> ## Documentation Index
> Fetch the complete documentation index at: https://docs.innkeepr.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# 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.
**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
**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
**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
**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`, p`urchase_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.
**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
**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
**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
**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
**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.
**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
**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
**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
**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
**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.
**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%
**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
**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
**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](/connections/spec-track)
* Objective configuration guide for defining business metrics
* [Destination setup guides for platform-specific signal activation](/guides/signals-and-activation/signal-overview)
* Signal quality monitoring for validating signal performance