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Customer lifetime value

        What it is
     Why it matters
    Using it in practice




     SnowPlow Analytics Ltd
What is customer lifetime value?

•   Prediction of the net profit attributed to the entire future relationship with a
    customer (wikipedia)




                       £50           £10          £1000           £100
•   The most important metric in business analytics (incl. digital)?

•   Not widely used… (Because it is hard to calculate, esp. in digital)

•   Example: using CLV to acquire customers for a mobile game




                                      SnowPlow Analytics Ltd
Why is customer lifetime value important?

      20% of our customers                                 Customer acquisition
       account for 80% of                                    costs keep rising
           our sales

 The best customers might be                         It is often more cost effective to
      – Brand loyal                                  spend money retaining existing
                                                     customers than acquiring new
      – Don’t “shop around”
                                                     customers
      – Rich
      – Different from the average




                                 SnowPlow Analytics Ltd
Where is customer lifetime value used?
Customer acquisition                                                          Customer relationship management
1. Use average CLV to inform                                                  •   Maximize customer lifetime value
   acquisition cost                                                                –   Instead of maximizing other metrics
     –    E.g. pay more for a customer than                                            e.g. utilisation
          recoup on first purchase, based on                                       –   E.g. email marketing to encourage




                                                  Increasing sophistication
          likelihood that he / she will make a                                         repurchase
          second / third / forth purchase)
                                                                              •   Differentiated approach for different
2. Calculate CLV per channel                                                      customer segments
     –    pay more more to acquire customers                                       –   Spend more cultivating loyalty in the
          on channels with higher CLV                                                  most valuable customers
     –    E.g. search engine marketing vs price                                        (personalisation) e.g. loyalty
          comparison sites                                                             schemes




         Acquire valuable customers                                                    Retain valuable customers

                                            SnowPlow Analytics Ltd
Calculating customer lifetime value: 2 challenges

•   We need to be able to attribute profit to a customer over his / her entire lifetime
     – Profit across sales channels (on and offline)
     – Single customer view?
     – Web analytics packages visit rather than customer-centric




•   We need to be able to forecast lifetime value based on past behaviour to date
     –   Need a model that matches the data (reasonably well)
     –   Needs to be done fast if used to acquire customers
     –   Limited data set
     –   Prediction is an art, not a science




                                     SnowPlow Analytics Ltd
Meeting those challenges:
1. Measuring actual customer lifetime value

1. Identify the moments in a customer journey where value is generated

2. Tie records for a specific customer together into a complete journey
    – E.g. using sales records, loyalty programmes, cookie IDs
    – If it is not possible to do at a customer level, then do at a segment level (and infer
      average CLV from segment lifetime value / number of customers)


3. Measure the profit made at each point
    – Normally use gross profit for simplicity                 Doing this is getting easier all
                                                               the time:
                                                               1. Improvements in
4. Sum them over the customer’s “lifetime”                         analytics solutions e.g.
                                                                   Universal Analytics
                                                               2. Companies are getting
                                                                   better at getting user’s to
                                                                   identify themselves e.g.
                                                                   via logins
                                      SnowPlow Analytics Ltd
Meeting those challenges:
2. Forecasting value based on past behaviour to date

1. Identify the moments in a customer journey where value is generated

2. Examine the value created at each moment: what is it a function of?
    – Does it vary much by customer / segment/ time / anything else? (I.e. wide variance in
      values)
    – If that variation is significant, what is it a function of?


3. Examine the likelihood of moving from one moment to-the-next: what is it a
   function of?
    – Does it vary much by customer / segment / time / anything else?
    – If that variation is significant, what is it a function of?


                                                         Developing a model is likely
                                                         much easier for a telecoms
                                                         operator (reliable subscription
                                                         revenue) rather than an online
                                                         clothing retailer
                                    SnowPlow Analytics Ltd
An example: using CLV to drive customer acquisition

•   Mobile game

•   Free to download, monetise by in-app purchases or virtual goods

•   Virtual goods can be bought at any stage of playing the game (i.e. very frequently or
    never at all)


•   Wide variety across customer base in terms of customer lifetime value
     – Zero value from majority of users. (Who play without ever buying an item.)
     – Small fraction account for disproportionate amount of value


•   Crucial to acquire users from channels where a high proportion of acquisitions
    have high CLV



                                     SnowPlow Analytics Ltd
Calculating CLV: the steps

•   Measuring the lifetime value of existing customers was easy:
     – All the data in a single system
     – Easy to track customer consistently (through single account)


•   Forecasting value based on behaviour to date was hard:
     – Massive variation number of purchases by customer (from 0 to a very high number)
     – Massive variations in the length of time consumers play game (download and never play
       vs download and play for months / years)
     – However, limited variation in each purchase value (all virtual goods cost roughly the
       same)




                                     SnowPlow Analytics Ltd
One key insight led to a simple model for CLV
•   Customer lifetime value varied widely between channels

•   The best predictor of whether a customer would purchase a virtual good in future was
    whether they had purchased a virtual good in the past

•   Within each channel, the likelihood that a customer would make another purchase was
    constant (i.e. independent of the number of purchases they had made to date)
     –    This means lifetime value can be modelled as a geometric series where each term in the series
          represents a purchase event
     –    The ratio between terms represents the probability that a user makes an nth purchase having made
          an (n-1)th purchase. That ratio, r, is what needs to be measured for each different channel
     –    Once you have r for a channel, then the lifetime value of the customers acquired can be estimated:
          (p = average price of virtual good)




    Value of 1st purchases                                                             Value of nth purchases




                                           SnowPlow Analytics Ltd
So what?
•   Easily prediction lifetime value by channel:
     –   Measuring r is easy: it is calculated simply from the ratio of 1st purchases, 2nd purchases etc.

                                     Keep the model as simple as possible. Use intuition about
                                     customer behaviour to derive key modelling insights
•   Fast results:
     –   Purchase events were, as a whole, frequent enough that a value could be calculated for r based on
         only a few days worth of data



•   Accurate results:
     –   Estimations of lifetime value were found to be accurate to 12%



•   Powerful results:
     –   Marketing budget was optimized to those channels driving the most valuable users

                                      If you have large variation in customer lifetime value
                                      between segments, your CLV prediction might not be very
                                      precise but canAnalytics incredibly useful
                                            SnowPlow still be Ltd
Questions

•   Where do you use CLV? Where do you want to be using it?

•   What type of models have you built?
     – What worked?
     – What didn’t?
     – Why?


•   Any other questions or insights?




                                  SnowPlow Analytics Ltd

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Customer lifetime value

  • 1. Customer lifetime value What it is Why it matters Using it in practice SnowPlow Analytics Ltd
  • 2. What is customer lifetime value? • Prediction of the net profit attributed to the entire future relationship with a customer (wikipedia) £50 £10 £1000 £100 • The most important metric in business analytics (incl. digital)? • Not widely used… (Because it is hard to calculate, esp. in digital) • Example: using CLV to acquire customers for a mobile game SnowPlow Analytics Ltd
  • 3. Why is customer lifetime value important? 20% of our customers Customer acquisition account for 80% of costs keep rising our sales The best customers might be It is often more cost effective to – Brand loyal spend money retaining existing customers than acquiring new – Don’t “shop around” customers – Rich – Different from the average SnowPlow Analytics Ltd
  • 4. Where is customer lifetime value used? Customer acquisition Customer relationship management 1. Use average CLV to inform • Maximize customer lifetime value acquisition cost – Instead of maximizing other metrics – E.g. pay more for a customer than e.g. utilisation recoup on first purchase, based on – E.g. email marketing to encourage Increasing sophistication likelihood that he / she will make a repurchase second / third / forth purchase) • Differentiated approach for different 2. Calculate CLV per channel customer segments – pay more more to acquire customers – Spend more cultivating loyalty in the on channels with higher CLV most valuable customers – E.g. search engine marketing vs price (personalisation) e.g. loyalty comparison sites schemes Acquire valuable customers Retain valuable customers SnowPlow Analytics Ltd
  • 5. Calculating customer lifetime value: 2 challenges • We need to be able to attribute profit to a customer over his / her entire lifetime – Profit across sales channels (on and offline) – Single customer view? – Web analytics packages visit rather than customer-centric • We need to be able to forecast lifetime value based on past behaviour to date – Need a model that matches the data (reasonably well) – Needs to be done fast if used to acquire customers – Limited data set – Prediction is an art, not a science SnowPlow Analytics Ltd
  • 6. Meeting those challenges: 1. Measuring actual customer lifetime value 1. Identify the moments in a customer journey where value is generated 2. Tie records for a specific customer together into a complete journey – E.g. using sales records, loyalty programmes, cookie IDs – If it is not possible to do at a customer level, then do at a segment level (and infer average CLV from segment lifetime value / number of customers) 3. Measure the profit made at each point – Normally use gross profit for simplicity Doing this is getting easier all the time: 1. Improvements in 4. Sum them over the customer’s “lifetime” analytics solutions e.g. Universal Analytics 2. Companies are getting better at getting user’s to identify themselves e.g. via logins SnowPlow Analytics Ltd
  • 7. Meeting those challenges: 2. Forecasting value based on past behaviour to date 1. Identify the moments in a customer journey where value is generated 2. Examine the value created at each moment: what is it a function of? – Does it vary much by customer / segment/ time / anything else? (I.e. wide variance in values) – If that variation is significant, what is it a function of? 3. Examine the likelihood of moving from one moment to-the-next: what is it a function of? – Does it vary much by customer / segment / time / anything else? – If that variation is significant, what is it a function of? Developing a model is likely much easier for a telecoms operator (reliable subscription revenue) rather than an online clothing retailer SnowPlow Analytics Ltd
  • 8. An example: using CLV to drive customer acquisition • Mobile game • Free to download, monetise by in-app purchases or virtual goods • Virtual goods can be bought at any stage of playing the game (i.e. very frequently or never at all) • Wide variety across customer base in terms of customer lifetime value – Zero value from majority of users. (Who play without ever buying an item.) – Small fraction account for disproportionate amount of value • Crucial to acquire users from channels where a high proportion of acquisitions have high CLV SnowPlow Analytics Ltd
  • 9. Calculating CLV: the steps • Measuring the lifetime value of existing customers was easy: – All the data in a single system – Easy to track customer consistently (through single account) • Forecasting value based on behaviour to date was hard: – Massive variation number of purchases by customer (from 0 to a very high number) – Massive variations in the length of time consumers play game (download and never play vs download and play for months / years) – However, limited variation in each purchase value (all virtual goods cost roughly the same) SnowPlow Analytics Ltd
  • 10. One key insight led to a simple model for CLV • Customer lifetime value varied widely between channels • The best predictor of whether a customer would purchase a virtual good in future was whether they had purchased a virtual good in the past • Within each channel, the likelihood that a customer would make another purchase was constant (i.e. independent of the number of purchases they had made to date) – This means lifetime value can be modelled as a geometric series where each term in the series represents a purchase event – The ratio between terms represents the probability that a user makes an nth purchase having made an (n-1)th purchase. That ratio, r, is what needs to be measured for each different channel – Once you have r for a channel, then the lifetime value of the customers acquired can be estimated: (p = average price of virtual good) Value of 1st purchases Value of nth purchases SnowPlow Analytics Ltd
  • 11. So what? • Easily prediction lifetime value by channel: – Measuring r is easy: it is calculated simply from the ratio of 1st purchases, 2nd purchases etc. Keep the model as simple as possible. Use intuition about customer behaviour to derive key modelling insights • Fast results: – Purchase events were, as a whole, frequent enough that a value could be calculated for r based on only a few days worth of data • Accurate results: – Estimations of lifetime value were found to be accurate to 12% • Powerful results: – Marketing budget was optimized to those channels driving the most valuable users If you have large variation in customer lifetime value between segments, your CLV prediction might not be very precise but canAnalytics incredibly useful SnowPlow still be Ltd
  • 12. Questions • Where do you use CLV? Where do you want to be using it? • What type of models have you built? – What worked? – What didn’t? – Why? • Any other questions or insights? SnowPlow Analytics Ltd