“Knowing who your customers are is great, but knowing how they behave is even better.”Jon Miller (Co-founder, Marketo)
Customer segmentation remains at the heart of marketing despite the digital evolution. If anything, it’s now even more important given the level of personalization in the customer experience. That’s why Jon Miller’s statement represents the ultimate aim of customer segmentation for modern businesses.
Identifying the complete personality of a user is a key feature in the marketing strategy of businesses today. That’s because it’s not good enough to only know who buys the product or service. Instead, companies need to know when, where and how consumers come into contact with their business. Only then would business owners be able to devise strategies that play on competitive competencies to cater to customer needs.
Fortunately, discovering these insights is no longer as difficult as it once was thanks to AI. Automated segmentation and RFM modeling make customer segmentation much more valuable and insightful for businesses. But that’s only if business owners understand how to utilize artificial intelligence to implement customer segmentation techniques.
This blog presents a modern view of customer segmentation and why it is necessary for modern businesses to adopt updated segmentation techniques. The subsequent sections of the blog introduce automated segmentation and walk you through the steps required to implement RFM modeling along with Customer Lifetime Value analysis for effective segmentation.
So let’s get started with a brief introduction of customer segmentation.
Customer Segmentation in the Modern Age
Any marketer will tell you that segmentation is the division of consumers and markets based on common characteristics. But dig a little deeper and you’ll see for yourself that it’s a bit more complicated than that, especially now.
Customer segmentation in the modern age is particularly complex because of the level of personalization. The consumer experience is customized to suit each individual consumer. This means each individual consumer can be a segment in and of itself.
The standard technique for customer segmentation involves segmenting according to characteristics such as demographics, psychographics, geographical etc. In the modern age however, these characteristics are much more value-driven. The subsequent sections of the blog discuss these very characteristics.
Instead of segmenting and targeting consumers on who they are, businesses now segment based on behavior. With artificial intelligence, it is not only possible to monitor the habits and behavior of consumers, but also perform cohort analysis to group them together.
Thus, modern segmentation techniques are much more sophisticated as those practiced by businesses in the previous century. But why do business owners need to perform customer segmentation at all? What benefit do they gain out of this practice? And how has artificial intelligence improved its implementation in the modern age?
Customer Segmentation is just one piece of the puzzle. Businesses also use Marketing Attribution to improve Customer Lifetime Value which helps segment consumers even more accurately. Find out how.
The Need for Customer Segmentation
We’ll address the first question in this section – Why do businesses need to segment consumers based on smart metrics?
The first and foremost reason is the growth and development of business. According to a recent report, the business sales cycle has increased by 22 percent in the last five years. That means it takes longer for businesses to close sales with consumers who have greater options.
If businesses do not correctly identify the best consumers for a transaction, the consequences can be dire. In the modern age, competition is fierce and mistakes are fatal for any company. The average lifespan of a business is only 15 years now, thanks to the ease of doing business. So customers have so many options that they can quickly move onto without a second thought.
In this climate, it is essential for modern companies to know who they are going to target and how. The beauty of modern customer segmentation techniques is that it pinpoints relevant customers as well as the best strategies to go after them. We will discuss more about these strategies later on.
For now, it is essential to understand the role artificial intelligence plays in modern customer segmentation.
So let’s dive in.
Rediscovering Segmentation Techniques with AI
Artificial intelligence is rediscovering the art of customer segmentation thanks to advanced analytics and modeling techniques. With so much consumer data on hand, it’s no surprise businesses need to turn to artificial intelligence to analyze it. The machine learning algorithms, once set, are capable of highlighting trends and offering insights that would have otherwise escaped human intellect.
In this section, we outline the major segmentation methods that artificial intelligence makes possible for businesses in the modern age. Chief among these methods is the automated segmentation technique that uses RFM modeling. So let’s look at the concept of automated segmentation and how it benefits businesses in the long run.
a. Getting to Grips with Automated Segmentation
The concept of automated segmentation is vital to understanding how AI helps in modern customer segmentation. The term automated segmentation refers to the grouping of consumers into groups by artificial intelligence and machine learning algorithms based on common characteristics. At surface level, this definition seems eerily similar to the definition of segmentation in the previous section.
However, automated segmentation requires a preliminary analysis of consumer habits and characteristics to program machine learning algorithms. Once businesses input the preliminary data, the algorithms take over to analyze all subsequent data and group it based on initial programming. As the algorithm keeps operating, it improves its functionality by continual learning. That means the artificial intelligence algorithm learns simultaneously as it operates. In time, the AI algorithm becomes apt at segmenting consumers in the most efficient way without any supervision from human agents.
b. The Benefits of AI-Driven Segmentation for Businesses
We’ve already discussed the need to segment consumers into relevant segments in the previous section. However, sticking to old fashioned segmentation methods may actually do more harm than good. After all, in the modern world data is the commodity in demand. Companies that fail to act on business intelligence are exposing themselves to inevitable demise.
That’s why AI-driven segmentation techniques hold innumerable benefits for businesses. While it is true that AI system integration is costly, it pays off in the long run. Once the algorithm is up and running, the AI dashboards display segmentation insights automatically. The benefits of this feature are two folds.
Google Analytics is one of the most sophisticated customer segmentation tools ever created. Find out how to do more with Google’s “Lifetime Value” report.
Firstly, with mounds of data, the artificial intelligence algorithm is able to make better predictions about consumer behavior. As a result, the algorithm segments consumers better than human agents ever could. Secondly, the customer segmentation process is a long and tedious one. Once this process is offloaded to AI algorithms, the human resources are free to direct their time and effort to more pressing tasks. Not to mention, automated segmentation processes mean more time for formulating and implementing strategies for each segment.
Now let’s move on to the crux of automated segmentation i.e. RFM modeling and CLV analysis. These two concepts combined together form the essence of the modern customer segmentation techniques.
Using RFM Modeling for Segmentation
The first step in customer segmentation using artificial intelligence is the RFM modeling. RFM stands for Recency, Frequency and Monetary, which are the three vital criteria that determine the value of a customer for an organization. In this section, we will understand the basic concept behind RFM analysis, the different segments that are created according to this modeling and the interpretation of the RFM models.
Finally, we’ll take a quick glance at how artificial intelligence systems perform RFM modeling. This discussion will also help understand why AI-driven customer segmentation techniques are far superior to older, more traditional, approaches.
1. What is RFM Modeling?
As mentioned before, RFM stands for Recency, Frequency and Monetary. RFM modeling is a type of analysis tool that analyzes the interactions with different customers to assess the most and least profitable ones. The basis of analysis are as follows:
- Recency: This criterion groups customers according to their most recent interaction with the business. The highest weight of any interaction is assigned to the purchase of a service or a product. The logic behind the use of this criteria is that a customer is more likely to purchase a product again if they have had a recent purchase, or interaction, with the business. It is important to note that the scale for a recency is numeric, with a smaller number representing a recent interaction and a larger number representing an older interaction. Thus, if the recency of a particular client is 1 that means this client interacted with or purchased a product from the business 1 day ago.
- Frequency: This criterion groups customers according to how frequently a customer does business with a company. Similar to recency, frequency is also represented by a numeric value. However, the smaller a number, the lower the frequency of purchase and vice versa.
- Monetary: This criterion groups customers based on the amount of money they spend with the business. Since this measure is directly related to the monetary value, therefore the numeric value for Monetary represents the amount of money. This means if the Monetary value for a customer is 400, it represents $400 of transaction, on average, with the business.
Ideally, companies want customers who rank highly in all three categories since these are the perfect customers. But such customers are often rare. Not to mention, it’s difficult for humans to dissect and analyze each factor on its own because of the sheer volume of data. That’s why AI algorithms are revolutionizing customer segmentation with quick and precise RFM analysis that makes it easier for companies to track the most profitable consumers.
The RFM modeling makes use of all three measures together to group consumers. The analysis on the basis of all three measures is necessary since it provides a holistic view at the overall value of a customer. This segmentation is explained below.
2. How Does the Model Segment Customers?
The customer segmentation itself is done with the simultaneous analysis of all three variables. The customers are segmented on a scale of 1 to 5 with 1 being the lowest and 5 being the highest. A customer with a ranking of 555 is classified as the most profitable customer to the organization. Such a customer purchases frequently from the company, having done so most recently and spent above average on each transaction.
Usually, the AI tool will create ‘cells’ that can be defined as separate segments of customers. However, it is upto the human agents to define the range within which to categorize the customers.
3. How does AI Perform RFM Modeling?
AI programs end up creating ‘cells’, representing each customer segment based on their RFM scores. The RFM scores are generated by monitoring the activity of customers. With the help of APIs, the behavior of customers is constantly monitored on the website. Each interaction is then stored and information is added to the artificial intelligence system. This information is then used to assign a particular score to each customer for Recency, Frequency and Monetary. The customers with the same score are then grouped together.
The implementation of the RFM model through an AI system requires five essential steps. These are as follows:
- The first step requires the selection of an AI tool suited to your business needs.
- The next step is to set up a constant supply of data for the AI tool to analyze. It is this data supply that will help in training the algorithm and allow it to perform continual learning.
- Consequently, a workflow needs to be established that defines the parameters of analysis.
- The categorization of the action or interaction with the business needs to be defined in order to group the action in a similar category and assign it a value on the scale for each measure.
- The implementation of the segmentation workflow model is the final step as it begins the segmentation process for the customers visiting the business.
Once the AI tool carries out RFM modeling, the next step in AI-driven customer segmentation technique is customer lifetime value analysis.
The Role of Customer Lifetime Value in Segmentation
Customer lifetime value is the total amount of money a customer is expected to spend with a business over their lifetime. The classification of customers based on customer lifetime value is a foolproof method of segmentation. In the past, businesses used a number of different metrics to segment consumers like demographics, psychographics or geographical. However, the segmentation of customers based on their value is more suitable.
This type of segmentation was difficult (not impossible) before the advent of artificial intelligence. But now, with AI algorithms analyzing consumer behavior and tracking thousands of user interactions, CLV segmentation is not only possible but preferred.
So where does RFM modeling play into CLV analysis?
Since customer lifetime value is an estimation of the customer’s total value over their lifetime, therefore metrics like recency, frequency and monetary are essential in determining that value. Once customers have been grouped according to RFM modeling, the next step in AI-driven customer segmentation is classifying the value of said consumers based on their overall value.
The benefit of grouping customers based on their value is the efficient utilization of organizational resources. Imagine if you spent thousands of advertising dollars targeting a consumer that had plenty of recent visits but absolutely no monetary value, and therefore poor CLV.
Instead artificial intelligence algorithms not only pinpoint customers with the highest CLV but also suggest strategies to target them across different platforms. With predictive analytics, AI algorithms predict the CLV of recently acquired customers as well. This provides an opportunity for your business to allocate resources efficiently by prioritizing targeting customers that have a long term highest CLV.
A Helping Hand Beyond Customer Segmentation
Ask any business owner what the ultimate goal of segmenting customers is and they will tell you ‘To maximize ROI’.
Maximizing Return on Investment (ROI) isn’t easy but finding the right customers and targeting them is a good start. Fortunately, artificial intelligence systems provide a helping hand beyond customer segmentation as well. As we mentioned before, AI tools analyze every consumer activity across all platforms with the help of APIs.
This feature allows AI systems to assess what works best for different types of customers. For example, AI algorithms record the user actions and cross reference them with the type of content that initiates the action on the website.
Essentially, this means the AI system can provide content recommendations based on the type of content that got users clicking in the first place. With this data, businesses can devise entire content marketing strategies that brought users to the website and initiated action. Since users come from all sorts of different channels, including search engines, social media, emails and referrals, therefore using the right type of content is bound to attract the right type of customers.
At Lucrative.ai, we are determined to help your business find the right customers. The advanced customer segmentation techniques discussed above are a part and parcel of the modeling and analytics capabilities of Lucrative.ai.