Enhancing Customer Segmentation with Predictive Data Modeling

It’s a fact that no business would exist if it didn’t have customers. However, businesses that are efficient at marketing and selling don’t just wait for customers to come to them. Understanding customer behavior and attributes is the best way to target audiences, implement effective marketing strategies, and drive sales.

These days, tracking the behaviors and data points for customers is a challenge. Not only is it difficult to manage large portions of data, but customer-based data often shifts and changes. Static segments don’t take the changing mindset of user’s data into consideration and may lead to missed opportunities.

This is where predictive data modeling becomes the next step. It doesn’t use old data or broad audiences but rather uses learning algorithms and real-time information to predict behavior and adapt to customer preferences and needs. The best part of predictive data modeling is that it targets individuals, not a huge group.

In this blog post, we’ll talk about how predictive data modeling leads to enhanced customer segmentation and the effect it has on marketing and sales efforts.

Predictive data modeling can drive strong results for your marketing campaigns

What is Customer Segmentation?

No two people are the same, and therefore neither is their data. Taking the time to understand what makes customers similar, as well as different, helps businesses to segment these customers. Segmentation divides customers into groups and subgroups based on things such as demographics, buying behaviors, and other attributes.

Customer segmentation allows businesses to create targeted marketing strategies and campaigns that speak directly to the needs of that group. It also typically helps improve engagement and sales, because the group is being targeted based on their propensity to buy related to that message.

There are two types of segmentation:

• Traditional segmentation: Uses fixed attributes to target groups. This might include age, gender, income, geographic location, and doesn’t account for flexibility

• Dynamic segmentation: Uses behaviors and preferences, including purchase history, website visits, values, interests, and life events that are flexible

Why is Predictive Data Modeling Important?

In the past, businesses and marketers have used static data such as age or income to target audiences. In the days before the digital revolution, this was a great method, because most customer behavior followed certain patterns based on static data.

Now, shifts in behavior, preferences, and opinions are as variable as ocean waves. Customers are constantly engaging with different brands across different outlets. This constant interaction stream gives business more data to analyze and turn to behavior and real-time based predictive modeling to drive marketing and sales.

What is Predictive Data Modeling?

Predictive data modeling uses algorithms to analyze historical data, identify patterns, and make predictions about customers and their future behaviors and purchasing decisions. This gives businesses a great way to address issues ahead of time and uncover sales opportunities that may have previously been missed.

Benefits include:

• Personalization: Predictive data modeling creates personalized marketing campaigns that are specific to a customer’s needs and attributes. This level of personalization leads to increased customer engagement and sales.

• Better ROI: Instead of marketing to a broad audience, predictive data modeling zeroes in on a specific group or target, leading to less money spent on uninterested leads

• Stronger Retention: Businesses don’t want one-time customers – a repeat customer is the best! Predictive data modeling can help identify customer churn and engage to prevent those customers from leaving with specific incentives

Beginning a Predictive Data Modeling Strategy

Data modeling is constantly evolving, and includes future use of this like AI, sophisticated technology integrations, and more. Our team uses Decision Tree, Neural Network, Bayes Net, CHAID, and XGBoost Tree to transform our client data into a predictive model to drive ROI.

Business owners who take the opportunity to use predictive data modeling and evolve it for their business will drive better customer segmentation to create better marketing and sales results.

Predictive data modeling isn’t for the faint of heart – it requires attention to detail, research, study, and analysis. The best way to use this method of data modeling is to partner with a team that’s experienced and can guide you in analyzing the results.

Milestone Marketing Solutions is here to assist you with your data modeling needs. We would love to speak with you to learn more about how we may be able to assist in creating marketing campaigns that segment your customers to provide the best possible results. Contact us using the form below and we will respond as soon as possible.