customer lifetime value

SnowPlow Analytics Ltd

Customer lifetime value

What it isWhy it matters

Using it in practice


What is customer lifetime value?

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

• 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

£50 £10 £1000 £100


Why is customer lifetime value important?

The best customers might be– Brand loyal– Don’t “shop around”– Rich– Different from the average

20% of our customers account for 80% of

our sales

Customer acquisition costs keep rising

It is often more cost effective to spend money retaining existing customers than acquiring new customers


Where is customer lifetime value used?

Customer acquisition

1. Use average CLV to inform acquisition cost– E.g. pay more for a customer than

recoup on first purchase, based on likelihood that he / she will make a second / third / forth purchase)

2. Calculate CLV per channel– pay more more to acquire customers

on channels with higher CLV– E.g. search engine marketing vs price

comparison sites

Customer relationship management

• Maximize customer lifetime value– Instead of maximizing other metrics

e.g. utilisation– E.g. email marketing to encourage

repurchase

• Differentiated approach for different customer segments– Spend more cultivating loyalty in the

most valuable customers (personalisation) e.g. loyalty schemes

Acquire valuable customers Retain valuable customers

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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


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

4. Sum them over the customer’s “lifetime”

Doing this is getting easier all the time:1. Improvements in analytics

solutions e.g. Universal Analytics

2. Companies are getting better at getting user’s to identify themselves e.g. via logins


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


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


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)


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

𝐶𝐿𝑉=𝑝𝑟1−𝑟


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.

• 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

Keep the model as simple as possible. Use intuition about customer behaviour to derive key modelling insights

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


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?

customer lifetime value

Documents