Easily create segments without needing help from a statistician. Use the latest techniques including latent class, K-means and hierarchical cluster.
Displayr can form segments with any type of data including numeric, categorical, ranking, MaxDiff, Conjoint, and text. Missing data isn’t even a problem. By using the best-practice MAR assumption, Display automatically deals with missing data, which is always a major headache in other software.
Form segments using cluster analysis or latent class analysis (latent class analysis is the best for most problems). Build predictive models using all the machine learning tools, from discriminant analysis through to random forest.
Rapidly create, profile, and compare alternative segments, quickly iterating to the best segmentation for your market. Tables specifically designed for comparing different segmentations. Experiment with inputs, number of segments, segment names. Use crosstabs, bubble charts, and other visualizations to compare segments.
Automatically update and report on segments. Build your report (or dashboard) once. Then just apply a filter to update it entirely for the next segment. Export to PowerPoint or share as interactive dashboards.
Research Analyst, dunnhumby
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
There is not necessarily a ‘best’ number of segments you should use. However, four, is by far the most common number of segments that people end up using in latent class analysis.
Segmentation in R can be done using packages like kmeans, cluster, and tidyverse. Analysts use clustering techniques, decision trees, and MaxDiff scaling to create meaningful customer segments. You can use R in Displayr by either entering R code directly into a calculation, creating an R variable, creating a new Data Set using R, or accessing pre-written R code using menus and forms.
A segmentation report typically includes:
