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02 // NATURAL LANGUAGE

Intent Memories

CROSS-AMAZON AI  • PERSONALIZATION ENGINE

The Problem

SECTION 01 // PROBLEM

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Amazon knows what customers do  every click, search, and purchase  but that data is fragmented across experiences. No single place tells you what a customer is trying to accomplish in the moment. A runner training for his first marathon browses running shoes on the Amazon app, then asks Alexa whether marathon-specific shoes hold up over high mileage. These are two disconnected signals in two different systems  nothing connects them into one story: this customer is training for a marathon and needs gear built to go the distance. As a result, every AI experience has to reconstruct intent from raw events on its own — slow, expensive, and inconsistent and the customer's intent ends up understood shallowly, late, and differently on every surface.

Persona and User Quote

SECTION 02 // PERSONAS

Runner in Race

Persona 1: Marcus — The First-Time Marathoner

Role: Training for his first marathon; building out running gear
 

CHARACTERISTICS

Browses running shoes on the Amazon app, then asks Alexa about shoe durability for marathon-distance training. Researches heavily, prioritizes durability and performance over price.

FRUSTRATION

When he goes back to the app, it shows him the same generic running shoes as before — nothing reflects what he told Alexa about training for 26.2 miles. It can't tell the difference between someone shopping for casual sneakers and someone training for a marathon.

CORE USER STORY

"I told Alexa I needed shoes that could handle 40+ miles a week without breaking down. Good conversation, real advice. Then I opened the app and it's showing me the same lightweight trainers I'd already scrolled past — like it has no idea I'm marathon training, not just going on a jog."

Grapefruit And Pills

​Persona 2: David — The Health-Conscious Runner (live today)

Role: A runner managing new knee pain and seasonal allergies; shops the Amazon Health storefront.

CHARACTERISTICS

Recently started experiencing knee pain from running. He's searched "vitamin D supplements," purchased allergy medication, and browsed knee support braces.

FRUSTRATION

​When he opens the Amazon Health storefront, he sees generic recommendations — popular vitamins, trending supplements, seasonal picks. Nothing connects to his specific health journey.

CORE USER STORY

"As someone managing a health condition, I want recommendations that reflect my actual health journey, not generic top sellers. "​
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The Solution: Intent Memories

SECTION 03 // SOLUTION

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What Are Intent Memories?

Intent memories are natural-language representations of what a customer is trying to accomplish, generated by deep reasoning over their browsing, searches, purchases, and conversations. The memory captures what the customer wants, why it matters, how they decide, and where they are in the journey. This understanding is also available as structured attributes and embeddings for retrieval, with natural language as the token-optimized format any AI experience across Amazon can read to understand customers the same way.

1. Understanding Intent, not just a category

From the constraints of a fixed catalog, to reflecting what the customer is actually doing, to recommendations that fit

A kids' school backpack and a hiking backpack once landed in the same "backpacks" bucket and got the same generic results. Now, using embedding similarity, they split into distinct intents - "preparing for her son's first day of school," and "comparing durable, mid-size hiking backpacks under $40.

2. Reading the signals behind the click

Not just what she clicks, How she engages: which filters, how long she lingers, what she compares, Captures how she decides, not just what she did

A shopper who's opened the same pair of trail-running shoes eight times over three days — spending close to two minutes each visit reading reviews and checking the sizing chart — is signaling far more serious intent than someone who glanced at a different pair once for fifteen seconds. Repetition and depth of engagement reveal how close someone is to deciding, not just that they looked.

3. Listening to what customers say

Not just behavior, What she tells Alexa or the chatbot, Stated intent shapes every surface

When she says she wants a "subtle sage-green woodland" nursery, that spoken preference joins her memory and reshapes what she sees on the app and desktop — intent no amount of clicking would ever reveal.
 

Customer Journey with Intent Memories

SECTION 04 // FLOW

Image by Nicolas J Leclercq

1

Alexa for Shopping

She asks, "What do I need for a newborn nursery?" → an intent memory is created — objective "setting up nursery," mission "researching cribs."

2

App homepage

She opens the app and it reflects that objective in her recommendations — cribs and crib sheets — and she starts searching for nursery books, browsing a few titles by one author.

3

Alexa for Shopping (chatbot)

Back in the chatbot, she talks more about the nursery. It now recommends newborn books from the author she just viewed. She also chats about wanting a subtle sage-green woodland theme for the room → the memory updates with that theme preference.

4

Desktop later

She opens Amazon on her laptop and the recommendations reflect her theme — sage-green woodland crib bedding, wall art, and décor — matched to the preference she expressed in the chatbot.

Product Evolution and Roadmap

SECTION 05 // ROADMAP

Phase 1 — Ideation: The Representation Problem

Scope: Move from structured to natural language intent memories

What we shipped:  Improve intent identification quality leveraging embedding based similarity, then generated natural-language intent memories

EXPERIMENTAL RESULTS

90.17%

Correct-cluster rate, improving from 81.78%.

46%

reduction in clustering defects.

Phase 2 — Intent Memories MVP

Scope: Amazon Health Services (AHS) personalized-pills experiment.

What We shipped:   Scaled to 4MM customers affordably with two levers — model routing (our most capable model for high-value customers, lighter models for the rest) and intelligent triggering (regenerate a memory only when intent meaningfully changes, not every visit).

EXPERIMENTAL RESULTS

Higher click-through rate compared to generic recommendations.

$143.5MM

Annualized sales generated through personalized recommendations.

+52.2MM

Additional clicks on recommended products.
 

Phase 3 — Alexa for Shopping (Chatbot) Experiment

Scope: Conversation with Alexa for Shopping chatbot using intent memories for 20MM customers

What We shipped: Integrated conversational intent from Alexa for Shopping into the customer's intent memory.

EXPERIMENTAL RESULTS

Experiment in progress

Phase 4 — Full Rollout

Full rollout planned for 50MM customers across 6 AI experiences by end of year.

EXPERIMENTAL RESULTS

Pending
Launch

Anushka Kher

SENIOR TECHNICAL PRODUCT MANAGER, AMAZON

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© 2026 Anushka Kher. All rights reserved.

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