One row per customer is a different problem from one row per customer per month. Shape decides which methods can apply to your file at all. More are in build.
That is the actual report this analysis produces, not a mock-up. Every analysis on this page delivers the same shape of output on your own data.
Every analysis classified under this data shape.
Should I accept this lot?
Do two measurement methods agree well enough to be used interchangeably?
Are two categorical columns related?
Which factors predict the outcome class, how clearly they separate, and where predictions fail.
Which measures rise and fall together, how strongly, and in which direction, with every pair tested.
How do two categorical variables map together? Biplot of category associations, brands vs attributes, segments vs behaviors.
Every column in your file: its type, what is missing, how its numbers are spread. Plus the fixes worth making before you analyse it.
Is my data normal, and are there outliers?
What distribution does my data follow? Fit and compare candidate distributions with goodness-of-fit tests.
How does response change with dose or intensity? Nonlinear curve fitting with EC50/IC50 estimates.
The shared factors behind several measures: how many the data accepts, what loads on each factor, and how much of every measure they…
Which predictors does the model actually depend on?
What matters most that we do worst? Importance vs performance quadrants from survey data for prioritization.
What drives an outcome, with the model that fits how it is measured: logistic for yes/no, Poisson for counts, gamma for amounts, linear…
Where does my data's family tree naturally split?
What is the causal effect when treatment is confounded? Two-stage least squares with instrument diagnostics.
How well do two raters agree beyond chance?
What moves satisfaction most?
If I only contact the top N%, how much of the value do I capture?
Ordinal survey items done right.
How much a set of drivers explains an outcome, which of them matter most and in which direction, and how well the model actually fits.
Does X drive Y through M; conditional effects.
Pool effect sizes across studies.
How bad is my missing data and does it bias my results? Missingness patterns, MCAR checks, imputation impact.
What is our Net Promoter Score, and is it moving or differing by segment?
Which few causes drive most of the problem?
Reduces many measures to a few dimensions, tested against random data, and names what drives each.
Did crossing the threshold cause the change?
Do your survey items hold together as one scale?
Where should I set the cutoff given MY costs?
What hidden segments exist in categorical responses? Model-based clustering for survey and choice data.
Fit several classifiers/regressors on one dataset, compare honestly (CV, metrics, stability).
What drives a 1-to-5 rating outcome? Proportional-odds models for ordered outcomes instead of pretending they are continuous.
What drives the WORST and BEST cases, not the average? Regression at chosen quantiles of the outcome.
Does my sample look like my population? Compute and apply post-stratification weights, compare weighted vs raw estimates.
Heterogeneous treatment effects; persuadables vs sure-things.
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