a measure of the present value of future cash flows attributed to the customer relationship.
This is the second in a series of articles on the predictive metric, Customer Lifetime Value (CLV). In part 1, we discussed the basic idea behind CLV and explained how the CLV analytics forms the cornerstone of customer value management.
This is the first in a series of articles on Customer Lifetime Value (CLV). CLV is an important predictive metric, at the intersection of marketing and finance, providing business managers and senior decision makers with forward-looking information on customer relationship performance. Customer relationship performance is a key driver of firm value, risk management and profits. In addition, a focus on CLV enables a customer centric business culture and is the core of customer value management.