Customer Lifetime Value (CLV) is a series of indicators that help you to understand the value of your customers and how likely they are to shop with you again. In Connectif, the machine learning model that calculates these metrics is unique to each account. Using the information you acquire from your business, you’ll obtain increasingly accurate data to help improve your performance against KPIs.
This article explains the properties of CLV in Connectif, the requirements for activating it in your account, how these predictions are made, and what the metrics in your dashboard mean.
- Each CLV model is unique and learns from the account's data to provide the best possible forecasts and predictions.
- Connectif's Customer Lifetime Value machine learning model is based on the Buy Till You Die models, which describe customer behavior in two main processes:
- The repeat purchase process indicates that, while active, a customer makes purchases at random around their average transaction rate, which varies from that of other customers.
- The dropout process, whose premise is that each client has an unobserved propensity to churn and that this propensity varies among customers.
Contacts' CLV Data
- The CLV is updated at various different times to get the most out of the data and to make the machine learning system increasingly accurate:
- Once a day, Connectif will verify if your account meets the requirements for CLV calculation 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 is active and the more data you collect, the more accurate it will be.
- When a purchase is made, 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 made at least one purchase).
2. How CLV works in Connectif
a. Requirements for having CLV in your account
In order to have the Customer Lifetime Value calculation and metrics in your Connectif account, you must meet some minimum requirements (so that the algorithm can calculate and extract the necessary predictions):
- Time: your account must have at least four months of purchases.
- Customers: you’ll need at least 500 different purchasers.
- Repeat purchase rate: of these 500 customers, at least one must have made three or more purchases.
b. CLV parameters in Connectif
Once the model is adjusted, you can start obtaining insights. CLV machine learning will offer you the following predictions:
- Customer Lifetime Value: indicates the total amount of money this contact is expected to spend with your business in the next 12 months.
- P-Alive: This metric indicates how likely the customer is to purchase again. The higher this value, the more likely it is that the customer will make another purchase.
- Churn rate: the opposite indicator to P-Alive, this refers to the probability that the customer has definitively stopped making purchases.
- Expected purchases: this figure represents how many transactions you can expect from the customer in the next 12 months.
This series of parameters makes up the Customer Lifetime Value machine learning model in your Connectif account. With these metrics you can make an estimate about the value of your contacts and their probability of repetition.
c. Where to find CLV in Connectif
You can find and use CLV metrics in your Connectif account in various locations on the platform:
- In the contact sheet: you can see the value of each contact’s CLV metrics on their contact sheet.
- Dynamic Segments: when creating your dynamic segments, you can filter by contacts that meet certain CLV conditions.
- Dynamic Plus Segments: select "Value Indicators" in the search engine and choose the CLV parameters you want to filter.
- Workflows: you can use the "Check value" node, filtering by Customer Lifetime Value metrics and activate certain actions within your workflows for those contact segments.
- In the export of contact data: when exporting your contacts from Connectif, you can also save their available CLV metrics.
Use this information to draw up automated strategies that allow you to optimize your databases and increase the value of your customers.
To make the most of your Connectif account, we recommend reading these articles next:
- RFM analysis, to understand these metrics and how to segment contacts based on their purchasing behavior over time.
- Contact fields, to learn more about the minimum units of information that make up the contact sheet.
- Identifying and merging anonymous and identified contacts, to connect a contact’s activity on different devices and browsers in the same profile.
- Reviving interest, to reactivate contacts who need some attention using personalized recommendations.