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Customer Relationship Management
Notes By the way, using customer behaviour to predict the relative Lifetime Value and loyalty of
customers is a 40 year old technique still used by mail order and TV shopping companies today.
Large sites with CRM analytics are using this technique, known as RFM, to predict customer
value and response to promotions.
Caselet Calculating the Customer Lifetime Value: SECOR
ECOR assisted a large European mobile telephony operator with the definition and
project management of a Customer Lifetime Value (CLV) strategic solution. The
Sobjective was to establish a company-wide system for CLV calculation and analysis
utilizing all relevant revenues and costs from across the organization. The system was
intended to underpin the company’s CRM strategy providing the foundation for activities
such as:
Ad-hoc segmentation and data analysis for data driven marketing
Automation of decisions against customer requests such as handset upgrades
Targeted retention activities and decisions to ensure retention effort is aligned to
CLV
Identification of customers for cross-selling and up-selling
Development of product portfolios aligned to high CLV customers
Alignment of customers to appropriate channels by CLV
The benefits of the project were identified as being in excess of £3m!
Source: http://searchcrm.techtarget.com/feature/CRM-case-studies
Let’s say you’re not satisfied with using relative Lifetime value as a proxy for absolute Lifetime
Value. You’re a glutton for punishment, or your boss wants a hard number. No problem. Here
are a few issues we need to put on the table when discussing the calculation of LTV:
1. If you haven’t been in business long enough to know the Lifetime of a customer, just put
a stake in the ground by looking for defected best customers. Look at customers who have
spent or visited the most with you and then of these, look at the ones who haven’t made a
purchase or visit in some time (6–9 months, for example). In all likelihood, the last purchase
or visit was the end of the Lifecycle when considering best customers who have stopped
buying or visiting. When best customers stop, they’re usually all done. Then look at first
purchase or visit date for these customers, calculate your Lifetime, and use this length of
time as the “standard” customer LifeTime, realizing the average lifetime is probably
much shorter.
2. Frequently, a customer will defect for a few years and then come back. This is cool, and
normal. Their life changed somehow and they left, and now they need you again. Most
offline marketers would call a customer who has had zero activity for over 2 years a
defected customer. Online, it’s more like 6 months for the average customer, unless you
are in a classic seasonal business. If the customer starts up again, they would be a “new
customer”, for marketing and modelling purposes. They will more likely behave like a
new customer than a current customer. The behaviour will ramp and fall off all over
again, just like it did in their previous Lifecycle with your business.
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