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Customer Lifetime Value

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Business Problem

Customer lifetime value (CLV) is a measurement of how valuable a customer is to your company over that customer’s lifetime. When properly mapped and calculated, it highlights those customers with whom the whole of your relationship is quantitatively greater than the sum of its individual transactions.

CLV is a difficult calculation for most companies. It’s made up of metrics like length of relationship (in years), website visits, coupons used, customer referrals, purchase volume, and product preferences — data points held in multiple disparate sources. It can be a costly, needle-in-haystack attempt to identify a small handful of extremely valuable customers, with little short- or medium-term return. That’s why convincing management of the benefits of understanding CLV — for the bottom line as well as the brand — is as important as the exercise itself. 

Analytics Solution

The essence of measuring CLV is to identify the transactions (visits, referrals, purchases, etc.) at which customers create value, then weave the data from those transactions into a customer journey. By adding up the revenue in each transaction and using predictors to extrapolate into the future, the company arrives at the lifetime value of that customer. The goals are to build relationships with high-value customers and to move other customers into higher-value segments through marketing.

Automation smooths the process of collecting transactions from a variety of data sources and predictive analytics extrapolates from historical data to help estimate future revenue.
Alteryx Customer Lifetime Value workflow in the Customer Analytics Starter Kit enables users to combine customer data from multiple sources and to use that data to train a forest model to predict spend for current customers. This model can then be applied to new customers to understand their expected lifetime value before they ever make a purchase. 

Customer Lifetime Value Workflow

1 - Data Access

Combine customer sales, demographic, and psychographic data

2 - Prep & Blend

Combine current and new customer streams while removing unwanted fields

3 - Predictive Analytics

Utilize a Forest Model tool to predict spend for new customers
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Additional Resources

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Starter Kit for Marketing Analytics

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Customer Analytics

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Marketing Analytics

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