Recommendation systems predicting user preferences. Drives personalization across industries.
51 implementations across 11 industries
Think of this as building your own ‘Netflix-style’ recommendation brain: it watches what each user does, learns their tastes, and then uses a mix of traditional recommendation models and modern generative AI to decide what to show or suggest next.
This is about making every shopper’s online store experience feel like a helpful salesperson knows their tastes — showing the right products, offers, and content to each person instead of the same generic website for everyone.
Imagine every shopper in your online store having a smart salesperson who remembers their tastes, budget, and goals, and quietly reorders the search results and product suggestions just for them every time they type in the same vague query like “running shoes.”
This is like having a smart DJ that senses how you feel and then builds a playlist to match or change your mood, using AI instead of you manually picking songs.
Imagine your streaming app as a smart host at a party who learns what each guest likes, suggests the right music and games at the right moment, and nudges people before they leave so they stay longer and have more fun. This system uses AI to do that automatically for every user in your mobile entertainment app.
Think of AI in retail as giving every shopper a smart, always‑on personal assistant plus a savvy store manager behind the scenes. It helps customers quickly find the right products, prices, and deals while quietly optimizing inventory, supply chain, and staffing so shelves are stocked and operations run cheaper and faster.
Imagine every time you open your TV, there’s a smart concierge who has watched everything you’ve ever seen, remembers what you liked, what you quit after 5 minutes, what you binged in a weekend, and what people like you enjoy. That concierge quietly rearranges the shelves so the things you’re most likely to love are always right in front of you. That’s what a Netflix-style recommender system does—at software scale for millions of viewers.
This is like hiring millions of super-fast digital editors who watch everything posted on a social network in real time—hiding abusive or illegal content, flagging rule‑breaking posts, and deciding what to show in people’s feeds based on their interests.
This describes how modern social platforms use AI as an always‑on assistant that decides what each person sees, when they see it, and how brands can talk to them—so every user’s feed and every ad feel custom‑made.
Think of AI in marketing as a team of tireless digital interns that watch every interaction your customers have with your brand and then help your marketers decide: who to talk to, what to say, when to say it, and on which channel—automatically and at massive scale.
Think of this as a very smart fashion brain that studies what people actually buy and wear, then helps brands decide what to design, how much to produce, and which customer to show it to—so you make more hits and fewer flops.
Think of Aidaptive as a digital concierge that quietly watches how every guest shops, browses and books online, then automatically rearranges your website and offers so each visitor sees the rooms, packages and prices they’re most likely to buy—without your team needing to constantly tweak campaigns by hand.
Think of a streaming service that knows not just what shows you like, but also when you watch, what device you use, and whether you usually binge or sample. Contextual recommendation algorithms use this extra situational information to put the right movie, song, or game in front of you at the right moment.
Think of AI in programmatic advertising as a super-fast trading bot for ad space: it constantly scans who is online, what they’re doing, and in a split second decides which ad to show, at what price, and on which website to maximize your marketing results automatically.
This is like giving every hotel guest a smart, invisible concierge that remembers their preferences—wifi, room settings, content, and services—and quietly adjusts everything so each stay feels tailor‑made without staff doing everything manually.
This is like a smart content clerk that quietly watches what each viewer reads or watches and then rearranges your website or app so everyone sees shows, videos, or articles they’re most likely to click next.
Think of this as a super-smart ad trader that watches billions of people’s clicks in real time and automatically decides which ad to show, to whom, at what price, and on which platform to get the best return—far faster and more accurately than any human team could.
This is like giving every shopper their own smart salesperson who knows what they like and automatically suggests the right products over SMS, WhatsApp, or other channels powered by Plivo.
Imagine an online fashion store that behaves like a really good personal stylist who knows your size, style, and budget—and gets smarter every time you browse or buy. AI quietly powers that stylist behind the scenes.
This is like a smart talent scout for sports teams: it automatically finds, scores, and organizes athlete prospects using AI, so coaches see the best-fit players first instead of digging through endless highlight reels and emails.
Think of this as a data “brain” for sports leagues, broadcasters, and betting operators that watches every game, learns what fans enjoy, and then helps serve them the right highlights, stats, and betting offers at the right moment on the right screen.
This is like a smart in-store salesperson for your online shop that learns what each shopper likes and rearranges the shelves, product suggestions, and emails for every person in real time.
This is the kind of AI that decides “Because you watched X, you’ll probably like Y” on Netflix, YouTube, or Spotify. It watches what each user does, compares that to millions of other users, and then builds a constantly updating list of shows, videos, or songs you’re most likely to click next.
Imagine an online fashion store that behaves like a top personal stylist who knows your size, taste, budget, and what’s trending right now—and instantly rearranges the whole store just for you, in real time. That’s what ASOS is building with AI.
Imagine every shopper in your store sees a shelf that magically rearranges itself to show the products they are most likely to buy at the best price for them and for you. AI personalization for retail media does that on your website and app ad slots in real time.
This is like giving your online store a very fast, very smart assistant that watches how customers browse, what they buy, and how the site behaves, then constantly tweaks recommendations, pricing, and operations to sell more with less waste.
This is like giving every shopper their own smart stylist who has read the entire store catalog, remembers what similar customers liked, and can instantly suggest the right products and bundles in natural language across web, app, email, and chat.
This is Meta’s “autopilot” for ads: instead of you manually picking every audience detail, Meta’s AI watches how people behave on Facebook and Instagram, learns who reacts to which ads, and then automatically shows your ads to the people most likely to care, in real time.
Think of Gucci’s AI in ecommerce as a smart digital stylist and shop assistant that learns what each shopper likes, shows them the right products, and makes the online store feel as personalized and polished as their best physical boutiques.
Think of GEM as a super-smart matchmaker that reads every ad, every user’s behavior, and a ton of context, then “imagines” which specific ad version and placement a person is most likely to respond to—millions of times per second across Meta’s apps.
Think of this as a super-analyst that watches every ad impression, every click, and every purchase in real time, then constantly tweaks who sees which ad, on which channel, and at what price to get more results for the same (or less) budget.
This is Netflix’s R&D lab for making sure every member quickly finds something they’ll love to watch. Think of it as a constantly learning concierge that rearranges the entire Netflix store for each viewer, in real time.
This is Netflix’s “smart brain” that watches what every viewer clicks, skips, and binges, then uses a giant AI model to decide which shows and movies to put in front of each person so they’re more likely to hit play.
Imagine every hotel guest has a smart concierge that silently learns what they like and then offers the right room upgrade, spa slot, or late checkout at exactly the right moment and price—automatically, at scale.
This is a study that asks: "How much value do Netflix-style ‘Because you watched…’ recommendations really create?" It measures what happens to user behavior and business outcomes when you turn personalized recommendations on vs. off.
This is like giving Netflix a smart brain that quietly watches what you watch, when you stop, what you search for, and then rearranges the entire app, recommendations, images, and streaming quality just for you—millions of people at once, all differently.
Think of this as Netflix building its own very smart "taste brain" that understands movies, shows, images, and text, then wiring that brain into all the ways it personalizes what you see — rows, artwork, search, and more — instead of relying on a bunch of separate smaller brains.
This is like having a super-curious librarian who learns what movies, songs, or shows you like and then quietly rearranges the shelves so that whenever you walk in, the things you’re most likely to enjoy are right in front of you.
Think of Lily AI as a smart retail stylist for your online store that understands products and shoppers the way a great in‑store associate does, then uses that understanding to improve search, recommendations, and product discovery.
Imagine every visitor walking into your online store and instantly seeing the products, offers, and content most relevant to them—like a smart shop assistant who remembers every past visit, what they liked, ignored, and bought, and rearranges the whole store in real time for that one person.
This is like giving every online shopper their own smart store assistant that instantly knows what they like, what’s in stock, and how to guide them to the right product and offer in real time.
Imagine every learner having a personal tutor who watches how they learn, what they get right or wrong, how fast they move, and then quietly rearranges the course so they only see what they need next. That’s adaptive learning inside an LMS: the course reshapes itself in real time for each person.
This is about giving every shopper their own ‘personal store window’ online. AI watches what each person browses, buys, clicks and ignores, then rearranges products, offers and content in real time so the site feels like it was built just for that one customer.
This is like a smart shop assistant for an online store that learns what each customer likes and then quietly rearranges the shelves for them—showing different products, bundles, and follow‑up suggestions before and after purchase, even around returns.
Think of this as a smart shop assistant built into your online store that quietly watches what each shopper does and then rearranges the shelves, product lists, and offers in real time so each person sees the items they’re most likely to buy.
Think of this as a smart engine inside an online store that automatically shows each shopper the most relevant products, content, and offers, based on everything SAP already knows about them and similar customers.
This is about how Netflix-style “Because you watched…” lists are created. The system watches what you watch, when you stop, what you rewatch, and then predicts what you’re most likely to enjoy next—like a super‑attentive video store clerk who’s seen your entire viewing history.
This is like a super-smart “TikTok/Netflix-style” recommender that looks at everything about a piece of content—its text, images, video, and user behavior—and learns end‑to‑end what people are most likely to enjoy, instead of relying on many hand‑tuned sub‑systems.
This is like giving every shopper their own digital sales associate who remembers what they like, what they looked at before, and what similar customers bought, then uses all that data to tailor offers, messages, and experiences in real time across stores, apps, and websites.
Think of this as a map of all the ways online stores are using AI today—like a guidebook that explains how Amazon‑style recommendations, smart pricing, chatbots, and fraud checks actually work and where they’re going next.
This is like Netflix-style recommendations, but for news and media, where editors set the rules of the game and algorithms handle the heavy lifting of matching each reader with the most relevant stories and content.