Growth Colony: Australia's B2B Growth Podcast
Rebroadcast | EP #132: How to Calculate Your Lifetime Value (LTV)
In this episode, Shainoh talks to Oren Cohen about calculating customer lifetime value (LTV) in SaaS companies. They discuss the components of LTV, factors affecting LTV prediction, different LTV models, the importance of the LTV to CAC ratio, improving LTV models, analyzing user engagement and data quality, and challenges and recommendations for B2B SaaS companies. The episode provides valuable insights into understanding and leveraging LTV for business success.
LTV is crucial for business decision-making
Calculating customer lifetime value (LTV) helps determine marketing budget, resource allocation, profitability, and forecasting. It is an essential metric for B2B tech companies.
Factors to consider in LTV prediction
Engagement from zero and first-party data, momentum indicators, and the spread of variance in subscribers' LTV are important factors to consider in LTV prediction models.
Different LTV models for different use cases
Different use cases require different LTV models, and there is no one-size-fits-all approach. Consider industry perspective, signup vs. subscription, and prioritize salespeople for upsells.
LTV to CAC ratio provides valuable insights
The LTV to CAC ratio should be considered alongside customer acquisition costs (CAC) alone. It helps determine the value of customer acquisition costs and provides valuable insights for B2B PLG SaaS companies.
Improving LTV models and data quality
Understanding and knowing your data is crucial for improving the LTV model. Asking users for information through onboarding processes and mapping interactions within a website or product can enhance data quality.
Analyzing user engagement and optimizing campaigns
Analyzing data from signups, looking at the base population, and using predictive intent models can help analyze user engagement. Optimize campaigns towards subscribers and top sign-ups to improve LTV.
Challenges and recommendations for B2B SaaS companies
B2B SaaS companies face challenges in obtaining event data in the early stages of the user journey. Predicting intent and leveraging LTV for Google and Facebook campaigns require expertise. Start understanding where your company stands in terms of LTV prediction to avoid falling behind competitors.
- Factors Affecting LTV Prediction
- LTV Models and CAC
- Improving LTV Models
- Analyzing User Engagement and Data Quality
- Challenges and Recommendations
00:03 - 07:50
- X Growth helps B2B tech companies with strategic target account campaigns, ABM programs, and selecting the right tools
- In this episode, Shainoh talks to Oren Cohen about calculating customer lifetime value (LTV) in SaaS companies
- LTV is the estimate of revenue a customer will generate throughout their lifespan as a customer
- It helps determine marketing budget, resource allocation, profitability, and forecasting
- LTV starts from zero and grows over time, with a plateau in most cases
- The components of LTV include total revenue streams, base population, and evaluation method
- Other factors to consider are actionable time frame and limitations on network side
- Advanced components like monthly commitment, chargebacks, refunds can be included for net LTV calculation
- To predict LTV accurately, factors like product engagement, onboarding quiz/questionnaire, lifecycle marketing should be considered
Factors Affecting LTV Prediction
07:29 - 15:04
- Engagement from zero and first-party data is crucial for LTV prediction.
- LTV prediction is an ongoing process that considers momentum indicators like product usage, new user pace, and feature adoption.
- Understanding the business case and minimizing false positives are important in assessing LTV.
- Root mean square error is commonly used to evaluate LTV prediction models.
- The spread of variance in subscribers' LTV is important to consider.
- There is no one-size-fits-all approach to calculating LTV; it depends on the specific use case.
- Different dimensions, such as industry perspective or signup vs. subscription, can be considered when looking at LTV data.
- Multiple methods can be used to calculate LTV based on different objectives, such as bit adjustment or prioritizing salespeople for upsells.
LTV Models and CAC
14:42 - 22:01
- LTV prediction models are important for various use cases, such as predicting the lifetime value of signups and prioritizing salespeople for upsells to enterprise accounts.
- Different use cases require different LTV models, some immediate and others without limitations.
- CAC is often emphasized but should not be seen as a goal. It is an outcome of budget allocation, competition, and optimization techniques.
- Many companies focus on CAC targets without considering LTV.
- Measuring CAC is challenging due to privacy concerns.
- CAC can be a measured metric under the constraint of attribution window or a deeper event in the funnel that correlates with revenue generation.
- The cost of reaching the 'aha moment' in the funnel is more important than traditional CAC.
- The LTV to CAC ratio provides valuable insights and should be considered alongside CAC alone.
- For B2B PLG SaaS companies with long lifecycles, focusing on subscribers and their 'aha moment' is crucial.
- The cost of the 'aha moment' should be evaluated rather than just looking at classic CAC metrics.
Improving LTV Models
21:36 - 28:45
- LTV to CAC ratio is important in determining the value of customer acquisition costs.
- There is no specific ratio to aim for, it depends on the company's strategy and cash flow needs.
- Understanding and knowing your data is crucial for improving the LTV model.
- Asking users for information through onboarding processes can provide valuable zero-party data.
- Mapping and measuring interactions within a website or product can also enhance data quality.
- Obstacles to building an LTV model include lack of evaluation framework, oversimplification, and assumption of same LTV across all users.
Analyzing User Engagement and Data Quality
28:25 - 35:45
- Average LTV might be stable, but there is a big variance among users or workspaces.
- Paying the same price for high-value and low-value users is inefficient.
- Analyzing data from signups can reveal differences in user engagement.
- Looking at the base population of signups provides valuable input for LTV prediction.
- Predictive intent models can also be used to analyze user behavior.
- Starting point for improving LTV calculation is having reliable and sorted data.
- Identify the biggest pain point within your business and focus on that area first.
- For sales, focus on subscribers who need attention from salespeople.
- For user acquisition, optimize campaigns towards subscribers and top sign-ups.
- Define objectives, collect data, define events to measure, and mix in additional relevant data if needed.
Challenges and Recommendations
35:19 - 42:13
- B2B SaaS companies often lack event data in the early stages of the user journey due to the Freemium model.
- Predicting intent and knowing how to act for these ad groups, companies, and channels is a secret source of success for many B2B premium companies.
- Understanding the concept of LTV (Lifetime Value) is crucial in today's market, especially with privacy concerns.
- Using LTV to improve Google and Facebook campaigns requires expertise in data science, growth, user acquisition, and working with these networks.
- Start understanding where your company stands in terms of LTV prediction to avoid falling behind competitors.
- Oren recommends the book 'Man's Search for Meaning' by Victor Franco as a life-changing resource.
- His advice to B2B marketers is to start using prediction models for LTV, intent sales scoring, etc., as it will be a secret sauce for success in the future.
- One influencer Oren follows is Eric Shefford from Mobile Dev Memo, which provides deep insights into important growth aspects.
- Oren is excited about collaborative B2B SaaS companies that leverage growth loops within workspaces and teams.