Brand tracking with funnel, significance & drivers

Render the brand funnel as net rows, test every wave and competitor for significance, smooth the trend, and use driver analysis to see which image dimensions move preference. ISO 27001-certified hosting, GDPR-compliant, made in Munich.

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DataLion renders the brand funnel — from aided and unaided awareness through consideration to preference and loyalty — as net/top-box rows and tests wave and segment differences at confidence levels from 80% to 99%. The trend is smoothed with SMA/EMA, image batteries feed a relative-importance analysis on the R engine, and data is weighted to your sample frame.

🇩🇪 Made in Munich
GDPR-compliant
DPA included
Hosted in Germany

Trusted by research, insights & media teams

aided + unaided awareness measured
80–99% significance levels
SMA / EMA trend smoothing
weighting to your sample frame

Why brand tracking loses its punch

Every wave is rebuilt from scratch in SPSS, Excel and PowerPoint — error-prone and slow.
Changes get celebrated without testing whether they are significant.
Image gets measured but is never distilled into clear drivers of preference.

The funnel as net and top-box rows

DataLion renders each funnel stage as a net/top-box row: unaided and aided awareness, consideration, usage, preference and loyalty — with percentages by row, column or base, and conversion between stages.

Place several brands side by side as subcolumns, so your brand and the competition sit in one table — consistent wave after wave.

Unaided & aided awareness reported separately
Funnel conversion via top-box/net rows
Competitors as side-by-side subcolumns
Percent by row, column and base

Every wave and segment, tested

Whether a brand really gained or just wobbled in the noise, DataLion checks right in the table: differences between waves, brands or audiences are tested at 80%, 90%, 95% and 99% and shown as stars or letters.

Behind the scenes run the z-test and chi² test (pairwise and complement, with an optional Yates correction) — the procedures that make brand tracking defensible.

Wave, brand and segment differences tested
Confidence at 80/90/95/99%, stars or letters
z-test and chi² test, pairwise and complement
Optional Yates correction

Automatic wave import with smoothing

Set the tracking up once: new waves are imported and recoded automatically, so variables stay consistent across every wave — removing the most common source of error in manual tracking.

Show the trend as a timeline and smooth it with SMA or EMA at a freely chosen window, to separate sampling noise from the real brand trend.

Automatic import and recoding of new waves
Variables consistent across all waves
Timelines with SMA/EMA smoothing
More on tracking studies

Distill image batteries into drivers

Measure brand image through matrix/Likert batteries and show it as a polarity profile, heatmap or stacked chart — per brand and segment.

With relative-importance analysis on the R engine you find which image dimensions most strongly drive preference and advocacy — the basis for clear brand decisions.

Image via matrix/Likert batteries
Polarity profiles, heatmaps, stacked charts
Relative importance: which dimensions drive preference
Complemented by regressions on R

Weighted to your sample frame

Brand tracking lives on representativeness. In DataLion you weight to your sample frame — with weights from the dataset, from a separate weights table (via a join), or computed in DataLion to a target distribution.

Weighting is recalculated automatically when filters change, so every subgroup stays correctly weighted.

Weights from the dataset, a separate table or computed
Several weight variables per project
Recalculated on filter change
Weighted bases per question and category

What you can build with DataLion

Brand funnel dashboard

Awareness to loyalty as net rows — significance-checked, wave after wave.
See how →
Image driver analysis

Relative importance on R: which dimensions drive preference.
See how →
Weighted tracking

Weight to your sample frame, automatically across all waves.
See how →

See DataLion with your own data

Start a free trial or book a personal demo — from raw data to a finished dashboard.

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**Jens Falkenau**, Vice President of Market Research · Nielsen Sports

The platform in detail

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Common questions about brand tracking

How does DataLion render the brand funnel?

Each funnel stage — unaided and aided awareness, consideration, usage, preference, loyalty — is shown as a net/top-box row with percentages. Several brands sit side by side as subcolumns, so your brand and the competition are comparable in one table.

Are wave and competitor differences significance-tested?

Yes, right in the table. Differences between waves, brands or segments are tested at 80/90/95/99% via z-test and chi² test (pairwise and complement, optional Yates) and shown as stars or letters.

How does tracking across waves work?

You set the tracking up once; new waves are imported and recoded automatically so variables stay consistent. The trend is shown as a timeline and can be smoothed with SMA or EMA at a freely chosen window.

Can I see which image dimensions drive preference?

Yes. Image batteries (matrix/Likert) feed a relative-importance analysis on the R engine that shows which dimensions most strongly drive preference and advocacy — complemented by regressions.

Can I weight to my sample frame?

Yes. Weights come from the dataset, from a separate weights table (via a join), or are computed in DataLion to a target distribution. Weighting is recalculated automatically when filters change.

Ready for defensible brand tracking?

Try DataLion free with your own brand study — from the funnel through significance to driver analysis. Or book a personal demo.