Chapter 5 Methods in Marketing Science
5.1 Customer Lifetime Value
What Is Customer Lifetime Value (CLV)?
Customer Lifetime Value (CLV) is a crucial business metric that estimates the total revenue a business can reasonably expect from a single customer throughout their entire relationship with the company. This metric is essential for understanding how much money customers will spend on your products or services over time.
For example, consider a customer loyal to an auto brand whose vehicles average $30,000 each. If this customer buys three cars from the brand in their lifetime, their CLV is $90,000. Similarly, a person who visits their local coffee chain five days a week and spends $4 on a coffee will have a CLV of $10,400 over the course of 10 years.
Understanding CLV is vital for businesses because it helps determine the appropriate amount of investment in acquiring and retaining customers. It takes into account a customer’s revenue value and compares it to the company’s predicted customer lifespan.
Customer support and success teams play a significant role in influencing CLV by enhancing the customer’s journey. The longer a customer continues to purchase from your company, the greater their lifetime value becomes.
5.1.1 From Forbes Magazine
Customer lifetime value is a business metric used to determine the amount of money a customer will spend throughout the business relationship. It helps businesses determine customer acquisition costs, improve forecasting and increase profits over time. It also serves as a guide for decisions about their overall business strategy.
A higher CLV indicates customers spend more money on your product or service throughout the business relationship. When customers spend more and purchase frequently, your business is more successful and profitable.
The industries with the highest customer lifetime value are architecture firms for $1,129,000, followed by business operations consulting firms for $385,800 and healthcare consulting firms priced at $328,600. These firms generate high amounts of revenue for hourly fees, consultation fees, material costs and projects.
The estimated value of each customer can play an integral role when making business decisions, such as whether to invest more in customer acquisition or retention.
5.1.1.1 Reasons To Know Your CLV
Let’s take a look at several reasons why CLV matters.
1. Determine customer acquisition cost
How much should you invest in hiring a new customer? When you can determine the amount a customer will spend on your business, you can gauge the amount of money to spend on marketing campaigns.
For example, when you find out a customer spends an average of $1,000 on your business over time—you might have the budget to spend more on advertising and targeting campaigns. Not only that, but you have room to invest more money to personalize your email marketing strategy or content strategy.
Alternatively, if the estimated CLV is $1,000, you would only invest this much in convincing a customer to stay. Otherwise, you wouldn’t profit from the relationship.
2. Improve profitability consistently over time
Optimizing CLV helps you focus on ensuring consistent profitability over time. If you only invest in acquisition and closing new deals, it won’t be easy to remain profitable during slow seasons.
In contrast, a high CLV means you can rely on enough people to return to your store throughout the year.
3. More accurate forecasting
Customer lifetime value (CLV) can help you make better production, workforce and inventory decisions.
It helps pinpoint the types of clients you have, the best-selling products you buy and the factors that drive customer loyalty. Otherwise, you may spend more on producing products with insufficient demand.
4. Improve overall business strategy
Understanding CLV helps you determine the most effective strategy for your business growth.
If your CLV is low, you may need to invest more in loyalty programs and initiatives to boost customer retention.
In contrast, a high CLV means you may need to look into the best-selling products and campaigns driving growth to keep the momentum going.
Over time, this strategy will help you create more cost-effective strategies around customer acquisition, marketing and sales.
5. Better understand loyal customers
Customer lifetime value helps you understand the most loyal brand advocates. How often do they shop from your business? What items are they more likely to purchase? Answering these questions can help you brainstorm ways to engage with your most loyal customers.
5.1.1.2 How To Improve Your Customer Lifetime Value
Improving your CLV can enhance your business’s profitability over time. To that end, here are a few ways to improve customer loyalty and retention.
1. Create a loyalty and rewards program
A growing body of research proves rewards programs effectively drive loyalty and retention. Gamify the experience by offering discounts and perks every time customers complete a milestone (e.g., making their first order and spending a specific amount).
For example, Victoria’s Secret Pink Nation loyalty program lets customers receive members-only perks such as exclusive content, early access to sales, mental health tips and playlists.
2. Increase average order value
Making customers spend more in your store can boost CLV. Offer free shipping or freebies for customers who reach a specific order amount, such as $50 or $100. Alternatively, you can bundle related products and sell them at a discounted price.
For SaaS businesses, you can give temporary upgrades such as seven-day or 14-day trials. Not only does this allow your customers to see the advanced features they’re missing out on, but it may also persuade them to upgrade and make the transition.
3. Launch post-purchase email campaigns
Offer next-order coupons or discounts to encourage customers to shop again. A good tip is to place them on order confirmation emails because they have a high open rate of around 65%, which is nearly four times higher than an average email. Another way is to deliver post-purchase emails seeking reviews to encourage customers to shop again.
4. Place product recommendations
Product recommendations matter. A study found 92.4% of consumers are influenced by reviews when purchasing. Nearly 90% of consumers believe in product reviews as much as advice from family and friends.
Having product recommendations lets customers evaluate whether or not a product is worth buying. So, recommendations are another way to get customers to buy more, which increases customer lifetime value.
Amazon’s algorithm selects product recommendations based on users’ past purchases and browsing behavior. Using this information, it can make suggestions such as “similar items viewed” or “frequently bought together” by consumers with the same interests or preferences. That’s why 35% of Amazon.com’s revenue comes from its recommendation engine. Judging by the numbers, recommendations are crucial to increasing CLV.
5. Create personalized experiences
Businesses that want to retain customers should focus on increasing their value and relevance. That’s why creating personalized experiences relevant to shoppers’ interests is essential. A survey of 1,000 U.S. adults by Epsilon and GBH Insights found that 80% of respondents want personalization from retailers. Likewise, McKinsey predicts shopping will feel incredibly personalized by 2030.
Moving forward, businesses must be able to segment customers based on their demographics, interests and purchasing behaviors. This could mean tailoring content recommendations based on browsing behavior.
6. Offer quality customer service
Good customer service is essential to encourage customers to be long-time patrons. It only takes one bad experience to prompt a customer to switch to your competitors. A Qualtrics study found 80% of customers have changed brands because of a poor customer experience.
A good tip is to increase communication channels for customer support. Ideally, it would help if you looked into the channels your consumers use the most and created touchpoints there. A study found companies with solid omnichannel customer engagement retain 89% of their buyers.
7. Create unified customer experiences
Thanks to the evolution of technology, most consumers adopt a hybrid approach when purchasing. They may discover a product on Facebook, visit the brand’s website and go to an in-store outlet to examine the physical product—and this won’t stop soon.
While brands can create different touchpoints, ensuring these experiences are streamlined is essential. For example, Timberland lets people stand in front of augmented reality mirrors to envision how apparel fits before going to the fitting room. Similarly, IKEA’s app allows shoppers to browse products online and use their smartphone’s camera to see how it looks in a room.
8. Make returns easy (and ideally, free)
Sometimes customers aren’t delighted with the product—and that’s perfectly okay. Just make it easy for customers to return products and services. A fast and easy return process will encourage customers to return to your online store and give it a try again.
9. Create actionable surveys
Understand your customers by creating actionable surveys. This information will help you understand customers’ level of satisfaction with your products or services. Not only that, but it will help you determine the most effective strategies to drive higher CLV while growing your customer base.
For example, Sephora collected consumer data and found that 70% of customers that visited its website within 24 hours before visiting the store spent 13% more than other customers.
After realizing the importance of online customer journeys, it launched online campaigns that improved in-store engagement. The results? It found a higher return on ad spend (ROAS) by 3.9 times and a threefold increase in conversion rates.
5.1.1.3 Common Mistakes Around CLV
CLV isn’t a magical metric that will solve all your problems. If not used wisely, businesses can fall prey to costly pitfalls. Remember these mistakes when examining your CLV.
No Segmentation
Sure, it’s excellent to increase CLV for your entire customer base—but it’s not an effective strategy. Marketing to everyone will lead you to invest more resources in low CLV customers.
Examine cohorts with a high CLV—which could be your top 20% spenders. Ideally, it would be best if you focused on increasing CLV among valuable customers who are likely to spend more on your products and services based on data. Identify, understand and engage with them to understand their preferences, lifestyle, attitudes and behaviors.
Wrong Segmentation
You can also identify CLV within a customer cohort or segment. A segment is a group of customers with similar characteristics, attitudes and behavior. Proper segmentation provides an overview of consumer behavior and what makes them distinct.
Creating campaigns that target specific segments is more effective due to personalization. However, incorrect segmentation may lead to a waste of precious company resources.
Targeting an Unrealistic CLV
Some customers will abandon your product or service no matter how hard you try. Only some people will pay thousands of dollars to transact with your business over time. There’s also no point in investing hundreds of dollars on low-value prospects.
As with any business objective, you have to be realistic. Think of ways to appeal to your target demographic but do not expect everyone to end up with a high CLV.
Failure To Be Flexible
Turning your small business into an empire is ideal—but things aren’t always smooth sailing. Sometimes recessions or inflation could decrease your CLV. Or, you may have to increase the prices of your products and services based on production costs and uncontrollable market forces. Be flexible when unpredictable situations arise and don’t be afraid to change your CLV based on these trends.
Bottom Line
Customer lifetime value (CLV) can help determine how much customers will spend on your business in the long term. It helps inform customer loyalty, acquisition, marketing and sales decisions.
Improving CLV can be done through various tactics such as creating a loyalty and rewards program, creating personalized experiences and offering quality customer service. Staying aware of common pitfalls when aiming for high CLV can also help prevent errors that could make your company waste valuable resources.
5.1.2 From a hubspot blog
It’s easier to sell to an existing customer than it is to acquire a new one.
Why is customer lifetime value important?
- Increasing CLV can increase revenue over time.
- It can help you find issues so you can boost customer loyalty and retention.
- It helps you target your ideal customers.
- Increasing CLV can help reduce customer acquisition costs.
- CLV can simplify financial planning.
- CLV trends can show you how to improve your products and services
Customer lifetime value helps you understand the growth and revenue value of each customer over time. This metric is important to any business because it can help your business:
- Boost customer loyalty
- Reduce churn
- Improve strategic decision-making
For example, you can use customer lifetime value to find the customer segments that are most valuable to your company.
Here are some other reasons why understanding your CLV is essential.
1. Increasing CLV can increase revenue over time.
The longer the lifecycle or the more value a customer brings during that lifecycle, the more revenue a business earns. Therefore, tracking and improving CLV results in more revenue.
CLV helps you find the specific customers that contribute the most revenue to your business. You can use this information to segment your audience by the value those customers bring.
Once you find those customers, you can encourage repeat purchases and find specific cross-selling and upselling opportunities for different segments of your audience. Or you can tailor your products or marketing to your highest spenders to keep them coming back for more.
2. It can help you identify issues so you can boost customer loyalty and retention.
If CLV is a priority in your business, you can use it to identify impactful trends in your customer data.
This insight can help you stay ahead of competition with action items to address those changes.
CLV helps you understand customer behavior, preferences, and spending patterns. With this analysis, you can improve your data-driven decision-making. This leads to more personalized marketing strategies for growth.
For example, say your CLV is low. You can work to optimize your customer support strategy or loyalty program to better meet the needs of your customers. Or you can optimize a new product to attract higher-value customers.
3. It helps you target your ideal customers.
Customer lifetime value tracking makes it easier to segment your customers. You can segment based on profitability, customer needs, preferences, or behavior.
When you know the lifetime value of a customer, you also know how much money they spend with your business over some time — whether it’s $50, $500, or $5000.
Armed with that knowledge, you can develop a customer acquisition strategy that targets customers who will spend the most at your business. You can personalize marketing to attract and retain them, and effectively allocate resources to get the most value from your efforts.
4. Increasing CLV can help reduce customer acquisition costs.
Acquiring new customers can be costly, and it’s less expensive to retain a customer than it is to acquire a new one.
Customer lifetime value can help reduce costs with a focus on retaining existing customers. If you can keep a customer happy long-term, then you can improve their value to the business.
Using CLV metrics can improve customer loyalty and word-of-mouth referrals — it can also reduce marketing and sales expenses.
5. CLV can simplify Financial planning.
The financial health of a business is often a big concern for CEOs and business owners.
Customer lifetime value helps you get a clear picture of your customers’ relationship with your business and products. It can offer insights into future revenue streams and changes in customer behavior.
This knowledge can help you make more accurate predictions about future cash flows. So, CLV helps you reliably forecast revenue and plan the financial future of your business.
6. CLV trends can show you how to improve your products and services.
Understanding CLV can give you a better understanding of the value customers get from specific products or services.
With insights from your CLV you’ll have a clear direction for further analysis. This may guide you to look at customer feedback and behavior, update pain points, or change your approach to product development.
Lifetime value data can help you find where to make key improvements that align with customer needs and boost satisfaction. This not only strengthens customer loyalty but also differentiates your company from competitors.
Now that we understand the importance of customer lifetime value, let’s talk about the two main customer lifetime value models.
Customer Lifetime Value Models
There are two models that companies will use to measure customer lifetime value.
Choosing between the two can result in different outcomes.
This depends on whether a business is looking at pre existing data, or trying to figure out the future behavior of customers based on current circumstances.
Predictive Customer Lifetime Value
The predictive CLV model forecasts the buying behavior of existing and new customers using regression or machine learning.
Using the predictive model for customer lifetime value helps you better identify your most valuable customers, the product or service that brings in the most sales, and how you can improve customer retention.
Historical Customer Lifetime Value
The historical model uses past data to predict the value of a customer without considering whether the existing customer will continue with the company or not.
With the historical model, the average order value is used to determine the value of your customers. You’ll find this model to be especially useful if most of your customers only interact with your business over a certain period.
But because most customer journeys are not identical, this model has certain drawbacks.
Active customers (deemed valuable by the historical model) might become inactive and skew your data.
In contrast, inactive customers might begin to buy from you again, and you might overlook them because they’ve been labeled “inactive.”
Customer Lifetime Value Formula
The customer lifetime value formula is
Customer Lifetime Value = Customer Value x Average Customer Lifespan. The CLV result is the revenue you expect an average customer to generate during their relationship with your business.
Typically, lifetime value (LTV) calculates the overall value of all customers. But customer lifetime value (CLV) can also focus on the business value of specific customers or groups of customers.The formula above is the standard formula to calculate CLV. But finding this important figure can be more complicated than it looks.
Customer Lifetime Value = (Customer Value * Average Customer Lifespan).
To find CLTV, calculate
customer value = the average purchase value x average number of purchases
Once you calculate the average customer lifespan, you can multiply that by customer value to determine customer lifetime value.
You can see both formulas below:
Customer Value = Average Purchase Value x Average Number of Purchases
Customer Lifetime Value = Customer Value x Average Customer LifespanAverage Purchase Value
Divide your company’s total revenue in a period (usually one year) by the number of purchases throughout that same period.
Average purchase value helps you see the average amount of revenue each customer generates during a period. Analyzing this number also shows you:
- Opportunities to increase the value of each transaction
- New options for cross-selling and upselling
- Whether your pricing and packaging strategies are working
- This data helps you find new and viable products or services and other strategies to increase value per transaction and revenue.
Average Purchase Frequency Rate
To calculate average purchase frequency rate:
Divide the number of purchases by the number of unique customers who made purchases during that period.Recent research says that a 5% customer retention increase can create a 25%+ increase in profit.
Average Purchase Frequency Rate is essential for calculating CLV because it shows you how often customers make repeat purchases. This metric also offers insights into:
Customer engagement and loyalty Trends in customer behavior over time Churn reduction Future revenue streams Average Purchase Frequency Rate Challenges Like average purchase value, inconsistent or incomplete data can also distort your purchase rate numbers.
Other challenges include: Purchase cycle timing, which can get skewed by industry trends or product releases Changing customer buying patterns Seasonality
Tips for Calculating Average Purchase Frequency Rate Track and analyze customer data to capture changing customer buying patterns Regularly review and update customer segmentation based on buying patterns ONer personalized promotions to inspire more consistent spending Conduct customer surveys or interviews for insights into reasons behind changing purchase patterns
Customer Value To calculate customer value, =gure out the average purchase value for your products. Then, calculate the average number of purchases per customer (also called purchase frequency rate). When you multiply these two =gures, it will give you the customer value.
Customer value is important in calculating CLV because it makes it easier to =nd the customers who have the most impact on your revenue. This leads to better strategies, because you can make more eNective decisions when you know what each customer is bringing to your business. Customer value is also important because it gives you what you need to segment customers by their purchasing habits. Segment insights help you create more targeted, customized experiences for your top customers.
Tips for Calculating Customer Value Implement a CRM to con=rm data accuracy Create a consistent process for assigning monetary value to each customer based on their transaction history Combine =nancial systems with customer data to show the monetary value of each customer, like these [nance integrations Watch customer feedback and sentiment through reviews and social listening to add it to customer value calculations
Average Customer Lifespan
To calculate average customer lifespan:First, figure out the average number of years a customer stays active with your company. Once you have your customer lifespan, you’ll divide that by your total customer base to get the average.
ou’ll need excellent data management for this =gure, and make sure you don’t have duplicate accounts in your data. Average customer lifespan is useful when calculating CLV. This is because it supports predictions on how long customer relationships will last with data. This helps you make more informed budgeting and resourcing decisions. It can also help you: Launch proactive strategies to build customer relationships and reduce churn Figure out the ROI for customer acquisition Optimize marketing strategies Find acquisition channels with higher CLV potential Average Customer Lifespan Challenges Calculating average customer lifespan can be tough because: Accurate customer lifecycle tracking needs a robust data management system DiNerent customer segments and subgroups can skew lifespan predictions Limited customer data or short relationships lead to projections that don’t align with actual customer behavior Tips for Calculating Average Customer Lifespan Use reliable customer service software to track the customer lifecycle Include data from diNerent sources and platforms to create a full view of the customer journey Capture and analyze data at each stage of the buyer journey to track engagement and retention Analyze the average lifespan of each customer segment individually to limit skewed results Conduct regular trend analysis to predict shifts or changes that may impact lifespan Gather data on customer satisfaction and loyalty Constantly con=rm and adjust lifespan average based on actual customer behavior and feedback Customer Acquisition Cost Customer acquisition cost is not a factor in most CLV formulas, but it can be useful to include in a customer lifetime value analysis. Comparing how much it costs to acquire a customer with their lifetime value to the business, you can =gure out how to: Decide how eNective marketing and sales strategies are Distribute resources wisely Find =tting opportunities to improve customer retention and acquisition Check out this guide to learn more about customer acquisition cost (CAC) and how to calculate. Then, review these tips for analyzing your CAC to LTV ratio.
5.2 CLV Calculations
5.2.1 From Forbes
There are four essential steps to calculate CLV.
1. Determine the average order value
Determine the average amount customers spend on your business. To get this information get an estimate based on customer transactions in the last few months.
this is the average spending per order.
2. Identify frequency of transactions
Next, identify how often customers come to your store. How many times do they come back, given a specific period? Do they return weekly, monthly or annually?
3. Measure customer retention
Figure out how long an average customer remains loyal to your business.
Some industries, including restaurants and retail, tend to have a lower CLV because customers tend to go to establishments that offer a better deal.
Meanwhile, industries such as technology and travel have a higher CLV because customers seek updated product features and personalized holiday experiences.
4. Calculate CLV
Once you have all this information, calculate CLV with this formula:
X = average order value
Y = number of transactions
Z = average length of the customer relationship (in years)\(CLV = X x Y x Z\)
Using this information, we can assume a father that regularly purchases smartphones for his family might be worth:
\(\$1,000 (per\ smartphone) × 2 (smartphones\ per\ year) × 10 (years) = \$20,000\)
The CLV is $20,000.
5.2.1.1 3 Examples of CLV
CLV varies based on the nature of the product or service. Let’s examine various industries to show how CLV could affect your bottom line.
1. Grocery Shop
Grocery stores inspire loyalty among residents within the vicinity. Let’s say a shopper frequents a grocery in New York every week. He spends around $100 per visit. He returned every week, 52 weeks a year, for an average of three years.
\(\$100 (purchase\ per\ visit) × 52 (visits\ per\ year) × 3 (years) = \$15,600 (CLV)\)
2. SaaS Service
A UX designer uses a cloud-based subscription service to conceptualize mobile apps. He spends $70 per month for 10 years on the software. In this example, the SaaS product is a necessary job-related expense, so the subscription lasted a long time.
\(\$70 (subscription\ fee\ per\ month) × 12 (payments\ per\ year) × 10 (years) = \$8,400 (CLV)\)
3. Interior Design
The interior design agency has higher average order values. For example, a homeowner spends $100,000 to renovate their home. Because they liked the initial experience, they became a patron of the interior design firm and renovated their property every 10 years within 20 years.
\(\$100,000 (per\ renovation) × 0.1 (annual\ purchase) × 20 (years) = \$200,000 (CLV)\)
These examples show CLV varies across industries.
While day-to-day products such as coffee are bought more frequently, you need to get customers to purchase often to get a high CLV.
In contrast, some products such as houses, automobiles or interior design agencies have a lower purchase frequency. But due to the nature of the product or service, they rack up thousands of dollars with only a few transactions.
5.2.2 Another example (AI)
Customer Lifetime Value (CLV) can be calculated at both the individual level and the brand level, depending on your objectives and the granularity of your analysis. Here’s how you can approach each:
5.2.2.1 Individual-Level CLV
Calculating CLV at the individual level involves estimating the total revenue that each specific customer will generate over their relationship with the brand. This method provides detailed insights into the value of each customer, which can help in personalizing marketing efforts and customer retention strategies. Here’s how you can calculate it:
Calculate Average Purchase Value (APV): \[ \text{APV} = \frac{\text{Total Revenue}}{\text{Number of Purchases}} \]
Calculate Purchase Frequency (PF): \[ \text{PF} = \frac{\text{Number of Purchases}}{\text{Number of Customers}} \]
Calculate Customer Value (CV): \[ \text{CV} = \text{APV} \times \text{PF} \]
Estimate Customer Lifespan (CL): \[ \text{CL} = \text{Average Customer Lifespan in Years} \]
Calculate Individual CLV: \[ \text{CLV} = \text{CV} \times \text{CL} \]
5.2.2.2 Brand-Level CLV
Calculating CLV at the brand level involves aggregating the CLV of all customers to get an overall estimate of the value that the customer base will generate over time. This method helps in understanding the overall financial health and growth potential of the brand. Here’s how you can calculate it:
Calculate Average Revenue Per User (ARPU): \[ \text{ARPU} = \frac{\text{Total Revenue}}{\text{Total Number of Customers}} \]
Estimate Average Customer Lifespan (CL): \[ \text{CL} = \text{Average Customer Lifespan in Years} \]
Calculate Brand-Level CLV: \[ \text{Brand CLV} = \text{ARPU} \times \text{CL} \times \text{Total Number of Customers} \]
5.2.2.3 Steps to Calculate CLV with Your Data
Given daily transactional individual-level data, here’s a step-by-step approach:
Aggregate the data to calculate total revenue and number of purchases per customer.
Calculate APV and PF for each customer.
Estimate the average customer lifespan using historical data.
Compute the individual CLV for each customer.
Aggregate individual CLVs to get the overall brand CLV if needed.
By calculating CLV at the individual level, you gain detailed insights that can be rolled up to understand the overall brand performance. This dual approach allows for both personalized customer engagement strategies and broader financial planning.
Calculating CLV for each individual customer provides a more granular view of your customer base. This approach allows you to:
- Identify your most valuable customers
- Segment customers based on their lifetime value
- Tailor marketing strategies for different customer segments
- Predict future value of specific customers
To calculate individual CLV, you would use each customer’s specific purchase history, frequency, and value over the 2-year period, and potentially project this into the future.
Given your dataset, here’s how you could approach the calculation:
Calculate individual CLV:
For each customer, determine their total purchase value over the 2-year period
Calculate their purchase frequency (number of purchases / 2 years)
Project these values forward to estimate future value
Sum up the historical and projected values to get individual CLV
5.2.2.4 Brand-level CLV:
Calculating CLV at the brand level gives you an average value across all customers. This approach:
Provides an overall measure of customer value for your chocolate brand
Helps in strategic decision-making at the brand level
Allows for easier comparison with industry benchmarks or competitors
To calculate brand-level CLV, you would aggregate data across all customers to determine average purchase value, frequency, and customer lifespan.
Remember that CLV is typically a forward-looking metric, so you may need to use your 2-year historical data to project future customer behavior and value. Additionally, consider factors like customer acquisition costs and profit margins to get a more accurate picture of customer value.
Citations:
[1] https://blog.hubspot.com/service/how-to-calculate-customer-lifetime-value
[2] https://www.unionkitchen.com/resources/customer-lifetime-value
[3] https://www.omnisend.com/blog/customer-lifetime-value-clv/
[4] https://getvoip.com/blog/customer-lifetime-value-formula/
[5] https://www.crazyegg.com/blog/customer-lifetime-value/
By calculating CLV, CPG brands can better understand the long-term value of their customers and make informed decisions about marketing strategies, customer retention efforts, and resource allocation. Keep in mind that CLV calculations are estimates and may be subject to adjustments based on changing market conditions and customer behaviors.
The Customer Lifetime Value (CLV) formula provided is typically calculated on a per-customer basis, representing the expected value of an average customer over their entire relationship with a brand. The formula considers the average purchase value, purchase frequency, and customer lifespan for an individual customer.
\[ CLV = \frac{{\text{Average Purchase Value} \times \text{Purchase Frequency} \times \text{Customer Lifespan}}}{{\text{Discount Rate}}} \]
So, each customer will have their own CLV based on their purchasing behavior and the estimated duration of their relationship with the brand. The overall CLV for a brand is often the sum of the CLVs for all its customers.
This individual customer focus allows businesses to understand the value of acquiring and retaining each customer and can inform marketing strategies, customer relationship management, and overall business decisions.
5.2.3 CLV Example (hubspot)
Using data from a Kissmetrics report, we can take Starbucks as an example for determining CLTV.
Its report measures the weekly purchasing habits of five customers, then averages their total values together.
By following the steps listed above, we can use this information to calculate the average lifetime value of a Starbucks customer.
1. Calculate the average purchase value.
Measure Average Purchase Value.
based on Kissmetrics, the average Starbucks customer spends about $5.90 each visit.
Average the money spent by a customer in each visit during the week.
For example, a person goes to Starbucks three times and spent nine dollars total, then average purchase value would be three dollars.
Repeat the process for the other five customers.
Then add each average together, divide that value by the number of customers surveyed (five) to get the average purchase value.
2. Calculate the average purchase frequency rate.
Now measure the average purchase frequency rate.
How many visits the average customer makes to one of a store within a week.
The average observed across the 5 customers in the report was found to be 4.2 visits.
This makes our average purchase frequency rate 4.2.
3. Calculate the average customer’s value. Now that we know what the average customer spends and how many times they visit in a week, we can determine their customer value. To do this, we have to look at all =ve customers individually and then multiply their average purchase value by their average purchase frequency rate. This lets us know how much revenue the customer is worth to Starbucks within a week. Once we repeat this calculation for all =ve customers, we average their values to get the average customer’s value of $24.30. 4. Calculate the average customer’s lifetime span. While it’s not explicitly stated how Kissmetrics measured Starbucks’ average customer lifetime span, it does list this value as 20 years. If we were to calculate Starbucks’ average customer lifespan, we would have to look at the number of years each customer frequented Starbucks. Then we could average the values together to get 20 years. If you don’t have 20 years to wait and verify that, one way to estimate customer lifespan is to divide 1 by your churn rate percentage.
- Calculate your customer’s lifetime value.
Once we have determined the average customer value and the average customer lifespan, we can use this data to calculate CLTV.
In this case, we first need to multiply the average customer value by 52.
Since we measured customers on their weekly habits, we need to multiply their customer value by 52 to reflect an annual average.
After that, multiply this number by the customer lifespan value (20) to get CLTV.
For Starbucks customers, that value turns out to be $25,272 (52 x 24.30 x 20= 25,272).
5.2.4 Study of Customer lifetime value model based on Survival analysis methods
Customer are at the center of business. Understanding the value of a customer is important in terms of personalized marketing efforts
5.2.4.1 Basic Model of CLV
Barbara, Jackson (1985) laid the foundation. CLV depends on the income from customer at every stage of the life cycle.
Berger, Nasr (1998) introduced parameter of customer retention rate.
\[CLV = \sum_{i=1}^{n}{\gamma . \pi(i).(1+d)^-i}\]
\(\pi():\) profit function of customer
\(i:\) time variable
\(\gamma:\) retention rate
\(d:\) discount rate
\(n:\) entire life cycle of time
Here are the topics criticized:
rate of customer retention is replaced by a constant, or by a function of time.
time of the customer lifetime is always evaluated as constant.
customer’s future profitablity is constant
5.2.4.2 Improved CLV model using Survival Analaysis
5.2.4.2.1 Estimating dynamic Customer retention rate
survival time is a measure of the time of an event. Survival function can be used to describe the distribution of survival time.
Customer retention rate is actually the distribution of customer life time.
\[r(t) = r_0(t)^{exp(\beta)}, t>0\]
\(r(t)\) is the cummulative retention rate of lost-for-good customers.
\(r(t)\) is also the dynamic customer retention rate which can be can be given by Cox regression analysis.
5.2.5 Look-alike Modeling
What is a Look-Alike?
Look-alike audiences are the prospects who are having similar traits, behavior like your already existing customers.
Look-alike modeling is a process that essentially helps you in finding look-alike audiences of your best, most profitable customers. It is a modelling approach that can be used by marketers to define customers who are most likely to engage with their marketing messages or activities. This model analyzes and considers common behaviors or traits among the current customers and seeks potential customers who have similar characteristics.
Seed Data is data of existing customers based on whom we want to find look-alike audiences.
Pool Data is the customer database, in which we would look for customers who are look-alikes to seed data. Pool data could be collected from various sources.
Extended Look-Alikes Audience are the look-alike audience generated by the model from pool audience, based on seed data.
Benefits of Look-Alike Modeling
Look-Alike model plays a key role in making business and marketing related decisions. Helps in understanding existing customer base and expand business reach by only focusing on your best customers with a stable business model.
1. Effective Targeting
Look-Alike model helps businesses and marketers to execute better marketing campaigns by limiting their focus to those prospects who are similar to the target customers on whom the business is interested in.
2. Lower Acquisition Cost
Customer Cost Acquisition (CAC) cost is, in general, approximately 6-7 times costlier than Customer Retention Cost (CRC). But by relying on look-alike modeling, businesses can reduce CAC as they would only spend their marketing efforts on potential customers (look-alikes) who are more likely to convert.
3. Loyal and Profitable Customer Base
Look-alike modeling helps you in building a highly profitable customer base, by allowing you to target the look-alikes of those customers with high Customer Lifetime Value (CLV) ensuring a highly profitable customer base for your business, in the long run.
What is Look-alike Modeling
Look-alike modeling is essentially finding groups of people (audiences) who look and act like your best, most profitable customers. For example, let’s say you run an ecommerce store and you’ve identified that your best audience are people whose average purchase is over $100, buy cosmetics and perfumes, and make a purchase at least twice a month; look-alike modeling would allow you to find more people like that.
How to Look-alike Modeling (LAL)?
Demand Side Platforms (DSP) can perform LAL modeling
Data Management Platforms can conduct LAL modeling
Requires data collection and modeling
Define the attributes and behaviors of your most valuable customers.
The stricter your look-alike model is (the more attributes you define), the better chance you have of finding your target — albeit smaller — audience, which will allow you to improve campaign performance. Meaning if the model is more complex, then accuracy of the model increase.
However, you could be less strict with the look-alike model (i.e. define less attributes and behaviors) if your goal is to focus on overall reach and awareness rather than higher conversion rates.
- A look-alike model that has tightly defined (more) attributes and behaviors, and one that has loosely defined (less) attributes and behaviors.
The third step involves using algorithms to extend the audience based on the look-alike model.
The DMP or DSP would analyze the seed audience (the pre-defined best customers) and then apply proprietary algorithms to the data you’ve collected in order to find user profiles that match the seed audience.
Goal: Prospecting and Increasing Campaign Reach
Look-alike modeling is mainly used for prospecting, which involves finding new potential customers and/or visitors. However, it can also be used to extend the reach of online advertising campaigns.
Let’s say you target audiences based on a set of attributes (e.g. age, gender, location, etc.). By applying look-alike modeling to your campaigns, you can find similar customers who perhaps don’t fit your current audiences either because we don’t have enough data (e.g. we lack the attributes needed to make a match) or they don’t fit your current audiences (i.e. they consists of other attributes) but are still similar in many ways to your best customers.
LAL Modeling vs Classification Modeling
Look-alike modeling and classification modeling are two distinct techniques used in data analysis and machine learning, often employed in marketing and predictive analytics contexts.
Look-alike Modeling: Definition: Look-alike modeling, also known as similarity modeling or clone modeling, involves identifying individuals or entities in a target group who resemble those in a source group based on certain characteristics. Process: It begins with a source group of individuals or entities with known traits or behaviors of interest. Machine learning algorithms then analyze these traits to identify patterns and similarities. These patterns are then applied to a larger population to find individuals or entities who closely resemble those in the source group. Application: Look-alike modeling is commonly used in marketing to identify potential customers who share characteristics with existing customers or high-value prospects. It helps in targeting marketing campaigns more effectively by focusing resources on individuals with a higher likelihood of conversion.
Classification Modeling: Definition: Classification modeling involves categorizing data points into predefined classes or categories based on their features or attributes. Process: In classification modeling, historical data with known outcomes is used to train machine learning algorithms. These algorithms learn patterns from the input data and assign new data points to predefined classes or categories based on their similarities to the training data. Application: Classification modeling is widely used in various domains such as finance, healthcare, and e-commerce. Examples include spam email detection, sentiment analysis, disease diagnosis, and credit risk assessment. The model predicts the class or category to which a new data point belongs based on its features. In summary, look-alike modeling focuses on finding individuals or entities similar to a known group, while classification modeling focuses on categorizing data points into predefined classes or categories based on their features. Both techniques are valuable in different contexts and can provide insights for decision-making and targeting in various industries.