Advanced analytics is a lot like magic. Most people don’t know how either works but are amazed by both. So when advanced analytics techniques yield valuable insights, brands usually don’t question them. They have neither the time nor motivation to dissect the system’s details so long as it churns out insights.
But when it comes to vital metrics like customer lifetime value, brands can’t afford to be ignorant. CLV shows the amount of money a business can expect to earn from a particular consumer throughout their lifetime. So the understanding of the customer lifecycle is necessary for the estimation of CLV as well. Fortunately, AI makes customer lifecycles easier to understand for businesses worldwide.
But the AI algorithm that calculates customer lifetime value is only as good as the preliminary data fed into it. That’s why understanding how advanced analytics does what it does is essential for pinpointing shortcomings in the CLV model.
So today, we’ll be discussing how advanced analytics calculates customer lifetime value. But before we get to that, let’s look at why CLV is so vital for businesses.
The New Age of Customer Lifetime Value
We’ve already defined customer lifetime value in general terms. It’s the amount of money a customer spends on business throughout their lifetime. The method of calculating CLV is relatively straightforward. Most brands still rely on the tried and tested CLV formula:
CLV = (Annual revenue per customer * Customer relationship in years) – Customer acquisition cost
Even though the definition remains the same today, what it means for businesses has changed drastically. Brands are now aware of the importance of CLV in relative as well as absolute terms. That means they view customer lifetime value relative to other metrics such as customer acquisition and customer retention. That’s a lot smarter, too, because a single metric alone can never truly capture a business’s state.
Yet, it’s a lot harder understanding metrics relative to each other. It takes more time and effort, not to mention greater analytical capabilities. In the past, it wasn’t possible to analyze several metrics simultaneously. Today, however, it’s a different story.
Practical applications of AI and machine learning allow brands to carry out analyses like never before. With AI, brands get faster, better, and more detailed insights into improving their operations.
But how do they predict the future CLV of specific segments in their general audience?
The Advent of Advanced Analytics
Enter advanced analytics – an amalgamation of the business intelligence techniques that define the 21st century’s brilliance. Advanced analytics tools rely on artificial intelligence and algorithms that employ deep learning. This means that once you set the initial data, the algorithm learns from past trends to make future predictions.
Advanced analytics is a combination of forecasting, data mining, machine learning, semantic analysis, and many other techniques. Among these, businesses rely heavily on predictive analytics for CLV predictions.
That’s because brands make a lot of business decisions based on accurate predictions about future CLV. Since predictive analytics uses predictive modeling and machine learning to calculate CLV, it is a more reliable method than others. You can read more about the role of predictive analytics on CLV here.
Integrating CLV with Advanced Analytics
So why is advanced analytics such a big deal when it comes to customer lifetime value?
That’s because businesses can’t afford to waste money on consumers who don’t have a positive ROI. Companies are now relying on AI for CLV insights because they don’t want to leave anything up to chance. With advanced analytics, businesses get accurate insights, which is a big step up from rough CLV estimations of the past.
Even though rough estimations were enough in the past, precision in customer metrics is necessary for the modern competitive marketplace. That’s why Google and other tech firms are rolling out advanced analytics tools that calculate CLV for clients. More importantly, these tools provide valuable insights on the effective utilization of budget and CLV maximization. As a result, businesses know which customer has the best CLV in a group. So they can use extra resources to target that specific customer if ROI warrants it.
Using Advanced Analytics to Find Customer Lifetime Value
We have already seen the basic framework of customer lifetime value formula. So the question is: Does advanced analytics use the same method to calculate customer lifetime value? If so, then what’s the benefit of using advanced analytics for CLV calculations? And if not, then how does advanced analytics calculate CLV?
It’s important to understand there are multiple ways of calculating CLV. In the past, businesses used the traditional way. This method requires computing the ratio of retention rate and discount rate multiplied by the gross margin per lifespan. However, historical and predictive methods of calculating CLV are far better. That’s because these methods account for purchase behavior.
Advanced analytics primarily uses predictive methods to calculate customer lifetime value. But businesses can also modify it to calculate CLV through a particular type of historical model called cohort analysis.
Advanced analytics predictive method takes into account the average of four main factors:
- The value of each purchase (AOV)
- The profit generated from each purchase after costs (AGM)
- The lifespan of a single consumer (ALT)
- The number of transactions in a given period (Avg. T)
These four factors are then divided by the total number of consumers in a given period. Hence the formula is:
CLV = {Avg. T x ALT x AGM x AOV} / No. of clients in given time period
According to this formula, AI algorithms calculate the CLV for businesses and display them on dashboards. However, some advanced analytics systems use historical CLV methods based on the brand’s preference. Usually, such historical analysis takes place as cohort analysis. Cohort analysis groups consumers based on similar purchase behavior and uses past behavior to predict future CLV.
Finding the Meaning Behind the Numbers
So we have CLV calculated for all consumers, great. Now what? Anyone can calculate customer lifetime value if they know the formula and have the data. The real benefit of advanced analytics is that it tells you how to act on CLV predictions.
For example, advanced analytics compared customer lifetime value with customer acquisition costs to predict the business’s long-term profitability. That means even if the CLV for a consumer is positive, the consumer may not be profitable. That’s because the CLV to CAC ratio could be off, and that’s precisely the sort of insight advanced analytics provides.
Advanced analytics is the best way to gain data-driven insights, whether for CLV or anything else. But it is essential to remember that this technique works with data. So if there is bias in the preliminary data, then the predictions will also be biased. Similarly, if users refuse to share their data, advanced analytics will be restricted by limited data.
The Benefits of Advanced Analytics for CLV Calculations
So it’s no secret that AI provides a better insight into customer lifetime value. The real question is – is it worth it? After all, advanced analytics tools are not cheap. You need to purchase the entire dashboards to get these insights. But if history is anything to go by, AI tools are bound to pay off.
Like Facebook and Google, some of the biggest tech firms in history, rely on AI for their dominance. With advanced analytics as your guide for CLV, you’ll expend less energy, time, and effort in the long run. So the initial investment is worth it as long as you can get the right data. But at the end of the day, these predictions are based on certain assumptions. So make sure you contextualize the predictions you get from analytics for the best results.
[hubspot portal=”6539536″ id=”0216077c-fa40-4ee1-b68e-2ed513ba8e02″ type=”form”]
