customer lifetime value – direct wines customer lifetime value tfm&a 2014 david lockwood:...

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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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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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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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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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