From survey file to client-ready findings

This guide maps one end-to-end workflow in AddMaple: file received → evidence reviewed → output ready.

It assumes you already run banners, judge subgroup differences, and shape client stories. Below is where each AddMaple feature fits in that workflow, with links to detailed help for each step.

Use it when a CSV, Excel, SPSS, or survey platform export needs to become a deck, dashboard, Insight Hub, or PowerPoint deliverable.

Upload and start reviewing immediately

AddMaple reads the file in your browser and summarizes every column as charts and tables as soon as the project loads. Column order matches your export, so you can sanity-check structure right away.

Quick checks at upload:

Column types for scales, multi-select, open ends, and dates
Banner, segment, country, wave, or audience fields you plan to use
Long export labels that need renaming before client-facing output

See How to analyze a survey for upload and the graph summary view.

Prepare variables in the same project

Rename labels, group answer options, create segments and top-box variables, apply weights, and stack tracker waves—all in the same project, then pivot against the updated fields immediately.

You do not need a perfect file before you start. Fix what the current analysis question needs, then keep working. For the detailed prep workflow, see Prepare messy survey data for analysis.

Other preparation guides:

Scan the full survey from the Chart Dashboard

The Chart Dashboard gives you an interactive summary across every column. Expand, filter, and pivot from any chart without specifying cuts upfront.

This is useful for a fast first pass across the whole file, and for following up on new questions as they come up. See Exploring your data.

Pivot and filter on demand

Pivot any question by audience, market, segment, buyer type, wave, or outcome column. Swap pivot columns, add filters, and open subgroup views from the same project.

See How to pivot your data and Filter your data.

Significant differences and related columns

AddMaple surfaces significant differences and related columns automatically—ranked by significance and effect size—so you can see which variables are most connected to your selected column.

For outcome-specific stats—key driver analysis, regression, NPS—see Understand what drives outcomes. For segment shading in banner tables, see Run significance testing and read the shading. Also useful:

Open ends in the same project as quant

Code open-ended responses into themes, summarize text where useful, and filter those themes by the same banners and filters used in the rest of the survey.

For the full workflow, see Connect open-text feedback with scores, segments, and outcomes. Also useful:

Build the deliverable while you analyze

Save charts to a dashboard, capture findings in Insights, and assemble client-facing views as you go.

See Create a dashboard, Add items to a dashboard, Create Insights, and Add to dashboard.

Move evidence into client-ready outputs

Once you know which findings belong in the deliverable, pick the output format your client or stakeholder needs—deck, published Insight Hub, explorable hub, or a mix.

For output-specific steps, see Turn findings into client-ready outputs. Also useful:

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