1. Customer Lifetime Value (CLV)
Mather Economics LLC
September, 2014

2. Agenda Firm overview Customer Lifetime Value (CLV)
Copyright 2013 Mather Economics LLC. All rights reserved.

3. Mather Economics – Firm Overview
Founded In 2002; leading practices in applied microeconomics
Extensive experience in price optimization for subscription services
Pricing strategy & analytics: Market based pricing process
Customer profitability & customer lifetime value (CLV)
Marketing effectiveness
$4 billion in subscription revenue under management
Data on 30 million subscribers received weekly
Additional suite of economic services
Predictive Modeling & Forecasting
Econometrics: digital and print advertising
Market Analysis
Digital Analysis: meter optimization, paywall strategies, web advertising yield
Industry Benchmarking
35 Employees and 10 academic affiliates
Copyright 2013 Mather Economics LLC. All rights reserved.

4. Mather Economics – Publishing Experience
Locations of Mather Clients in North America
Mather Economics has advised over 400 publishers in North America, including 36 of the top 50 and the largest media holding companies at the corporate level.
Locations of Mather Global Clients
Include Publishers in: Toronto, Ottawa, Montreal, Vancouver, Halifax, Calgary, Edmonton, Windsor, Sydney, Melbourne, Auckland, Helsinki and Tampere, Finland.
Several pilots going on in Europe in South America
Copyright 2013 Mather Economics LLC. All rights reserved.

5. Mather Economics – Services Offered
Targeted renewal pricing
Customer lifetime value (CLV)
Targeted acquisition and reacquisition Customer segmentation
Targeted upgrade offers
Digital subscription pricing and monetization strategy
Dynamic paywalls Meter monetization
Advertising pricing and reporting (print and digital)
Benchmarking and digital dashboards
11/8/2018 Copyright 2009 Mather Economics LLC. All rights reserved.

6. Customer Lifetime Value (CLV)

7. Customer Lifetime Value (CLV) – Introduction
The power of CLV (the present value of a customer’s total contribution to cash flow) is that it clarifies key strategic considerations, such as:
What is the likelihood a specific customer will leave?
How is a customer’s value affected by attributes or targeted incentives? What will happen to my bottom line if those attributes change by 10%?
What customers should we acquire? What incentives should we use?
What specific conditions optimize profitability for a given level of risk?
How do we operationalize CLV to impact retention and drive increased customer satisfaction through targeted customer experiences?
CLV allows you to conduct marketing analyses with increased rigor and develop a scoring system to drive optimal action based on individual customer value.

8. Customer Lifetime Value (CLV) – Definition
CLV is the risk-adjusted operating margin for an individual customer
CLV = [(Circ Rev + Adv rev – Del Cost – Prod Cost)*(Expected Retention)] – Acquisition Cost
Expected Lifetime
(Area Under Curve)
Likelihood of Survival:
New Customer
CLV metric allows you to allocate acquisition and retention resources to their most profitable use through fact-based analysis

9. Customer Lifetime Value (CLV) – Building Model
Once the source data is identified and available in a central location, the CLV model can be implemented in a scientific way
Construct a customer service history
Start and end date
Usage statistics (e.g., prices, payments, service changes, print/digital/bundled)
Attach indicators to the service history
HH demographics (e.g., income, age, gender, children, education)
Marketing-specific metrics (e.g., insert revenue, ROP adv. value)
Organization-specific metrics (e.g., acquisition response rates and costs)
Digital metrics (sub and non sub web traffic consumption and usage patterns)
Develop a retention (survival) model
Calculate “best fit” (regression) model correlating customer variables to retention
Calculate the CLV using the retention rates, revenue and cost metrics
How the model is built – and implemented – is crucial to making CLV scores actionable and statistically significant

10. Customer Lifetime Value (CLV) – Building Model

11. Customer Lifetime Value (CLV) – Acquisition
CLV = [(Circ Rev + PP rev – Del Cost – Prod Cost)*(Expected Lifetime)] PV – Acquisition Cost
One method of decomposing the data – by zip
Ranking each one of the components
USAGE EXAMPLE – one zip has higher CLV score than another, but perhaps one of worst performing carriers delivers there, adjust to prioritize better service to most profitable subs

12. CLV Dashboard – Sample screen shot

13. CLV Dashboard – Sample screen shot

14. CLV Dashboard – Sample screen shot

15. CLV Dashboard – Sample screen shot

16. Customer Lifetime Value (CLV) – Distribution of Operating Margin Shows Profitable vs. Unprofitable Subscribers

17. Customer Lifetime Value (CLV) – Acquisition
Targeted acquisition lists developed specifically for channels such as:
Direct mail and
Samples and Inserts
Telemarketing
Social media
Reflects the cost-per-order and offers to be employed by channel to determine sales source efficiency by geography and households
Non subscriber HH’s are scored based on projected profitability to the organization
Granular reporting of results and strategic insight

18. Customer Lifetime Value (CLV) – Retention
Targeted lists for retention campaigns
Upgrade or bundled pricing offers for multiple publications
Custom approach developed for subscribers deemed at risk in survival model
Prioritize service to most valuable subs
Metrics available to CSRs that will guide them in assigning stop-save offers, credits, and premiums
CSRs talk to customers more than anyone else, yet normally have access to least information
Proactive and reactive with CLV scores integrated into CIS
Can develop customized call center scripting around CLV scores
Can develop custom call center strategy around pricing reverts with appropriate fall back steps by CLV score
Improve subscriber relationship matching skills based call center routing with custom messaging for renewal, retention, and upgrade efforts
Reporting of results and strategic insight

19. Customer Lifetime Value – Conclusions
Retention is modeled using survival analysis
Calculate “best fit” (regression) model correlating customer variables to retention
A/B testing validates price elasticity for customer segments
Calculate the CLV using the retention rates, revenue, and cost metrics
Every customer is tracked as they enter the billing cycle
Recommendations of strategy and best practices continuously provided
Regular phone calls and communication with analysts and clients
On-site visits can be arranged

20. Contact Information
Matthew Lulay
Manager
Mather Economics LLC
main: 
direct:   
fax:     
11/8/2018 Copyright 2010 Mather Economics LLC. All rights reserved.