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In [[marketing]], '''customer lifetime value''' (CLV), '''lifetime customer value''' (LCV), or '''user lifetime value''' (LTV) is a prediction of
This is a preview for the new '''MathML rendering mode''' (with SVG fallback), which is availble in production for registered users.
the net profit attributed to the entire future relationship with a customer. The prediction model can have varying levels of sophistication and accuracy, ranging from a crude [[heuristic]] to the use of complex [[predictive analytics]] techniques.


==Origins==
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One of the first accounts of it is in the 1988 book Database Marketing, and includes detailed worked examples.<ref>Shaw, R & Stone, M. (1988) Database Marketing, Gower, London</ref><ref>Shaw, R & Stone, M. (1990) Database Marketing, Wiley US Edition</ref>
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== Uses and Advantages ==
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Customer lifetime value has intuitive appeal as a marketing concept, because in theory it represents exactly how much each customer is worth in monetary terms, and therefore exactly how much a marketing department should be willing to spend to acquire each customer, especially in [[direct response marketing]].


Lifetime value is typically used to judge the appropriateness of the costs of acquisition of a customer. For example, if a new customer costs $50 to acquire (COCA, or cost of customer acquisition), and their lifetime value is $60, then the customer is judged to be profitable, and acquisition of additional similar customers is acceptable.
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:<math forcemathmode="mathml">E=mc^2</math>


Additionally, CLV is used to calculate [[customer equity]].
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Advantages of CLV:
'''source'''
* management of customer relationship as an asset
:<math forcemathmode="source">E=mc^2</math> -->
* monitoring the impact of management strategies and marketing investments on the value of customer assets
* determination of the optimal level of investments in marketing and sales activities
* implementation of sensitivity analysis in order to determinate getting impact by spending extra money on each customer<ref>Gary Cokins (2009). Performance Management: Integrating Strategy Execution, Methodologies, Risk and Analytics. ISBN 978-0-470-44998-1. p. 177</ref>
* optimal allocation of  limited resources for ongoing marketing activities in order to achieve a maximum return
* a good basis for selecting customers and for decision making regarding customer specific communication strategies
* measurement of customer loyalty (proportion of purchase, probability of purchase and repurchase, purchase frequency and sequence etc.)<ref>V. Kumar (2008). Customer Lifetime Value. ISBN 978-1-60198-156-1. p. 6</ref>


==Misuses and Downsides==
<span style="color: red">Follow this [https://en.wikipedia.org/wiki/Special:Preferences#mw-prefsection-rendering link] to change your Math rendering settings.</span> You can also add a [https://en.wikipedia.org/wiki/Special:Preferences#mw-prefsection-rendering-skin Custom CSS] to force the MathML/SVG rendering or select different font families. See [https://www.mediawiki.org/wiki/Extension:Math#CSS_for_the_MathML_with_SVG_fallback_mode these examples].
===NPV vs Nominal Prediction===
The most accurate CLV predictions are made using the [[net present value]] (NPV) of each future net profit source, so that the revenue to be received from the customer in the future is recognized at the future value of money. However, NPV calculations require additional sophistication including maintenance of a [[discount rate]], which leads most organizations to instead calculate CLV using the nominal (non-discounted) figured. Nominal CLV predictions are biased slightly high, scaling higher the farther into the future the revenues are expected from customers


===Net Profit vs Revenue===
==Demos==
A common mistake is for a CLV prediction to calculate the total [[revenue]] or even [[gross margin]] associated with a customer. However, this can cause CLV to be multiples of their actual value, and instead need to be calculated as the full [[net profit]] expected from the customer.


===Segment Inaccuracy===
Here are some [https://commons.wikimedia.org/w/index.php?title=Special:ListFiles/Frederic.wang demos]:
Opponents often site the inaccuracy of a CLV prediction to argue they should not be used to drive significant business decisions. For example, major drivers to the value of a customer such as the nature of the relationship are often not available as appropriately structured data and thus not included in the formula.


===Comparison with Intuition===
More, predictors such as specific [[demographics]] of a customer group may have an effect that is intuitively obvious to an experienced marketer, but are often omitted from CLV predictions and thus cause inaccuracies in certain customer segments.


==Effects on Business Practices==
* accessibility:
Its use as a marketing metric tends to place greater emphasis on customer service and long-term customer satisfaction, rather than on maximizing short-term sales.
** Safari + VoiceOver: [https://commons.wikimedia.org/wiki/File:VoiceOver-Mac-Safari.ogv video only], [[File:Voiceover-mathml-example-1.wav|thumb|Voiceover-mathml-example-1]], [[File:Voiceover-mathml-example-2.wav|thumb|Voiceover-mathml-example-2]], [[File:Voiceover-mathml-example-3.wav|thumb|Voiceover-mathml-example-3]], [[File:Voiceover-mathml-example-4.wav|thumb|Voiceover-mathml-example-4]], [[File:Voiceover-mathml-example-5.wav|thumb|Voiceover-mathml-example-5]], [[File:Voiceover-mathml-example-6.wav|thumb|Voiceover-mathml-example-6]], [[File:Voiceover-mathml-example-7.wav|thumb|Voiceover-mathml-example-7]]
** [https://commons.wikimedia.org/wiki/File:MathPlayer-Audio-Windows7-InternetExplorer.ogg Internet Explorer + MathPlayer (audio)]
** [https://commons.wikimedia.org/wiki/File:MathPlayer-SynchronizedHighlighting-WIndows7-InternetExplorer.png Internet Explorer + MathPlayer (synchronized highlighting)]
** [https://commons.wikimedia.org/wiki/File:MathPlayer-Braille-Windows7-InternetExplorer.png Internet Explorer + MathPlayer (braille)]
** NVDA+MathPlayer: [[File:Nvda-mathml-example-1.wav|thumb|Nvda-mathml-example-1]], [[File:Nvda-mathml-example-2.wav|thumb|Nvda-mathml-example-2]], [[File:Nvda-mathml-example-3.wav|thumb|Nvda-mathml-example-3]], [[File:Nvda-mathml-example-4.wav|thumb|Nvda-mathml-example-4]], [[File:Nvda-mathml-example-5.wav|thumb|Nvda-mathml-example-5]], [[File:Nvda-mathml-example-6.wav|thumb|Nvda-mathml-example-6]], [[File:Nvda-mathml-example-7.wav|thumb|Nvda-mathml-example-7]].
** Orca: There is ongoing work, but no support at all at the moment [[File:Orca-mathml-example-1.wav|thumb|Orca-mathml-example-1]], [[File:Orca-mathml-example-2.wav|thumb|Orca-mathml-example-2]], [[File:Orca-mathml-example-3.wav|thumb|Orca-mathml-example-3]], [[File:Orca-mathml-example-4.wav|thumb|Orca-mathml-example-4]], [[File:Orca-mathml-example-5.wav|thumb|Orca-mathml-example-5]], [[File:Orca-mathml-example-6.wav|thumb|Orca-mathml-example-6]], [[File:Orca-mathml-example-7.wav|thumb|Orca-mathml-example-7]].
** From our testing, ChromeVox and JAWS are not able to read the formulas generated by the MathML mode.


==Predictive Models==
==Test pages ==
===Simple Ecommerce Example===
(Avg Monthly Revenue per Customer * Gross Margin per Customer) / Monthly Churn Rate


You should have something that looks like:  
To test the '''MathML''', '''PNG''', and '''source''' rendering modes, please go to one of the following test pages:
$100 avg monthly spend * 25% margin / 5% monthly churn = $500 LTV <ref>http://www.quora.com/How-do-you-calculate-Customer-Lifetime-Value#</ref>
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*[[Help:Formula]]


===A Retention Example===
*[[Inputtypes|Inputtypes (private Wikis only)]]
====4 Steps====
*[[Url2Image|Url2Image (private Wikis only)]]
# forecasting of remaining customer lifetime in years
==Bug reporting==
# forecasting of future revenues year-by-year, based on estimation about future products purchased and price paid
If you find any bugs, please report them at [https://bugzilla.wikimedia.org/enter_bug.cgi?product=MediaWiki%20extensions&component=Math&version=master&short_desc=Math-preview%20rendering%20problem Bugzilla], or write an email to math_bugs (at) ckurs (dot) de .
# estimation of costs for delivering those products
# calculation of the net present value of these future amounts<ref>Lynette Ryals (2008). Managing Customers Profitably. ISBN 978-0-470-06063-6. p.85</ref>
Forecasting accuracy and difficulty in tracking customers over time may affect CLV calculation process.
 
====Inputs====
* '''[[Churn rate]]''', the percentage of customers who end their relationship with a company in a given period.  One minus the churn rate is the '''retention rate'''.  Most models can be written using either churn rate or retention rate.  If the model uses only one churn rate, the assumption is that the churn rate is constant across the life of the customer relationship.
* '''Discount rate''', the [[cost of capital]] used to discount future revenue from a customer.  Discounting is an advanced topic that is frequently ignored in customer lifetime value calculations.  The current [[interest rate]] is sometimes used as a simple (but incorrect) proxy for [[discount rate]].
* '''[[Contribution margin]]'''.
* '''Retention cost''', the amount of money a company has to spend in a given period to retain an existing customer.  Retention costs include customer support, billing, promotional incentives, etc.
* '''Period''', the unit of time into which a customer relationship is divided for analysis.  A year is the most commonly used period.  Customer lifetime value is a multi-period calculation, usually stretching 3–7 years into the future.  In practice, analysis beyond this point is viewed as too speculative to be reliable.  The number of periods used in the calculation is sometimes referred to as the model '''horizon'''.
 
====Model====
<ref>Berger, P. D. and Nasr, N. I. (1998), Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing, 12: 17–30. {{doi|10.1002/(SICI)1520-6653(199824)12:1<17::AID-DIR3>3.0.CO;2-K}}</ref>:
 
<math>\text{CLV}  = \text{GC} \cdot \sum_{i=0}^n \frac{r^i}{(1+d)^i} - \text{M} \cdot \sum_{i=1}^n \frac{r^{i-1}}{(1+d)^{i-0.5}}</math>,
 
where <math>\text{GC}</math> is yearly gross contribution per customer, <math>\text{M}</math> is the (relevant) retention costs per customer per year (this formula assumes the retention activities are paid for each mid year and they only affect those who were retained in the previous year), <math>n</math> is the horizon (in years), <math>r</math> is the yearly retention rate, <math>d</math> is the yearly discount rate.
 
==Simplified Models==
It is often helpful to estimate customer lifetime value with a simple model to make initial assessments of customer segments and targeting. Possibly the simplest way to estimate CLV is to assume constant and long-lasting values for contribution margin, retention rate, and discount rates, as follows <ref>Adapted from "Customer Profitability and Lifetime Value," HBS Note 503-019</ref>:
 
<math>\text{CLV} = \text{GC} \cdot (\frac{1+d}{1+d-r})</math>
 
==See also==
[[Gompertz distribution]]
 
==References==
 
<references/>
 
==External links==
 
#[https://www.custora.com/home/customer_lifetime_value custora.com/home/customer_lifetime_value]
#[http://www.mineful.com/customer-retention-roi-calculator.html Free customer lifetime value calculator]
#[http://www.kaushik.net/avinash/analytics-tip-calculate-ltv-customer-lifetime-value/ kaushik.net/avinash/analytics-tip-calculate-ltv-customer-lifetime-value/]
#[http://ariegoldshlager.posterous.com/recommend-reading-customer-lifetime-value-pot ariegoldshlager.posterous.com/recommend-reading-customer-lifetime-value-pot]
#[http://www.quora.com/Turki-Fahad/Great-Stuff/How-do-you-calculate-Customer-Lifetime-Value quora: How-do-you-calculate-Customer-Lifetime-Value]
 
{{DEFAULTSORT:Customer Lifetime Value}}
[[Category:Marketing]]
[[Category:Consumer behaviour]]
 
[[de:Customer Lifetime Value]]
[[es:Valor del tiempo de vida del cliente]]
[[fr:Valeur vie client]]
[[it:Lifetime value]]
[[pl:LTV (marketing)]]
[[pt:Lifetime value]]

Latest revision as of 22:52, 15 September 2019

This is a preview for the new MathML rendering mode (with SVG fallback), which is availble in production for registered users.

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