EBOOK

99 Personalization Use Cases

What Works, What Doesn’t, and When

Every personalization use case that matters to a streaming operator, mapped on the subscriber lifecycle stage where it belongs and the KPIs it affects.

Written by Renato Bonomini, VP Sales Engineering, ContentWise. Foreword by Paolo Cremonesi, Co-founder and CTO. With Gianluca Leucci, Paolo Romano and Matteo Fabiano.

99Use cases

7Lifecycle stages

225Pages

FreeNo paywall

Twenty-five years ago the hard problem in television was distribution. Today it is attention.

From the foreword by Paolo Cremonesi, Co-founder and CTO, ContentWise by Moviri

WHAT'S INSIDE

The ultimate collection of video streaming use cases

Seven parts, one per stage of the subscriber lifecycle, plus the foundations that set the language and a playbook that turns the rest into a build order. Every chapter opens with the question a product leader actually asks, and answers it in the first paragraph.

The seven stages of the streaming subscriber lifecycle, the question each one answers, and the KPI it moves
Part	Stage	The question it answers	The KPI it moves
01	Acquisition	How do we convert a non-subscriber?	Cost per acquisition
02	Onboarding	Does the new user reach value before they doubt?	Time to first value
03	Conversion	Does the surface turn interest into action?	Conversion to play
04	Engagement	Do they keep finding things worth watching?	Streaming minutes, take-rate
05	Satisfaction	Did the experience build trust, or only minutes?	Return frequency, NPS
06	Monetization	Can we grow value per user without breaking trust?	ARPU, upsell take-rate
07	Retention	Do we keep the subscribers the product can hold?	Churn, LTV

Before the seven parts, the foundations: what personalization actually is as distinct from recommendations, why metadata is what makes any of it work, how to place your own service on the maturity curve, and the sequencing that decides what to build first. After them, the implementation playbook, which turns 99 use cases into an order of operations.

ANATOMY OF A USE CASE CARD

Every use case answers the same six questions

Most lists of personalization features name the feature and stop. Each of the ninety-nine entries tells you where it fits in the subscriber lifecycle, what business case funds it, which KPI it moves, the conditions under which it fails, and what has to exist before you build it.

ALL 99 USE CASES

Which ones are you running?

Nobody had catalogued them, so we did. Once you see all ninety-nine in one place the useful question is not how many you run. It is whether the ones you run map to the correct lifecycle stage, and whether they move your KPIs.

Search

15use cases

The viewer asks. Lean forward, goal-directed.

SEA-001Personalized Search
SEA-002Semantic Search
SEA-003Search by Metadata
SEA-004Fuzzy Search
SEA-005Alias-Aware Search
SEA-006Search Suggestions
SEA-007Top Searches
SEA-013Grouped / Multi-Row Search Results
SEA-008Strategic Search Ranking
SEA-009Conversational Search & Discovery
PLY-003EPG-via-Chat
SEA-010Shadow Catalog (Ghost Search)
SEA-012Augmented Search (Recommendations-in-Search)
AGG-002Federated / Cross-Service Search
LIN-003A-Z Catalog Browse

Discovery

72use cases

The system proposes. Lean backward, and where most personalization earns its keep.

REC-001Top Picks For You
REC-007More Like This
REC-008People Who Enjoyed This Also Enjoyed
REC-004Dynamic Categories
REC-005Hidden Gems
REC-020Visually-Similar Recommendations
REC-014Contextual Recommendations
REC-015In-Session Adaptive Recommendations
REC-017Decision-Budget-Aware Recommendations
REC-019Play Something / Auto-Play
REC-021Swipe Feed / Short-Form Infinite Feed
REC-024Shuffle / Play Random Episode
MOO-002Mood Selector
MOO-003Contextual Mood Triggers
NBA-001Next-Best-Action
EDT-009Last Chance To Watch
EDT-001Hero Banner
EDT-004Algotorial
EDT-003Editorial Layout
EDT-002Personalized Promotions
EDT-014Personalized Upcoming Movies
EDT-005Thematic Collections
EDT-006Box Set Collections
EDT-019Seasonal / Occasion Rails
PAG-001Page Personalization
MSG-001Behavioral / Dynamic-Title Messaging Overlay
EDT-007Recently Added
EDT-008New Releases
EDT-010Most Viewed
EDT-011Trending Now
EDT-012Most Liked
EDT-013Highest Critics Score
EDT-015Trending Entities
EDT-020Popular in Your Region
FBK-002Explicit Ratings (Thumbs / Stars)
SOC-001Social / Friends Recommendations
PRE-001Personalized Imagery / Artwork
PRE-002Dynamic Titles
PRE-003AI Widget Selection
PRE-004Personalized Preview Autoplay
REC-016Session-Intent-Adaptive Homepage
AGG-001Third-Party App Recommendations
AGG-003Cross-Media Recommendations
LIN-001Personalized Channel Discovery
LIN-002Live & Upcoming
LIN-005Personalized Linear For You Channel
LIN-004FAST Channel Auto-Generation
MON-001Top Rental
MON-002Pack Related
MON-003Marketing Engine Promotion Packages
MON-004TVOD / Add-on Upsell Overlay
PLY-001Preroll Optimization
CRM-004Win-Back / Churn-Discount Overlay
CRM-006Cancel-Flow Save Offers
OB-001New User Onboarding
OB-002Personalization Post Ad-Click
MOO-001Mood Board / Taste Profile
MOO-004Clip-Based Mood Inference
SPT-001Fandom-Keyed Sport Pages
SPT-002Live Event Prioritization & Notification
SPT-003Post-Event Cards
KID-001Kids / Parental-Control Onboarding
REC-003Continue Watching
REC-009Next-To-Watch
PLY-002In-Player End Cards (Next Episode)
REC-010Watch Again
REC-011From Your Watchlist
REC-012Unwatched in My Stuff
REC-023Recently Viewed
DWN-001Smart Downloads / Downloads For You
RCP-001Personalized Recaps / Catch Up
RCP-002What You Missed Yesterday

Trust and explanation

5use cases

Understanding and believing the system, rather than finding content.

REC-006Because You...
EDT-016Why This?
EDT-017Percentage Match Score
FBK-001Two-Way Feedback Overlay
REC-018Long-Term Satisfaction Ranking

Off-platform

7use cases

Reaching the viewer when they are not in the app at all.

CRM-001Behavioral Trigger Comms & Notifications
CRM-002Recommendation API into CRM
CRM-003Propensity-Scored Lifecycle Tracks
NOT-001Coming Soon / Notify-Me Reminders
YIR-001Year-in-Review / Your Year
ACQ-001Cohort & Look-alike Ad Targeting
SEG-001Audience Segmentation for Campaigns

THE KEY ARGUMENT

Engagement is not loyalty

The systems that lift retention and LTV are the ones that align personalization to where the user is in their journey. Aggression in the algorithm is not the variable. A new user, a trialist, a power viewer, and a subscriber halfway out the door need different things, and the same recommendation row serves none of them well. Every part of this book serves that single thesis.That is the right instinct pointed at the wrong target.

Too many options, not enough guidance, the same friction every night. A good system answers it before the user feels the fatigue. That is the job. This book is about doing it at every stage, not just on the home page.

WHO THIS IS FOR

What this book is, and who it is for

Renato Bonomini

VP Sales Engineering, ContentWise by Moviri

This is the reference I wished existed. Every personalization use case that matters to a streaming operator, organized by where it pays off in the subscriber lifecycle, with real benchmarks and the counter-narratives most vendors will not print.

I wrote it for the leader who already runs personalization and owns the engagement, conversion and retention numbers. It assumes you have a recommender in production, an editorial strategy you can defend, and retention targets someone is measuring you against.

Two choices shaped it. Running the catalogue along the subscriber journey rather than down a feature list forces every use case to answer a question you actually have: who is this for, at what moment, and what number should move. And it prints the use cases that do not work, the ones that lift a click and move nothing that matters, along with the conditions under which the good ones fail.

Product leaders: You own the discovery surfaces and answer for engagement.
Editorial and programming leads: You decide what gets promoted, to whom, and when.
Engineering and data leads: You build the rails and keep them fed with metadata.
Strategy and commercial leads: You carry ARPU, churn and LTV.

EBOOK FAQ

What operators want to know

How many personalization use cases are there for a streaming service?

ContentWise catalogued 99, grouped by what the viewer is doing: discovery, search, trust and explanation, and off-platform. Each one is placed at the subscriber lifecycle stage where it pays, with the KPI it moves, the effort it takes, and the build phase it belongs to.

What are the stages of the streaming subscriber lifecycle?

Seven: acquisition, onboarding, conversion, engagement, satisfaction, monetization, and retention. Each stage has its own KPI, and a use case that works in engagement can be the wrong instrument in onboarding.

What is the difference between recommendation, curation, and personalization?

Recommendation is one use case: ranking titles inside a single carousel against a single objective. Personalization adapts the whole experience, page plus search plus collections, to the viewer's journey, device, and context, which means deciding which rails exist and in what order before deciding what goes in them.

Should editorial curation or the algorithm decide what users see?

Neither, exclusively. Every rail carries a dial between hand-picked and machine-ranked: editors define the blocks and priorities, the algorithm fills and ranks them per profile, and the blend covers both failure modes, an algorithm's popularity bias and editorial's inability to scale to thousands of micro-segments.

Why is metadata the thing that decides whether personalization works at all?

Because algorithms only know what the metadata tells them, and bad metadata is the most common reason personalization efforts fail. The book gives a four-metric self-diagnostic you can run this quarter: coverage, distribution, number of values per item, and consistency.

Once content is commoditized, what actually differentiates a streaming operator?

The experience. Most content is ubiquitous and operator catalogs are largely undifferentiated, so the durable differentiator is the experience aligned to where the viewer is in the journey, not the one running the loudest algorithm.

How mature is our personalization, and what comes next?

Maturity runs on two axes: how much you know about the viewer, and how much of the page you act on. The sequencing rule that wins is content first, community second, full UX personalization last, because content-based and statistics rails work on day one with zero behavioral history.

Which personalization use cases lift a click and move nothing that matters?

Click-through and take-rate are local, short-term wins that do not guarantee retention, satisfaction, or monetization. The book names the use cases where that gap appears and the conditions under which the reliable ones fail.

Join us at IBC 2026

We are at IBC in Amsterdam from 11 to 14 September, Hall 5, Stand 5.G50.

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