The Clear Insights Method™
Most review analysis stops at surface-level sentiment. "75% positive" tells you almost nothing.
Clear Insights goes deeper. We built a methodology specifically designed for customer reviews—one that extracts attribute-level meaning, identifies emerging trends, catches false alarms, and surfaces the feedback that actually moves the needle.
COLLECT
Building the foundation for accurate analysis. Most tools grab a snapshot; we build a continuous, normalized, and cleansed dataset.
We don't just pull recent reviews. Our systems continuously monitor products over time, building historical datasets that reveal patterns invisible in point-in-time analysis. A product that was 4.5 stars six months ago and 3.8 stars today tells a very different story.
Reviews come from multiple platforms with different formats. Our ingestion pipeline normalizes everything into a unified structure—so "5 stars" and "9/10" can be meaningfully compared.
Capture entire product categories, automatically detecting new brands and products.
Duplicate reviews, bot-generated content, and data corruption pollute analysis. Our pipeline identifies and removes duplicates and flags suspicious patterns so downstream analysis starts clean.
"Clean, continuous, categorized data. Everything that follows depends on getting this right."
DECODE
Extracting what customers actually care about. We move beyond generic sentiment to identify attributes and decode exactly what drives them.
Theme Identification
We identify specific product attributes—not generic categories. For a sleep supplement: effectiveness, taste, time-to-work, morning grogginess.
Sentiment Scoring by Attribute
Each theme gets its own analysis. A product might score 87% on "taste" but only 34% on "packaging." Granularity reveals where to focus.
Accurate Frequency Counting
Proprietary methods solve LLM hallucinations. When we say "423 reviews mention taste," that number is accurate.
Themes evolve. Our temporal analysis tracks sentiment shifts: Is "shipping speed" getting worse? Did "taste" complaints spike after a formula change? These patterns reveal the story behind the numbers.
VALIDATE
Separating real issues from noise. Not every complaint is a product defect—some are misunderstandings or edge cases.
Case Study: False Alarm Detection
Wave of complaints: "Bottle appears half-empty."
Label states 8 fl oz. Reviews confirm exactly 8 fl oz contents. Bottle is 16 oz to accommodate settling. Product is as advertised.
Pattern Correlation
A single "arrived damaged" is a fluke. Fifteen in a month is a fulfillment failure. We distinguish isolated incidents from systemic issues.
Evidence Weighting
Reviews with specific details carry more diagnostic weight than vague complaints. Our analysis weights evidence quality, not just volume.
Segment Discovery
Widespread "setup difficulty"? We segment feedback to find that desktop users are fine while mobile users struggle. The issue isn't "setup"—it's "mobile app setup." Precision makes problems fixable.
ILLUMINATE
Clarity shaped for action. Intelligence isn't useful until it's usable. We deliver insights structured, contextualized, and prioritized.
Actionable Recommendations
Specific next steps prioritized by potential impact on the product or purchase decision.
Comparative Context
How does the product stack up against competitors, benchmarks, and its own history?
Prioritized Findings
Issues ranked by severity, frequency, and impact—highlighting where attention matters most.
Confidence Indicators
Every insight includes transparency about data depth, validation status, and certainty level.