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most surprising valuesmost predictive insights

What drives revenue across segments?|

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• 21% of "exchange_vertical" records were "social media"

• 31% of "marketing" user needs were "marketing research"

Turn every search into instant explorations of real metrics

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Understand Related Findings

mktg + mkt research

31%

public figure + social

26%

mastery + language

32%

list suggest + social

24%

Base Column

user_need ▾

Value Column

exchange_vertical ▾

31%ofmarketing=market research

predictive power

76%

research count

marketing count

200

see examples

3.platform

● Moderate Insight Urgency

AI Score Highlight: This column has findings in the upper ranges of relevance score.

Finding Highlight: 82% of "platform" → "chatgpt" records were "0-89" for "avg prompt word count"

Highlight Reason: Most ChatGPT conversations use brief prompts (0-89 words), suggesting users prefer short, direct questions.

More Value Findings:

• 92% of "platform" records were "chatgpt"

• 6% of "platform" records were "gemini"

• 2% of "platform" records were "grok"

AI + ML scores surface impactful insights

Base Column

platform ▾

Value Column

Select an option ▾

Base Column platform ×Value Column ×

92%ofplatform=chatgpt

select for report

surprising score

100%

chatgpt count

3023

total count

3286

see examples

6%ofplatform=gemini

select for report

surprising score

78%

gemini count

181

total count

3286

see examples

AI Report

AI Usage Patterns in YouTube-Related Conversations

This report examines key patterns from AI exchanges where YouTube was mentioned, revealing trends in platform usage, user intent, and content focus.

ChatGPT dominates as the platform of choice, with 92% of platform records attributed to it.¹ This extends to 88% of list suggestion interactions² and 83% of market research interactions on ChatGPT.³

Marketing emerges as a recurring theme. It accounts for 32% of revenue optimization records,⁴ 31% of market research⁵ and content strategy records,⁶ dropping to 7% of list suggestion⁷ and 6% of context source records.⁸

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Insight Citations

¹ 92% of "platform" records were "chatgpt"

² 88% of "list suggestions" records were "chatgpt" for "platform"

³ 83% of "user_need" → "market research" records were "chatgpt" for "platform"

⁴ 32% of "revenue optimization" records were "marketing"

⁵ 31% of "user_need" → "market research" records were "marketing"

⁶ 31% of "content strategy" records were "marketing"

⁷ 7% of "list suggestions" records were "marketing"

⁸ 6% of "user_need" → "context sources" records were "marketing"

Premium Analysis Features

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Talk to Your Data

Ask in plain English and get answers pulled from real metrics

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Eureka Score AI Rankings

Every finding ranked, so what matters most reaches you first

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Regression Predictive Power

See what actually moves revenue as one clear percentage

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Automated Value and Relational Metric Breakouts

Every value and column pair calculated before you log in

Optional Enhancement Services

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AI Topic Mapping

Turn long-form text into searchable, measurable concepts

Removed column "address_line_2" that was 96% blank

"utm_source" values "fb", "FB" and "facebook.com" condensed into "Facebook"

Converted value "1" to "shipped" for column "order status"

Removed column "currency" because 100% of values were "USD"

generating new dataset

NEW DATA

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Data Preparation

Messy noise becomes analysis-ready data you approve first

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AI Search Engines

One secure search across every file your team owns

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Journey Mapping

Every customer journey gets an ID and a measurable story

Premium Use Cases

iPhone shopping intent in AI?

ChatGPT

92%

Gemini

How does iPhone compare for AI shopping?

iPhone leads in shopping intent across 88k conversations…

0 1

AI Search Intelligence

How your product appears in 90,000 real AI conversations

Creative Performance

Top creativeVideo A3.2× ROAS lift

0 2

Expanded Ad Analytics

Turn your ad reports into actionable growth insights

Austin 1889 water rights

July 12, 1889Council Meeting #4821

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Historical Document Search

History comes alive with Steeped AI's OCR technology

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turns any dataset into an AI-guided exploration of real statistical findings. The output is always the actual math. Zero hallucinations.

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Rank RevPAR Drivers

Direct Booking

61%

Loyalty Member

47%

Suite Category

35%

Long Lead Time

22%

Base Column

booking_attribute ▾

Value Column

revpar_tier ▾

61%ofDirect Booking=High RevPAR

predictive power

89%

high RevPAR count

183

stay count

300

see examples

All Features:

Talk to Data

Ask Steeped AI anything about your data and it will surface related "insight deeplinks" that are strictly accurate calculations called "findings", leaving zero room for hallucination (because output is not AI).

Clicking on the "insight deeplinks" brings up an exploration of related findings to understand more and select for reporting later.

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Did the price increase hurt conversion?

What's driving our churn this quarter?

Any weird spikes in refunds?

Answered in 0.3s

Conversion dropped 12% after the price hike

62% of churn traces back to onboarding

Refunds spiked 3x, mostly one SKU

AI Rankings

Each finding is ranked by an AI-based "Eureka Score" that judges how good the stats/ metrics are and how surprising or relevant the finding is.

Each column is ranked has AI score diagnostics on why it is important to the dataset and related metrics worth exploring further.

1.deal_signal

● High Insight Urgency

AI Score Highlight: This signal has findings in the upper ranges of relevance score.

Finding Highlight: 62% of "closed-lost" calls raised a "competitor"

Highlight Reason: Late-stage competitor mentions are the strongest loss signal across the pipeline, pulling down win rates in every segment.

More Value Findings:

• 62% of "closed-lost" calls had a "competitor mention"

• 44% of "stalled" deals showed "no economic buyer"

• 31% of "closed-won" deals raised "urgency" early

AI Reports

After exploring the important insights of the dataset, you can create a research-grade report on all the findings you have selected.

The report sources every insight with the exact finding citation that wasn't created by AI.

AI REPORT Cited

Q3 Cancellation Drivers

ChatGPT dominates as the platform of choice across the sampled conversations¹ which holds across every segment we tested.

Market research is the single largest recorded user need² and marketing accounts for most revenue optimization records.³

Onboarding confusion is the clearest recoverable driver in the cancellation set.⁴

Model blind-tested this week · rubric score 94

INSIGHT CITATIONS

192% of "platform" records were "chatgpt"

231% of "user_need" is "market research"

324% of "revenue optimization" is "marketing"

418% of "cancellation" mentions "onboarding"

RAW DATA EXPORT

the mathscore artifactsraw examples

Relational Metrics

All the hidden intersecting metrics between two columns are calculated automatically, expanding your dataset with more accurate connections and aggregations.

The most critical metrics are surfaced to the top of rankings and the "Talk to Your Data" feature for your goals.

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Rank Flavor Drivers

Spicy-Sweet

63%

Crisp Texture

48%

Citrus Aroma

37%

Low Sugar

26%

Base Column

flavor_profile ▾

Value Column

repurchase ▾

63%ofSpicy-Sweet=Repurchase

predictive power

89%

repurchase count

189

panel count

300

see examples

Regression

Every Relational Metric has a predictive power score that is built from regression models.

The predictive metric shows which connections are so strong, they are predictive.

87%Prediction Power

vs. random baseline

Engagement → Revenue82%

Visits → Conversion69%

Text Examples

Large text documents, articles, reviews or quotes associated with the data can be directly embedded within the quant metrics.

This essentially blends the quant with the qual, enriching the reporting at the end with examples explaining the "why".

Examples

See Examples For

13% of "topic_appearance" records were "Bipedal / Upright"

"Motorist has a possible nighttime sighting west of Albion. I was coming home from work, heading from Albion towards Fairfield, which would have been about 11:30 that night. I started smelling something first, I had my window down. And all of a sudden I saw this huge figure off to the side of the road, between the ditch and the field. It is walking upright, it wasn't no coyote or wolf, we don't have bears up there that I know of, I grew up there."

select for report

Column: sightingfortopic_appearance: Bipedal / Upright

The insights are already in your data.

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