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

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When the great Gary Vaynerchuk famously asked a conservative CMO how she assessed the ROI of her mother, he wasn’t trying to be funny. He presented this question as a means of highlighting the importance of engaging customers and nurturing them.

With Vaynerchuk’s question in mind, you should rethink how you view the lifetime value (LTV) of your customers. Let’s face it, this exercise has got to be easier than calculating your mother’s ROI! Moreover, once you have obtained the LTV data on your customers, you can generate phenomenal ROI in the long term. Predictive marketing will help you achieve such success.

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

This explosion in organizational data requires companies to hire teams of data analysts and scientists to conduct the processing and interpretation of collected data. Predictive analytics come into play here too. These tools can help you gauge and assess the available data and then predict future trends on multiple fronts.

What is predictive analytics?

‍Predictive analysis is a means of using real-time or historical data to help you predict consumer behaviors and decisions. Doing so will enable you to determine what leads them to make purchases, upsize and undertake other crucial actions.

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

The benefits of predictive analytics

There are two main benefits for your whole organization, and particularly your marketing team, that come with using predictive analytics.

1. Combat churn

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 cut your costs, but also lead to increased loyalty to your brand.

Predictive analytics can help you identify the most likely candidates for churn by correlating data on customers’ profiles, feedback and transactions. The subsequent personalized offerings from this correlated data give you something a little special to win back your customers most at risk of churn.

‍2. Improved forecasting

Utilizing rich data will enable you to improve your forecasting and other predictions. These insights will prove incredibly beneficial to your marketing team and other departments. They’ll allow you to optimize your pricing structures and improve inventory management, ultimately improving your revenue. As a sales tool, it is invaluable because it enables you to forecast deals better.

How predictive analytics can boost revenue

‍One of the more significant D2C brands I have worked with was struggling with pricing structures and managing its inventory during the earlier stages of its operation. The brand was running out of stock within hours for certain products while other lines were not shifting 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 pricing and more accurately predict future demand.

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

In the post-pandemic world, you may find that your marketing budget is a little more restricted than it previously was. Indeed, this is the case for many companies. You could find that you have to do more with fewer resources. Therefore, you need to allocate your resources to that which will provide you with 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 enable you to draw up predictions for similar future events.

In the past, this was a domain that only mathematicians would enter. However, today, most of the top brands are turning to predictive analytics models to help solve complex problems and discover hidden opportunities.

You can benefit from these models too. Some of the most common areas in which predictive analytics help you are risk reduction, fraud detection, operational-efficiency improvement and market-campaign optimization.

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

Forecast models. These models are versatile and used across many industries and for various business purposes. They provide metric value predictions based on estimates of new data values from what has been gleaned from historical data. You can use forecast models to generate numerical values for historical data and can input multiple data parameters.
Classification models. This predictive-analytics model is one of the most commonly used. Their popularity comes down to a feature that allows you to categorize information based on historical data. Also, you can quickly retrain these models with new data, providing you with a broad range of analysis options.
Time series models. Time series models focus on data in which time is the input parameter. The model uses various data points from the previous year, for instance, to develop numerical metrics predicting trends and patterns within specified periods. You will find this predictive-analytics model useful if you desire to see how specific variables change over time.
Clustering models. This model will sort your data into groups depending on specific common attributes. You may find this particularly useful for your marketing activities.

You can use several platforms to streamline your predictive-analytics process, many of which offer automated tools and features to help you adapt them to your in-house purposes. One such platform is Google’s BigQuery, which provides you with an ML template library, making life easier if you use GA4.

Overall, predictive analytics can help you to learn from your old data and optimize your customers’ 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 plenty through predictive models. For instance, they will aid you with user acquisition and increasing digital interactions. Predictive analytics also help reduce CAC, discover lookalike audiences and determine your customers’ LTV. These benefits will help scale your marketing campaigns and increase your ROI. The rich data you’ll receive from a predictive-analytics model will also enable you to give your customers personalized experiences.

Getting started

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

You should consider predictive analytics 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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