What is CLV ?Customer Lifetime Value (CLV) is one of the most important metrics you need to know. Because it shows who your best customers are and what they have in common. Calculating CLV can help optimize business practices, reduce customer acquisition costs, reduce customer churn, and improve retention. It is also a practical metric for tracking customer quality and the health of business. Using this metric will revolutionize the approach to user acquisition and retention marketing.
Why is CLV important?CLV is a metric that estimates how much value any customer will bring to your business over the total time (past, present and future) ，while interacting with your brand. It comprises three different factors:01. Average order value02. Purchase frequency03. Customer lifeIt is more important to calculate the CLV at the individual customer level than all customer scopes. That’s because your customers are individuals, each with unique attributes. If you want to take full advantage of CLV, you need to make predictions for each one.Why is CLV important? A good CLV model assesses the commonalities of all customers in your business and then combines this information with each customer’s behavior to predict how much they will spend on your brand in the future.The CLV model provides more than just a useful metric. In its developing progress, you will also use data analytics to discover the characteristics of various customer values to find the most profitable customer groups.Characteristics are personal attributes based on behavioral or identifiable information, such as whether someone opens an email on Thursday or Saturday, or which Internet Protocol Address they buy from when shopping online.
What can you do with this information?Both the features and final output of the CLV model provide key data points for brand development in order to focus resources on the most attractive touchpoints. But the most important thing is how to deal with these data points. There are many uses for CLV metrics, and the top 5 uses to be most impactful on the DTC brand are:01. Data-based customer segmentation02. Customer journey optimization03. Customer lifetime value model (CLV model)04. Digital marketing optimization05. Reduce churn and improve retentionFeatures derived from the CLV model can serve as the data basis for clustering algorithms to help you segment your customers. The resulting segments can give you an idea of what your best customers engage with, what they dislike, and how to attract new customers like them.You can also combine CLV forecasting and customer acquisition cost
(CAC) to understand and optimize the customer journeys. Use machine
learning algorithms to examine customers' various paths to achieve
specific conversion points with minimal friction or cost. The
algorithm then scores each path based on the customer's cost
relative to their CLV.Once scored, reinforcement learning can consider a range of touchpoints and determine which interactions will maximize the odds of a certain outcome, such as repeat purchases. This suggests that CLV can predict how much revenue each customer is likely to bring in and encourage them to make more purchases. You can also use CLV to identify the most profitable channels.
What can CLV solve for your business? In an increasingly data-driven world, menstruating ROI has been the biggest challenge. Predicting CLV and regularly benchmarking customer data and behavior are the best solutions to this challenge. It also allows you to show exactly how much revenue an activity can generate.A new challenge is to understand and respond to changes in paid social and shrinking platforms. For example, privacy changes and unified targeting strategies today have made Facebook ads more expensive while overall reach has declined. CLV can help companies understand how marketing and customer experience efforts are conducted unbiased, helping to develop strategies to restore underperforming channels and show the value of all efforts to execution stakeholders.
What does it take to calculate CLV in your business? Calculating CLV on an individual level is not as easy as one might think. The process is much more complicated than opening Excel, searching the web, and coming up with a number. No Excel formula can predict each customer’s CLV while considering their respective buying tempo and price sensitivity. What makes this process so complicated is that perfectly calculating CLV requires tedious information, such as the amount customers spend and the frequency and duration of their purchases. Without a large enough sample size, models can not be accurately trained to understand changes in the personalities of individuals interacting with brands.To calculate individual-level CLV without the help of a software solution, you need::
- Clarify what CLV stands for your business
- A data scientist trained in machine learning
- Complex model developed by researchers
- A large amount of customer data over a year
- Comprehensive and valid data on customer behavior
- Powerful computing power
How does the process proceed?When you have everything you need for a CLV, you can delve into how to build and apply the CLV model in your organization.1. Locate the necessary dataThere are five types of relevant datasets in the database: transaction history, customer demographics, profile data, marketing campaigns, and interactions between products, websites, and applications.2. Predict the behavior of each existing customerUse the collected data to predict the number of future transactions, churn dates, and expected net payouts. 3. Split dataDivide data into two groups for training and testing. The training data will form the basis of the model, while the test data will allow you to benchmark the performance of the model before rolling out new information. A common criterion is to use 80% of the data for training and 20% for testing.4. Add features and attributesMake sure your customer dataset uses as many features as possible. Ideally, models going into production should be able to make predictions about brand new customers. This is an additional challenge because new customers have little (if any) historical data to measure. Early in the customer lifecycle, it is important to identify some features or attributes that have a high impact on CLV predictions. You can predict CLV by building market segments and similar audiences even before one becomes a customer.5. Training the modelOnce the dataset is ready, modern machine learning methods can be performed. The most critical of which is building models that capture causal relationships between variables and metrics. Then use algorithms to apply the model’s rules to the data to produce an output similar to the final CLV calculation. 6. Validate the model The built model may perform well on training data and prediction
data, but not on other samples. Compare the customer's recent actual
purchases with the model's predictions based on past data to verify
the usefulness of the model.7. Put the model into productionAt this stage, make sure that the built model can be integrated with the actual way it works, whether linked to the dashboard via an API, or with other custom solutions. 8. Observe the offset of the modelThe accuracy or usefulness of the model changes over time. CLV
changes as new customer data is observed, especially when marketers
take effective action based on CLV metrics to increase customer
purchase value, purchase frequency, and customer longevity. Make
sure to recalculate CLV regularly and record offsets to better
understand how each customer's value has changed over time.