How to Use Predictive Customer Lifetime Value to Empower Your Business

how to use predictive customer lifetime value to empower your business

Customer Lifetime Matters

In business, customers are everything. If companies manage to have a thorough customer relationship management strategy that allows clients to feel a bond with the business, they can actually see a huge increase in sales. Therefore, customer relationships should be dealt with taking the ultimate blessing of customer lifetime value into account.

But what is customer lifetime value and how will predictive customer lifetime value change the customer acquisition game in the years to come? By reading this informative article, you will be able to understand how to use predictive customer lifetime value to gather data, get more sales, and increase customer retention. Moreover, you will also get a glimpse into the future, getting to know how Artificial Intelligence and machine learning will team up with predictive customer lifetime value analytics to get results.

What is Customer Lifetime Value?

Customer Lifetime Value (CLV) is the current value of the future cash flows or the specific value of business attributed to a particular client during the entirety of their relationship with a company. This is a remarkably important metric since it allows business leaders to make intelligent decisions regarding sales, product development, marketing, and even customer support. By taking full advantage of this crucial marketing metric, you can effectively get to know what is the average value associated with a long-term bond with clients.

Even though it is quite hard to actually predict the duration of a customer relationship, most people can clearly get to have a good sense of an estimate.

How to calculate CLV? Multiply the Average Order Value by the Number of Repeat Sales and the Average Retention Time.

When you get to have a sense of what your customer lifetime value is, you will be able to know how much you can spend on paid advertising, media, video, email marketing, etc. At the end of the day, nobody needs to feel overwhelmed by numbers and stats. People just need to be aware of the specific value the client will eventually provide in the long haul.

By getting to become fully acquainted with the customer experience and by being able to measure important feedback every step of the way, you can be sure that you are in touch with your CLV. The most common use of CLV has to do with what is known as Historical CLV. This basically means that the customer lifetime value is calculated based exclusively on what a client has previously spent on a business.

What is Predictive Customer Lifetime Value?

Now that we know what customer lifetime value is all about, it’s important to get to know what predictive customer lifetime value is. The goal of maximizing customer lifetime value is simple: to be able to actually model the particular purchasing behavior of clients in order to gather insights into what their future actions will be. Whereas Historical CLV has to do with what a client has spent in the business, predictive CLV will leverage both the historical behavior and the predicted retention to estimate a discounted stream of prospective future lifetime revenue.

What is the disadvantage of focusing only on Historical CLV? The fact that - it being that it is the sum of previous profit for a particular client or group of clients - it will only provide specific insights and give clues as to what has happened before. This means Historical CLV fails to shed a bright light on the actual value of new subscribers.

What about predictive CLV? It represents a whole other level in the world of customer lifetime value analysis because it effectively has the ability to actually incorporate the expected retention, allowing marketing professionals to get various key insights.

These include the following takeaways:

  • What will the most profitable types of subscribers be
  • Where the company earns the highest return
  • What are the customer attributes which are driving customer retention

This incredible method allows everyone to create specific regression models that can actually predict the customer lifetime value either for a new or recent client, based exclusively on their latest buying patterns. This means this value relies heavily on analytical data which is not only recent but also shows the purchasing frequency. Basically, people that want to take full advantage of predictive CLV must be able to build incredibly complex data sets that can provide the monetary value of specific purchases for each of the first three months of a customer for all other current clients.

This important value can actually be measured over a finite period of time, based on the business and the kind of historical data that professionals manage to gather. A predictive customer lifetime value model can actually allow you to become knowledgeable and informed, coming up with data-driven decisions that will promote remarkable savings, efficiency, and ultimately increase revenue.

What are the Applications of Predictive CLV?

There are a plethora of different applications you can start to leverage by embracing the boon of predictive customer lifetime value models. First and foremost, predictive CLV supercharges acquisition optimization.

How? Because it allows people to upgrade the targeting of specific campaigns, making sure they are tailored to customer service prioritization. CLV will basically help professionals prioritize acquisition efforts to make sure to acquire subscribers who will represent the biggest lifetime value in the future.

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Another application is the fact that predictive CLV can help shape upgrade campaigns. By taking CLV into account, businesses will get to know what the value of a customer is, which means they can create a strategy that promotes an engagement boost, therefore increasing the value of all current subscribers.

Another amazing application is the fact that predictive CLV allows everyone to actually prioritize the customer service experience. When a current client has been effectively scored, their lifetime value is estimated. This allows customer service squads to leverage that fundamental customer data to improve the experience for all clients, streamlining it for max efficiency.

This can be done by having certain members of the customer service personnel become fully-dedicated to high-value clients. This will mean that the calls or interactions of these clients are prioritized, reducing wait times, and providing impeccable customer service.

Last but not least, it can also be used in the customer service department. Indeed, it can help create customized retention scripting which will be based on the estimated value of a subscriber.

What this means is that professionals will be able to provide more aggressive offers so as to make sure high-value subscribers are not only easily acquired but also retained. With its dynamic, data-driven ability, predictive CLV allows its dedicated users to calculate the risk-adjusted value of a particular customer’s lifetime. By taking advantage of specific econometric tools such as survival analysis, you will be able to easily start adding this value to your list of profitable marketing strategies.

Once it is calculated, CLV will basically allow you to have analytical data you can use to supercharge acquisition, customer service, and customer retention. Ensuring businesses make use of predictive CLV is not a choice but a fundamental part of being a consummate professional in the 21st century.

AI and Predictive CLV

Even though the future is always a mystery, it seems clear that Artificial Intelligence and customer lifetime value are bound to team up. AI and machine learning platforms are now offering remarkably intelligent insights. In fact, successful companies can now know what is the average customer lifespan and the revenue that the highest-value clients are bringing throughout a lifetime.

Moreover, they can also know how much managing that long-term relationship costs. Indeed, machine learning platforms can now analyze CLV across all the different marketing channels, providing a 360º view on all interactions.

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Once these intelligent systems get to analyze important characteristics of high-value clients, they will be effectively ready to learn how to optimize predictive customer lifetime value. As a result, machine learning users will be able to utilize data from current high-value clients to optimize touch points and campaigns, ensuring they reach other high-value users.

How to supercharge Artificial Intelligence and machine learning and use them to ensure the aforementioned value reaches new heights? People should follow these specific guidelines:

  • Focus on measuring crystal-clear business goals
  • Avoid wasting time with vanity metrics
  • Understand who their highest-value clients are
  • Get to know exactly how much lifetime revenue these clients drive
  • Promote data sharing and cross-functional collaboration to get full insight into the customer
  • Effectively promote a customer-centered ability

Artificial Intelligence will take this value to a whole new level by using current data without cognitive bias. Learning and optimizing on a regular basis, AI will allow predictive customer lifetime value to predict even more aspects and marketing nuances, making sure that - together - these two can effectively help businesses drive value, increase engagement, retain customers, and grow.

Predictive CLV is a Business Blessing

You already know that customer lifetime value in marketing allows you to save money, create better marketing campaigns that take highly-targeted clients into account, encourage the power of brand loyalty, get more sales, and save time. Now, with predictive customer lifetime value marketing models and applications, you will get to not only have access to historical CLV but also gain new insights from predictive regression models, machine learning, and AI.

By having access to an intro to predictive modeling for customer lifetime value and to important customer data, businesses can improve retention techniques, create a customer-centered customer experience, and increase their overall customer lifetime value taking advantage of predictive power.