customer lifetime value – direct wines customer lifetime value tfm&a 2014 david lockwood:...
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Customer Lifetime Value – Direct Wines
Customer Lifetime Value
TFM&A 2014
David Lockwood: Direct WinesTerry Hogan: Golden Orb
Customer Lifetime Value – Direct Wines
How Direct Wines use customer lifetime value
Discover profitable/unprofitable activities, sources, mechanics
Previous curve-based approach– Problems with LTV changing from week to week– Purchase of a few additional cases had substantial
impact
Needed to extend model to other markets
Customer Lifetime Value – Direct Wines
Customer Lifetime Value: What is it and why does it matter?
New customers are acquired at an average cost per recruit (CPR)
Different campaigns generate customers at a different cost per recruit– Drivers: response rate, media, message costs etc.– Relatively simple to measure – Easy to fall into the trap of focusing too much on CPR
Different cohorts of recruits will have different values to the business over their lifetimes (LTV)
– Drivers: proportion who never buy again, number of orders, average order value (AOV), attrition rate
What matters is the difference between CPR and LTV– Added value per recruit
Customer Lifetime Value – Direct Wines
Meaning: Cost per recruit
What level of ‘cost’ to include?
Direct marketing costs (MCPR)
Discounts? Extra free? Multibuys?
Free shipping?
Direct Wines use ‘Full cost per recruit’ (FCPR) – net contribution loss divided by recruits
Customer Lifetime Value – Direct Wines
Meaning: Lifetime Value
‘Value’ – Net contribution‘Lifetime’ – how long?
Simple approach– Look at first x years e.g. 3
years– Simple calculation – easily
understood
Advanced approach– Consider the full lifetime of
the customer– Could be 20 years or more– Essential to discount future
cash flows
Revenue attrition over time
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Year after recruitment
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Customer Lifetime Value – Direct Wines
Calculation of Lifetime value
•Technique– Curves – sensitive to ‘lumpy’ purchases– Time-series – difficult with infrequent purchases– Regression – most powerful/appropriate in this case
•What level?
Individual
•Data intensive
•Time-consuming
•Use sample data to build models
Campaign
•Quick
•Good reference point
•Lose detail of tests, sources
Response code
•Not too data-intensive
•Distinguishes lists, tests
•May need to split by time
More detail Less detail
Customer Lifetime Value – Direct Wines
Regression: a bit of theory
Equation: Y = k + ax (+ a2x2 +a3x3 ...)
Aims to minimise the square of the residuals
R2: measure of goodness of fit – % variance explained by line
Standard error of estimate: measure of the absolute size of residuals
Regression graph
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InterceptSlope
Residuals
Customer Lifetime Value – Direct Wines
Regression model: predict what?
Need to decide a time-frame – let us take 3 years for now
What are we trying to predict?– Total revenue?– Revenue per recruit?
Total revenue (schematic)
y = 11613x - 24697
R2 = 0.85
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Revenue to date
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Regressing total revenue – typically high R2
– Dominated by cohort size – Doesn’t distinguish well between different campaigns
Better to predict revenue per recruit
Customer Lifetime Value – Direct Wines
Regression model: Level of detail?
Total or incremental revenue per recruit?– Either is OK, but total revenue includes one of the numbers we
want to use as a predictor (revenue to date)– Better to make it harder by predicting incremental revenue
• Lower R2 but same standard error
By line of business? Category? Product?– Direct Wines have both standard and continuity businesses– We found it more accurate to predict total revenue– Errors tend to be greater if predicting standard and continuity
separately
Customer Lifetime Value – Direct Wines
Building the model
Assemble lots of likely measures about historic campaigns– Need to be on the same basis as the predicted variable
• Total revenue to date• Continuity revenue• Gross/Net orders• Deseasonalised, last 6 months’ revenue• Dummy variables (1/0) to represent categorical variables
Point your preferred statistics tool at these numbers – stepwise regression
– Beware! Increasing number of variables always increases R2
without improving model– Many will be highly correlated
• individually quite predictive, but don’t add much• A good statistics package will identify the significant factors• Include the measures that significantly improve the R2
Customer Lifetime Value – Direct Wines
Spread the sales over time using a curve
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Standard sales
Continuity sales
Not essential, but can aid interpretation/understanding - Use remaining portion of curve
Curves can be different for different revenue streams
Best to do it before calculating contribution so that we can treat past times differently
Use the standard error of the model to estimate a confidence interval around the numbers
Customer Lifetime Value – Direct Wines
Calculate net contribution from projected revenueDepends on what data is available
– Actual margin for past sales– Most costs can be calculated
as standard percentage of revenue for simplicity
• Within margin of error of predictions
– Additionally/alternatively £ per original recruit for marketing costs
• E.g. for the first 6 months/ 1 year
Customer Lifetime Value – Direct Wines
When can we start to build a reliable model?
R-squared and std. error by month after recruitment
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R-squared (leftaxis)Std error (rightaxis)
From about 6 months we can account for half of natural variation
We need to build a series of models for different time periods
Early on, recruitment details feature. Later, recent sales are more relevant
Customer Lifetime Value – Direct Wines
Over time, the results become better
These are real people with real lives
– Move away
– Change in circumstances
– It is only a model
Bigger campaigns are more predictable
– Random variation tends to cancel out
After about 6 months we can generally make pretty good predictions
– Different in different markets
Customer Lifetime Value – Direct Wines
Thank You
www.golden-orb.ltd.uk
@GoldenOrbLtd