Use Cases Reference

Documentation Index

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This reference guide provides technical specifications for each Innkeepr use case, including required tracking events, objective definitions, signal types, and destination configurations. Use this guide to validate your implementation and ensure your signal optimization infrastructure is configured correctly for your business objective.

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Use Cases by business goal

Your business goal determines which signal strategies and use cases Innkeepr configures during setup. This table maps each business goal to its corresponding use cases:

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Use case specifications

Each use case section below details the required configuration for signal generation:

• Required events: Tracking calls needed to power the causal engine
• Objective definition: How to configure the business metric Innkeepr optimizes for
• Signal types: Which audience and conversion signals are generated
• Destinations: Platform connections where signals activate
• Expected data requirements: Minimum conversion volume and traffic thresholds

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Improve new customer acquisition

These use cases generate signals optimized for growing your customer base with high-quality, incremental new customers.

Build seeds based on predicted ad impact

Objective: Identify users most likely to deliver incremental response to ad campaigns and use them as seed audiences for lookalike expansion.Required events:

• page - Track page views with url, path, referrer
• track: product_viewed - Include product_id, category, price
• track: checkout_started - Include cart_value, product_ids
• track: order_completed - Include order_value, order_id, is_new_customer
• identify - Include user_id, anonymous_id

Objective configuration:

• Primary metric: Purchase probability or conversion rate
• Segmentation: Filter by is_new_customer: true
• Lookback window: 7-30 days

Signal types generated:

• Audience signals: High-incrementality seed audiences (top 10-20% predicted responders)
• Syncs as Custom Audiences (Meta), Customer Match (Google), Custom Audiences (TikTok)

Destinations:

• Google Ads (Customer Match)
• Meta Ads (Custom Audiences)
• TikTok Ads (Custom Audiences)
• Amazon Ads (Audience segments)

Minimum requirements:

• 500+ conversions per month
• 10,000+ monthly sessions
• At least one connected media platform with conversion tracking

Exclude recent purchasers or converters

Objective: Suppress users who recently converted to improve seed purity and reduce wasted prospecting spend.Required events:

• track: order_completed - Include order_id, order_value, timestamp
• identify - Include user_id, email, phone (for platform matching)

Objective configuration:

• Exclusion window: 7-90 days (configurable)
• Event trigger: order_completed or custom conversion event
• Refresh frequency: Daily

Signal types generated:

• Exclusion signals: Audiences of recent converters
• Syncs as exclusion lists to prospecting campaigns

Destinations:

• Google Ads (exclusion lists)
• Meta Ads (exclusion audiences)
• TikTok Ads (exclusion audiences)

Minimum requirements:

• 100+ conversions per month
• Email or phone tracking enabled for platform matching

Tune conversion values based on predicted ad impact

Objective: Weight conversion values by incremental contribution so bidding algorithms optimize for true causal impact.Required events:

• track: order_completed - Include order_value, order_id, user_id
• page - Include UTM parameters, referrer, landing_page
• identify - Include user_id, anonymous_id

Objective configuration:

• Primary metric: Revenue or order value
• Attribution: First-touch, 7-day window
• Value adjustment: Multiplier based on treatment effect (0.5x - 2.0x typical range)

Signal types generated:

• Conversion signals: Incrementality-weighted conversion values
• Replaces standard conversion events with adjusted values
• Real-time sync on each conversion

Destinations:

• Google Ads (conversion value adjustments)
• Meta Ads (conversion value optimization)
• TikTok Ads (value-based optimization)

Minimum requirements:

• 300+ conversions per month
• Revenue tracking implemented
• UTM parameters or referrer tracking enabled

Predict signup likelihoods to grow user base

Objective: Model signup intent from behavioral signals to generate predictive seed audiences for user acquisition.Required events:

• page - Include url, path, time_on_page
• track: form_started - Include form_type, timestamp
• track: signup_completed - Include user_id, signup_method
• Session-level traits: page_depth, session_duration, source

Objective configuration:

• Primary metric: Signup probability
• Features: Session depth, engagement time, page sequence, referrer
• Model type: Logistic regression or gradient boosting

Signal types generated:

• Audience signals: High-signup-propensity users (top 15-25%)
• Updates daily based on recent behavioral patterns

Destinations:

• Google Ads (Customer Match for signup campaigns)
• Meta Ads (signup-focused audiences)
• TikTok Ads (signup campaigns)

Minimum requirements:

• 200+ signups per month
• Session tracking implemented
• At least 5,000 monthly anonymous sessions

Identify high-value customers

Objective: Build seed audiences from high-LTV or low-churn customer segments for quality-focused acquisition.Required events:

• track: order_completed - Include order_value, order_id, user_id
• identify - Include user_id, lifetime_value, order_count
• Calculated traits: ltv_90d, purchase_frequency, avg_order_value

Objective configuration:

• Primary metric: Customer lifetime value (CLV) or predicted LTV
• Segmentation: Top 20-30% by LTV or retention probability
• Refresh: Weekly or as LTV calculations update

Signal types generated:

• Audience signals: High-LTV customer seeds
• Exclusion signals: Low-LTV or high-churn segments (optional)
• Conversion signals: LTV-weighted conversion values (optional)

Destinations:

• Google Ads (Value-based lookalikes)
• Meta Ads (Value-based Custom Audiences)
• TikTok Ads (High-value lookalikes)

Minimum requirements:

• 500+ customers with repeat purchase data
• LTV calculation or lifetime revenue tracking
• 90+ day historical data

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Suppress spend on unlikely converters

These use cases generate exclusion signals that prevent wasted spend on low-incrementality traffic.

Exclude low-LTV visitors

Objective: Identify and suppress visitors with low predicted monetization potential before retargeting budget is spent.Required events:

• page - Include url, referrer, session_id
• track: product_viewed - Include product_id, price, category
• identify - Include user_id, anonymous_id
• Session traits: traffic_source, device_type, geo

Objective configuration:

• Primary metric: Predicted LTV or purchase probability
• Exclusion threshold: Bottom 20-40% of predicted LTV distribution
• Refresh frequency: Daily

Signal types generated:

• Exclusion signals: Low-LTV visitor segments
• Syncs as exclusion audiences to retargeting campaigns

Destinations:

• Google Ads (exclusion lists for remarketing)
• Meta Ads (exclusion audiences)
• Criteo (exclusion segments)

Minimum requirements:

• 1,000+ monthly sessions
• Product view or engagement tracking
• At least 200 conversions for model training

Pre-qualify traffic before retargeting

Objective: Filter retargeting audiences based on session quality signals to focus spend on high-intent visitors.Required events:

• page - Include time_on_page, bounce, session_depth
• track: product_viewed - Include view_count, time_spent
• Session-level traits: pages_per_session, session_duration, scroll_depth

Objective configuration:

• Qualification criteria: Minimum session depth (2+ pages), time on site (30+ seconds), or engagement events
• Exclusion logic: Suppress sessions below quality thresholds
• Refresh: Real-time or daily

Signal types generated:

• Exclusion signals: Low-quality session audiences
• Audience signals: High-intent qualified visitors (optional)

Destinations:

• Google Ads (remarketing exclusions)
• Meta Ads (retargeting exclusions)
• TikTok Ads (retargeting filters)

Minimum requirements:

• 5,000+ monthly sessions
• Session depth and time tracking enabled

Suppress low-propensity app installs

Objective: Exclude users with low predicted install likelihood from app install campaigns.Required events:

• track: app_install - Include install_source, user_id
• page (app landing page) - Include referrer, device_type, os
• Pre-install engagement: track: video_played, track: cta_clicked

Objective configuration:

• Primary metric: App install probability
• Features: Device type, OS, referrer, landing page engagement
• Exclusion threshold: Bottom 30-50% predicted probability

Signal types generated:

• Exclusion signals: Low-install-propensity users
• Audience signals: High-propensity install audiences (optional)

Destinations:

• Google Ads (app install campaigns)
• Meta Ads (app install exclusions)
• TikTok Ads (app install campaigns)

Minimum requirements:

• 200+ app installs per month
• App event tracking implemented
• Landing page or pre-install engagement data

Filter cart abandoners unlikely to convert

Objective: Segment cart abandoners by recovery probability and suppress those unlikely to complete purchase.Required events:

• track: checkout_started - Include cart_value, product_ids, timestamp
• track: order_completed - Include order_id, order_value
• Session context: time_in_checkout, payment_method_added

Objective configuration:

• Primary metric: Cart recovery probability
• Features: Cart value, time spent in checkout, product category, abandonment count
• Exclusion threshold: Bottom 40-60% recovery probability

Signal types generated:

• Exclusion signals: Low-recovery cart abandoners
• Audience signals: High-recovery abandoners for prioritized retargeting (optional)

Destinations:

• Google Ads (cart abandonment exclusions)
• Meta Ads (dynamic retargeting exclusions)
• Klaviyo (email suppression lists)

Minimum requirements:

• 300+ cart abandonment events per month
• Order completion tracking
• 30+ day historical abandonment data

Suppress low signup intent

Objective: Remove users with weak behavioral indicators from signup-focused campaigns.Required events:

• page (signup page) - Include time_on_page, bounce
• track: form_started - Include form_type, abandonment
• track: signup_completed - Include signup_method
• Session traits: referrer, landing_page, session_depth

Objective configuration:

• Primary metric: Signup probability
• Exclusion criteria: Low engagement (quick bounce, no form interaction)
• Threshold: Bottom 30-50% predicted signup likelihood

Signal types generated:

• Exclusion signals: Low-signup-intent visitors
• Updates in real-time or daily batches

Destinations:

• Google Ads (signup campaign exclusions)
• Meta Ads (lead gen exclusions)
• TikTok Ads (signup campaign filters)

Minimum requirements:

• 200+ signups per month
• Form tracking and signup page analytics
• 5,000+ monthly sessions

Suppress churn-prone free users

Objective: Prevent remarketing to users showing early churn signals during onboarding or trial periods.Required events:

• track: trial_started - Include user_id, trial_type, timestamp
• track: feature_used - Include feature_name, usage_count
• identify - Include user engagement traits: days_active, feature_adoption
• track: trial_ended or track: subscription_cancelled

Objective configuration:

• Primary metric: Churn probability during trial or onboarding
• Features: Feature adoption, days active, engagement frequency
• Exclusion threshold: Top 30-50% churn risk

Signal types generated:

• Exclusion signals: High-churn-risk trial users
• Prevents upsell or paid conversion spend on likely churners

Destinations:

• Google Ads (trial conversion exclusions)
• Meta Ads (subscription campaign exclusions)
• Email platforms (suppression lists)

Minimum requirements:

• 100+ trial starts per month
• Feature usage tracking
• Trial outcome data (conversion or churn)

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Boost retention, upsell, and cross-sell

These use cases optimize signals for maximizing value from existing customers through repeat purchases and product expansion.

Target customers with next-best offer

Objective: Use purchase history and product affinity to identify cross-sell or upsell opportunities and generate personalized targeting signals.Required events:

• track: order_completed - Include product_ids, categories, order_value
• track: product_viewed - Include product_id, category, timestamp
• identify - Include purchase history traits: last_purchase_date, favorite_category, total_orders

Objective configuration:

• Primary metric: Product affinity score or next purchase probability
• Recommendation logic: Complementary products, bundle opportunities, category expansion
• Refresh: Weekly or after each purchase event

Signal types generated:

• Audience signals: Product-specific cross-sell or upsell audiences
• Segmented by next-best-offer category or product

Destinations:

• Google Ads (cross-sell campaigns)
• Meta Ads (dynamic product audiences)
• Email platforms (personalized recommendations)

Minimum requirements:

• 500+ customers with purchase history
• Product catalog with categories or SKU relationships
• Multiple product categories or SKUs

Accelerate first purchase

Objective: Optimize signals for users in early journey stages to drive faster time-to-first-purchase.Required events:

• page - Include landing page, session sequence
• track: product_viewed - Include view count, time spent
• track: cart_added - Include product_id, timestamp
• track: order_completed - Include days_to_purchase, is_new_customer

Objective configuration:

• Primary metric: Time to first purchase or purchase probability
• Segmentation: New visitors or users without prior purchases
• Optimization: Shortest time-to-conversion predictions

Signal types generated:

• Audience signals: High-first-purchase-propensity new visitors
• Conversion signals: Weighted by speed to first conversion (optional)

Destinations:

• Google Ads (new customer acquisition)
• Meta Ads (first-purchase campaigns)
• TikTok Ads (conversion campaigns)

Minimum requirements:

• 300+ first-time purchases per month
• New vs. returning customer tracking
• Session and product view data

Promote first-time offers

Objective: Generate audience signals for new users most likely to respond to incentive offers like discounts or free shipping.Required events:

• page - Include landing_page, referrer, session_id
• track: coupon_viewed or track: offer_displayed
• track: order_completed - Include coupon_used, discount_amount, is_new_customer
• identify - Include is_new_customer, first_visit_date

Objective configuration:

• Primary metric: Offer response probability or discount sensitivity
• Segmentation: New users only (first 1-7 days)
• Model type: Propensity to convert with incentive

Signal types generated:

• Audience signals: Incentive-responsive new users
• Prioritized for first-purchase discount campaigns

Destinations:

• Google Ads (promotion campaigns)
• Meta Ads (offer-based acquisition)
• Email platforms (welcome discount flows)

Minimum requirements:

• 200+ new customer conversions per month
• Discount or coupon tracking
• New vs. returning segmentation

Convert browsers to buyers

Objective: Identify high-intent product viewers and create signals that prioritize them in retargeting campaigns.Required events:

• track: product_viewed - Include product_id, price, time_spent
• track: cart_added - Include product_id (optional, for intent scoring)
• track: order_completed - Include product_ids, order_value
• Session traits: view_count, category_views, price_range_viewed

Objective configuration:

• Primary metric: Purchase probability for product viewers
• Features: View frequency, time on product page, price range, category depth
• Threshold: Top 20-40% purchase probability

Signal types generated:

• Audience signals: High-intent browser segments
• Can be product-specific or category-specific

Destinations:

• Google Ads (dynamic remarketing)
• Meta Ads (dynamic product ads)
• Criteo (product retargeting)

Minimum requirements:

• 1,000+ product views per month
• 200+ conversions per month
• Product catalog connected

Recover abandoned carts

Objective: Detect and signal users who abandoned carts with high recovery probability for urgency-based messaging.Required events:

• track: checkout_started - Include cart_value, product_ids, timestamp
• track: order_completed - Include order_id, order_value
• identify - Include email, phone, user_id (for matching)
• Session context: abandonment_reason (if tracked), payment_method_added

Objective configuration:

• Primary metric: Cart recovery probability
• Features: Cart value, abandonment timing, product type, prior abandonment behavior
• Segmentation: High-recovery probability (top 30-50%)

Signal types generated:

• Audience signals: High-recovery abandoners for prioritized retargeting
• Exclusion signals: Low-recovery abandoners to suppress spend (optional)

Destinations:

• Google Ads (cart recovery campaigns)
• Meta Ads (dynamic cart retargeting)
• Klaviyo (abandoned cart emails)

Minimum requirements:

• 300+ cart abandonment events per month
• Email or user ID tracking for matching
• Order completion tracking

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Increase long-term profitability

These use cases optimize signals for sustainable, profitable growth focused on customer lifetime value and margin contribution.

Optimize for customer lifetime value

Objective: Generate signals weighted by predicted CLV to guide platforms toward customers who deliver the most long-term value.Required events:

• track: order_completed - Include order_value, product_ids, user_id
• identify - Include calculated traits: lifetime_value, order_count, avg_order_value, days_since_first_purchase
• track: subscription_started or recurring revenue events (if applicable)

Objective configuration:

• Primary metric: 90-day or 180-day predicted CLV
• Model inputs: Purchase frequency, AOV, recency, product mix, cohort behavior
• Refresh: Weekly or as new transaction data arrives

Signal types generated:

• Audience signals: High-CLV prospect segments (for lookalikes)
• Conversion signals: CLV-weighted conversion values for bidding optimization

Destinations:

• Google Ads (value-based bidding)
• Meta Ads (value optimization)
• TikTok Ads (value-based campaigns)

Minimum requirements:

• 500+ customers with repeat purchase data
• 90+ days of transaction history
• Repeat purchase rate >20%

Weight conversions by incremental margin

Objective: Adjust conversion values based on product margin contribution so bidding algorithms optimize for profitability, not just revenue.Required events:

• track: order_completed - Include order_value, product_ids, order_id
• Product catalog with margin data: product_id, cost, margin_percentage
• identify - Include user_id

Objective configuration:

• Primary metric: Contribution margin or profit per order
• Value calculation: (Revenue - COGS) weighted by incremental effect
• Sync frequency: Real-time on each conversion

Signal types generated:

• Conversion signals: Margin-weighted conversion values
• Replaces revenue-based conversion values with profit-based values

Destinations:

• Google Ads (profit-optimized bidding)
• Meta Ads (margin-based optimization)
• Amazon Ads (profitability signals)

Minimum requirements:

• 300+ conversions per month
• Product margin data available
• Multiple products with varying margins

Suppress low-AOV traffic

Objective: Exclude users predicted to generate low average order values to focus budget on higher-value transactions.Required events:

• track: order_completed - Include order_value, user_id
• track: product_viewed - Include price, category
• Session traits: price_range_viewed, product_count_viewed

Objective configuration:

• Primary metric: Predicted AOV
• Exclusion threshold: Bottom 20-40% of AOV distribution
• Refresh: Daily or weekly

Signal types generated:

• Exclusion signals: Low-AOV visitor segments
• Suppresses from prospecting or retargeting

Destinations:

• Google Ads (AOV-focused exclusions)
• Meta Ads (value campaign exclusions)
• TikTok Ads (exclusion audiences)

Minimum requirements:

• 300+ conversions per month
• Wide AOV distribution (3x+ range between high and low)
• Price or product view tracking

Target high-retention segments

Objective: Build audience signals around users with high predicted retention or repeat purchase probability.Required events:

• track: order_completed - Include order_id, user_id, timestamp
• identify - Include retention traits: days_since_last_purchase, order_count, purchase_frequency
• track: subscription_renewed (if applicable)

Objective configuration:

• Primary metric: Repeat purchase probability or retention likelihood
• Model type: Survival analysis or repeat purchase propensity
• Segmentation: Top 20-30% retention probability

Signal types generated:

• Audience signals: High-retention customer seeds for lookalikes
• Exclusion signals: Low-retention or one-time buyer suppression (optional)

Destinations:

• Google Ads (retention-focused lookalikes)
• Meta Ads (high-LTV audiences)
• Email platforms (loyalty campaigns)

Minimum requirements:

• 500+ customers with purchase history
• 20%+ repeat purchase rate
• 90+ days of transaction data

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Implementation support

For detailed implementation guidance on any use case, refer to:

• Innkeepr tracking specification for event structure and properties
• Objective configuration guide for defining business metrics
• Destination setup guides for platform-specific signal activation
• Signal quality monitoring for validating signal performance