Use Cases Reference

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.

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:

Business Goal	Use Cases
Improve new customer acquisition	Build seeds based on predicted ad impact Exclude recent purchasers or converters Tune conversion values based on predicted ad impact Predict signup likelihoods to grow user base Identify high-value customers
Suppress spend on unlikely converters	Exclude low-LTV visitors Pre-qualify traffic before retargeting Suppress low-propensity app installs Filter cart abandoners unlikely to convert Suppress low signup intent Suppress churn-prone free users
Boost retention, upsell, and cross-sell	Target customers with next-best offer Accelerate first purchase Promote first-time offers Convert browsers to buyers Recover abandoned carts
Increase long-term profitability	Optimize for customer lifetime value Weight conversions by incremental margin Suppress low-AOV traffic Target high-retention segments

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

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

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

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

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)

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

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

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

Home

Getting Started

Guides

Connections

Use Cases by business goal

Use case specifications

Build seeds based on predicted ad impact

Exclude recent purchasers or converters

Tune conversion values based on predicted ad impact

Identify high-value customers

Exclude low-LTV visitors

Pre-qualify traffic before retargeting

Suppress low-propensity app installs

Filter cart abandoners unlikely to convert

Suppress churn-prone free users

Target customers with next-best offer

Accelerate first purchase

Promote first-time offers

Convert browsers to buyers

Recover abandoned carts

Optimize for customer lifetime value

Weight conversions by incremental margin

Suppress low-AOV traffic

Target high-retention segments

Implementation support

Home

Getting Started

Guides

Connections

​Use Cases by business goal

​Use case specifications

​Build seeds based on predicted ad impact

​Exclude recent purchasers or converters

​Tune conversion values based on predicted ad impact

​Predict signup likelihoods to grow user base

​Identify high-value customers

​Exclude low-LTV visitors

​Pre-qualify traffic before retargeting

​Suppress low-propensity app installs

​Filter cart abandoners unlikely to convert

​Suppress low signup intent

​Suppress churn-prone free users

​Target customers with next-best offer

​Accelerate first purchase

​Promote first-time offers

​Convert browsers to buyers

​Recover abandoned carts

​Optimize for customer lifetime value

​Weight conversions by incremental margin

​Suppress low-AOV traffic

​Target high-retention segments

​Implementation support

Use Cases by business goal

Use case specifications

Build seeds based on predicted ad impact

Exclude recent purchasers or converters

Tune conversion values based on predicted ad impact

Predict signup likelihoods to grow user base

Identify high-value customers

Exclude low-LTV visitors

Pre-qualify traffic before retargeting

Suppress low-propensity app installs

Filter cart abandoners unlikely to convert

Suppress low signup intent

Suppress churn-prone free users

Target customers with next-best offer

Accelerate first purchase

Promote first-time offers

Convert browsers to buyers

Recover abandoned carts

Optimize for customer lifetime value

Weight conversions by incremental margin

Suppress low-AOV traffic

Target high-retention segments

Implementation support