The Role of Predictive Analytics in Customer Lifetime Value

Predictive analytics has revolutionized the way we forecast. Businesses couldn’t have dreamt of the type of insights they gain today from predictive tools and software. That’s because artificial intelligence algorithms weren’t powerful enough to provide such insights in the past. 

Luckily, we have come a long way in a short time. 

Today, AI dictates all sorts of analytics and forecasting purposes. From providing social media analytics to generating consumer insights, there isn’t much AI cannot do. But by far, the most crucial indicator of a business’s sustainability that AI helps with is the customer lifetime value. 

CLV indicates how much money a customer is likely to spend with the business throughout their lifetime. Even though this metric has been around for a long time, AI is rediscovering CLV in a whole new way. Predictive analytics is spearheading this rediscovery with its sophisticated forecasting capabilities. 

Today, we shall look at the role of predictive analytics in evolving customer lifetime value for businesses. But before we get to that, it’s necessary to understand how predictive analytics works.

predictive analytics for CLV

Understanding the Science Behind Predictive Analytics

Despite its popularity, many people are still confused about how predictive analytics works. That’s because the perception around artificial intelligence is that it’s insanely complicated. In reality, the science behind these technologies is relatively simple if you get to know about it. That’s why we’ll try to explain it simply.

Predictive analytics is essentially a branch of data analytics that uses current and historical data to predict future trends. This prediction capability is the essence of predictive analytics and what makes it so powerful. 

But how does it predict future trends?

The answer: ‘Modeling and machine learning.’

Modeling is the basis of predictive analytics since this technique allows the assimilation of past data. Among the most widely used predictive modeling models are customer segmentation models, predictive maintenance models, and, you guessed it, customer lifetime value models.

Each model is a template that uses machine learning to find patterns in historical data. These patterns are then replicated for future scenarios, depending on the quality of information. Simply put, more accurate historical data leads to more accurate models that help gain the best insights. 

So, where does machine learning fit into all this?

The predictive analytics techniques, such as decision trees and neural networks, rely on AI and machine learning algorithms. These techniques employ algorithms for predictive analysis of current data. Once the algorithm starts running, it learns how to perfect predictive analysis with each data interaction. 

And that’s how predictive analytics works!

The Relation between Predictive Analytics and Customer Lifetime Value

Now, we come to the direct relation between predictive and customer lifetime value. As mentioned earlier, organizations use customer lifetime predictive value models globally. That’s because businesses need to know their customers’ lifecycle and its corresponding value. These customer lifetime models identify individual consumers who are likely to spend more with a company than their peers.

So why is this important?

Imagine you’re running a business and keep targeting Consumer X with paid digital advertisements. After six months, you’ve spent over $1,000 in advertising dollars on Consumer X. But after some calculations, you realize Consumer X has spent only $300 with your business. 

Now imagine if you had known Consumer X would spend $300 on your business six months ago. Would you have spent $1,000 on advertising to Consumer X? Of course not!
That’s where predictive customer lifetime value models come into play. These models predict how much money a consumer will spend on a business’s products and services based on past data. This prediction then dictates the advertising and targeting strategies companies adopt with regards to that consumer.

predictive analytics in CLV

Long Term Implications of Predictive Analytics on CLV

So what does this mean for the long-term for businesses that use predictive analytics to calculate CLV?

The beauty of predictive analytics tools is that each one relies on an initially established model. Human analysts devise this preliminary model using past data. But machine learning algorithms learn from historical data to improve predictive capabilities. This means that as the model keeps running, it keeps getting smarter.

Eventually, the algorithm can predict, with exceeding certainty, which consumers will be most profitable for the business. As such, the companies that can perfect their predictive analysis can boost ROI rapidly. Such businesses outstrip their competition in terms of profitability and customer satisfaction. That’s because they expend resources on consumers they know to be profitable in the long run.

Tech giants like Apple, Google, and Amazon are perfect examples of companies that use accurate predictive CLV modeling to capture the market. With products such as Lucrative.ai that provide user-friendly dashboard interface and predictive modeling capabilities, predictive CLV modeling is sure to take off in the future.

Steps to Improve Predictive Modeling for Customer Lifetime Value

It can be somewhat overwhelming when your company is new to predictive analytics for customer lifetime value. After all, this analysis is right at the deep end of data analytics. With so many models, regressions, and decision tree techniques, it’s easy to get lost. 

So we’ve put together a brief outline of how to get the most out of customer lifetime value predictive analysis. Let’s dive in.

1. Set Time Frame

The first question you need to ask is: “What time frame would give us an accurate estimate of CLV?”. That’s because CLV varies depending on the type of business you run and the stage of the business cycle. So the time frame you choose is exceptionally vital to the accuracy of the predictive model. Remember, the model needs to be run manually at first so that the algorithms can learn and improve from it. 

The best way to select a time frame is to base it off of historical data. Take a look at the past 1-2 years of your business (or six months if you’re new). That should give you an accurate estimate of when most customers are flowing in.

2. Determine Features for Prediction

This step is crucial in the predictive model since it decides the characteristics to consider. The customer lifetime value model predicts the behavior of consumers. But it can only do that if it knows what features or queues to look for during analysis. Generally speaking, the most commonly used consumer features include purchase frequency, repurchase rate, and one-time purchase value. 

The better the selection of consumer features, the more accurate CLV predictions will be. That’s why companies are always tweaking their predictive models to find the best combination of features. As seasonality changes and customers move through lifecycle stages, the elements need to be adjusted accordingly.

3. Calculate CLV to Train ML Algorithm

Now you need to calculate CLV and set the model on its way. There are multiple equations for calculating customer lifetime value. The one you use needs to take into account factors relevant to your business. For example, the traditional CLV equation is as follows:

Gross margin * (Retention rate / [1+ Rate of discount – Retention rate]

The initial phase of the predictive CLV model is challenging in terms of keeping track of the analytics. However, once the machine learning algorithm is up and running, then it gets easier. The ML algorithm observes and learns from the CLV model to do predictive modeling. If the historical data is sound, then the algorithm becomes increasingly accurate with time.

4. Test Accuracy of Model

A predictive model’s accuracy is the difference between the predicted values and the actual values of customer lifetime value. Each predictive model operates with a certain margin of error or uncertainty. The accuracy of the model verifies if it can predict CLV within the prescribed margins. 

Of course, there is no way a predictive model will be 100 percent accurate. That’s because, in the real world, there are simply too many variables. Keep in mind that the model is initiated with a particular set of features to look for and consider. But so far, the predictive models are not powerful enough to account for every relevant factor affecting consumer behavior.

A Word of Advice

Before you set out on the journey of predictive CLV modeling, there are a few things to note. The predictions of machine learning algorithms rely on the data entered. So if there is bias in the preliminary data input, there will be bias in machine learning algorithms. Therefore, it is essential to consider how you choose input data because factors excluded in the data set will ultimately affect the CLV predictions.

Secondly, companies should take the predictive analysis with a pinch of salt. As we’ve discussed before, no predictive analytics model can be 100 percent accurate. So the predictions made through regression models and decision trees have a limited chance of panning out. That’s why you need to think long and hard about how to allocate resources based on predictive models. 

After all, what predictive model saw a pandemic coming?

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