Ask a marketer about customer lifetime value, and they’ll tell you it’s all about ‘relationship.’ Ask a financial analyst, and they’ll let you know it’s all about ‘returns.’
The truth is that perspective dictates the definition, but all streams lead to the same pond. The concept of customer lifetime value is crucial to business sustainability and success. That is why most, if not all, businesses use it as a mandatory metric in their long term calculations.
But if you’re struggling to understand CLV and its significance, don’t worry – you’re not the only one. There are plenty of marketers out there who have no idea what CLV means or how important it is.
The good news is, you’ve come to the right place. We’re going to take you through everything CLV-related, from its interpretation to its AI-led evolution. So let us embark on the journey of rediscovering customer lifetime value through the lens of AI.
Getting to Grips with Customer Lifetime Value
This section deals with the basic understanding of customer lifetime value. We’ll go over CLV’s primary definition, its different types, the interpretation of its kinds, and CLV’s importance for businesses.
What is Customer Lifetime Value?
The definition of customer lifetime value is pretty straightforward. It is merely the expected amount of money a customer spends over their lifetime with the business. As such, customer lifetime value is always in dollar terms.
So if you ever read: “Customer X CLV = $9,000”, it means customer X spends an expected $9,000 buying the business’s products or services over his or her lifetime.
Even though the definition itself is simple, it’s important to remember one thing. Customer lifetime value is a forecast or an estimation. Remember that the definition has the word ‘expected‘ in it, which means that CLV isn’t conclusive.
This characteristic is a cause of considerable discomfort for businesses. If brands can’t know for sure how much money a customer will spend over their lifetime, how is the company supposed to account for future costs?
Keep this question in mind, because we will address it later on. For now, let’s look at the different types of customer lifetime value.
What are Different Types of Customer Lifetime Value?
There are essentially two types of customer lifetime value.
The first is the historical customer lifetime value. This type of CLV determines the cost of a customer based on historical purchase behavior. Essentially, this model assumes that the buying behavior of the customer will remain unchanged throughout their lifetime.
The predictive customer lifetime value, on the other hand, takes future behavior into account. This model predicts future behavior and alters the CLV of a customer based on this prediction.
How Do Businesses Interpret Types of CLV?
For example, John buys a $5 coffee from a local coffee shop every Tuesday. If the coffee shop manager uses historical customer lifetime value, he assumes John will continue buying $5 coffee every Tuesday for the rest of his life. Based on his past purchase behavior, his CLV comes out to be $X.
Now let’s assume the coffee shop manager uses the predictive customer lifetime value. The manager knows he will be offering a special deal of free $1 bagel with each purchase of $5 coffee. This deal is likely to increase John’s purchase frequency. A predictive customer lifetime value model takes into account John’s change in future behavior. Thus, John’s CLV changes from $X to $Y.
Why Is It Important?
The reason customer lifetime value is so crucial for businesses is because of planning expenditures. As we know, CLV is the total amount of money a customer will spend over their lifetime with a company. The more accurately a business estimates the CLV of a customer, the better it can plan its expenditure.
Think of it this way. Customer X has a customer lifetime value of $5,000. So if a business spends $5,500 to retain that customer over his lifetime, then the company is losing money in the long run. As such, customer lifetime value helps plan out customer acquisition and customer retention costs for businesses.
Redefining Customer Lifetime Value in the Modern Age
Customer lifetime value has been around for quite some time now. The digital age, however, is bringing it into a whole new light. For the first time, businesses understand the significance of CLV concerning other KPIs. Let’s look at how this change occurred.
How has Digitization Changed Customer Lifetime Value?
The wave of industrial internet and the shift to digital has certainly changed CLV for good. Rewind the clocks to the 20th century, and brands utilized CLV independently. That means it was, more often than not, not integrated with overall performance objectives.
Even though that seems insane today, it was standard practice back then. That’s because there wasn’t a way to integrate it efficiently. Without online databases and advanced software to do the job for us, understanding CLV in light of other metrics was one hell of a task. That’s why businesses used this metric for short term individual customers.
But not anymore!
Ever since digitization revolutionized the way businesses operate, customer lifetime value is the king of the heap. Now it’s easier to integrate metrics and gain valuable insights thanks to advanced analytics. As such, companies analyze CLV in lockstep with customer acquisition, retention, equity, and a whole bunch of other metrics. As a result, businesses are smarter about the customers they target and the way they target them.
How Modern Businesses Utilize CLV Insights?
Modern businesses use customer lifetime value to understand their customer better. As mentioned earlier, the predictive CLV model takes into account future purchase behavior. Therefore, brands can predict customer behavior and adjust strategies accordingly.
This feature makes customer lifetime value a vital metric for making crucial business decisions. Understanding how a customer’s lifetime value impacts the brands’ profitability, the decision to retain or ax products and services becomes that much easier. So even though CLV is all numbers and stats, it’s interpretation is vital for insights.
By comparing their own customers’ CLV to the industry standard, brands can better gauge their performance. For example, a lower average CLV means lesser inflowing capital in the long run. That means a business can’t spend more on advertising if it doesn’t pay off through customer equity.
The Impact of Artificial Intelligence on Customer Lifetime Value
CLV wasn’t the only thing impacted by the advent of the 21st century. The rise of artificial intelligence in business analytics is unprecedented. The way machine learning algorithms analyze data and output insights have changed the game for businesses worldwide. That is why the AI trend has had a significant impact on CLV as well.
Let’s discuss this impact in detail and assess whether it comes with more pros than cons.
What Does Artificial Intelligence have to do with Customer Lifetime Value?
So the first question we need to address is the relation of AI to customer lifetime value. Like with so many other metrics, AI helps analyze CLV more efficiently. This doesn’t necessarily mean that AI-powered analysis does something entirely different. Instead, it does the same thing humans do – just better.
Before AI was so widely used, data was hard to collect and even harder to analyze. It would take days to interpret the meaning behind the numbers and getting actionable insights from them. Not to mention, CLV isn’t an easy metric to calculate and integrate with other KPIs.
So when AI algorithms and machine learning capabilities came along, it did the job that much easier for analysts. After all, you can only rely on insights you can trust. With humans, there is always a chance of error in calculation or biased forecasts. But with artificial intelligence, the whole process is much more reliable.
Machine learning models base CLV calculations on RFM, which stands for Recency, Frequency, and Monetary. This feature makes CLV calculations faster and the insights more relevant. As a result, AI eliminates human error and effort from CLV insights enhancing speed and efficiency along the way.
What are the Benefits & Drawbacks of using AI for CLV Analysis?
We’ve already discussed some of the benefits of using AI for CLV analysis. But let’s dig a little deeper. According to Marketing Land, over 80 percent of users churn three months after downloading an app. That’s because businesses target the wrong audience and pay the price (literally). Using AI to understand which customer is likely to continue business in the future is essential to avoid these mistakes.
As mentioned above, AI uses machine learning algorithms to determine CLV according to the RFM method. This tells business owners which customers will be more profitable and how soon! That’s the sort of insight you can’t get without AI.
But it costs a pretty penny to get access to these insights, and that’s the drawback. Of course, you already knew this was coming. Any AI software powerful enough to provide reliable insights is expensive to obtain. Not to mention, employees need the training to operate the system. That requires additional capital and time, something that businesses don’t always have.
Predictive Analytics & Customer Lifetime Value
At this point, it is apt to discuss predictive analytics and their contribution to CLV analysis. It’s important to remember that predictive analytics (or predictive modeling) is also a form of AI. But, it is highly specialized for making predictions for a group of consumers based on past data.
How is Predictive Analytics Useful for Customer Lifetime Value?
You may be asking, how does that help in customer lifetime value?
Glad you asked.
Imagine you have data for 1,000 consumers. It might be a little tedious and nonsensical to devise a strategy based on the individual CLV of each consumer. It probably makes more sense to group the data for analysis and decide based on group behavior.
For that, you have the magic of predictive analytics!
Based on similar behavior patterns, the predictive analytics model groups consumers together. It then calculates and suggests strategies relevant to increase CLV for that group of consumers.
Choosing the Right AI for CLV Insights
Using AI for CLV insights is enticing, but there’s more than one software. Making the right decision about the type of AI your business relies on can be the difference between success and failure.
It’s important to remember that not all AI systems provide the same benefits. The trick is to test out the ones that satisfy your business needs. For example, it’s not enough to get generic CLV for consumer clusters. It would help if you had CLV according to channels, to know which channel provides better conversion and acquisition costs. Lucrative.ai does precisely that with advanced artificial intelligence algorithms that measure and analyze CLV by channels.
But what if you want to map the consumer buying journey and integrate it with customer lifetime value? Will you need to employ another AI algorithm for that? Not really.
Lucrative.ai maps consumer buying journeys and attributes budget according to the weightage of each medium. All in all, it’s best to go for an AI system that can provide a whole host of analytic services. In that way, you’ll minimize costs and maximize results.
Google Analytics – The Next Evolution in Customer Lifetime Value Analysis
You thought we’d talk about AI and customer lifetime value without bringing up Google Analytics? Think again.
The truth is, Google Analytics is probably the most sophisticated analytics tool out there. As a serious business owner, you are likely to use it at some point or another. That’s why it’s essential to know the proper usage of GA to understand CLV and other metrics. Besides, Google Analytics is at the forefront of introducing cutting edge analytics to must-have KPIs, CLV included.
How is GA Revolutionizing Customer Lifetime Value?
There are many metrics in Google Analytics, and among them, is the ‘lifetime value’ metric. This metric allows business owners to assess the value of each customer based on past purchases. But that’s not a big deal because every other AI software presents the same analysis.
The beauty of Google Analytics’ lifetime value feature lies in detail. It presents the performance of each user and cross-references it with channels, engagement, and sources. This gives an accurate picture of a consumer’s value beyond the simple CLV figure. Check out how to run the lifetime value report here.
Integrating Customer Lifetime Value with Marketing Attribution
It’s essential to understand the link between customer lifetime value and marketing attribution here. Marketing attribution is the allocation of marketing budget among channels according to the value of each channel. There are different models for marketing attribution. For example, the first touch attribution model gives all of the credit of a lead to the first contact channel.
So what’s that got to do with customer lifetime value? Everything.
CLV is all about knowing the lifetime value of a consumer. Marketing attribution understands the importance of the channel.
So imagine if you knew where the most valuable consumers were making contact with your brand. And you knew how much to spend on each channel to maximize conversions from it.
Voila! Marketing attribution and customer lifetime value working hand in hand to maximize ROI for your business. So always remember to integrate marketing attribution with CLV to drive maximum value from business capital.
The Relation between Customer Lifetime Value & Customer Lifecycle
Finally, we come to the relation between customer lifetime value and customer lifecycle. This relationship is the last fundamental business owner needs to know about to maximize ROI. So let’s dive in.
Seeing how we’ve talked about CLV a gazillion times by now, let’s look at the customer life cycle. The customer lifecycle consists of the stages a customer goes through in their relationship with the brand. From reach and acquisition to nurturing and retention, every step is necessary to be tracked and optimized.
So what’s the link with CLV?
Each consumer is at a different stage of the customer lifecycle. That means the lifetime value of each consumer is directly related to the lifecycle stage. For example, a consumer at the ‘acquire’ phase of the lifecycle will have a CLV of maybe $500. Whereas, a consumer at the ‘advocacy’ stage would have a CLV of $50,000.
See the difference?
So the business owner would automatically know which consumer would have a higher CLV depending on the stage of the customer lifecycle. As a consumer moves through the life cycle steps, an AI-powered CLV tool adjusts the analysis accordingly. As such, brands need to link CLV with the customer lifecycle to know which consumer is most valuable in the long run.
Conclusion
CLV is an essential concept for business success. Despite how it’s defined, CLV is always about the value of a customer. AI is fast revolutionizing how this concept is analyzed and implemented in business strategy. That is why up and coming brands need to get with the trend and utilize AI for long-term customer lifetime value insights.
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