Black Friday Index

Who wins when AI becomes the shopping shelf?

A seasonal benchmark for AI shopping answers, SKU inclusion, product attribute accuracy, retailer citations, and review sentiment.

Haynechi Index

Your category has an answer board.

See prompt, source, and narrative gaps before competitors make them default.

1 Category leader

2 Editorial incumbent

3 Your brand

4 Comparison challenger

5 Niche specialist

Seasonal Benchmark System

Peak-demand answers need a tighter operating protocol.

The Black Friday Index helps commerce teams inspect where AI shopping answers include, misread, cite, or ignore priority products before the demand window compresses decision-making.

Commerce workflow

Benchmark ScopePre-peak packet

Category

Beauty, consumer electronics, home, apparel, wellness, toys, or any product class where AI shopping answers shape demand.

market

Product set

Priority SKUs, hero bundles, competitive alternatives, retailer availability, approved attributes, and seasonal claims.

catalog

Answer surfaces

ChatGPT, Gemini, Perplexity, Copilot, AI Overviews, Amazon Rufus, retailer search, Reddit, and review-led answers.

engines

Decision window

Pre-peak research, sale-week comparison, availability checks, gifting prompts, and post-sale review learning.

seasonal

Prompt WatchlistSample

best gifts for sensitive skin under $50 gift intent

review language

which serum is better for winter dryness comparison

attribute accuracy

Catalog

best black friday skincare bundle promotion

retailer freshness

Commerce

alternatives to [competitor product] displacement

citation quality

Growth

Runbook4 steps

01 Lock scope

Choose category, SKUs, retailers, regions, competitors, and prompts before the seasonal window opens.

02 Capture shelf state

Record SKU inclusion, competitor displacement, attributes, cited retailers, review language, and answer quality.

03 Route fixes

Assign PDP updates, retailer corrections, schema work, review-response insights, PR source plans, and offer-page fixes.

04 Review movement

Compare answer state, citation freshness, crawl windows, and referral quality after fixes ship.

Readout OutputsDecision-ready

Seasonal scorecard

SKU inclusion, competitor position, answer quality, attribute accuracy, and citation freshness.

Retailer work queue

PDP issues, bundle mismatches, stale inventory, missing GTINs, and source freshness actions.

Answer-risk brief

Wrong claims, omitted products, negative review language, and competitor-favorable recommendation patterns.

Executive readout

What moved, what is still inferred, which actions shipped, and what to fund for the next demand window.

Benchmark BoundariesHonest proof

Sample surface

Visible rows demonstrate product mechanics; they are not published Black Friday market rankings.

Shelf-control boundary

Haynechi can route evidence and fixes, but AI shopping systems decide recommendations independently.

Approved attributes only

Product corrections use brand-approved catalog, retailer, legal, and claims language.

Seasonal proof caveats

Peak demand creates noise; readouts separate observed answer changes from inferred influence.

AI Shopping Shelf

See which products AI systems include, misread, or ignore.

This sample product surface shows how commerce teams would track SKU inclusion, product attributes, retailer citations, and review signals without presenting sample rows as customer outcomes.

Shopping index

SKU Visibility Sample catalog

SKU-104 Hydrating serum Included 3 engines

attribute gap

SKU-217 Repair cream Competing 2 engines

review drift

SKU-342 Travel kit Missing 0 engines

retailer gap

SKU-509 SPF moisturizer Included AI Overview

price mismatch

Retailer Citations Sample

Owned product page trusted fresh attributes

Retailer PDP mixed stale inventory

Marketplace listing risk wrong bundle

Review roundup rising positive sentiment

Signal Queue Sample

Attribute fragrance-free missing Catalog

Review sensitive skin concern CX

Retailer bundle unavailable Commerce

Schema GTIN not resolved Web

Start with the map

Start with one market map.

Request the pilot