How Tracksuit data works
Tracksuit measures how real people think and feel about brands. Every month, thousands of consumers across multiple markets answer short surveys about the brands they know, would consider, use, and prefer. Respondents are nationally representative, matched to each market's population by age, gender, and region, so the data reflects the real world, not just a narrow slice of it.
Responses are cleaned and weighted, then turned into the metrics you see in the API: simple percentages (e.g. “68% of people in this category are aware of your brand”). Data refreshes monthly.
This glossary is a quick reference for the terms, metrics, and data fields you'll run into when working with the Tracksuit API v2 today.
The Funnel
Tracksuit measures brand health through a funnel of consumer sentiment. Every metric is a percentage of people in the category, returned as a decimal between 0 and 1 (0.68 = 68%).
Unprompted / Unaided Awareness (paid add-on)
API value: UNPROMPTED_AWARENESS
The share of people who can name your brand without prompting.
Survey question: “Thinking about different [category brands], which ones come to mind?” Respondents see an open text field and type in any brands in the category they can think of.
Not every tracker includes this. It's a paid add-on, so if your subscription doesn't have it, it won't appear in the API response.
Prompted / Aided Awareness
API value: PROMPTED_AWARENESS
The share of people who recognise your brand when shown its name and logo. This is the top of the standard funnel.
Survey question: "Which of the following [category brands] have you heard of?" Respondents see a list of brands with logos and select all they've heard of.
Consideration
API value: CONSIDERATION
Of the people aware of your brand, how many would consider buying or using it?
Survey question: "Which of the following [category brands] would you consider buying?" Multiple select.
Investigation
API value: INVESTIGATION
Whether someone has actively looked into your brand. Only used in service or high-involvement categories (insurance, banking, automotive) where the purchase journey involves research. If your tracker doesn't include it, it won't appear in the API.
Survey question: "Which of the following [category brands] have you actively looked into or researched?" Multiple select.
Usage
API value: USAGE
The share of people who've recently bought or used your brand. This is claimed usage, not sales data.
Survey question: "Which of the following [category brands] have you used in the last week?" Multiple select.
Preference
API value: PREFERENCE
Your brand's share of first-choice picks. The only single-select question in the survey, making it a strong signal of loyalty.
Survey question: "Which is your preferred brand?" Single select.
Media consumption
Endpoint: /v2/category-views/{id}/media-consumption
Where people in your category spend their media time. Returns a ranked list of channels with percentage of respondents who selected each one. Unlike the funnel, this isn't a timeline. You get one aggregated data point per channel for the period you request.
Each channel in the response includes a rank field (its position by consumption within the category) and a value object with the same percent, weight, and base_weight structure as the funnel.
Survey question: "In the past month, which of the following media channels have you used, read, seen, or listened to?" Multiple select.
Conversion
Endpoint: /v2/category-views/{id}/conversion
How well your brand moves people from one funnel stage to the next. Returns the rate at which respondents who reach one stage (e.g. Awareness) go on to reach the next (e.g. Consideration), across the time range you specify.
Each data point is a FROM-TO pair, so one call can return multiple conversion metrics at once — for example, Awareness→Consideration and Consideration→Usage side by side.
Requesting specific metrics: Pass a metrics parameter as a JSON array of FROM-TO strings, e.g. ["PROMPTED_AWARENESS-CONSIDERATION", "CONSIDERATION-USAGE"]. Valid pair names are listed in the conversion_metrics field from GET /v2/category-views/{id}. If you omit metrics, all available pairs are returned.
Response fields unique to this endpoint:
value.from_weight: weighted count of respondents who reached the from stage (rolling).value.to_weight: weighted count of respondents who also reached the to stage (rolling).value.base_weight: total weighted respondents in the group (rolling).value.conversion_rate:to_weight ÷ from_weight, expressed as a percentage. This is the headline number — e.g. a value of0.42means 42% of people who are aware of your brand would also consider it.
Supports: start_period, end_period, filters, smoothing, brand_ids.
⚠️ Important note
Conversion rates are calculated within the aware base, not the total category. A 42% Awareness→Consideration rate means 42% of aware people would consider the brand — not 42% of everyone. Don't compare conversion rates directly to funnel percentages.
Statements
Endpoint: /v2/category-views/{id}/statements
Brand imagery scores — what people who know your brand actually think about it. Each statement is a short association (e.g. "Is innovative", "Is good value for money") and the response tells you what share of respondents agree it applies to each brand.
Available statements vary by category and subscription. Call GET /v2/category-views/{id} and check statement_metrics and statement_funnel_metrics to see what's available for your view.
Gating by funnel stage: The metric parameter controls which respondents are included in the base. For example, metric=PROMPTED_AWARENESS means only people who are aware of the brand are asked the statement questions. metric=WHOLE_CATEGORY uses all respondents, with no funnel gating. Defaults to PROMPTED_AWARENESS.
Valid values: WHOLE_CATEGORY, PROMPTED_AWARENESS, CONSIDERATION, PREFERENCE, USAGE, INVESTIGATION.
Requesting specific statements: Pass a statements parameter as a JSON array of human-readable statement strings (e.g. ["Is innovative", "Is good value for money"]). Returns all available statements if omitted.
Survey question: "For each of the brands you selected, please indicate how strongly you agree or disagree with the following statements." Respondents rate each statement on a scale; Tracksuit reports the share who agree.
Supports: start_period, end_period, filters, brand_ids, metric, statements.
⚠️ Important note
Statements don't have a smoothing parameter — they're aggregated over the full date range you supply. Keep this in mind when comparing to funnel data pulled with a smoothing window.
Profile
Endpoint: /v2/category-views/{id}/profile
How your brand skews by demographic. For a given funnel metric, this endpoint slices across all available demographic dimensions simultaneously — age, gender, region, income, and so on — and tells you what percentage of each group sits at that funnel stage.
Unlike the funnel endpoint (where you filter to a specific demographic to get a single time series), Profile gives you a snapshot across all demographics at once. It's the equivalent of the demographic breakdown view in the dashboard.
metric is required. Pass one funnel stage to profile, e.g. metric=PROMPTED_AWARENESS. Valid values: UNPROMPTED_AWARENESS, PROMPTED_AWARENESS, CONSIDERATION, PREFERENCE, USAGE, INVESTIGATION.
Response fields unique to this endpoint:
demographic: the dimension slice for this row (e.g.{"name": "Age", "value": "18 to 24 years"}).is_significant:trueif the brand's value for this demographic is statistically significantly different from the category average. Useful for highlighting standout segments without manual significance testing.category: a comparison object containing the category-level value and sample for the same demographic slice. Lets you contextualise the brand result — e.g. your brand's 18–24 Awareness vs. the category's 18–24 Awareness.
Supports: start_period, end_period, metric, brand_ids.
⚠️ Important note
Profile doesn't accept a filters parameter — because it's designed to show all demographic breakdowns at once, filtering to a demographic would create a circular or redundant result. If you want a time series for a specific demographic, use the funnel endpoint with a filters parameter instead.
Data fields
Percent (value.percent): the metric value as a decimal (0–1). Multiply by 100 for display, or format as a percentage in your BI tool.
⚠️ Important note
Percentages come back as decimals between 0 and 1, not 0–100. If your numbers look 100× too small, check the format. This is the most common source of confusion in early integrations.
Weight (value.weight): weighted count of positive responses (numerator). Adjusted for demographic imbalances in the sample.
Base Weight (value.base_weight): total weighted base (denominator). Percent = weight ÷ base_weight. Useful for aggregating over time: sum the weights and base_weights, then divide. Don't average percentages directly. Sample sizes vary by month.
Sample (sample): every data point includes a sample object with two fields:
sample.indicator: how much to trust the data point (see table below).sample.size: the actual number of survey respondents behind the data point.
Sample indicator (sample.indicator)
Indicator | Respondents | Meaning |
| 200+ | Enough data to act on confidently. Safe for reports and presentations. |
| 100–200 | Directional read. Don't rely on it alone, and be upfront about that when sharing. Try extending the date range or removing demographic filters to increase sample size. |
| Under 100 | Too few respondents for reliable analysis. Try removing demographic filters or extending the date range to increase sample size. Filter these out or flag clearly in reports. |
⚠️ Important note
Sample sizes shrink when you stack demographic filters. The quality indicator tells you when you've sliced too finely.
Minimum indicator filter
The funnel endpoint accepts a minimum_indicator query parameter (default: DIRECTIONAL). This filters out data points below the specified quality threshold before they reach your response. Set it to RELIABLE for only high-confidence data, or INSUFFICIENT if you want everything and will handle quality filtering yourself.
Smoothing
Also known as “rolling averages” in the dashboard.
API parameter: smoothing
Controls the rolling average window. Any value from "1mo" to "12mo" / "1y".
"3mo"(default): pools three months per data point. Matches the dashboard. More stable."1mo": single-month data. More volatile (~5–7% margin of error nationally) but useful for MMM models or specific campaign windows.
Don't compare 1mo data as though it has the same precision as 3mo.
Dimensions and filters
Slice data by age, gender, region, income, household status, etc. Available filters depend on your category view and geography. Call GET /v2/category-views/{id} to check.
Combining filters: Selecting across types (age 18–24 + Female) gives the intersection. Selecting within a type (18–24 and 25–34) expands the group.
⚠️ Important note
You can't sum demographic slices to get a total. Each slice is independently weighted. Request data without filters to get the total.
Category View
A specific Category × Geography combination (e.g. "Beer in Australia"). Equivalent to the account picker in the dashboard.
A category view also includes your account's configuration: which competitor brands are tracked, what statements are available, which extra demographics are enabled, and so on.
Call GET /v2/category-views/{id} to see the full metadata for a given view.
Start with GET /v2/category-views to list your available views. The id from that response is used in every other endpoint.
Pagination
Token-based. Each response includes a next_token field.
If next_token is not null, pass it as next_token in your next request to fetch the next page. When next_token is null, you've reached the last page.
Dates
All dates use ISO 8601 format (e.g. 2026-03-01). Always use the first day of the month. Tracksuit data is collected monthly, so mid-month dates will be snapped to the nearest period boundary.
start_period is inclusive. end_period is inclusive.
Brand
An individual brand tracked within a category view (e.g. "Corona" within "Beer in Australia"). This includes your own brand(s) and the competitors in your tracker. Funnel metrics are calculated per brand, per category view.
Wave
A period of data collection, aggregated monthly. Tracksuit surveys continuously, and data is rolled up into monthly waves. When you request data with smoothing=3mo, each data point pools three consecutive waves.