How to Determine Your Customers’ Lifetime Value and Take Your Brand to the Top

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When the great Gary Vaynerchuk asked a conservative chief marketing officer how he evaluated his mother’s ROI, he wasn’t trying to be funny. He presented this question as a means of highlighting the importance of engaging and nurturing customers.

With Vaynerchuk’s question in mind, you need to reconsider how you view the lifetime value (LTV) of your customers. Let’s face it, this exercise has to be easier than calculating your mother’s ROI. Plus, once you’ve obtained LTV data from your customers, you can generate phenomenal ROI in the long run. Predictive marketing will help you achieve that success.

Research and Markets published a report on the potential of the global predictive analytics market. The report stated that by 2025, the market will grow to $ 21.5 billion from its 2020 level of $ 7.2 billion. This increase equates to a healthy compound annual growth rate (CAGR) of 24.5%.

Related: How to Increase Customer Lifetime Value and Increase Profits

This explosion of organizational data requires companies to hire teams of data analysts and scientists to perform the processing and interpretation of the collected data. Predictive analytics also comes into play here. These tools can help you measure and evaluate available data and then predict future trends on multiple fronts.

What is predictive analytics?

‍ Predictive analytics is a means of using real-time or historical data to help you predict consumer behaviors and decisions. Doing so will allow you to determine what drives them to make purchases, increase their size, and take other crucial actions.

Predictive analytics data and solutions are designed to make your life easier. For example, how much easier would your life be if you could identify the customers who offer you the highest LTV? Identifying the engagement patterns and buying habits of these prospects will allow you to determine future buying behaviors based on predictive analytics projections.

The benefits of predictive analytics

There are two main benefits for your entire organization, and in particular for your marketing team, that come with the use of predictive analytics.

1. Combat rotation

You can correlate your data to help you combat churn by offering more personalized options, and your ability to minimize your customer churn rate will not only lower your costs, but increase your brand loyalty as well.

Predictive analytics can help you identify the most likely candidates for churn by correlating data in customer profiles, comments, and transactions. Subsequent personalized offers from this correlated data give you something a little special to win back your customers at higher risk of churn.

two. Improved forecast

Using rich data will allow you to improve your forecasts and other predictions. This knowledge will be incredibly beneficial to your marketing team and other departments. They will allow you to optimize your pricing structures and improve inventory management, ultimately improving your revenue. As a sales tool, it is invaluable because it allows you to better forecast sales.

Related: Why Industry Leaders Are Turning To Predictive Analytics

How Predictive Analytics Can Drive Revenue

‍One of the biggest D2C brands I have worked with was struggling with pricing structures and managing its inventory during the early stages of its operation. The brand was running out of stock within hours for certain products, while other lines weren’t changing at all.

Facing significant revenue losses, the brand turned to predictive analytics to help get things back on track. By using historical sales data, the brand was able to optimize prices and more accurately predict future demand.

The result was an increase in revenue of between 10% and 13% in the different departments. Having achieved such a significant boost in sales, the brand’s marketing team uses predictive analytics to help it understand purchasing patterns and market trends. Predictive analytics also aids in customer retention, inventory management, and developing future growth campaigns.

In the post-pandemic world, you may find that your marketing budget is a bit more restricted than it was previously. In fact, this is the case for many companies. You may find that you have to do more with fewer resources. Therefore, you must allocate your resources to what will give you the best ROI: repeat customers.

The best predictive analytics models

‍ You can use predictive analytics on historical data to identify patterns and trends. Your findings here will allow you to make predictions for similar future events.

In the past, this was a domain only mathematicians entered. However, today, most major brands are turning to predictive analytics models to help solve complex problems and uncover hidden opportunities.

You can also benefit from these models. Some of the most common areas in which predictive analytics help you are reducing risk, detecting fraud, improving operational efficiency, and optimizing market campaigns.

To help you decide which predictive analytics model might be best for your business, here is an overview of a few:

  • Forecast models. These models are versatile and are used in many industries and for various business purposes. They provide predictions of metric values ​​based on estimates of new data values ​​from what has been obtained from the historical data. You can use forecasting models to generate numeric values ​​for historical data, and you can enter multiple data parameters.

  • Classification models. This predictive analysis model is one of the most used. Its popularity comes down to a feature that allows you to classify information based on historical data. Plus, you can quickly retrain these models with new data, giving you a wide range of analysis options.

  • Time series models. Time series models focus on data where time is the input parameter. The model uses various data points from the previous year, for example, to develop numerical metrics that predict trends and patterns within specific time periods. This predictive analytics model is useful if you want to see how specific variables change over time.

  • Clustering models. This model will classify your data into groups based on specific common attributes. You may find this particularly useful for your marketing activities.

You can use various platforms to streamline your predictive analytics process, many of which offer automated tools and features to help you tailor them for your internal purposes. One such platform is Google’s BigQuery, which provides you with a library of ML templates, making your life easier if you use GA4.

Overall, predictive analytics can help you learn from your old data and optimize your customer experience.

With predictive analytics so widely available, it makes sense to use these models even before acquiring users. In an oversaturated market, you can achieve a lot through predictive modeling. For example, they will help you acquire users and increase digital interactions. Predictive analytics also helps reduce CAC, uncover similar audiences, and determine your customers’ LTV. These benefits will help scale your marketing campaigns and increase your ROI. The large amount of data you will receive from a predictive analytics model will also allow you to provide your customers with personalized experiences.

Starting

Understanding your business and technical requirements is the first step in the predictive marketing process. Once you understand these requirements, you can create a solution that meets your needs. Of course, there may be more than one suitable solution, so which one you choose will depend on factors such as your budget, equipment, scale, and available internal resources. Your marketing team needs to understand what features and functionalities your chosen solution has and how they can capitalize on it.

Related: How Various Industries Depend on Predictive Analytics

You should view predictive analytics as a long-term process. Together with your team, work out the results you want to achieve. Feeding your solution with data from other systems, such as CRM applications or other marketing tools, would also be helpful and save a significant amount of time.

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