Getting to know all about you

Editor's note: Ed Lorenzini is chief executive officer and Scott Chase is chief technology officer at Analyze Corporation. They can be reached at and

The abundance of data in the digital age is enabling a deeper understanding of consumers’ behaviors, attitudes and psychographics. This wealth of information has shifted customer segmentation from the surface-level insights provided by traditional demographic methods to more precise segmentations and the implementation of personalized marketing strategies using advanced segmentation techniques such as behavioral and predictive modeling.

In this article we present case studies that delve into the influence of advanced customer segmentation tools on marketing and business performance. We’ll discuss their use, features and potential advantages over traditional methods. We’ll explore the latest trends in this field and examine ongoing debates around limitations, with the aim of stimulating discussion and offering insights for businesses and market researchers.

Case study 1: Strategic expansion through advanced customer segmentation – Citron Clothing

Company profile: Citron Clothing, established in 1992, is a distinguished women's apparel retailer offering “wearable works of art.” Its products are made available through physical boutiques, online sales and select department stores across the western United States.

Objective: Citron aimed to boost its online visibility and diversify its product range. It intended to use data-driven insights into its existing customers’ retail, apparel and accessory trends to guide its expansion strategy.

Challenge: Citron faced an essential product direction decision: Should it branch into women’s petite apparel or branch into fashion accessories? Comprehensive consumer insights were needed to make an informed decision.

Solution: Citron leveraged a consumer data segmentation tool to develop psychographic profiles and identify the retail behaviors of 10,000 existing online customers. This process revealed its core market demographic as upper middle-income Boomer women with interests spanning fashion, fine arts, fine dining, domestic travel and philanthropy. This demographic showed a higher inclination towards fashion jewelry, accessories, luxury items and leather goods, while petite-sized apparel had lower appeal. Using these insights, Citron strategically expanded its jewelry and accessories lines, saving time and money by deciding against investing in petite-sized apparel. Additionally, Citron used advanced customer segmentation technology to reach new customers within its target demographic. It executed a comprehensive marketing strategy involving direct e-mail marketing, social media promotion and geographic expansion.

Results: The segmentation tool identified a total potential market of 500,000 U.S. consumers for Citron’s offerings. A targeted e-mail and social media campaign significantly outperformed previous new-customer contact campaigns, yielding four times the performance.

Case study 2: Enhancing marketing response through
targeted customer engagement – Angi

Company profile: Angi, previously known as Angie’s List, is an American home services platform. Founded in 1995 by Angie Hicks and William S. Oesterle, it allows users to find contractors for paid home improvement work. The value of its offering primarily lies in providing customer reviews, aiding potential customers in making informed decisions.

Objective: Angi aimed to concentrate its marketing efforts on its most valuable customers and those likely to contribute reviews. It recognized that only a small percentage of its users ever contributed a review and sought to increase this number.

Challenge: Angi’s initial solution was to call 20,000 customers monthly who had previously used its service, encouraging them to write a review. However, this approach was costly and only increased reviews by about 5%. The company needed a strategy to boost the response rate to its calls.

Solution: Angi turned to a consumer data segmentation tool to analyze those who had written a review in the past. It generated a segmentation report and a unique model on the platform and, using this model, identified 20,000 high-potential reviewers and focused its monthly calls on this group instead of making random calls.

Results: Switching to targeted calling led to a significant increase in response rate – from 5% to 30%. This improvement in effectiveness was attributed entirely to the insights provided by the consumer data segmentation tool.

Case study 3: Leveraging big data to drive new car buyers to dealerships – Temes Consulting

Company profile: Temes Consulting is a marketing firm working on behalf of new car manufacturers including Fiat Chrysler, Ford and Toyota. Its aim was to drive dealership visits for new model releases and major promotional events. To achieve this, it turned to a consumer data segmentation tool to utilize consumer sociometric and automobile registration data.

Objective: Identify prospective buyers and match them with the ideal car and offer.

Challenge: The purchase of a new automobile is one of the average consumer’s largest investments. However, identifying customers who are in-market for a specific vehicle at a particular time can be a daunting task for manufacturers and dealerships.

Solution: Temes built demographic, psychographic and financial models for each make and model using the consumer data segmentation tool. It was able to construct ideal customer profiles for each car on the lot and even for competing models at other dealerships. Additionally, it combined these profiles with lease- and loan-expiry data from current car owners within driving distance of the dealership. This allowed Temes to develop highly personalized telesales, direct mail and social campaigns to reach qualified buyers with the car that matches their needs, enabling dealer sales consultants to focus on closing the deal.

Results: By integrating sociometric and psychographic data for every U.S. household with existing car ownership information for 125 million vehicles, Temes gained insight into the automobile buying habits and preferences of all Americans. These insights were leveraged into omnichannel, personalized marketing campaigns for vehicle promotions. As a result, Temes boosted buyer visits to its customers’ dealerships by 317% over a one-year period.

Play their part

As with a symphony, the various components of behavioral data interpretation all need to play their part to create a harmonious whole.

Clustering: Like a conductor identifying similar tones within an orchestra, clustering groups data points based on shared attributes. In customer segmentation, this technique discerns customer cohorts with parallel behaviors, needs or preferences.

Predictive modeling: This technique is the crystal ball of data interpretation. Utilizing historical data, predictive modeling forecasts future behavior – an invaluable asset for crafting targeted marketing campaigns or personalizing product recommendations.

Machine learning: The virtuoso in the lineup, machine learning algorithms are the maestros that learn from data, improving their performance over time. They have the knack for uncovering complex patterns and relationships that might evade traditional analysis techniques.

The mastery of data interpretation is a pivotal cog in the wheel of effectively leveraging behavioral data. By deploying techniques like clustering, predictive modeling and machine learning, businesses can unearth actionable insights to steer strategy and turbocharge growth.

Moreover, by weaving the threads of behavioral data into both advanced segmentation and data interpretation, businesses can enrich their customer value proposition, optimize their marketing ROI and maintain a competitive edge. The power lies not only in what data you collect but how you interpret and apply it – transforming raw data into a strategic asset.

Presents challenges

While customer segmentation technology offers numerous benefits, it also presents certain challenges. The quality of insights derived heavily depends on the accuracy and completeness of the data collected. Inaccurate or incomplete data can lead to misleading results, significantly impacting business decisions. Furthermore, businesses need to adapt swiftly to changing consumer behavior, especially in the wake of events like the COVID-19 pandemic. Regular updates to models are crucial to ensure that segmentation strategies remain relevant. 

Implementing advanced customer segmentation also requires substantial investments in technology and talent. This requirement may pose significant challenges for small businesses, potentially leading to a digital divide in the market.

The debate around customer segmentation technology primarily revolves around striking the right balance between personalization and privacy. Personalized marketing, facilitated by this technology, significantly enhances customer experience and engagement. However, concerns about the extent of data collection persist, with businesses grappling with setting appropriate boundaries. With consumers becoming increasingly conscious of their digital footprint, the demand for more transparency and control over personal data is on the rise.

Alongside privacy concerns, ethical considerations also come into play when AI and machine learning are used for customer segmentation. There's a growing concern that these technologies may reinforce existing biases in the data, potentially leading to unfair or discriminatory practices. Businesses, therefore, need to ensure that their algorithms are transparent, fair and inclusive.

Brace for regulatory changes

The future of market research with customer segmentation technology appears promising. Advancements suggest a move towards real-time segmentation, predictive modeling and hyper-personalization as standard practices. However, businesses must brace for regulatory changes concerning data privacy and ethics. Prioritizing transparency and consent in data collection practices and ensuring fairness and inclusivity in technology usage will be vital.

The potential benefits of embracing this technology far outweigh these challenges. By generating detailed insights into customer behavior, companies can tailor strategies that enhance business performance and customer satisfaction, equipping them with a competitive edge and propelling sustainable growth in the digital age.