Trends & Demand

First PublishedMay 24, 2026Last UpdatedMay 25, 2026ByAtif Alam

The decision this page enables: is the market moving toward or away from this category, and at what speed?

What it is

Trends and demand research is the passive side of market intelligence: you watch the signals the world is already publishing — search volume, public reports, funding rounds, hiring patterns, social conversation — and infer where the market is heading.

It pairs with demand validation experiments, which is the active side (running tests to see if real demand exists at price). Trends tell you whether the wave is forming; experiments tell you whether you can surf it.

Why it matters

The cost of misreading the trend curve is huge — building for a flat or shrinking market eats years.
ICP sometimes needs to widen or narrow based on adjacent-segment growth signals.
Pricing can be anchored higher if the category is heating up (alternatives’ prices are rising).
Workbook → Channel mix — emerging channels with low CPC are often where pre-trend products quietly win cheap acquisition.

How to distinguish a real trend from a fashion

The biggest single skill in trend research is telling the difference between:

Fashion — a short hype cycle. Sharp rise, sharp fall. Lots of conference talks, few durable products. Examples: many specific framework or platform fads.
Trend — a durable shift in customer behavior. Gradual rise, then plateau at a new normal. Becomes invisible because it’s the default. Examples: remote work as a default; SaaS pricing as a default.

The test:

Signal	Fashion	Trend
Search volume curve	Sharp spike then drop	Steady growth, then plateau
Time at elevated interest	Months	Years
Job titles changing	No	Yes
Workflow defaults changing	No	Yes
Pricing of alternatives	Volatile	Anchoring at a new level

If you can’t point to changed defaults (job titles, workflows, pricing anchors), you’re looking at a fashion. Building a durable product on a fashion is a hit-driven business with bad odds.

How to gather signal

A practical 5-source stack, ranked by cost-to-signal ratio:

Google Trends + Glimpse / Exploding Topics + niche search-volume tools. Free or cheap. Shape of the curve matters more than absolute level. Look at 5-year window, not 12-month — short windows hide the inflection point.
Industry analyst reports (Gartner, Forrester, IDC, CB Insights). Expensive ($X-XX K each) but the structured market data is real. Most early-stage teams should quote analyst data, not buy it.
Public funding rounds and S-1 filings. Crunchbase / PitchBook. Where is venture capital flowing? Where are public competitors growing revenue?
Job-posting analysis. Companies hire for what they’re building next quarter. Tools like LinkedIn or Indeed scraped for category-relevant titles (“growth engineer,” “RevOps lead”) reveal staffing trends 6-12 months ahead of product trends.
Niche community signals. Subreddit growth, Discord/Slack member counts in topic-specific communities, podcast download trends, newsletter subscriber growth.

You don’t need all five. Pick 2-3 that match your stage and check them on a quarterly cadence (more often is noise).

How to do trends research

A 4-step quarterly cadence:

Pick your category keywords. 5-10 phrases customers actually use (from discovery interviews). Avoid your own product or category jargon.
Build a quarterly dashboard. One number per signal per quarter. Trend over time matters; absolute levels rarely do.
Look for confirming signals across sources. A trend that shows up in search + funding + job postings is real. A trend that shows up only in search is probably a fashion.
Form one hypothesis per quarter. Pick the strongest signal and write down “we believe X is happening — here’s what we’d expect to see next quarter if true.” Then check next quarter.

Templates

Trend signal dashboard (quarterly)

Quarter: [YYYY-QN]

Search volume (Google Trends, 5-year window, indexed):
  Keyword 1: ____ (vs. last quarter: ↑/↓ ____%)
  Keyword 2: ____
  ...

Funding (rounds in your category, last quarter):
  Total $ raised: $____
  Distinct companies: ____
  Notable round: ____

Job postings (LinkedIn search count):
  Title 1: ____ (vs. last quarter: ↑/↓ ____%)
  Title 2: ____
  ...

Community signals:
  Subreddit/community 1: ____ members (↑/↓ ____)
  Subreddit/community 2: ____ members

Analyst signal (if you have access):
  [most relevant analyst note from this quarter]

Hypothesis to test next quarter:
  "..."

Fashion vs. trend test

Signal: [the thing you're tracking]

Defaults changing?
  - Job titles:      yes / no  (evidence: ____)
  - Workflows:       yes / no  (evidence: ____)
  - Pricing anchors: yes / no  (evidence: ____)

If 0-1 yes:  treat as fashion; don't build durable product around this.
If 2 yes:    plausible trend; gather one more quarter of data.
If 3 yes:    treat as durable trend; consider it core to plan.

Metrics to track

Search volume trajectory — slope over a 5-year window, not absolute volume.
Category mention growth in target communities — month-over-month % change in posts/discussions in 2-3 watering-hole communities.
Funding velocity in category — $ raised per quarter and round count; sudden acceleration is often the early-stage pre-trend.
Job posting count for category-relevant titles — month-over-month or quarter-over-quarter. Leads product activity by ~6 months.
Analyst quadrant placement (for mid/late-stage) — appearances in industry quadrants, even as “challenger,” materially shorten enterprise sales cycles.

Examples

SaaS workspace (B2B through-line)

Tracked five signals over 4 quarters:

“workflow automation small teams” search: +28% Y/Y on a 5-year window — durable upward slope.
Funding into adjacent workflow tools: 11 rounds Q1 → 19 rounds Q4. Sharp acceleration.
Job postings for “RevOps” + “ProductOps” + “engineering manager small team”: +40% Y/Y. Defaults shifting.
Subreddit member count in r/[redacted-community]: +35% Y/Y.
Analyst signal: Gartner moved “team workflow consolidation” from “Innovation Trigger” to “Slope of Enlightenment.”

Three of three defaults changing (titles, workflows, pricing anchors). Treated as durable trend; doubled down on content / community channels which were cheap precisely because category was pre-trend.

Consumer fitness app (B2C contrast)

Tracked four signals:

“home workout” search volume: spiked 2020, plateau 30% above pre-2020. Default workflow changed.
App-store downloads in category: steadily up Y/Y.
“Habit tracking” related searches: rising, but stable; no sharp inflection.
Public competitors (Peloton, Apple Fitness+): revenue continuing to grow, pricing stable.

The lesson: the 2020 spike was a fashion (the at-home-workout boom of lockdown) that resolved into a trend (home fitness as a permanent default at a higher base level). The team built for the trend, not the spike. Marketing copy avoided “the new at-home boom” framing — which would have been correct in 2020 and stale by 2023.

Common pitfalls

Chasing fashion. The cycle from spike to product launch to plateau to crash is faster than the cycle from idea to revenue. Most fashion-chasers ship after the spike has passed.
Looking at short windows. A 12-month Google Trends chart hides the inflection. Always look at 5-year minimum.
Single-source confirmation. A trend that shows up only in search data is suspect. Real trends show up in 2-3 sources (search, funding, hiring).
Confusing analyst hype with adoption. Gartner’s “Hype Cycle” is named that for a reason. Top of the hype cycle is almost the worst entry point.
No hypothesis discipline. Without a written quarterly hypothesis (“we believe X — here’s what we’d expect to see”), trend dashboards become decoration. The discipline is in the prediction-and-check loop.
Ignoring your own data. The most actionable trend signal is sometimes your own funnel: are users from a specific source / vertical growing 3x quarter-over-quarter? That’s a trend, locally proven.