Customer Lifetime Value
Last updated
Last updated
Detecting early signs of reduced customer engagement is pivotal for businesses aiming to maintain loyalty. A notable signal of this disengagement is when a customer's once regular purchasing pattern starts to taper off, leading to a significant decrease in activity. Early detection of such trends allows marketing teams to take swift, proactive measures. By deploying effective retention strategies, such as offering tailored promotions or engaging in personalized communication, businesses can reinvigorate customer interest and mitigate the risk of losing them to competitors.
Our objective is to utilize a model that not only alerts us to customers with an increased likelihood of churn but also forecasts their potential purchasing activity and, importantly, estimates the total value they are likely to bring to the business over time.
These analytical needs are served by what is known in data science as Buy 'Til You Die (BTYD) models. These models track the lifecycle of a customer's interaction with a business, from the initial purchase to the last.
While customer churn models are well-established within contractual business settings, where customers are bound by the terms of service agreements, and churn risk can be anticipated as contracts draw to a close, non-contractual environments present a different challenge. In such settings, there are no defined end points to signal churn risk, making traditional classification models insufficient.
To address this complexity, our model adopts a probabilistic approach to customer behavior analysis, which does not rely on fixed contract terms but on behavioral patterns and statistical assumptions. By doing so, we can discern the likelihood of future transactions for every customer, providing a comprehensive and predictive understanding of customer engagement and value.
The Customer Lifetime Value (CLV) model is a robust tool employed to ascertain the projected revenue a customer will contribute over their entire relationship with a business. The model employs historical data to inform predictive assessments, offering valuable foresight for strategic decision-making. This insight assists companies in prioritizing resources and tailoring customer engagement strategies to maximize long-term profitability.
The CLV model executes a series of sophisticated calculations. Yet, its operations can be conceptualized in a straightforward manner:
Historical Analysis: The model comprehensively evaluates past customer transaction data, noting the frequency and monetary value of purchases alongside the tenure of the customer relationship.
Engagement Probability: It assesses the likelihood of a customer’s future engagement based on their past activities, effectively estimating the chances of a customer continuing to transact with the business.
Forecasting: With the accumulated data, the model projects the customer’s future transaction behavior, predicting how often they will make purchases and the potential value of these purchases.
Lifetime Value Calculation: Integrating these elements, the model calculates an aggregate figure representing the total expected revenue from a customer for a designated future period.
The Customer Lifetime Value model uses historical customer data to predict the future value a customer will generate for a business. It leverages algorithms and statistical techniques to analyze customer behavior, purchase patterns, and other relevant factors to estimate the potential revenue a customer will bring over their lifetime.
The dataset on which you will run your model must contain a time-related column.
We need to distinguish all customers, so we need an identifier variable like Customer ID. If you might have data about Customer Names, great, if not, don't worry, just select the same column as in the Customer ID field.
We need to choose the numeric variable regard to which we will observe customer behavior, called Monetary (amount spent).
Finally, you need to choose the Starting Date from which you'd like to calculate this model for your dataset.
When you're looking at this option for calculating Customer Lifetime Value (CLV), think of it as setting a starting line for a race. The "race" in this case is the journey you're tracking: how much your customers will spend over time.
The "Starting Date for Customer Lifetime Value Calculation" is basically asking you when you want to start watching the race. You have a couple of choices:
Max Date: This is like saying, "I want to start watching the race from the last time we recorded someone crossing the line." It sets the starting point at the most recent date in your records where a customer made a purchase.
Today: Choosing this means you want to start tracking from right now, today. So any purchases made after today will count towards the CLV.
-- select date --: This would be an option if you want to pick a specific date to start from, other than today or the most recent date in your data.
Let's see how to interpret the results after we have run our model.
And then, the results consist of 2 tabs: CLV Insights and Details Tabs.
On the summary of repeat customers, we have:
the Total Repeat Customers: the customers came that keep returning (the loyal customers)
the Total Historical Amount: the past earnings from loyal customers
the Average Spend per Repeat Customer
the Average no. of Repeat Purchases: shows the customers' loyalty with the average number of repeat purchases
the Average Probability Alive Next 90 days: estimate the likelihood that a customer stays alive or active for their business in the next 90 days
the Predicted no. of Purchases next 90 days: the number of purchases you can expect the next 90 days based on our analysis
Predicted Amount Next 90 days: the revenue you can expect the next 90 days with our predicted amount feature
CLV Customer Lifetime Value: average revenue that one customer generated in the past and will generate in the future
The CLV Insights Tab shows some charts on the lifetime of customers.
The forecasted number of purchases chart estimates the number of purchases that are expected to be made by returning customers over a specific period.
The forecasted amount chart is a graphical representation of the projected value of purchases to be made by returning customers over a certain period.
Finally, the average alive probability chart illustrates the average probability of a customer remaining active for a business over time, assuming no repeat purchases.
Last but not least, on the Details Tab, you can find a detailed table where you can see all relevant values which were used for the above results.
You have all the information in each column if you click on the link on the details tab.
The Details Tab within the Customer Lifetime Value Model offers an extensive breakdown of metrics for in-depth analysis. Each column represents a specific aspect of customer data that is pivotal to understanding and predicting customer behavior and value to your business. Below are the descriptions of the available columns:
amount_sumDescription: This column showcases the total historical revenue generated by an individual customer. By analyzing this data, businesses can identify high-value customers and allocate marketing resources efficiently.
amount_countDescription: Reflects the total number of purchases by a customer. This frequency metric is invaluable for loyalty assessments and can inform retention strategies.
repeated_frequencyDescription: Indicates the frequency of repeated purchases, highlighting customer loyalty. This metric can be leveraged for targeted engagement campaigns.
customer_ageDescription: The duration of the customer's relationship with the business, measured in days since their first purchase. It helps in segmenting customers based on the length of the relationship.
average_monetaryDescription: Average monetary value per purchase, providing insight into customer spending habits. Businesses can use this to predict future revenue from a customer segment.
probability_aliveDescription: Displays the current probability of a customer being active. A score of 1 means 100%, the customer is likely active, aiding in prioritizing engagement efforts.
probability_alive_7_30_60_90_365Description: This column shows the probability of customers remaining active over various time frames without repeat purchases. It's critical for developing tailored customer retention plans.
predicted_no_purchases_7_30_60_90_365Description: Predicts the number of future purchases within specific time frames. This forecast is essential for inventory planning and sales forecasting.
CVL_30_60_90_365Description: Estimates potential customer value over different time frames, aiding in strategic financial planning and budget allocation for customer acquisition and retention.
In this given example, we have a snapshot of customer data from the CLV model. The model considers various unique aspects of customer behavior to predict future engagement and value. Let's analyze the key data points and what they signify in a non-technical way, while emphasizing the model’s ability to tailor predictions to individual customer behavior:
amount_sum: This customer has brought in a total revenue of $4,584.14 to your business.
amount_count: They have made 108 purchases, which shows a high level of engagement with your store.
repeated_frequency: Out of these purchases, 106 are repeat purchases, suggesting a strong customer loyalty.
customer_age: They have been a customer for 364 days, indicating a relatively long-term relationship with your business.
average_monetary: On average, they spend about $42.73 per transaction.
probability_alive: There’s an 85% to 86% chance that they are still actively engaging with your business, which is quite high.
probability_alive_7: Specifically, the probability that this customer will remain active in the next 7 days is about 44.48%.
Alex, with a remarkable 106 repeated purchases and a customer_age of 364 days, has shown a pattern of strong and consistent engagement. The average monetary value of their purchases is $42.73, contributing significantly to the revenue with a total amount_sum of $4,584.14. The current probability_alive is high, indicating Alex is likely still shopping.
However, even with this consistent past behavior, the probability_alive_7 drops to about 44.48%. It highlights a nuanced understanding of Alex's habits; a sudden change in their routine is notable, which is why the model predicts a more significant impact if Alex were to alter their shopping pattern even slightly.
On the other hand, we have Casey, who has made 2 purchases, with only 1 being a repeated transaction. Casey’s amount_sum is $185.93, with an average_monetary value of $84.44, and a customer_age of 135 days. Despite a high current probability_alive, the model shows a minimal decline to 83.73% in the probability_alive_7.
This slight decrease tells us that Casey's engagement is inherently more sporadic. The business doesn't expect Casey to make purchases with the same regularity as Alex. If Casey doesn't return for a week, it isn't alarming or out of character, as reflected in the gentle decline in their seven-day active probability.
The contrast in these profiles, painted by the CLV model, enables the business to craft distinct customer journeys for Alex and Casey. For Alex, it's about ensuring consistency and rewarding loyalty to maintain that habitual engagement. Perhaps an automated alert for engagement opportunities could be set up if they don't make their usual purchases.
For Casey, the strategy may involve creating moments that encourage repeat engagement, possibly through sporadic yet impactful touchpoints. Since Casey's behavior suggests openness to larger purchases, albeit less frequently, the focus could be on highlighting high-value items or exclusive offers that align with their sporadic engagement pattern.
The CLV model's behavioral predictions allow the business to personalize customer experiences, maximize the potential of each interaction, and strategically allocate resources to maintain and grow the value of each customer relationship over time. This bespoke approach is the essence of modern customer relationship management, as it aligns perfectly with the individualized tendencies of customers like Alex and Casey.
This detailed data is a treasure trove for businesses keen on data-driven decision-making. Here’s how to utilize the information effectively:
Custom Segmentation: Use customer_age, amount_sum, and average_monetary to segment your customers into meaningful groups.
Detect Churners: Use probability_alive to segment customers currently being active for non contractual business like eCommerce and Retail. A score of 0.1 means 10% probability the customer is active ("alive") for your business.
Targeted Marketing Campaigns: Leverage repeated_frequency and probability_alive columns to identify customers for loyalty programs or re-engagement campaigns.
Revenue Projections: The CVL_30_60_90_365 column helps in projecting future revenue and understanding the long-term value of customer segments.
Strategic Planning: Use predicted_no_purchases_7_30_60_90_365 to plan for demand, stock management, and to set realistic sales targets.
By engaging with the columns in the Details Tab, users can extract actionable insights that can drive strategies aimed at optimizing customer lifetime value. Each metric can serve as a building block for a more nuanced, data-driven approach to customer relationship management.
