Question 1

Do Retina models go beyond RFM?

Caroline Kratofil · Accepted Answer

Recency, frequency, and monetary value (RFM) is a simple method to determine customer value. Retina models take into account data beyond RFM, including demographic and behavioral attributes, customer journeys, quiz and session data, and more. All of these data points make up a comprehensive data set that allows us to use machine learning to impute missing values from messy data and make customer lifetime value predictions early in the customer journey.

Question 2

What is customer lifetime value?

Caroline Kratofil · Accepted Answer

Customer Lifetime Value (CLV) is a metric that estimates how much value (usually revenue or profit margin) any given customer will bring to your business over the course of the total time they interact with your brand—past, present, and future.

Customer Lifetime Value (CLV) is the single most important metric for you to know because it demonstrates who your best customers are and what they have in common. Using this metric effectively revolutionizes the approach to both acquisition and retention marketing, separating true industry disruptors from the rest of the pack.

Think about it—how would your marketing, sales, and product strategies and budget allocations change if you could predict which audiences will remain loyal to your brand for years to come? And which will visit your site and make just one highly-discounted purchase before falling off the radar completely? What if you knew that some customers you are already planning to spend lots of your budget to retain actually aren’t likely to bring lots of value to your organization anyway?

Question 3

How do you calculate customer lifetime value?

Caroline Kratofil · Accepted Answer

There are many formulas available to calculate lifetime value. However, calculating predictive customer lifetime value at the individual level requires a complex model. The steps include:

Locate the necessary data
Forecast every existing customer’s behavior
Split the data into two sets for training and testing
Add customer features and attributes to the model
Train and validate the model
Put the model into production

Read our whitepaper to learn 8 steps to calculate CLV. Check out this blog post to learn how to make three types of CLV models: analytic aggregate CLV, analytic cohort-based CLV, predictive CLV using statistical models.

Question 4

How do you know if your CLV metric is accurate?

Caroline Kratofil · Accepted Answer

Not all CLV models are created equal. If you're reviewing CLV from your organization or a vendor, start by asking yourself a few questions to see if the metric is accurate and useful.

Is the CLV at the aggregate level or individual level? If it is aggregate, is it the mean or median? It's pretty easy to calculate CLV at the aggregate level. Because most business use cases require individual level CLV, you'll want a model that calculates CLV for each customer.
Is CLV historic, predictive or some combination? If CLV is provided at the individual level, you want it to be predictive and not just historic.
How much of CLV is already observed vs predicted future revenue/profit? The danger of using only future-predicted revenue is that you can no longer compare future-predicted revenue of a highly active current customer with a previous cohort of customers.

Read more about the CLV questions you should be asking in this blog post.

Question 5

What is the difference between CLV and LTV?

Caroline Kratofil · Accepted Answer

Customer Lifetime Value is calculated at the individual level, while Lifetime Value is an aggregate metric.

Question 6

Why is it important to calculate CLV at the individual level?

Caroline Kratofil · Accepted Answer

The most popular and commonly used customer lifetime value (CLV) models benchmark their strength on aggregate metrics. However, these models are incredibly inaccurate at the individual level. This becomes an issue because most business use cases require a strong CLV model at the individual level. Aggregate CLV simply does not allow you to adjust who you’re targeting with re-engagement or acquisition efforts to maximize the benefits of each campaign. Read more about the importance of individual CLV in this blog post.

Question 7

Can CLV change over time?

Caroline Kratofil · Accepted Answer

Yes, customer lifetime value is not a static metric. It changes over time as you observe new data about a customer. Knowing this, you can adjust your retention and product strategies to increase customer lifetime value. Learn more about how CLV changes with time in this blog post.

Question 8

How can CLV improve marketing strategies?

Caroline Kratofil · Accepted Answer

Customer lifetime value is an important tool for marketers: CLV can help you target, retain, and provide exceptional service to your best customers. Once you know what attributes and behaviors make up your high-CLV customer, you can use that information to build lookalike audiences. Read more in this blog post. If you’re ready to start using CLV to inform your marketing strategy, start here.

Question 9

Why is CLV important for segmentation?

Caroline Kratofil · Accepted Answer

It’s relatively simple to segment your current customers by attributes and/or past behavior. But if you want to predict the future behavior of your customers, it’s better to segment by features and attributes that impact customer lifetime value. Read more about the process in this blog post.

Question 10

What makes a customer high value?

Caroline Kratofil · Accepted Answer

Customer lifetime value (CLV) is the metric leading companies use to understand their customers’ purchasing habits. Simply put, customers who purchase higher-value products, who purchase more frequently, and who continue to purchase on an ongoing basis are your high-CLV customers.

Question 11

What should your CLV to CAC ratio be?

Caroline Kratofil · Accepted Answer

Once you’ve calculated CLV, you can use it to determine your target customer acquisition cost (CAC). Instead of simply targeting customers that bring in high initial revenue, focus your acquisition efforts on high CLV customers. Even if their first purchase is small, they will bring your business more value in the future.

A customer acquisition is unprofitably only if CAC exceeds CLV. For most businesses, your CLV to CAC ratio should be 3:1 for each marketing segment. If you spend too much (e.g. 1:1 ratio), acquiring those customers won’t be profitable. But if you spend too little (e.g. 7:1 ratio), you will be missing out on profitable customers whose acquisition cost is above your current bid cap.

Question 12

How do you incorporate discounting when calculating the CLV?

Caroline Kratofil · Accepted Answer

You can take your annual discount rate and turn it into a daily discount rate, and apply a discount factor, which is: 1 / (1 + X)^Y where X is the discount rate and Y is the time variable.

Question 13

How do you determine the right time frame for churn?

Caroline Kratofil · Accepted Answer

Typically, we compute the lifespan of every single customer using survival analysis. Once we’ve done that, you get a sense of what the longest life is of a customer. Based on that, you can choose the truncated time spans for computing customer lifetime value. We will typically calculate CLV for multiple year horizons, depending on the business. Sometimes, we will also compute full lifetime horizons as well.

Question 14

Do Retina models go beyond RFM?

Caroline Kratofil · Accepted Answer

Recency, frequency, and monetary value (RFM) is a simple method to determine customer value. Retina models take into account data beyond RFM, including demographic and behavioral attributes, customer journeys, quiz and session data, and more. All of these data points make up a comprehensive data set that allows us to use machine learning to impute missing values from messy data and make customer lifetime value predictions early in the customer journey.

Question 15

What types of companies does Retina work with, and what data do I need to work with Retina?

Caroline Kratofil · Accepted Answer

Retina works best with companies whose customers have repeat purchase behavior. This can include, but is not limited to: eCommerce, retail, CPG, financial services, healthcare, and even paid smartphone apps. In addition, customers ideally have 10,000 repeat purchases, 18-months or more worth of data, and customer identity resolution. Retina’s models use orders data and customer attributes, so we have integrations with Shopify as well as CDPs like mParticle or Segment to make data transfer a breeze.

Question 16

In what format will I receive my eCLV scores?

Caroline Kratofil · Accepted Answer

Possible outputs of Retina’s models are: Customer ID, As Of Date, Retina CLV (up to 15-year predictions), Lapse Probability, Retina Persona, Likely Next Transaction , Typical Order Value , and Predicted Lapse Date. Each can be modified to fit a client’s needs, and will be sent through an established pipeline with your data warehouse.

Question 17

How can Retina predict CLV even before a prospect’s first transaction?

Caroline Kratofil · Accepted Answer

Unlike “Buy ‘Til You Die” models, Retina’s CLV model can predict customer lifetime value at or before a prospect’s first transaction. To do this, we first collect all available customer data, including estimates of RFM signals (AOV, survival rate, time between purchases). Next, our model denoises and imputes missing data. Finally, we simulate expected behavior at the customer-level using cleaner RFM signals. To learn more about how our model works, check out our CLV Framework Whitepaper.

Question 18

What does a typical Retina engagement look like?

Caroline Kratofil · Accepted Answer

Retina’s customers typically receive early CLV scores for every customer within a month of starting a project. To begin, Retina conducts a data audit on a one-time data transfer. Once the model is built, customers can opt to include other services such as: training modules, strategy sessions, and Quality of Customers reports.

Question 19

How much does Retina cost?

Caroline Kratofil · Accepted Answer

Retina’s pricing is dependent on how many customers you have, how often those customers are scored, and the length of your commitment with Retina. Of note, Retina has no integration or upfront fee, and you only pay for what you use.

Question 20

When can I expect to see return on my investment and in what form will it be?

Caroline Kratofil · Accepted Answer

As soon as you receive early CLV scores (~1 month), you can put them to use. An immediate application building lookalike audiences based on high LTV customers. On Facebook, Google, and other DSPs our clients achieve a 44% incremental lift in LTV. Other clients measure incremental LTV of each marketing campaign to optimize their ad spend. By doing so, our clients improved their marketing efficiency by 30%. Even outside of CLV scores, Retina clients save approximately $1.5 million in data science and model development costs.

Question 21

What is customer lifetime value?

Caroline Kratofil · Accepted Answer

Customer Lifetime Value (CLV) is a metric that estimates how much value (usually revenue or profit margin) any given customer will bring to your business over the course of the total time they interact with your brand—past, present, and future.

Customer Lifetime Value (CLV) is the single most important metric for you to know because it demonstrates who your best customers are and what they have in common. Using this metric effectively revolutionizes the approach to both acquisition and retention marketing, separating true industry disruptors from the rest of the pack.

Think about it—how would your marketing, sales, and product strategies and budget allocations change if you could predict which audiences will remain loyal to your brand for years to come? And which will visit your site and make just one highly-discounted purchase before falling off the radar completely? What if you knew that some customers you are already planning to spend lots of your budget to retain actually aren’t likely to bring lots of value to your organization anyway?

Question 22

How do you calculate customer lifetime value?

Caroline Kratofil · Accepted Answer

There are many formulas available to calculate lifetime value. However, calculating predictive customer lifetime value at the individual level requires a complex model. The steps include:

Locate the necessary data
Forecast every existing customer’s behavior
Split the data into two sets for training and testing
Add customer features and attributes to the model
Train and validate the model
Put the model into production

Read our whitepaper to learn 8 steps to calculate CLV. Check out this blog post to learn how to make three types of CLV models: analytic aggregate CLV, analytic cohort-based CLV, predictive CLV using statistical models.

Question 23

How do you know if your CLV metric is accurate?

Caroline Kratofil · Accepted Answer

Not all CLV models are created equal. If you're reviewing CLV from your organization or a vendor, start by asking yourself a few questions to see if the metric is accurate and useful.

Is the CLV at the aggregate level or individual level? If it is aggregate, is it the mean or median? It's pretty easy to calculate CLV at the aggregate level. Because most business use cases require individual level CLV, you'll want a model that calculates CLV for each customer.
Is CLV historic, predictive or some combination? If CLV is provided at the individual level, you want it to be predictive and not just historic.
How much of CLV is already observed vs predicted future revenue/profit? The danger of using only future-predicted revenue is that you can no longer compare future-predicted revenue of a highly active current customer with a previous cohort of customers.

Read more about the CLV questions you should be asking in this blog post.

Question 24

What is the difference between CLV and LTV?

Caroline Kratofil · Accepted Answer

Customer Lifetime Value is calculated at the individual level, while Lifetime Value is an aggregate metric.

Question 25

Why is it important to calculate CLV at the individual level?

Caroline Kratofil · Accepted Answer

The most popular and commonly used customer lifetime value (CLV) models benchmark their strength on aggregate metrics. However, these models are incredibly inaccurate at the individual level. This becomes an issue because most business use cases require a strong CLV model at the individual level. Aggregate CLV simply does not allow you to adjust who you’re targeting with re-engagement or acquisition efforts to maximize the benefits of each campaign. Read more about the importance of individual CLV in this blog post.

Question 26

Can CLV change over time?

Caroline Kratofil · Accepted Answer

Yes, customer lifetime value is not a static metric. It changes over time as you observe new data about a customer. Knowing this, you can adjust your retention and product strategies to increase customer lifetime value. Learn more about how CLV changes with time in this blog post.

Question 27

How can CLV improve marketing strategies?

Caroline Kratofil · Accepted Answer

Customer lifetime value is an important tool for marketers: CLV can help you target, retain, and provide exceptional service to your best customers. Once you know what attributes and behaviors make up your high-CLV customer, you can use that information to build lookalike audiences. Read more in this blog post. If you’re ready to start using CLV to inform your marketing strategy, start here.

Question 28

Why is CLV important for segmentation?

Caroline Kratofil · Accepted Answer

It’s relatively simple to segment your current customers by attributes and/or past behavior. But if you want to predict the future behavior of your customers, it’s better to segment by features and attributes that impact customer lifetime value. Read more about the process in this blog post.

Question 29

What makes a customer high value?

Caroline Kratofil · Accepted Answer

Customer lifetime value (CLV) is the metric leading companies use to understand their customers’ purchasing habits. Simply put, customers who purchase higher-value products, who purchase more frequently, and who continue to purchase on an ongoing basis are your high-CLV customers.

Question 30

What should your CLV to CAC ratio be?

Caroline Kratofil · Accepted Answer

Once you’ve calculated CLV, you can use it to determine your target customer acquisition cost (CAC). Instead of simply targeting customers that bring in high initial revenue, focus your acquisition efforts on high CLV customers. Even if their first purchase is small, they will bring your business more value in the future.

A customer acquisition is unprofitably only if CAC exceeds CLV. For most businesses, your CLV to CAC ratio should be 3:1 for each marketing segment. If you spend too much (e.g. 1:1 ratio), acquiring those customers won’t be profitable. But if you spend too little (e.g. 7:1 ratio), you will be missing out on profitable customers whose acquisition cost is above your current bid cap.

Question 31

How do you incorporate discounting when calculating the CLV?

Caroline Kratofil · Accepted Answer

You can take your annual discount rate and turn it into a daily discount rate, and apply a discount factor, which is: 1 / (1 + X)^Y where X is the discount rate and Y is the time variable.

Question 32

How do you determine the right time frame for churn?

Caroline Kratofil · Accepted Answer

Typically, we compute the lifespan of every single customer using survival analysis. Once we’ve done that, you get a sense of what the longest life is of a customer. Based on that, you can choose the truncated time spans for computing customer lifetime value. We will typically calculate CLV for multiple year horizons, depending on the business. Sometimes, we will also compute full lifetime horizons as well.

Question 33

Do Retina models go beyond RFM?

Caroline Kratofil · Accepted Answer

Recency, frequency, and monetary value (RFM) is a simple method to determine customer value. Retina models take into account data beyond RFM, including demographic and behavioral attributes, customer journeys, quiz and session data, and more. All of these data points make up a comprehensive data set that allows us to use machine learning to impute missing values from messy data and make customer lifetime value predictions early in the customer journey.

Question 34

What types of companies does Retina work with, and what data do I need to work with Retina?

Caroline Kratofil · Accepted Answer

Retina works best with companies whose customers have repeat purchase behavior. This can include, but is not limited to: eCommerce, retail, CPG, financial services, healthcare, and even paid smartphone apps. In addition, customers ideally have 10,000 repeat purchases, 18-months or more worth of data, and customer identity resolution. Retina’s models use orders data and customer attributes, so we have integrations with Shopify as well as CDPs like mParticle or Segment to make data transfer a breeze.

Question 35

In what format will I receive my eCLV scores?

Caroline Kratofil · Accepted Answer

Possible outputs of Retina’s models are: Customer ID, As Of Date, Retina CLV (up to 15-year predictions), Lapse Probability, Retina Persona, Likely Next Transaction , Typical Order Value , and Predicted Lapse Date. Each can be modified to fit a client’s needs, and will be sent through an established pipeline with your data warehouse.

Question 36

How can Retina predict CLV even before a prospect’s first transaction?

Caroline Kratofil · Accepted Answer

Unlike “Buy ‘Til You Die” models, Retina’s CLV model can predict customer lifetime value at or before a prospect’s first transaction. To do this, we first collect all available customer data, including estimates of RFM signals (AOV, survival rate, time between purchases). Next, our model denoises and imputes missing data. Finally, we simulate expected behavior at the customer-level using cleaner RFM signals. To learn more about how our model works, check out our CLV Framework Whitepaper.

Question 37

What does a typical Retina engagement look like?

Caroline Kratofil · Accepted Answer

Retina’s customers typically receive early CLV scores for every customer within a month of starting a project. To begin, Retina conducts a data audit on a one-time data transfer. Once the model is built, customers can opt to include other services such as: training modules, strategy sessions, and Quality of Customers reports.

Question 38

How much does Retina cost?

Caroline Kratofil · Accepted Answer

Retina’s pricing is dependent on how many customers you have, how often those customers are scored, and the length of your commitment with Retina. Of note, Retina has no integration or upfront fee, and you only pay for what you use.

Question 39

When can I expect to see return on my investment and in what form will it be?

Caroline Kratofil · Accepted Answer

As soon as you receive early CLV scores (~1 month), you can put them to use. An immediate application building lookalike audiences based on high LTV customers. On Facebook, Google, and other DSPs our clients achieve a 44% incremental lift in LTV. Other clients measure incremental LTV of each marketing campaign to optimize their ad spend. By doing so, our clients improved their marketing efficiency by 30%. Even outside of CLV scores, Retina clients save approximately $1.5 million in data science and model development costs.

Question 40

What is customer lifetime value?

Caroline Kratofil · Accepted Answer

Customer Lifetime Value (CLV) is a metric that estimates how much value (usually revenue or profit margin) any given customer will bring to your business over the course of the total time they interact with your brand—past, present, and future.

Customer Lifetime Value (CLV) is the single most important metric for you to know because it demonstrates who your best customers are and what they have in common. Using this metric effectively revolutionizes the approach to both acquisition and retention marketing, separating true industry disruptors from the rest of the pack.

Think about it—how would your marketing, sales, and product strategies and budget allocations change if you could predict which audiences will remain loyal to your brand for years to come? And which will visit your site and make just one highly-discounted purchase before falling off the radar completely? What if you knew that some customers you are already planning to spend lots of your budget to retain actually aren’t likely to bring lots of value to your organization anyway?

Question 41

How do you calculate customer lifetime value?

Caroline Kratofil · Accepted Answer

There are many formulas available to calculate lifetime value. However, calculating predictive customer lifetime value at the individual level requires a complex model. The steps include:

Locate the necessary data
Forecast every existing customer’s behavior
Split the data into two sets for training and testing
Add customer features and attributes to the model
Train and validate the model
Put the model into production

Read our whitepaper to learn 8 steps to calculate CLV. Check out this blog post to learn how to make three types of CLV models: analytic aggregate CLV, analytic cohort-based CLV, predictive CLV using statistical models.

Question 42

How do you know if your CLV metric is accurate?

Caroline Kratofil · Accepted Answer

Not all CLV models are created equal. If you're reviewing CLV from your organization or a vendor, start by asking yourself a few questions to see if the metric is accurate and useful.

Is the CLV at the aggregate level or individual level? If it is aggregate, is it the mean or median? It's pretty easy to calculate CLV at the aggregate level. Because most business use cases require individual level CLV, you'll want a model that calculates CLV for each customer.
Is CLV historic, predictive or some combination? If CLV is provided at the individual level, you want it to be predictive and not just historic.
How much of CLV is already observed vs predicted future revenue/profit? The danger of using only future-predicted revenue is that you can no longer compare future-predicted revenue of a highly active current customer with a previous cohort of customers.

Read more about the CLV questions you should be asking in this blog post.

Question 43

What is the difference between CLV and LTV?

Caroline Kratofil · Accepted Answer

Customer Lifetime Value is calculated at the individual level, while Lifetime Value is an aggregate metric.

Question 44

Why is it important to calculate CLV at the individual level?

Caroline Kratofil · Accepted Answer

The most popular and commonly used customer lifetime value (CLV) models benchmark their strength on aggregate metrics. However, these models are incredibly inaccurate at the individual level. This becomes an issue because most business use cases require a strong CLV model at the individual level. Aggregate CLV simply does not allow you to adjust who you’re targeting with re-engagement or acquisition efforts to maximize the benefits of each campaign. Read more about the importance of individual CLV in this blog post.

Question 45

Can CLV change over time?

Caroline Kratofil · Accepted Answer

Yes, customer lifetime value is not a static metric. It changes over time as you observe new data about a customer. Knowing this, you can adjust your retention and product strategies to increase customer lifetime value. Learn more about how CLV changes with time in this blog post.

Question 46

How can CLV improve marketing strategies?

Caroline Kratofil · Accepted Answer

Customer lifetime value is an important tool for marketers: CLV can help you target, retain, and provide exceptional service to your best customers. Once you know what attributes and behaviors make up your high-CLV customer, you can use that information to build lookalike audiences. Read more in this blog post. If you’re ready to start using CLV to inform your marketing strategy, start here.

Question 47

Why is CLV important for segmentation?

Caroline Kratofil · Accepted Answer

It’s relatively simple to segment your current customers by attributes and/or past behavior. But if you want to predict the future behavior of your customers, it’s better to segment by features and attributes that impact customer lifetime value. Read more about the process in this blog post.

Question 48

What makes a customer high value?

Caroline Kratofil · Accepted Answer

Customer lifetime value (CLV) is the metric leading companies use to understand their customers’ purchasing habits. Simply put, customers who purchase higher-value products, who purchase more frequently, and who continue to purchase on an ongoing basis are your high-CLV customers.

Question 49

What should your CLV to CAC ratio be?

Caroline Kratofil · Accepted Answer

Once you’ve calculated CLV, you can use it to determine your target customer acquisition cost (CAC). Instead of simply targeting customers that bring in high initial revenue, focus your acquisition efforts on high CLV customers. Even if their first purchase is small, they will bring your business more value in the future.

A customer acquisition is unprofitably only if CAC exceeds CLV. For most businesses, your CLV to CAC ratio should be 3:1 for each marketing segment. If you spend too much (e.g. 1:1 ratio), acquiring those customers won’t be profitable. But if you spend too little (e.g. 7:1 ratio), you will be missing out on profitable customers whose acquisition cost is above your current bid cap.

Question 50

How do you incorporate discounting when calculating the CLV?

Caroline Kratofil · Accepted Answer

You can take your annual discount rate and turn it into a daily discount rate, and apply a discount factor, which is: 1 / (1 + X)^Y where X is the discount rate and Y is the time variable.

Question 51

How do you determine the right time frame for churn?

Caroline Kratofil · Accepted Answer

Typically, we compute the lifespan of every single customer using survival analysis. Once we’ve done that, you get a sense of what the longest life is of a customer. Based on that, you can choose the truncated time spans for computing customer lifetime value. We will typically calculate CLV for multiple year horizons, depending on the business. Sometimes, we will also compute full lifetime horizons as well.

Question 52

Do Retina models go beyond RFM?

Caroline Kratofil · Accepted Answer

Recency, frequency, and monetary value (RFM) is a simple method to determine customer value. Retina models take into account data beyond RFM, including demographic and behavioral attributes, customer journeys, quiz and session data, and more. All of these data points make up a comprehensive data set that allows us to use machine learning to impute missing values from messy data and make customer lifetime value predictions early in the customer journey.

Question 53

What types of companies does Retina work with, and what data do I need to work with Retina?

Caroline Kratofil · Accepted Answer

Retina works best with companies whose customers have repeat purchase behavior. This can include, but is not limited to: eCommerce, retail, CPG, financial services, healthcare, and even paid smartphone apps. In addition, customers ideally have 10,000 repeat purchases, 18-months or more worth of data, and customer identity resolution. Retina’s models use orders data and customer attributes, so we have integrations with Shopify as well as CDPs like mParticle or Segment to make data transfer a breeze.

Question 54

In what format will I receive my eCLV scores?

Caroline Kratofil · Accepted Answer

Possible outputs of Retina’s models are: Customer ID, As Of Date, Retina CLV (up to 15-year predictions), Lapse Probability, Retina Persona, Likely Next Transaction , Typical Order Value , and Predicted Lapse Date. Each can be modified to fit a client’s needs, and will be sent through an established pipeline with your data warehouse.

Question 55

How can Retina predict CLV even before a prospect’s first transaction?

Caroline Kratofil · Accepted Answer

Unlike “Buy ‘Til You Die” models, Retina’s CLV model can predict customer lifetime value at or before a prospect’s first transaction. To do this, we first collect all available customer data, including estimates of RFM signals (AOV, survival rate, time between purchases). Next, our model denoises and imputes missing data. Finally, we simulate expected behavior at the customer-level using cleaner RFM signals. To learn more about how our model works, check out our CLV Framework Whitepaper.

Question 56

What does a typical Retina engagement look like?

Caroline Kratofil · Accepted Answer

Retina’s customers typically receive early CLV scores for every customer within a month of starting a project. To begin, Retina conducts a data audit on a one-time data transfer. Once the model is built, customers can opt to include other services such as: training modules, strategy sessions, and Quality of Customers reports.

Question 57

How much does Retina cost?

Caroline Kratofil · Accepted Answer

Retina’s pricing is dependent on how many customers you have, how often those customers are scored, and the length of your commitment with Retina. Of note, Retina has no integration or upfront fee, and you only pay for what you use.

Question 58

When can I expect to see return on my investment and in what form will it be?

Caroline Kratofil · Accepted Answer

As soon as you receive early CLV scores (~1 month), you can put them to use. An immediate application building lookalike audiences based on high LTV customers. On Facebook, Google, and other DSPs our clients achieve a 44% incremental lift in LTV. Other clients measure incremental LTV of each marketing campaign to optimize their ad spend. By doing so, our clients improved their marketing efficiency by 30%. Even outside of CLV scores, Retina clients save approximately $1.5 million in data science and model development costs.

Question 59

Do Retina models go beyond RFM?

Caroline Kratofil · Accepted Answer

Recency, frequency, and monetary value (RFM) is a simple method to determine customer value. Retina models take into account data beyond RFM, including demographic and behavioral attributes, customer journeys, quiz and session data, and more. All of these data points make up a comprehensive data set that allows us to use machine learning to impute missing values from messy data and make customer lifetime value predictions early in the customer journey.

Do Retina models go beyond RFM?

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