Congratulations, your software as a service (SaaS) company just landed a deal with one of the most sought-after businesses in the industry. Before the celebrations begin, do you know the value this customer will bring to your organization? Put another way… will the resources and money invested in acquiring this customer yield the anticipated revenue and profitability? This blog aims to answer the above questions, as well as cover:
- The metrics needed to know and understand customer lifetime value in SaaS
- What it takes to reduce customer churn and improve customer lifetime value (CLV)
- The role predictive analytics plays in forecasting CLV
What You Need to Know About Customer Lifetime Value in SaaS
As described by Gartner, customer lifetime value is the total revenue or profit generated by a customer over the entire course of their relationship with your business. Simply put, CLV is a metric that estimates the total revenue or profit you can expect from a single customer over the duration of their relationship with your brand. While an important metric for all industries and business models, CLV is especially critical for SaaS businesses.
Somewhat unique, SaaS organizations rely heavily on recurring revenue from customer subscriptions. With renewals, upgrades, and cross-sells playing a critical role in customer lifetime value in SaaS, software as a service companies use this metric to determine the amount of money they can spend in acquiring new customers, focus on customer retention strategies, improve the forecasting of future revenue, and develop informed strategies for ongoing business growth. In a nutshell, by knowing the long-term value of your customers, you’re in a better position to make more informed decisions regarding customer acquisition, retention strategies, and overall business growth.
Chewy – a U.S. based online retailer of pet food and other pet-related products – is a classic example where CLV is central to the organization’s business strategy. With a focus on customer retention, they rely on personalization and data-driven strategies to improve customer engagement and boost loyalty. For instance, long-term subscription retention is encouraged by their auto-ship program, and customer service strategies such as surprise gift packages to loyal subscribers and personalized condolences when a subscriber’s pet dies are handled seamlessly. Their winning CLV strategies are highlighted by their revenue, which increases 15% year-over-year.
Why Customer Lifetime Value Matters
As you can see from Chewy’s customer lifetime value example, CLV is a critical metric for organizations. Not only does it directly affect growth, profitability, and long-term sustainability; this metric is relied upon to:
Justify customer acquisition costs (CAC): SaaS organizations typically have high upfront costs (marketing, sales, and onboarding) to acquire customers. If the CAC is significantly higher than the CLV, the business will eventually lose money. As a rule of thumb, the ideal CLV:CAC ratio is >3:1.
Make informed business decisions: CLV provides the information needed to determine marketing spend, plan sales compensations, forecast revenue, and prioritize high-value customer segments. Additionally, by knowing which customer segments have higher CLV, you can tailor and target your spending towards this segment.
Determine pricing strategies: Companies can experiment with pricing tiers or add-ons by analyzing how changes impact CLV. For example, by encouraging longer subscription periods and upselling premium features, companies can maximize CLV.
Highlight retention and expansion strategies: To put it bluntly, customer churn kills growth. CLV only rises when customers are loyal to the brand through the purchase of upgrades or additional features/functionality.
Prioritize features and the product roadmap: Teams are better able to focus on what matters most to high-value customers, e.g. introduction of new features/functionality, retention or removal of product lines, and up-sell/cross sell initiatives.
Provide a valuation metric and attract investors: Investors and stakeholders often look to CLV as a key performance indicator (KPI). When SaaS companies have a high CLV, it’s an indicator of good product-market fit, loyal customers, and the potential to scale the business.
How to Determine and Use Customer Lifetime Value
To calculate CLV, you will need the following data:
- Average order value: The value of all customer purchases over a particular time.
- Average purchase frequency: This can be obtained by dividing the number of purchases in a time period by the number of customer transactions during the same period.
- Customer value: The average purchase frequency multiplied by the average purchase value.
- Average customer lifespan: The average length of time a customer buys from your company.
The above metrics provide the basis to calculate CLV. Depending on the type of CLV you are calculating, other figures (described within) may be required.
While CLV can be calculated in a variety of ways and varying degrees of complexity, we will focus on the 3 most frequently used formulas – historical CLV, predictive CLV, and traditional CLV.
Historical CLV
This figure is based on the gross profit from purchases customers made in the past. To calculate this figure, you will need the following:
- Total revenue for the chosen time period.
- Number of customers during the same time period.
There are 2 methods to calculate historical CLV – average revenue per user (ARPU) approach and cohort analysis. Note: a cohort is a group of customers who share a common characteristic, such as their average spend.
Using the ARPU method, we’ll assume that the time period is the 4th quarter. During that period, 20 customers purchased $2,000 from your SaaS business.
Your 3-month ARPU would be $100.
$2,000 / 20 = $100
Taking this one step further, your 12-month ARPU would be $1,200.
$100 x 12 = $1,200
Predictive CLV
Based on a customer’s transactional behavior, predictive CLV provides insight into how much the average customer will spend during their relationship with your brand. To calculate this figure, you will need the following:
- Average number of transactions per a time period.
- Average order value.
- Average gross margin.
- Average customer lifespan (in months).
- Number of customers for the period.
To illustrate predictive CLV, we’ll assume that during a 6-month period there were 100 subscribers and 120 total subscriber transactions (20 transactions per month). During the same time, the average order value was $600 per month. Additionally, we’ll assume the average gross margin was 33% and an average customer lifespan of 6 months.
Your predictive CLV is $238 per month.
20 x $600 x 0.33 x 6 / 100 = $238
Traditional CLV
Frequently used when annual sales per customer vary year-over-year, traditional CLV provides a more detailed view of how CLV can change over time. To calculate traditional CLV, you will need the following:
- Average gross margin per customer lifespan.
- Customer retention rate.
- Discount rate, which is typically 10% for SaaS businesses.
For this example, we’ll assume that your gross margin per customer lifespan is $2,200, your customer retention rate is 70%, and you’ve applied a 10% discount during the time period.
Gross margin per customer lifespan x retention rate / (1 + rate of discount – retention rate)
Your traditional CLV is $3,850
1 + 0.70 – 0.1 = 0.4
.70 / 0.4 = 1.75 x $2,200 = $3,850
Using CLV
Now that you know your CLV, what’s next? Once you know your CLV, there are plenty of ways to use it. But first, how should CLV be interpreted? Since this metric is all about finding balance, CLV provides the data needed to determine how much you should invest to retain current subscribers, as well as acquire new customers.
Here’s some ways you can put CLV to work.
Maximize return on investment (ROI): CLV enables you to identify which customer segments are most profitable. Use this information to prioritize marketing campaigns to target and attract the right subscribers.
Set realistic marketing/advertising budgets: CLV provides the insights needed to know how much you can afford to spend on marketing and advertising campaigns – without tipping your CLV:CAC ratio.
Optimize pricing and offers: Know how much customers tend to spend during the relationship with your brand, CLV provides the data needed to know their perceived value of your offerings.
Identify upsell/cross-sell opportunities: By knowing your high-value subscribers, you can target this group with targeted marketing and loyalty programs.
Drive enhanced customer experiences: Grow the business and increase CLV by identifying positive experiences such as buying through specific channels. Use the information to drive similar experiences to wider subscriber segments.
Identify customer experience gaps: Leverage CLV to not only identify positive impacts, but also ones across the customer journey that have had a negative impact. Use this information to understand the root cause(s) and make improvements.
Reduce customer churn: When CLV is shared across customer-facing teams, they’re in a position to make better decisions when it comes to nurturing high-value subscribers, as well as preventing at risk subscriber churn.
The Effects of Churn on Customer Lifetime Value in SaaS
Given that customer lifetime value in Saas is a metric that estimates how much revenue a business can expect from a subscriber over the duration of their relationship, customer churn has a direct and significant impact on CLV. Let’s assume the average revenue per subscriber per month is $100 and the churn rate is 5%, CLV ≈ 1 / 0.05 = 20 months.
CLV = $100 x 20 = $2,000
Let’s now consider if churn increases to 10%. The new CLV would be $1,000 – a whopping 50% decrease in CLV from just a 5% increase in churn.
In a nutshell, reducing customer churn is one of the fastest ways to boost CLV and overall profitability.
Reduce Churn and Improve Customer Lifetime Value
Reducing customer churn is all about delivering continuous value and exceptional user experiences. The most effective churn reduction strategies fall into the following categories:
- Subscriber onboarding: Did you know that a significant number of users churn within the first 30 days? Reduce or eliminate immediate churn through personalized onboarding, providing interactive training, and offering early software activation.
- Subscriber activity: It goes without saying, inactive users are at a high risk of churn. Keep subscribers using your product(s) by sending texts, email, or in-app reminders to prompt usage. Additionally, target low-activity users with adoption campaigns of underutilized functionality/features.
- Subscriber support: Poor support remains a top reason for subscriber churn. Nip it in the bud by offering live chat, self-service knowledge bases, quick response times, proactive outreach on common issues and problems, and customer feedback loops.
- Product stagnation: Subscribers naturally churn when product(s) no longer meet their needs. Keep your products fresh with regular updates and bug fixes, user-driven roadmaps and product personalization.
- Subscriber / business relationships: Especially true for B2B, strong customer/business relationships drive retention. Detect at-risk subscribers early through regularly monitored customer health scores and track satisfaction through surveys and NPS. Offer larger accounts a dedicated customer success manager, and provide all subscribers with quarterly business reviews.
- Pricing flexibility: When pricing doesn’t match perceived value, subscribers will quickly opt out. Retain subscribers by offering dynamic pricing schemes, granting grace periods or subscription pauses in lieu of cancellation, and locking in longer term commitments through annual plans or discounts.
Reducing churn improves user engagement metrics, which also positively impacts CLV, including:
- Increased subscriber retention
- More frequent purchases
- Stronger brand loyalty
- Higher average order value
- Improved cross-sell / upsell opportunities
- Increased customer advocacy
Simply put, improving subscriber metrics typically leads to higher customer retention, more sales and reduced churn.
Using Predictive Analytics to Forecast Customer Lifetime Value
Using historical data, statistical algorithms, and machine learning (ML) techniques, predictive analytics identifies the likelihood of future outcomes – in this case forecasting CLV. Here’s how predictive analytics adds value to CLV.
Historical data analysis: It examines past customer behavior like brand engagement, purchasing patterns, purchasing frequency, and average spend. Analyzing historical data helps to estimate how much a subscriber may contribute to future revenue. This is one of many reasons why having accurate customer data is so important to overall success.
Segmentation: Eliminates the one-size-fits-all model by segmenting customers based on attributes such as demographics, transaction history, or engagement, enabling SaaS organizations to develop more accurate customer segmentation predictions.
Churn prediction: By analyzing signals such as decreased purchase frequency or reduced brand engagement, predictive analytics is able to identify customers who are at a risk of churn. This prediction aids in forecasting a reduction in CLV for certain customer segments and can trigger customer retention strategies.
Behavioral modeling: Behavioral models can help in identifying factors that influence a customer’s lifetime value. For instance, predictive analytics may find that customers who engage with certain products are more likely to have higher CLV.
Personalized marketing and subscriber retention: Predictive analytics provides insights into how personalized offers, product recommendations, or promotions can increase CLV over the long term. With this knowledge SaaS organizations can improve CLV by targeting their most valuable customers.
Dynamic forecasting: As new customer data becomes available and you are forecasting your ARR, predictive analytics can adjust CLV forecasts – in real time.
By combining predictive analytics and CLV SaaS companies can make data-driven decisions, improving subscriber retention and maximizing profitability.
Don’t Wait… Improve CLV Today
In addition to predictive analytics, there are other tools you should consider to improve customer lifetime value in SaaS. For instance, customer relationship management (CRM), data analytics platforms, customer data platforms, marketing automation tools, advanced analytics and data science tools, and financial platforms… to name a few.
A significant factor in increasing CLV is pricing optimization. And doing this requires the ability to manage dynamic pricing for one-time charges, usage, tiered, subscription, overages, minimum commitment, event based… and many others. BillingPlatform provides everything you need to unlock the creative pricing strategies that will improve CLV and maximize revenue. Our experts are ready to help you meet the challenge of modern monetization, contact us to learn how.
