Based on modules currently available in the platform.
1. E-commerce
Context: high-frequency purchases, broad assortment, rich behavioral data (page views, add-to-cart, purchases). This is the standard scenario most Inspire modules were built for.
|
Use Case |
Module |
Example |
|---|---|---|
|
Homepage personalization |
Personalized Product Recommendations |
"For you" section based on the user-item model |
|
PDP complements/alternatives |
Contextual Product Recommendations |
"Frequently bought together" / "You might also like" |
|
Reorder / consumables |
Frequently Bought Products |
"Want to restock your regular coffee beans order?" |
|
Cart/browse recovery |
CDP Behavior Trigger + Event-based Retargeting |
Abandoned cart pop-up, follow-up email after 24h |
|
Customer segmentation for email |
RFM Scorer |
Target loyal customers more frequently / with premium items |
|
Bestsellers without personal data (cold start) |
Item Enricher |
"Trending now" for new or anonymous visitors |
|
Own-brand / margin steering |
Boosting & Business Rules |
Boost own-brand items on PDP, push high-margin products |
Client examples: VerlichtingNL, Akzo Nobel, Prénatal.
2. Travel & Leisure
Context: low repeat-purchase frequency (often once a year, seasonal), but high order value.
|
Use Case |
Module |
Example |
|---|---|---|
|
Upsell during booking |
Contextual Product Recommendations |
Travel insurance, extra luggage, transfer as a complement to a flight booking |
|
Destination recommendations |
Personalized Product Recommendations |
Suggest destinations matching past bookings/browsing behavior |
|
Abandoned booking recovery |
CDP Behavior Trigger |
Flight/hotel viewed but not booked → retargeting email/SMS |
|
Post-trip re-engagement |
Event-based Retargeting |
Recommend next trip based on season/previous destination |
|
Content-driven engagement between bookings |
Attribute Tagger + newsletter |
Inspirational content instead of transactional pressure, since the purchase cycle is long |
Strategy for low-frequency purchasing: shift focus from "more transactions" to session behavior (browsing/add-to-cart as strong intent signals), content/inspiration engagement, and timing (predicting when someone is "ready to travel again"). Measure success via wishlist additions, email engagement, and time-to-next-booking rather than repeat-purchase rate.
3. LIVE — Festivals & Venues
Context: short, intense customer journey around the event itself, with many one-time/anonymous visitors who become "known" only through their ticket purchase.
|
Use Case |
Module |
Example |
|---|---|---|
|
Pre-event upsell |
Contextual / Personalized Recommendations |
Recommend VIP upgrade or camping option based on behavior |
|
Abandoned ticket cart |
CDP Behavior Trigger |
Visitor viewed ticket page but didn't purchase → follow-up workflow |
|
Line-up/content personalization |
Attribute Tagger |
Genre preference → personalized line-up content in app/website |
|
On-site cross-sell |
Personalized Product Recommendations |
Merchandise, side-events during the festival itself |
|
Next-event recommendation |
Event-based Retargeting |
Recommend the next event after the current one, based on genre/behavior |
4. Subscription & Membership
Context: small product assortment (sometimes just one core product), so classic recommendation modules (PPR, Frequently Bought) have little to work with. Personalization here is about customer value and retention, not product selection.
|
Use Case |
Module |
Example |
|---|---|---|
|
Find churning customers |
RFM Scorer |
Predict who is likely to cancel |
|
Upgrade/downgrade triggers |
Attribute Tagger + Behavioral Triggers |
Usage behavior → upsell to a premium tier |
|
Re-engagement on declining usage |
CDP Behavior Trigger |
Low check-ins/logins → win-back campaign before cancellation |
5. B2B / Wholesale
Context: buyers often reorder routinely, and customer segments need to remain strictly separated (e.g. horeca vs. retail buyers) within the same catalog.
|
Use Case |
Module |
Example |
|---|---|---|
|
Reorder recommendations |
Item enricher |
A set of products every customer "should" be buying |
|
Buyer segmentation for campaigns |
RFM Scorer |
Identify at-risk or high-value accounts for targeted follow-up |
|
Cart/browse recovery on B2B portal |
CDP Behavior Trigger |
Product added but order not completed → follow-up |
|
Catalog personalization |
Personalized / Contextual Product Recommendations |
Personalized homepage or complement/substitute suggestions per buyer |
|
Post-order cross-sell |
Event-based Retargeting |
Recommend complementary products after an order is placed |