Whether you want to use the familiar cluster analysis techniques, or state-of-the-art latent class analysis, it’s easy and efficient to do it in Displayr.
Hierarchical clustering, k-means clustering, latent class analysis, and all the associated techniques for predicting class membership in new data sets (e.g., regression, machine learning).
Displayr can be used to form segments with any type of data. Numeric. Categorical. Ranking. MaxDiff. Conjoint. Even text.
Drag and drop variables to create a cluster analysis and latent class analysis.
If you’re an expert you know that missing data can entirely derail cluster and latent class analysis, as the simple techniques, such as mean replacement, lead to the results being wrong. Displayr’s tools automatically deal with missing data problems using the best-practice MAR assumption, so you can focus on strategy and interpretation rather than trying to get the analysis working.
Displayr’s not just a clustering and latent class analysis tool. It is designed to do all your analysis and reporting. This makes it a snap to quickly profile and compare segments, whether you like to do this using crosstabs or some of our various visualizations designed specifically for comparing segments.
Vice President, Analytics, Leger
SQL, databases, Excel, CSV, text, SPSS, survey platforms, APIs, integrations, & more.
Summary tables, crosstabs, pivot tables, regression, text analysis, segmentation, machine learning, & more.
Data visualization, interactive data apps, dashboards, presentations, PowerPoint, Excel, PDF, web pages, & more.
AI automatically identifies and categorizes themes within your text data, providing deeper insights.
Understand and analyze complex emotions like frustration and sadness, helping you understand customer motives.
Extract key entities like names, places, and organizations to enrich your analysis.
Fine-tune and adjust categories to match your specific needs and preferences.
Create stunning word clouds, charts, and dashboards that help tell the story behind your text.
Analyze text data in any language, with true native language support to a global audience.
Analyze large volumes of text to gauge positive, negative, or neutral sentiments.
Extract insights with unrivalled accuracy, utilizing NLP to reduce manual effort and free up time.
Displayr helps MAC Research cut reporting and analysis time by 2/3rds
Cluster Analysis is a statistical technique used to group similar data points together into groups called clusters. Clusters are distinct from one another, while the objects within each cluster are broadly similar. It is widely used in market segmentation, customer profiling, and pattern recognition.
As Cluster Analysis is relatively simple in theory, there are a number of different methods that have been created to help segment data into clusters. Common examples include:
Cluster Analysis is best used when you need to uncover hidden patterns in data, such as segmenting customers based on behavior, detecting anomalies, or simplifying complex datasets into meaningful groups.
Like Cluster Analysis, Latent Class Analysis is a statistical technique used to find segments in data. It works essentially the same way as k-means clustering, only it can be used with non-numeric data and can be used alongside MaxDiff, Conjoint, Driver Analysis, and Choice Modeling data sets without needing to first compute respondent-level coefficients. The underlying maths of Latent Class Analysis is also a bit more advanced, making it a more accurate technique.
R provides a wide range of packages and libraries designed for cluster analysis, such as “stats,” “cluster,” “fpc,” and “dbscan.”
