Understanding CLTV: How to Calculate Lifetime Value of a Customer in Retail and E-commerce
There are lots of factors that have an impact on the business environment. It depends on your business industry, but the main aspects are economic, leadership, political and legal, social and cultural, and technological. While certain prominent metrics might take center stage, sometimes it's the nuances, like understanding how to calculate customer lifetime value (CLTV), that make a significant difference in strategic decision-making.
Previously, we highlighted the essence of inventory management; now, let’s delve into the significance of Customer Lifetime Value (CLTV), or simply LTV, as commonly known in the e-commerce world.
What is CLTV and How It Can Impact your Business
Customer Lifetime Value, or simply LTV in E-commerce is an estimated revenue that you can have within your future relationship with current customers. It also can be defined as the monetary value of a customer relationship, based on the present value. One of the most crucial contributions had been made by Dr. Peter Feder with his book "Customer Centricity" where he helps businesses radically rethink how they relate to customers, describes his research on lifetime value and explains how to calculate it. Different industries have various data for LTV analytics, but it's obviously important for any kind of business. With the help of LTV management, you can simply predict your profit, optimize your marketing budget, identify your target audience and, as a result, increase your profits.
CLTV vs LTV: Distinguishing Between the Two
At first glance, CLTV (Customer Lifetime Value) and LTV (Lifetime Value) might seem synonymous. But there's a subtle distinction. LTV often refers to the general concept of a product's or service's value over its lifespan. On the other hand, CLTV specifically zeroes in on the value of a customer over the entirety of their relationship with a business. While LTV might focus on product profitability, CLTV concentrates on customer profitability, highlighting the nuances of customer behavior, purchasing patterns, and loyalty. Recognizing the distinctions and the interplay between the two can aid businesses in crafting more effective strategies. For more insights on how business intelligence tools are shaping modern banking, explore our article on Business Intelligence Tools for Modern Banking in 2023-24.
Customer Life Time Value Metrics
The simplest prediction you can get by calculating average purchase value, and then multiply that number by the average purchase frequency rate to determine customer value. When you have calculated the average customer lifespan, you can multiply that by customer value to determine customer lifetime value. But we suggest diving deeper and taking into account more details. Many companies predict LTVs only by looking at the total monetary amount of sales, without understanding the wider context. For example, a customer who makes one big order might be less valuable than another customer who buys multiple times, but in smaller amounts. Lifetime modeling can help you better understand the buying profile of your customers and help you value your business more accurately. We also suggest taking into account the kind of business setting that you're in, the demography and gender of your customers. Discover the pivotal role of data mining in banking by readingData Mining: A Key Tool to Improve Banking Business Processes.
We can define the three most important inputs into LTV models: recency, frequency, and monetary value.
Recency means the last time when was the customer's order. Frequency shows us how often do customers make purchases. By monetary value we imply the amount of money that they spend.
How to calculate customer LTV?
As a metric, LTV calculation in principle is simple. But in practice, it is rarely that easy. LTV can keep changing forever. It's not easy to keep track of LTV across campaigns and channels if one is managing a moderately large marketing effort. Individual modeling of the behavior of every single LTV curve is possible to be done in tools like Excel, but it can eventually become very time-consuming. Different software can simplify your LTV calculation, such as AI platforms, that deal great with complex algorithms and Machine Learning models for analytics. Learn how AI is revolutionizing predictive analysis in insurance in our detailed articlehere.
You can use different models and ways to calculate your LTV. In most cases, you will follow the next steps, regardless of the method you've chosen:
Define your target audience with all the specifications like gender, demography, etc
Purchase Frequency Curve based on your transactional data
Get Customized Lifetime Value Forecast
Based on this data you can manage your marketing campaigns. With LTV management, companies can plan campaigns and predict impact on revenue substantially better. Choose the marketing channel, time period, budget, and then analyze the simulated results.
Lifetime value platforms can also help you with loyal customer identification and increase your retention rate. Apply different loyalty programs and measure different effects based on key results. LTV management helps with: Churn Rate, increasing net revenue, marketing budget optimization, retention level improving, defining customer acquisition cost. For an in-depth understanding of automated reporting, read our articleWhat is Automated Reporting? A Key to Data Intelligence.
Customer LTV Models: Predictive vs. Historical
Customer Lifetime Value (LTV) is a critical metric that companies use to understand the value each customer brings over the long term. Businesses employ two main models to measure this: Predictive and Historical. Explore how machine learning aids in bank churn prediction in our articleBank Churn Prediction: Using ML to Retain Customers.
Predictive CLV leverages advanced techniques like regression or machine learning to forecast future customer behavior. This model is valuable for identifying potential high-value customers and optimizing strategies for customer retention. It allows businesses to take proactive steps based on current data to enhance future customer interactions.
Historical CLV, on the other hand, uses past transaction data to calculate the average value that existing customers have already brought to the company. This model is useful for short-term strategies and for businesses where customer interactions are more period-specific. However, it comes with limitations, like the potential to misclassify customers who may become active or inactive over time. . Read more about customer retention strategies in modern bankinghere.
Choosing between these models hinges on whether a business aims to analyze past data or predict future customer behavior, each offering unique advantages and limitations.
The CLTV Formula: Key to Calculating Customer Lifetime Value
CLTV=(Average Purchase Value Purchase Frequency)x Average Customer Lifespan 1.Average Purchase Value: This is calculated by dividing the company's total revenue in a time period by the number of purchases in that same period. 2. Purchase Frequency: This is the average number of times a customer makes a purchase during a set period. 3. Average Customer Lifespan: This represents the average number of years a customer continues to buy from the company.
Using the CLTV formula, businesses can predict future revenue, tailor their marketing strategies for high-value customers, and allocate resources more efficiently. Gain insights on leveraging AI and ML for advanced customer segmentation in banking in our detailed articlehere.
Exploring Customer Lifetime Value with a Practical Example
Let's delve into the concept of Customer Lifetime Value (CLTV) through an illustrative case study centered around Starbucks, utilizing data insights from a Kissmetrics report. The process we follow here can provide valuable insights into your own business's CLTV calculation. Understand the importance of demand forecasting in retail with our articleDemand Forecasting and Planning in Retail: Datrics Use Cases.
To start your journey, consider utilizing Datrics, an end-to-end data science platform that streamlines optimization and automation for businesses. Looking to enhance your business operations? Reach out to us and embark on a tailored transformative journey.
Deciphering CLTV with the Starbucks Example Let’s walk you through the steps used to determine Starbucks' CLTV. Learn about the value of inventory management in retail businesses and its impact on CLTV in our comprehensive guidehere.
Calculating Average Purchase Value First, gauge the average purchase value. A typical Starbucks customer spends approximately $5.90 per visit– it’s an essential metric in the CLTV equation.
Analyzing Average Purchase Frequency Rate Next, assess the average purchase frequency rate. For Starbucks, this involves understanding how often a customer visits in a week. As per studies, an average customer visits Starbucks 4.2 times weekly, which establishes the purchase frequency rate.
Determining Average Customer Value Now, calculate the average customer value by multiplying the average purchase value with the purchase frequency rate. This computation offers insights into the customer's monetary contribution to Starbucks within a week. Considering the above numbers, as average customer spends $24.78 per week at Starbucks. We can further extrapolate, that the average customer spends $1,239 per year at Starbucks (considering 50 weeks a year).
Deriving CLTV To derive CLTV, we need to consider another factor apart from an average annual spend by a customer: The churn rate.
What’s churn rate? It’s the probability of a customer dropping off. In the case of Starbucks, the churn rate is strikingly low - about 5%. In other words, 95% of customers who visit Starbucks in a particular year, are likely to visit in the next year as well.
To calculate CLTV (Customer Lifetime Value), we need to divide the annual spend with the churn rate.
In this case, the CLTV works out to : $1,239/5% = $24,780!
Doesn’t it explain why Starbucks shouldn’t mind spending a few hundred dollars to acquire a new customer?
Elevating CLTV: Strategies to Implement
With a grasp of CLTV, it's essential to explore strategies that can bolster it. Here are actionable approaches to consider:
Revamp Your Onboarding Process Enhance customer onboarding to build strong foundations for lasting relationships. Utilize customer data to offer tailored recommendations and follow up with personalized interactions, thereby creating a framework for sustained CLTV growth.
Boost Average Order Value Elevate your CLTV by increasing the average order value. Implement upsell and cross-sell tactics to introduce complementary products, driving incremental purchases and subsequently amplifying the customer's lifetime value.
Foster Meaningful Authentic connections with customers beyond transactions. Engage via social media, offer personalized gestures aligned with their interests, and cultivate a genuine rapport that transcends the business-customer dynamic.
Embrace Customer Feedback Listen to your customers; their insights can shape your business strategies. Create avenues for feedback, involving them in decisions and acknowledging their contributions, ultimately fostering loyalty and extended CLTV.
Facilitate Seamless Connections Reduce response times and empower easy interactions. Active social media engagement and streamlined communication channels can lead to increased connectivity, strengthening customer relationships and overall
Customer Service Prioritize exceptional customer service, as it directly impacts CLTV. Personalized experiences, multi-channel support, and customer-centric policies contribute to customer satisfaction, translating into sustained value.
As you explore the world of CLTV, consider leveraging Datrics, your partner, in optimizing and automating business processes. Connect with us to craft a tailored solution that aligns with your unique challenges and aspirations. Your journey towards enhanced CLTV and operational excellence starts with Datrics.
Are you facing challenges in your business domain and looking for optimization and automation solutions? At Datrics, we offer an end-to-end data science platform designed to simplify and elevate your business processes. With our innovative 'no-code' data science and analytics approach, even those without technical expertise can harness the power of advanced data analytics. Don't let complexities hold you back. Contact us, and let's build a custom pipeline tailored to your unique needs.