This presentation provides insight into how to forecast and calculate customer lifetime value (CLV). Here a startup applied a scientific approach to maximise customer retention and minimise churn. The outputs of the analytics were built into the system and business processes driving the success of the company and helping it to win the customer service of the year award, and to achieve a successful exit through acquisition.

1. Customer Lifetime Value
IIR Allocating Cost & Calculating Profitability In Telecoms
Hilton Olympia London – December 2007
Paul Woods
Commercial Director

2. About Perlico
• Ireland’s No. 1 alternative operator in fixed residential market
• Recently acquired by Vodafone for €80 million
• Winner of Telecommunications & IT Customer Service Team of 2007
• Winner of the Inspired Telecommunication, Media, Technology & IT award for
2007
• First Irish telecommunications company to publish its daily customer service
statistics online
• ‘Will to win’ culture
Slide 2 of 17

3. Perlico | Objective today
• Introduce concept of customer lifetime value (CLV)
• High level overview of calculating CLV and potential customer value
• Consider customer retention & ability to predict / reduce churn
• Take account of ‘Word of mouth’ marketing
• Implications of CLV for pricing strategy
• Conclusions & feedback
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4. • Acquiring new customers significantly more expensive than retaining existing
customers – can range from 1x to 20x+
• Customer lifetime value = discounted value of future profit the customer
yields to the company
v CLV can be calculated before and after marketing and retention
activities for decision making purposes
v Customer profitability a summary of past – CLV is forward looking
• Concept of CLV shifts focus from the sales ‘transaction’ alone to maintaining
an ongoing relationship with the customer
• Importance of understanding CLV
v Ensure company doesn’t overspend to retain unprofitable customers
v Or underspend on retaining highly profitable customers
Perlico | Introduction
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5. Perlico | Averages ‘the white rainbow’
Revenue
Gross
Margin
Ebitda
• No such thing as an average customer!
v ‘In saying a little about a group, they can obscure all that matters about
its parts’
v 80/20 Rule: 80% profit generated from 20% customers
v High ARPU may not result in high CLV
v Manage average CLV up but differentiate key activities based on customer
segments
Difficulty to calculate on customer basis
Value to
business

6. • Recognises importance of good customers
v Quality insight - re-engineer processes that ‘touch the customer’
• Cracking CLV at individual customer level the Holy Grail
v Easier to implement for online trading than offline
v But acquisition / pricing / propositions by CLV segments very worthwhile
• Provides scope for more strategic reporting
v Capital investment usually focused on geography and/or products
v P&L and Balance Sheet also focused on geography and/or products
v Cost allocations for CLV may have to be computed offline
• Churn management
v Identifying customers with higher propensity to be loyal
v Identifying existing customers most likely to churn
Perlico | CLV – why?
Slide 6 of 17

7. • CLV for the customer (i) using current service can be denoted as:
Where
CFi,k = the net cash flow
D = discount factor
K = time period
• Time period (K) = Customer length of service (LOS)
v Number of transactions a customer will make
v Impacted by contract duration, competitiveness & special promotions
v Long LOS not necessarily correlated to higher CLV
v Acquire customers when CLV > customer acquisition costs
Perlico | CLV formula
Slide 7 of 17

8. • Customer retention – reducing churn of profitable customers critical
• Customer segmentation
v Separate heterogeneous base into homogenous groupings
• Product portfolio
v Transactional & recurring income
v Efficient bundling & implications for churn / switching costs
• Discount factor – future revenue worth less today
• Model should highlight
v Strategic marketing insight – tactics for customer acquisition
v Identify ‘handle with care’ targets for customer care
v If possible correlate CLV variations to channel
Perlico | CLV – model parameters
Slide 8 of 17

9. • Current value does not provide insight into potential value via up/cross
selling of add-ons or new products
• Potential customer value can also be calculated as:
Where
i = customer who uses service j from competitive options available
ij = profit company can make from customer i
• Decision tree can be used to evaluate potential value for distribution of
customers taken into account probability of upselling options
• Acquisition threshold: weighted potential value > acquisition costs
Perlico | Potential value
Slide 9 of 17

10. Perlico | Potential value – decision tree
• Cost of upsell usually significantly lower than cost of new acquisition
• Potential value should be greater than existing CLV
• Influences customer acquisition strategy
1000 Prospects
20% Voice
(200 Customers)
20% add-­on
(40 Customers)
Potential value X
80% No add-­on
(0) Customers)
Potential value Y
20% Bundle
(200 Customers)
20% VoIP
(40 Customers)
Potential Value Z
20% Software
(40 Customers)
60% No add-­o
(0 Customers)
60% No Sale
Target for
Acquisition

11. Perlico | Customer Retention
• Customer loyalty = 1 – churn rate
v 5% improvement in retention could equal 100% increase in gross
margin
v Case for separate budget targeted at churn reduction
v Retention costs need to be directly attributed to customer segments
• Avoid customer churning when customer profit < acquisition costs
v key consideration when developing aggressive promotions
• Key to retention is the ability to leverage & understand business intelligence
• Churn can be caused by everything:
v Competitor propositions
v Poor sales practice / customer care
v Billing errors
v Faults
Slide 11 of 17

12. Perlico | Example: Care – decision tree
• ‘Silent killer’ are the customers who don’t complain
• Each dissatisfied customer tells others
v Loss of (CLV * X) + (customer acquistion * X) to hold base static
• Important to make it easy for customers to complain & fix process issues
100 Dissatisfied
Customers
4% Complain
75% Retained
(3 Customers)
25% Exit
(1 Customer)
Tell their family
/ friends
96% Do not
complain
5% Retained
(1 Customer)
95% Exit
(95 Customers)
Tell their family
/ friends
Slide 12 of 17

13. Perlico | Predicting churn
• Models only useful if they can predict outcome more accurately than without
a model
v Possible to develop complex multiple regressions
v Also possible to develop & hone intuitive customer satisfaction index
• Churn model should consider analysis of customers who have churned and
customers with high CLV who have not churned
• Data must be very clean over lifetime
v Customer care issues clearly categorised
• Possible to estimate in real-time probability of churn
v Algorithms can be calculated using CRM data
• Volume & category of customer interactions
• Credit management history
• Customer network effects
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14. Perlico | Reducing churn
• Once churn can be predicted – manipulate key levers to reduce
• Deploying anti-churn measures should only target customers with CLV net of
acquisition cost > 0
Or CLV net acquisition cost + weighted additional potential value > retention
costs
v Greater frequency of contact with care = higher propensity to churn
v Avoid billing errors
v Fix process issues highlighted by care cases = minimise dissatisfied
customers that don’t complain
Customer Churn
Poor
processes
Misinformation
Billing errors
Slide 14 of 17

15. • Effect of referrals from ‘Word Of Mouth’ not often taken into CLV calculations
• Rewarding existing customers for help in acquiring new customers should
increase CLV of both existing and new customer
• If referral award < customer acquisition cost then CLV new customer on
average higher
• Important in telecoms as service more intangible and thus fair hypothesis
that WOM plays important role
• Weighted probability of referrals can also be taken into account when
calculating weighted potential value
• Do not over reward for customer referrals
v Potential of cognitive dissonance due to set expectations
Perlico | CLV – referrals
Slide 15 of 17

16. Perlico | CLV & Pricing Strategy
• CLV provides new perspective on pricing decisions
v Tariffs based on contract duration
v Flat rate users – distribution of CLV when costs variable
v Pricing options differentiated by customer – loyalty bonuses
v Introduce staggered offers to extend LOS
v Value focused investment in retention
v Avoid unviable promotions
• Price to drive customer network effects
v Family / friend calling plans
v Referrals
• Price to take account of switching cost
v Higher switching cost = lower probability churn
Strategic
Bundling
Ramsey
Pricing
Value
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17. Perlico | Conclusions
• Without predicting the future value of a customer its difficult to quantify how
much to invest in acquiring new or retaining existing customers
• CLV modelling should include past value, potential value and propensity to
churn
• Modelling CLV encourages research on link between satisfaction, loyalty &
profitability
v Outputs should drive more efficient business processes
• Important to calculate CLV before and after acquisition / retention activity
v CLV upon acquisition > acquisition cost
v CLV after retention activity > marginal retention cost
• Strategic pricing decisions should take account of CLV
• Customers should be for life, not just for Christmas!
• Questions welcomed
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