Customer Value Maximization
Customer value maximization is about personalizing customer marketing efforts to maximize the total revenues that every customer generates for the company.
Introduction to Customer Value Maximization
Once a company has acquired a new customer, the ultimate goal of marketing and retention efforts is to maximize the revenues that the customer generates for the company (assuming a stable relationship between revenue and profit). The three primary factors which contribute to the total revenues that any particular customer will generate are time (how long the customer remains an active customer), purchase frequency (how often the customer purchases something from a company) and monetary value of purchases (how much money the customer spends with the company). Thus, maximizing the value of the customer to the business means maximizing the time × purchase frequency × monetary value equation.
How to Achieve Customer Value Maximization
Providing customers with the products or services they seek, at competitive prices and with terrific customer service is obviously the best way to ensure that a customer remains with a company for the long term and continues to buy from it. However, there are always specific actions available to a company which will encourage customers to stick around longer and spend more. The challenge is knowing which actions to apply to which customers and when to do so for maximum results.
Optimove introduces customer value maximization modeling methods which are far more advanced and effective than anything else available, manual or automated. By combining a number of technologies into an integrated, closed-loop system, marketers enjoy a highly-accurate customer value maximization machine in an easy-to-use Web application:
Optimove achieves market-leading customer value maximization modeling with the combination of the following capabilities:
- Customer segmentation – It is important to segment customers into small groups and address individual customers based on actual behaviors – instead of hard-coding any pre-conceived notions, making assumptions of what makes customers similar to one another or looking only at aggregated/averaged data (which hides important facts about individual customers).
- Tracking customers over time – It is critical to follow how customers move among different segments over time (i.e., dynamic segmentation), including customer lifecycle context and cohort analysis – instead of just determining in what segments customers are now without regard for how or when they arrived there.
- Accurate prediction of future customer behavior (e.g., convert, churn, spend more, spend less) – One should always use predictive customer behavior modeling techniques – instead of just looking in the rear-view mirror of historical data.
- Customer lifetime value (LTV) – Customer value maximization modeling should be based on the use of advanced calculations to determine the customer lifetime value (LTV) of every customer and to base decisions on it – instead of looking only at the short-term revenue that a customer may bring the company.
- Marketing action optimization – The marketer or retention expert should know, based on objective metrics, exactly what marketing actions to do now, for each customer, in order to maximize the long-term value of every customer – instead of trying to figure out what to do based on a dashboard or pile of reports.
One way to think of the difference between conventional approaches and the Optimove approach is that the former is like a customer snapshot whereas the latter is a customer animation. The animated view of the customer is far more revealing, allowing much more accurate customer behavior predictions and, thus, customer value maximization.
Start Using the Most Advanced Customer Value Maximization System Available Today!
Contact us today – or request a Web demo – to learn how you can use Optimove to predict customer behavior and easily maximize the impact of every marketing action in order to convert more customers, increase the spend of existing customers and reduce customer churn.
Last updated December 2015