The Customer Lifetime Value (CLV) is a set of indicators that allow you to understand the value of your customers and the likelihood of repeat purchases. In Connectif, the machine learning model that calculates these metrics is unique to each account. By utilizing the information it gathers from your business, you will obtain increasingly accurate data to improve your KPIs.
This article explains the properties of CLV in Connectif, the requirements for it to be activated in your account, how these predictions are made, and what the metrics you’ll find on your dashboard mean.
1. Properties
CLV Model
- Each CLV model is unique, learning from the data of each account to provide the best possible forecasts and predictions.
- The machine learning model of Connectif’s Customer Lifetime Value is based on Buy Till You Die models, which describe customer behavior through two main processes:
- The transaction process: indicates that while active, a customer makes purchases randomly around their average transaction rate, which varies compared to other customers.
- The churn process: the premise here is that each customer has an unobserved propensity to churn, and this propensity varies among customers.
Contact CLV Data
- The CLV is updated at various times to make the most of the data and ensure that the machine learning system becomes increasingly accurate:
- Once a day, Connectif will check if your account meets the requirements for calculating CLV, and if so, CLV will be activated in your account.
- Once a month, CLV will be recalculated for all contacts with at least one purchase. Therefore, the longer it remains active and the more data it collects, the more accurate it will be.
- Upon a purchase, the contact's CLV will be recalculated.
- When merging contacts, the system will recalculate the contact's CLV (if enabled and the anonymous contact has at least 1 purchase).
2. How CLV Works in Connectif
a. Requirements to Have It in Your Account
To have the Customer Lifetime Value calculation and metrics in your Connectif account, you must meet minimum requirements (so the algorithm can calculate and extract the necessary predictions):
- Time: your account must have at least four months of purchases.
- Customers: you need at least 500 different buyers.
- Recurrence: of these 500 customers, at least one must have 3 or more purchases.
b. CLV Parameters in Connectif
Once the model is set, you can start making inferences. With CLV machine learning, you’ll have the following predictions:
- Customer Lifetime Value: indicates the total amount of money this contact is expected to spend in your business over the next 12 months.
- P-Alive: this metric indicates the likelihood that the customer will buy again. Therefore, the higher this value, the more likely the customer is to purchase again.
- Churn rate: the opposite indicator to P-Alive, referring to the likelihood that the customer has stopped buying permanently.
- Expected Purchases: this figure represents how many transactions you can expect from that customer over the next 12 months.
Together, these parameters make up the Customer Lifetime Value machine learning model in your Connectif account. With these metrics, you can estimate the value of your contacts and their likelihood of repeat purchases.
c. Where It Is Located in Connectif
You can find and use CLV metrics in different places within your Connectif account:
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- In the contact card: within each contact's card, you can see their CLV metrics to understand their value.
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- Dynamic Segments: when creating your dynamic segments, you can filter by contacts that meet certain CLV conditions.
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- Dynamic Plus Segments: search by "Value Indicators", and you can choose the CLV parameters you want to filter.
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- Workflows: you can use the "Check Value" node to filter by Customer Lifetime Value metrics and trigger specific actions within your workflows for those contact segments.
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- In the contact data export: when exporting your contacts from Connectif, you can also save the available CLV metrics for each one.
Leverage this information to design automated strategies that help you optimize your databases and increase customer value.
Keep Learning!
To make the most of your Connectif account, we recommend continuing with the following articles:
- RFM Analysis, to understand these metrics and how to segment contacts based on their purchase behavior over time.
- Contact fields, to learn about the minimum units of information that, together, form the contact card.
- Preferred Access, to send an email to your most valuable contacts before the rest.
- Interest Reactivation, to reactivate contacts who need attention with personalized recommendations.