Overview Metrics FAQ

This article defines commonly referenced metrics in Spate.

Popularity Index

The Popularity Index is Spate’s unified measure of overall consumer interest.

It combines data from:

Google Search (validated demand)
TikTok (cultural acceleration)
Instagram (aesthetic adoption)

The result is a normalized index that allows trends of different sizes and across different platforms to be compared consistently.

Instead of looking at search and social separately, the Popularity Index provides one authoritative view of scale.

What the Popularity Index Represents

The index reflects:

Overall consumer awareness
Cross-platform traction
Relative scale within a category
Cultural penetration

It is designed to answer one question:

How big is this trend right now?

Important: Popularity Is Relative

The Popularity Index is not a raw volume metric.

It reflects relative interest within a category, not absolute search volume or views.

This means:

A “Very High” trend in a niche category may still have lower total demand than a “Medium” trend in a larger category
Popularity levels are designed for comparison within a category, not across unrelated markets

Popularity Levels

Very High Popularity → Mass awareness, culturally embedded
High Popularity → Strong recognition and engagement
Medium Popularity → Established but not dominant
Low Popularity → Limited awareness
Very Low Popularity → Emerging or niche

Popularity measures size, not direction.

To understand growth or decline, refer to velocity metrics and lifecycle classification.

The Popularity Share metric shows which platform contributes most to a trend’s overall performance.

It indicates whether a trend is strongest on:

Google (search-driven demand)
TikTok (attention and cultural momentum)
Instagram (visual normalization and creator adoption)

This helps you identify where consumer engagement is concentrated and where to activate.

For example:

High Google share → Strong purchase or problem intent
High TikTok share → Cultural acceleration
High Instagram share → Aesthetic diffusion

Convergence (Cross-Platform Consistency)

Convergence measures how similarly a trend performs across platforms.

It answers:

Is this trend platform-specific, or broadly adopted?

Very High Convergence → Strong across all platforms
High Convergence → Performing similarly across platforms
Medium Convergence → Moderate differences
Low Convergence → Skewed toward one platform
Very Low Convergence → Dominant on one platform only

High convergence often signals durable, scalable trends.

Low convergence can signal early-stage emergence or platform-specific hype.

Predicted Growth (Will It Last?)

Predicted Growth forecasts the likelihood that a trend will grow or decline over the next 12 months.

This metric supports forward-looking decision-making.

Growth Categories

Very Likely → >90% confidence in continued growth
Likely → Strong probability of growth
Uncertain → Equal probability of growth or decline
Unlikely → Strong probability of decline
Very Unlikely → >90% confidence in decline

Predicted Growth is one of the primary inputs into Lifecycle classification.

How Predictions Are Calculated

Spate uses advanced forecasting models trained on millions of historical data points across platforms.

Forecast horizon: 12 months
Historical directional accuracy: ~72%
Each forecast includes a confidence score

Predictions use:

Historical data
Seasonality
Behavioral/platform shifts
Emerging signals

Confidence matters.

Some projections carry very high certainty (90%+ confidence).

Others are marked as uncertain (~50% confidence).

We aim to be transparent: if the data does not strongly support a directional call, we indicate that.

Predictions inform decisions — they do not replace them.

Interpreting Prediction Graphs

You may notice sharper volatility further into the forecast period.

This is a normal statistical effect: uncertainty increases over time.

Focus on:

The overall 12-month directional outlook
The confidence level

Avoid over-indexing on individual monthly fluctuations at the end of the forecast window.

Note: a trend can be declining today, but still predicted to grow, this typically indicates:

Seasonal rebound
Cyclical behavior
Early signs of recovery

Predictions for Brands

Brand trajectories are inherently more volatile than trend trajectories.

Future brand search behavior can shift due to:

Product launches
Marketing campaigns
Retail expansion
PR events

For brands, predictions should be interpreted as directional guidance, not absolute outcomes.

Trend forecasts tend to be more stable than brand forecasts.

Example: How to Use Popularity + Prediction Together

High Popularity + High Predicted Growth
→ Category driver with continued upside
High Popularity + Low Predicted Growth
→ Mature trend likely entering Fade
Low Popularity + High Predicted Growth
→ Emerging opportunity