How Dynamic Customer Optimization Maximizes the Lifetime Value of a Customer (CLV)

Dynamic optimization is a system widely used in development and machine learning. Using variables and predictive analytics, programs are created to aid in a range of new and developing tech (e.g., unmanned vehicles). As we’ll see, companies are now using dynamic customer optimization to garner predictable profitability and increase the lifetime value of their customers (CLV).

Definition of Dynamic Customer Optimization

To better understand dynamic customer optimization, we first need to understand dynamic creative optimization. Essentially, creative optimization uses real-time data and buyer behavior to put specific messages, advertisements, and buying prompts (or calls-to-action) in front of people who are most likely to be influenced. Marketing efforts can be significantly improved using creative optimization.

Dynamic customer optimization, much like creative, also uses real-time data and buyer behavior to guide and influence individuals — the primary difference being that customer optimization is for marketing to current buyers in order to increase their value.

Benefits of Dynamic Customer Optimization

The broad benefit of dynamic customer optimization is the increased value of customers. Increased value can come in a number of ways, such as:

• Alternate Product Sales: If a customer or client purchases one product, optimization reveals indicators showing the potential for customers to purchase another product — or be “upsold” to a different version of the current product.
• Repeat Transactions: Repeat purchases from current customers makes up more than half of revenue for over 60% of SMBs (source). Using things like predictive analytics to optimize post-sale marketing can significantly improve the number of transactions per customer.
• Customer Retention: SaaS, banking, and other businesses with revenue dependent on how long customers stick around can see major increases with the right messaging prompted by the behaviors of current clients.

How Dynamic Customer Optimization Improves CLV

To best explain how this form of marketing works, let’s look at a simple example to illustrate the fundamentals of all dynamic optimization at the basic level. 

Customer Modeling

In the simplest terms, modeling is a method used for determining how and why things are the way they are. For customers, it’s how they became customers and what those customers are currently doing. Such factors may include:

• Things that prompted them to buy (i.e., timing, pain points, marketing, etc.)
• Other spending habits in related industries (e.g., previous solutions)
• Buying cycle time (how long it took them to actually purchase)
• How they have used the product since purchasing (heavy/light usage)
• Level of success using the product

Essentially, modeling is like mapping, showing the evolution of your buyers and factoring them based on known variables. A somewhat similar process to modeling is creating buyer personas.

Example: A local bank has a significant number of customers who move/open a single checking account from major banks.

Customer Data

While pre-sale data can come into play, customer data focuses on how customers are behaving on the other side of the funnel. This data is read and interpreted to determine the most likely ways your current customers will react in the future.

Example: Of the customers who have moved checking accounts, many have other accounts (e.g., savings, loans) and may be testing the new bank to decide whether to move all of their accounts.

Customer Estimation

Estimation is where the past and current data of customers and customer groups come together to inform potential marketing behaviors on behalf of the company. Predictive analytics can aid in determining potential outcomes based on the data.

Example: Data suggests that once a customer has opened the checking account, the next likely accounts to be moved are basic savings accounts.

Note: Of course, this data could be much more detailed. For instance, the checking customer could be three times more likely to move if the current account has more than $3,000 in it and has been a customer for 30 to 45 days. Machine learning and AI solutions, such as NGDATA’s Customer Data Platform, can be an incredibly useful marketing tool when it comes to dynamic customer optimization. Not only will the program notice current factors, but also has the ability to highlight data that isn’t currently being used to influence customers.

Customer Predictive Control

It’s at this level that the actual marketing comes into play. Once the data shows certain indicators, messaging begins. There are untold methods, but the overarching goal is to trigger particular messaging that is, by revealed data, likely to influence the behavior of those who receive the message in desired ways. This process gives predictive control of the overall trend, creating a very predictable outcome.

Example: Of the number of new checking accounts, 30% are moved from larger banks. 25% of those accounts hold at least $3,000. For these accounts, marketing (specifically to open a savings account) is delivered between the 30th and 45th day of a new account. 60% of those customers are likely to open a new savings account within the following 30 days.

One piece builds upon another and creates an automated, dynamic marketing funnel for current customers. The data can be used to forecast results, and if using AI, these forecasts can become even more accurate and predictable — making dynamic customer optimization an incredibly powerful tool for influencing consumer behavior and driving revenue by maximizing the lifetime value of each and every customer.