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Customer lifetime value


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Presentation given to Measurecamp Feb 16 2013

Customer lifetime value

  1. 1. Customer lifetime value What it is Why it matters Using it in practice SnowPlow Analytics Ltd
  2. 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. 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. 4. Where is customer lifetime value used?Customer acquisition Customer relationship management1. 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 different2. 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. 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. 6. Meeting those challenges:1. Measuring actual customer lifetime value1. Identify the moments in a customer journey where value is generated2. 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 in4. 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. 7. Meeting those challenges:2. Forecasting value based on past behaviour to date1. Identify the moments in a customer journey where value is generated2. 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. 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. 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. 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. 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. 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