Verb AI Query Cookbook

Learn what you can do with Verb AI MCP tools.

Verb AI MCP Query Guide

How to Transform Business Questions into Effective Data Queries

Overview

The Verb AI MCP provides access to real-time behavioral data from a compensated panel of 2,000+ US-based users (ages 21–34, skewing female). The data captures what people actually do on their devices — not what they say they do.

The key principle: The MCP answers objective, data-level questions well. It does not answer subjective or interpretive questions directly. Your job is to decompose business questions into structured queries the system can execute, then synthesize the results.

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Database Schema

Every event table links to AGENT_SYNC via USER_ID (or USER_IDENTIFIER for network traffic).

1. AGENT_SYNC — User Demographics

The panel's demographic backbone. Join this to any event table to segment behaviors by audience.

Column	Type	Description
USER_ID	PK	Unique user identifier. Links to all event tables.
DATE_OF_BIRTH / YEAR_OF_BIRTH	Date/Int	Use for age segmentation. Panel is 21–34.
GENDER	String	Values: Female, Male, Non-binary, Prefer not to say
HOUSEHOLD_INCOME	Numeric	Annual USD. Brackets: $1–50K, $50–100K, $100K+
EDUCATION_LEVEL	String	Highest completed (High School → Master's+)
EMPLOYMENT_STATUS	String	Full-time, Part-time, Student, Unemployed, Self-employed
MARITAL_STATUS	String	Single, Married, etc.
COUNTRY_OF_RESIDENCE	String	Currently 100% US
FULL_ADDRESS	String	Street-level address. Use for state/city extraction.
CITIZENSHIP / COUNTRY_OF_BIRTH	String	Origin demographics
RACE	String	Racial/ethnic background

Common patterns:

• Age calculation: YEAR(CURRENT_DATE) - YEAR_OF_BIRTH

• State extraction: Parse from FULL_ADDRESS

• Income brackets: GROUP BY ranges of HOUSEHOLD_INCOME

2. PRODUCT_EVENTS_FLAT_DYM — E-commerce Interactions

The richest table for purchase funnel analysis. Captures views, cart adds, and purchases across all e-commerce platforms.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
ACTION	String	Critical field. Values: viewed, added_to_cart, purchased
PRODUCT_NAME	String	Full product name as displayed on retailer site
BRAND	String	Brand name (may be NULL — check PRODUCT_NAME too)
CATEGORY	String	Product category from the retailer's taxonomy
PRICE	Numeric	Listed price at time of interaction
CURRENCY	String	Usually USD
DESCRIPTION	String	Full product description text. Use for keyword filtering.
PRODUCT_URL / PAGE_URL	String	Source URL. Extract retailer domain from this.
PROMOTION	String	Any active promotion text (coupons, BOGO, rebates)
RATINGS	Numeric	Product star rating
REVIEWS_COUNT	Numeric	Number of reviews
FEATURES	String	Product feature list
AVAILABILITY	String	In-stock status
IMAGE_URLS	String	Product images
PRODUCT_ID	String	Retailer's product identifier
EVENT_TIME	Timestamp	When the interaction happened
CREATE_TIME	Timestamp	When the record was created

Critical notes:

• ACTION is the funnel stage indicator. Always filter or group by this.

• BRAND is often NULL. Search PRODUCT_NAME and DESCRIPTION with ILIKE for brand detection.

• PAGE_URL tells you which retailer (Amazon, Target, Instacart, DoorDash, etc.)

• PROMOTION captures deal text — useful for understanding price sensitivity.

3. AMAZON_SHOPPING_FLAT_DYM — Amazon Shopping Funnel

Parsed from Amazon proxy traffic. One row per shopping interaction on www.amazon.com. Captures the full Amazon shopping funnel: search → product view → sponsored ads → cart → checkout → orders.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
EVENT_TYPE	String	SEARCH, PRODUCT_VIEW, SPONSORED_AD, SPONSORED_CLICK, FUNNEL, AUTOCOMPLETE
SEARCH_KEYWORDS	String	What the user searched on Amazon (SEARCH events)
ASIN	String	Amazon Standard Identification Number (product ID)
PRODUCT_TITLE	String	Product name from the listing page
PRODUCT_BRAND	String	Brand name (from URL slug or title heuristic)
PRICE	Numeric	Listed price at time of interaction
CURRENCY	String	Currency code (USD)
TOTAL_RESULTS	Numeric	Number of search results returned (SEARCH events)
PAGE_NUMBER	Numeric	Search results page number
FIRST_RESULT_ASIN	String	ASIN of the first organic search result
FIRST_RESULT_PRICE	Numeric	Price of the first organic search result
MERCHANT_NAME	String	Seller name (SPONSORED_AD events)
CAMPAIGN_ID	String	Ad campaign identifier (SPONSORED_AD events)
FUNNEL_STEP	String	cart, checkout, your-orders, your-returns (FUNNEL events)
PAGE_URL	String	Raw Amazon URL
EVENT_TIME	Timestamp	When the interaction happened

Critical notes:

• EVENT_TYPE drives what fields are populated. SEARCH events have keywords/results, PRODUCT_VIEW has title/ASIN/price, SPONSORED_AD has merchant/campaign.

• SPONSORED_AD and SPONSORED_CLICK capture Amazon's advertising ecosystem — which ads are shown and which are clicked.

• FUNNEL events (cart, checkout, your-orders) are behavioral signals without item-level detail.

• AUTOCOMPLETE captures search suggestions typed by the user.

• Prices are in USD. FIRST_RESULT_PRICE is from the first organic search result.

4. SHOPIFY_SHOPPING_FLAT_DYM — Shopify E-commerce Events

Parsed from proxy traffic across 400+ Shopify-powered stores. Auto-detects Shopify stores via standardized URL patterns. Captures product views with structured price/brand/currency data, cart contents, checkout, and search.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
EVENT_TYPE	String	PRODUCT_VIEW, PRODUCT_PAGE_VIEW, COLLECTION_BROWSE, CART_VIEW, ADD_TO_CART, CART_STATE, CART_CHANGE, CART_UPDATE, CHECKOUT, SEARCH, PAYMENT
STORE_DOMAIN	String	The Shopify store domain (e.g., "nathanandsons.com", "www.popflexactive.com")
PRODUCT_HANDLE	String	Shopify product slug (e.g., "remineralizing-gum")
PRODUCT_TITLE	String	Full product name from Shopify JSON API
PRODUCT_VENDOR	String	Brand/vendor name (e.g., "POPFLEX®", "Amish Country Popcorn")
PRODUCT_TYPE	String	Product category from Shopify
PRICE	Numeric	Price in the store's currency
COMPARE_AT_PRICE	Numeric	Original price before discount (if on sale)
CURRENCY	String	Currency code (USD, NZD, NOK, JPY, etc.)
SKU	String	Product SKU
VARIANT_TITLE	String	Selected variant (e.g., "Mastic Mint / 2 Packs", "XXS")
PRODUCT_ID	String	Shopify numeric product ID
TAGS	String	Product tags from Shopify
SEARCH_QUERY	String	What the user searched for (SEARCH events)
COLLECTION_HANDLE	String	Collection/category browsed (COLLECTION_BROWSE events)
PAGE_URL	String	Raw URL
EVENT_TIME	Timestamp	When the interaction happened

Critical notes:

• PRODUCT_VIEW (from /products/HANDLE.json) is the richest event type — 100% title, 99% vendor, 86% price, 95% SKU, 36% compare_at_price populated.

• CART_VIEW (from /cart.json) gives total cart price (in cents, divided by 100) and currency. Many carts show price=0 (empty carts).

• STORE_DOMAIN identifies which of 400+ stores the event occurred on. Major stores include Alo Yoga, Skims, Sanrio, New Era, Dolls Kill, Princess Polly, Tarte Cosmetics, and hundreds of DTC brands.

• Multi-currency: USD, NZD, NOK, JPY all detected.

• COMPARE_AT_PRICE > PRICE indicates a product is on sale — useful for discount analysis.

5. DELIVERY_APPS_FLAT_DYM — Food Delivery Events (DoorDash + Uber Eats)

Unified food delivery behavioral events from DoorDash and Uber Eats. Covers the full ordering funnel: store browsing → item view → cart → order → rating. Prices are in USD (converted from cents).

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
PLATFORM	String	DOORDASH or UBEREATS
EVENT_TYPE	String	STORE_VIEW, CART_VIEW, ADD_TO_CART, ORDER_PLACED, ITEM_VIEW, ORDER_RATING, ORDER_HISTORY, ACTIVE_ORDER, DRAFT_ORDER, INVOICE, PAGE_VIEW, PAYMENT
STORE_NAME	String	Restaurant/store name (e.g., "7-Eleven", "DashMart", "The Juicy Crab")
STORE_ID	String	Platform-specific store identifier
STORE_RATING	Numeric	Average store rating (0–5 scale)
NUM_RATINGS	Numeric	Total number of ratings for the store
DELIVERY_FEE	Numeric	Delivery fee in USD
IS_SUBSCRIPTION_PARTNER	Boolean	Whether store is DashPass/Uber One partner
DISTANCE_MI	Numeric	Distance from consumer in miles
STORE_ADDRESS	String	Full store address
STORE_LAT / STORE_LNG	Numeric	Store geolocation coordinates
ITEM_NAME	String	Menu item name (e.g., "Classic - Mixed Plate", "Chocolate Filled") or item ID
ITEM_PRICE	Numeric	Item price in USD
QUANTITY	Numeric	Number of items
ORDER_TOTAL	Numeric	Total order amount in USD
TIP_AMOUNT	Numeric	Tip amount in USD
ORDER_UUID	String	Unique order identifier
ORDER_STATUS	String	PLACED, COMPLETED, CANCELLED, ACTIVE, DRAFT
PAYMENT_METHOD	String	Payment method brand (e.g., "Visa")
CURRENCY	String	Currency code (USD)
PAGE_URL	String	Raw request URL
EVENT_TIME	Timestamp	When the interaction happened

Critical notes:

• DoorDash STORE_VIEW is the richest event — store name, rating, delivery fee, address, lat/lng, DashPass status, distance from consumer.

• DoorDash ORDER_PLACED has order total, tip amount, payment method, and order UUID. Prices are in cents divided by 100.

• Uber Eats ORDER_HISTORY has item-level detail: item name, price, quantity, customizations, and order status (COMPLETED/CANCELLED).

• DoorDash ITEM_VIEW shows numeric item IDs from the URL (item names not available because the app caches item details locally).

• DoorDash CART_VIEW gives store name, subtotal, and number of items in cart.

• Use PLATFORM to filter by DoorDash vs Uber Eats, or combine for cross-platform delivery analysis.

6. SEARCH_EVENTS_FLAT_DYM — Search Activity

Captures every search query across Google, Bing, YouTube, and other engines. The best table for understanding intent and discovery.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
QUERY	String	The actual search terms typed. Most valuable field.
SEARCH_ENGINE	String	google, bing, youtube, other
RESULTS_COUNT	Numeric	Number of results returned
RESULTS_DESCRIPTION	String	Summary of top results
PAGE_URL	String	Search results page URL
LANGUAGE	String	Interface language
EVENT_TYPE	String	Search event classification
EVENT_TIME	Timestamp	When the search happened

Critical notes:

• QUERY is case-sensitive in some contexts — always use ILIKE for matching.

• For topic analysis, use keyword patterns: QUERY ILIKE '%white claw%' or QUERY ILIKE '%ramen%'

• Combine with time windows to see search-to-action sequences.

7. YOUTUBE_EVENTS_FLAT_DYM — YouTube Interactions

Captures video views, including full metadata about what panelists watch.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
SNIPPET_TITLE / API_TITLE	String	Video title. Use for content categorization.
CHANNEL_TITLE / CHANNEL_ID	String	Creator/channel info
DESCRIPTION_SNIPPET / DESCRIPTION_TOP_LEVEL	String	Video description text
VIEW_COUNT	Numeric	Total video views (public metric)
LIKE_COUNT	Numeric	Total likes on the video
COMMENT_COUNT	Numeric	Total comments
TAGS	String	Video keyword tags
CATEGORY_ID	String	YouTube category
DURATION_ISO8601	String	Video length (ISO format, e.g., PT10M30S)
VIDEO_ID / API_VIDEO_ID	String	YouTube video identifier
PAGE_URL	String	Full YouTube URL
GEO_CITY / GEO_COUNTRY_NAME / GEO_REGION	String	Viewer location
PUBLISHED_AT	Timestamp	When the video was published
EVENT_TIME	Timestamp	When the panelist watched it

Critical notes:

• VIEW_COUNT and LIKE_COUNT are the video's total public metrics, not the panelist's personal view count. Use COUNT of events per user for panelist engagement.

• For creator tier analysis, use VIEW_COUNT as a proxy for creator size.

• TAGS and DESCRIPTION_SNIPPET are valuable for content topic classification.

8. REDDIT_POSTS_FLAT_DYM — Reddit Post & Comment Viewing

Parsed from Reddit GraphQL API traffic. Captures posts and comments that panelists viewed, including full post metadata, subreddit context, and engagement metrics.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
OPERATION_TYPE	String	GraphQL operation: SubredditFeedElements, PostDetailQuery, CommentsListingQuery, CommunityAboutQuery, ProfileOverviewFeed
SUBREDDIT_NAME	String	Subreddit (e.g., "AskReddit", "nba", "SkincareAddiction")
POST_TITLE	String	Full post title text
POST_ID	String	Reddit post identifier
AUTHOR_NAME	String	Post/comment author username
SCORE	Numeric	Upvotes minus downvotes
COMMENT_COUNT	Numeric	Number of comments on the post
SUBREDDIT_SUBSCRIBERS	Numeric	Subreddit subscriber count at time of viewing
FLAIR_TEXT	String	Post flair (topic/category tag)
IS_NSFW	Boolean	Whether the post is marked NSFW
CREATED_UTC	Timestamp	When the post was originally created
PAGE_URL	String	Reddit URL
EVENT_TIME	Timestamp	When the panelist viewed it

Critical notes:

• OPERATION_TYPE tells you what the user was doing: browsing a subreddit feed, reading a post's comments, viewing a community about page, or browsing a user profile.

• SUBREDDIT_NAME is the key interest-mapping field. Group by it to understand what communities panelists engage with.

• SCORE and COMMENT_COUNT are the post's public engagement metrics, not the panelist's interaction.

• Use SUBREDDIT_SUBSCRIBERS to understand community size context.

9. REDDIT_ACTIONS_FLAT_DYM — Reddit User Actions

Parsed from Reddit GraphQL API traffic. Captures active user behaviors: voting, saving, joining communities, hiding posts, and managing subscriptions.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
OPERATION_TYPE	String	GraphQL operation: Vote, Save, JoinSubreddit, UpdateNotificationPreferences, HidePost, MarkAllNotificationsRead, etc.
ACTION_DETAILS	String	Specifics of the action (e.g., vote direction, subreddit joined)
POST_ID	String	Target post identifier (for votes, saves, hides)
SUBREDDIT_NAME	String	Target subreddit (for joins, subscriptions)
PAGE_URL	String	Reddit URL
EVENT_TIME	Timestamp	When the action happened

Critical notes:

• This captures active engagement, not passive viewing. Votes, saves, and community joins are high-signal indicators of genuine interest.

• Combine with REDDIT_POSTS_FLAT_DYM to understand the view-to-action conversion: how many posts viewed vs. voted on vs. saved.

• JoinSubreddit actions indicate strong community affinity.

10. NETWORK_TRAFFIC_EVENTS_DYM — Network Traffic

Raw domain-level traffic. Shows which apps and websites panelists access.

Column	Type	Description
USER_IDENTIFIER	FK	Links to AGENT_SYNC.USER_ID (note: different column name)
SNI	String	Server Name Indication — the domain being accessed

Critical notes:

• This is the most granular but least structured table. It shows raw domain hits.

• Use SNI with ILIKE to detect app/platform usage: SNI ILIKE '%instagram%', SNI ILIKE '%tiktok%'

• Best for: understanding which platforms users are on before/after other events, mapping media exposure pathways.

• High volume table — always filter by time and user segments.

11. NEWS_EVENTS_FLAT_DYM — News Interactions

Captures news article consumption and engagement.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
ARTICLE_URL / PAGE_URL	String	Article URL
NEWS_OUTLET	String	Publishing organization
AUTHOR	String	Article author
CONTENT_SUMMARY	String	Article summary text
KEYWORDS	String	Topic keywords
NEWS_TYPE	String	Content category
COMMENTS_COUNT / LIKES	Numeric	Engagement metrics
LANGUAGE	String	Content language
GEO_*	Various	Geographic location fields
PUBLICATION_DATE / EVENT_TIME	Timestamp	When published / when read

12. DONATIONS_EVENTS_DYM — Donation Events

Tracks donation-related activity on digital platforms.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
EVENT_KEY	String	Unique donation identifier
EVENT_TYPE	String	Type of engagement
PLATFORM	String	Platform (tiktok, instagram)
DATA	JSON	Raw event details
DONATION_EVENT_TIME / EVENT_TIME	Timestamp	Timestamps

13. BETTING_EVENTS_FLAT_DYM — Betting Activity

Captures sports betting and gambling platform interactions.

Column	Type	Description
USER_ID	FK	Links to AGENT_SYNC
ACTION	String	User action type
BET_TYPE	String	Type of bet
PLATFORM	String	Betting platform
EVENT_NAME / EVENT_CATEGORY	String	Event details
PARTICIPANTS	String	Event participants
SELECTION	String	Selected outcome
ODDS	Numeric	Betting odds
STAKE_AMOUNT / POTENTIAL_PAYOUT	Numeric	Financial amounts
IS_LIVE_EVENT	Boolean	Live event indicator
MARKET_CATEGORY	String	Market classification
GEO_*	Various	Geographic location fields
EVENT_DATE / EVENT_TIME	Timestamp	Timestamps

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Query Translation Patterns

The core skill is translating business questions into multi-step, objective queries. Below are common patterns.

Pattern 1: "What's popular?" → Aggregate Event Counts

Business question: "What are the most popular RTD brands?"

Wrong approach: Asking the MCP "what's popular" directly.

Right approach — multi-step:

1. Identify keywords first: Ask: "What distinct values of BRAND or PRODUCT_NAME in PRODUCT_EVENTS_FLAT_DYM contain keywords like 'seltzer', 'cocktail', 'hard', 'white claw', 'truly', 'high noon'?"

2. Aggregate: "Count distinct USER_IDs and total events grouped by BRAND for products matching those keywords, ordered by count descending."

3. Segment by action: "Break this down by ACTION (viewed, added_to_cart, purchased) to see funnel performance."

Pattern 2: "What's the average price?" → Multi-Step Keyword + Aggregation

Business question: "What's the average price of RTD drinks?"

Wrong approach: Asking for "average RTD price" directly — the MCP has no RTD category tag.

Right approach — multi-step:

1. Define the universe: "Find all distinct products in PRODUCT_EVENTS_FLAT_DYM where PRODUCT_NAME or DESCRIPTION ILIKE any of: '%hard seltzer%', '%canned cocktail%', '%RTD%', '%ready to drink%', '%white claw%', '%truly%', '%high noon%', '%cutwater%'"

2. Extract prices: "For those products, what are the distinct PRODUCT_NAME and PRICE combinations?"

3. Calculate: "What is the average, median, min, and max PRICE across those products?"

Pattern 3: "Where do people discover X?" → Channel/Platform Analysis

Business question: "Which channels drive RTD discovery?"

Right approach:

1. Search behavior: "In SEARCH_EVENTS_FLAT_DYM, how many users searched queries containing RTD brand names? Group by SEARCH_ENGINE."

2. Content exposure: "In YOUTUBE_EVENTS_FLAT_DYM, how many users watched videos with RTD-related keywords in SNIPPET_TITLE or TAGS?"

3. Network traffic: "In NETWORK_TRAFFIC_EVENTS_DYM, which SNI domains (instacart, doordash, target, amazon, etc.) are most visited by users who also have RTD product events?"

4. Product source: "In PRODUCT_EVENTS_FLAT_DYM, extract the domain from PAGE_URL for RTD products. Group by domain to see which retailers."

Pattern 4: "Does X lead to Y?" → Temporal Sequence Analysis

Business question: "Does watching creator content lead to purchases?"

Right approach — event sequencing:

1. Define exposure group: "Find USER_IDs in YOUTUBE_EVENTS_FLAT_DYM who watched videos with beverage/RTD keywords in the title between [date range]."

2. Define action group: "Find USER_IDs in PRODUCT_EVENTS_FLAT_DYM who had ACTION = 'purchased' for beverage products in the same or subsequent time window."

3. Compare: "What % of the exposure group also appears in the action group? What % of users NOT in the exposure group appear in the action group?"

4. Time sequence: "For users in both groups, what was the time gap between YouTube event and purchase event?"

Pattern 5: "Who is our audience?" → Demographic Segmentation

Business question: "Who are the people engaging with Japanese content?"

Right approach:

1. Define the behavior: "Find USER_IDs from SEARCH_EVENTS where QUERY ILIKE any of '%japan%', '%anime%', '%ramen%', '%sushi%', '%sake%', '%matcha%'"

2. Also check YouTube: "Add USER_IDs from YOUTUBE_EVENTS where SNIPPET_TITLE or TAGS contain those same keywords."

3. Join to demographics: "For those USER_IDs, pull GENDER, YEAR_OF_BIRTH, HOUSEHOLD_INCOME, EDUCATION_LEVEL, EMPLOYMENT_STATUS, FULL_ADDRESS from AGENT_SYNC."

4. Aggregate: "What's the distribution of each demographic field?"

Pattern 6: "What content performs best?" → Engagement Metrics

Business question: "Which types of creator content drive the most engagement?"

Right approach:

1. Categorize content: "In YOUTUBE_EVENTS_FLAT_DYM, classify videos by keywords in SNIPPET_TITLE: 'tasting', 'tutorial', 'review', 'challenge', 'reaction', 'reveal'"

2. Per-category metrics: "For each category, count: distinct users, total events, average VIEW_COUNT, average LIKE_COUNT, average COMMENT_COUNT"

3. Engagement rate: "Calculate engagement per participant (total events / distinct users) for each category"

4. Cross-reference downstream: "For users who watched each content type, how many also appear in PRODUCT_EVENTS with beverage-related actions?"

Pattern 7: "Is there a trend?" → Time-Series Analysis

Business question: "Is awareness for brand X growing week over week?"

Right approach:

1. Define the signal: "Count events per week in SEARCH_EVENTS where QUERY ILIKE '%brand_name%'"

2. Add YouTube: "Count events per week in YOUTUBE_EVENTS where SNIPPET_TITLE or DESCRIPTION ILIKE '%brand_name%'"

3. Add product interactions: "Count events per week in PRODUCT_EVENTS where PRODUCT_NAME or BRAND ILIKE '%brand_name%'"

4. Compare periods: "Show week-over-week change for each signal."

Important caveat: Watch for false positives. Brand names like "Toki" or "Hibiki" may match unrelated content (anime characters, rappers). Always validate by checking the actual content context.

Pattern 8: "What are people buying on Amazon?" → Amazon Shopping Funnel

Business question: "What products are trending on Amazon in our category?"

Right approach:

1. Search intent: "In AMAZON_SHOPPING_FLAT_DYM, what are the top SEARCH_KEYWORDS by count where EVENT_TYPE = 'SEARCH'?"

2. Product discovery: "For PRODUCT_VIEW events, what are the most viewed PRODUCT_TITLE values? What are the average PRICE values by brand?"

3. Ad exposure: "For SPONSORED_AD events, which MERCHANT_NAME values appear most? What CAMPAIGN_IDs are active?"

4. Funnel conversion: "How many users have SEARCH events who also have FUNNEL events with FUNNEL_STEP = 'cart' or 'checkout'?"

5. Cross-reference: "Join to AGENT_SYNC to segment Amazon shoppers by demographics."

Pattern 9: "What DTC brands are people buying?" → Shopify Cross-Store Analysis

Business question: "Which Shopify DTC brands have the highest engagement?"

Right approach:

1. Store popularity: "In SHOPIFY_SHOPPING_FLAT_DYM, count events grouped by STORE_DOMAIN, ordered by total events descending."

2. Product-level detail: "For PRODUCT_VIEW events, show STORE_DOMAIN, PRODUCT_TITLE, PRODUCT_VENDOR, PRICE, and COMPARE_AT_PRICE. Group by vendor to find top brands."

3. Discount analysis: "Find products where COMPARE_AT_PRICE > PRICE — these are on sale. What % of product views are discounted?"

4. Full funnel: "Count events by EVENT_TYPE (PRODUCT_VIEW → ADD_TO_CART → CHECKOUT → PAYMENT) per store to see conversion signals."

5. Search behavior: "What SEARCH_QUERY values appear in SEARCH events? These show what people look for within Shopify stores."

Pattern 10: "What are people ordering for delivery?" → Food Delivery Analysis

Business question: "What restaurants and food items are most popular on delivery apps?"

Right approach:

1. Restaurant popularity: "In DELIVERY_APPS_FLAT_DYM, for STORE_VIEW events on DOORDASH, what are the top STORE_NAME values by count? What are their average STORE_RATING and DELIVERY_FEE?"

2. Order details: "For ORDER_PLACED events on DOORDASH, what are the average ORDER_TOTAL and TIP_AMOUNT? Group by PAYMENT_METHOD."

3. Item-level data: "For ORDER_HISTORY events on UBEREATS, what ITEM_NAME values appear most frequently? What is the average ITEM_PRICE?"

4. Cross-platform: "Compare DOORDASH vs UBEREATS by total events, distinct users, and average order totals."

5. Geography: "For DOORDASH STORE_VIEW events, use STORE_LAT/STORE_LNG and STORE_ADDRESS for geographic analysis of which areas have the most delivery activity."

6. DashPass analysis: "What % of STORE_VIEW events have IS_SUBSCRIPTION_PARTNER = TRUE? Do DashPass stores have higher engagement?"

Pattern 11: "What's happening on Reddit?" → Reddit Community Analysis

Business question: "What topics are trending in our category on Reddit?"

Right approach:

1. Subreddit mapping: "In REDDIT_POSTS_FLAT_DYM, count events grouped by SUBREDDIT_NAME, ordered by count descending. This shows which communities panelists are browsing."

2. Content analysis: "For specific subreddits, what POST_TITLE values appear? Use ILIKE to filter titles by category keywords."

3. Engagement signals: "In REDDIT_ACTIONS_FLAT_DYM, count actions grouped by OPERATION_TYPE (Vote, Save, JoinSubreddit) to see what drives active engagement."

4. View-to-action conversion: "Compare users who viewed posts (REDDIT_POSTS) vs. users who voted/saved (REDDIT_ACTIONS) to measure engagement depth."

5. Community growth: "Track JoinSubreddit actions over time to see which communities are gaining panelist members."

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Common Pitfalls

❌ Asking interpretive questions directly

• Bad: "Why do consumers prefer White Claw?"

• Good: "How many distinct users have events for White Claw products broken down by ACTION type? What QUERY terms do users search before or after White Claw product events?"

❌ Assuming category labels exist

• Bad: "Show me all RTD beverage data"

• Good: "Show me products where PRODUCT_NAME or DESCRIPTION contains keywords: [list of RTD brands and category terms]"

❌ Confusing video-level metrics with user-level metrics

• Bad: "How many views did RTD content get?" (this gives you public YouTube view counts)

• Good: "How many distinct panelists watched videos with RTD-related titles?" (this gives you panel reach)

❌ Forgetting to validate keyword matches

• Bad: "How many people searched for 'Yamazaki'?" (could be the whisky OR a Japanese actor)

• Good: "How many people searched for 'Yamazaki'? Show me the top 10 QUERY values to verify context."

❌ Single-step queries for complex questions

• Bad: "What's the conversion rate from creator content to purchase?"

• Good: Break into 3 steps: define exposure group → define conversion group → calculate overlap percentage.

❌ Confusing platform-specific pricing formats

• Bad: Assuming all prices are in dollars

• Good: DoorDash and Uber Eats store prices in cents (already converted to dollars in the table). Shopify CART_VIEW prices are in cents (divided by 100 in the table). Amazon prices are in dollars. Always check the table documentation.

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Quick Reference: Question → Table Mapping

Question	Table	Key Columns
What people search for	SEARCH_EVENTS_FLAT_DYM	QUERY, SEARCH_ENGINE
What people buy / view / cart	PRODUCT_EVENTS_FLAT_DYM	ACTION, PRODUCT_NAME, BRAND, PRICE, PAGE_URL
What people search on Amazon	AMAZON_SHOPPING_FLAT_DYM	SEARCH_KEYWORDS, EVENT_TYPE, ASIN
What people buy on Amazon	AMAZON_SHOPPING_FLAT_DYM	PRODUCT_TITLE, PRICE, ASIN, FUNNEL_STEP
What Amazon ads are shown	AMAZON_SHOPPING_FLAT_DYM	MERCHANT_NAME, CAMPAIGN_ID, EVENT_TYPE='SPONSORED_AD'
What DTC brands people shop	SHOPIFY_SHOPPING_FLAT_DYM	STORE_DOMAIN, PRODUCT_TITLE, PRODUCT_VENDOR, PRICE
What Shopify stores people visit	SHOPIFY_SHOPPING_FLAT_DYM	STORE_DOMAIN, EVENT_TYPE, COLLECTION_HANDLE
What products are on sale	SHOPIFY_SHOPPING_FLAT_DYM	PRICE, COMPARE_AT_PRICE, PRODUCT_VENDOR
What restaurants people browse	DELIVERY_APPS_FLAT_DYM	STORE_NAME, STORE_RATING, PLATFORM='DOORDASH'
What food people order	DELIVERY_APPS_FLAT_DYM	ITEM_NAME, ITEM_PRICE, ORDER_TOTAL, PLATFORM
DoorDash vs Uber Eats usage	DELIVERY_APPS_FLAT_DYM	PLATFORM, EVENT_TYPE, ORDER_TOTAL
Delivery tipping behavior	DELIVERY_APPS_FLAT_DYM	TIP_AMOUNT, ORDER_TOTAL, PAYMENT_METHOD
What videos people watch	YOUTUBE_EVENTS_FLAT_DYM	SNIPPET_TITLE, CHANNEL_TITLE, VIEW_COUNT
What apps/sites people use	NETWORK_TRAFFIC_EVENTS_DYM	SNI
What news people read	NEWS_EVENTS_FLAT_DYM	KEYWORDS, NEWS_OUTLET, CONTENT_SUMMARY
What subreddits people browse	REDDIT_POSTS_FLAT_DYM	SUBREDDIT_NAME, POST_TITLE, SCORE
What Reddit actions people take	REDDIT_ACTIONS_FLAT_DYM	OPERATION_TYPE, ACTION_DETAILS, SUBREDDIT_NAME
Who the users are	AGENT_SYNC	GENDER, YEAR_OF_BIRTH, HOUSEHOLD_INCOME, FULL_ADDRESS
Donation behavior	DONATIONS_EVENTS_DYM	PLATFORM, EVENT_TYPE
Betting behavior	BETTING_EVENTS_FLAT_DYM	PLATFORM, BET_TYPE, EVENT_NAME

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Multi-Table Query Templates

Template A: Content Exposure → Purchase Funnel

Find users exposed to content → check if they purchased → compare to unexposed users.

Template B: Platform Usage Before Purchase

Use NETWORK_TRAFFIC to see what platforms users visited before product interactions.

Template C: Category Interest Sizing

Combine SEARCH, YOUTUBE, PRODUCT, and REDDIT tables to size total interest in a category.

Template D: Price Point Analysis

Use AMAZON_SHOPPING (PRICE), SHOPIFY_SHOPPING (PRICE, COMPARE_AT_PRICE), and PRODUCT_EVENTS (PRICE) for cross-platform price comparison.

Template E: Cross-Platform Shopping Journey

Trace users from SEARCH_EVENTS (discovery) → AMAZON_SHOPPING or SHOPIFY_SHOPPING (product browsing) → DELIVERY_APPS (food ordering) to understand daily commerce patterns.

Template F: Restaurant & Food Discovery

Combine SEARCH_EVENTS (food-related queries) → YOUTUBE_EVENTS (food content) → DELIVERY_APPS_FLAT_DYM (actual orders) to measure content-to-order conversion.

Template G: Reddit-to-Purchase Pipeline

Track REDDIT_POSTS (community browsing) → REDDIT_ACTIONS (upvotes/saves on product recommendations) → PRODUCT_EVENTS or SHOPIFY_SHOPPING (actual purchases) to measure Reddit's influence on buying decisions.

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Tips for Power Users

• Always start by identifying which table(s) contain the signal you need, using the Quick Reference table above.

• For brand analysis, search across multiple tables: PRODUCT_EVENTS (purchases), SEARCH_EVENTS (intent), YOUTUBE_EVENTS (content), AMAZON_SHOPPING (Amazon-specific), SHOPIFY_SHOPPING (DTC), and REDDIT_POSTS (community discussion).

• Use AGENT_SYNC joins to segment any behavior by demographics.

• For time-series analysis, use DATE_TRUNC('week', EVENT_TIME) to aggregate events into weekly bins.

• For funnel analysis across platforms, use USER_ID to trace the same person across tables.

• For Shopify discount analysis, compare PRICE vs COMPARE_AT_PRICE — when compare_at is higher, the product is on sale.

• For delivery app analysis, DoorDash has the richest store-level data (ratings, fees, addresses, geolocation) while Uber Eats has the richest item-level data (individual menu items with prices from order history).