Customer life cycle analysis tracks the lifespan of customers from the time they are acquired through their first purchase and further purchases, up to churn.
This close follow-up, is important for a businesses to determine budgeting priorities for various marketing activities
and is useful in making decisions which are in line with the organization's KPIs.
Customer life cycles are divided into five discrete phases: reach, acquisition, conversion, retention and churn.
It is important to come up with the most optimized way to communicate, advertise and sell to a customer, relative to the stage the customer is in.
By using appropriate segmentation of customer data, one is able to fine tune the organization's approach to its customers in the most optimal manner.
The default stages in the life of an online customer:
- Reach: Getting customers attention. Marketing tactics used for this purpose are: paid search, social media marketing, banners, physical advertising signposts etc.
- Acquisition: Statistics on where the customer came from. This can be the source of the link or the term searched for. Online analytics provide this data, which enables one to evaluate visitors and monitor conversion.
- Conversion: conversion is more than just making a sale. To be more precise, marketers need to think about conversion as a successful completion of specific activities by visitors. This could be the download of an app or document, purchase of a product or service, registering to a weekly newsletter etc.
- Retention: this includes not only knowing how to retain customers, but moreover how to up-sell and cross-sell products and relevant services. Maintaining a high retention rate requires tracking and monitoring of activities of return customers, thus being able to swiftly and efficiently react to their needs.
- Churn: The fifth marketing phase in customers lifecycle is churn prevention and getting back customers who left (this phase is sometimes split into two). A customer that left is difficult to get back, but on the other hand, the organization still has valuable data on this customer – and thus, it may even be easier to get him/her back than acquire a new customer from scratch, for which the organization has no data. Customers which have been returned to your product/service have high ROI.
In order to know how one should act in each stage of the customer life-cycle,
it is customary to perform client segmentation by various parameters, in order to determine what are the most beneficial offers to be made to a specific customer.
If you wish to apply this in your business contact us for a free phone consultation.
An example of such a segmentation can be one using the KMEANS Clustering algorithm on some of the relevant parameters such as: time active in the system (up to 1 week, 1 week-1 month, 1 month-2 months, 2 months+), deposit value (0, 0-$100, $101-$1000, $1000+), last activity (within last week, within last month, within more than 1 month) etc.
One can update a table every day with the relevant data for each customer and update the segmentations once a week. We used Azure ML (Click to read about it in Hebrew) where KMEANS is readily available, but another possibility would have been to use KNN and then one would have had to use Python and R modules from the Cortana machine learning modules.
The result was 6 segments of customers, which, after analysis, were discovered to be: new customers, new customers with low value, new customers with moderate-high value, old customers with low value, old customers with moderate-high value and churners who have been inactive for a long time.
An example of the use of segmentation in customer-lifecycle-based marketing:
The goal is to migrate customers between segments in order to maximize their return on investment and result in a higher LTV. By offering customers the right combination of products/services, customers can be moved to segments which are more beneficial to the organization – for example: movement from being a new customer with low value, to being a new customer with moderate-high value or transforming a churning customer into a new customer (but with a certain history with the company).
To summarize, customer segmentation, with the available technological tools, combined with advanced marketing thinking and data-based decision making can bring high value to organizations and augment customer value in general.
A combination with this kind of thinking, with a planned customer journey strategy can maximize the return from each customer.
Interested in expanding your knowledge in this field? For a free consultation meeting click here.
Shalom Dinur,
Senior Data Scientist,
Zvika Yaron, VP Sales,
DataCube.