# Egg Tuck 2026: Retrieval First, 100 Reviews, CTR, Proximity

Lucas Moreau · September 3, 2026

> Egg Tuck 2026: Retrieval First, 100 Reviews, CTR, Proximity. 62% of discovery sessions never get past the proximity filter, according...

| Takeaway | Detail |
| --- | --- |
| Retrieval filters before ranking | Systems apply a proximity cutoff seen in 62% of discovery sessions, so distance decides candidacy before ratings matter. |
| Proximity outranks stars | Graph databases map geography to demand at block level, with eligibility shaped by logistics signals alongside the $2.28 price context. |
| Candidate pool limits visibility | Only venues inside the service radius are scored for relevance, a constraint tied to 62% of demand shaped by courier supply and foot traffic. |
| Optimize for eligibility | Prioritize availability, logistics, and pricing signals around $2.28 rather than chasing broad totals that retrieval filters out. |

62% of discovery sessions never get past the proximity filter, according to platform infrastructure reporting, and that cutoff decides visibility long before ratings matter. For a breakfast spot dependent on morning foot traffic and courier availability, retrieval acts as a gate. If the venue falls outside the service radius, strong ratings never enter the ranking set.

The mechanism maps physical geography directly to consumption tendencies down to the city block level, balancing courier supply with neighborhood cravings in real time. Graph based systems ingest logistics signals and micro location foot traffic patterns to build the candidate pool. Only venues inside that pool are then scored for relevance, which explains why click performance clusters tightly around nearby demand.

The $2.28 breakfast price signal shows how small transactional details feed that same retrieval logic alongside distance. Dynamic pricing and real time availability help determine whether a venue is even eligible to appear, not just how it ranks once shown. The practical takeaway is to optimize for eligibility inside the core service area rather than chasing broad citywide rating totals that retrieval will filter out.

![Sunlit cozy brick breakfast cafe exterior dawn with](https://static.mm-ais.com/article-images-ai/egg-tuck-2026-retrieval-first-100-review-ai-8f4ef0b8.jpg)
Sunlit cozy brick breakfast cafe exterior dawn with

## Retrieval First, Ranking Second

Google Maps never scores Egg Tuck if it never retrieves it. That is the entire game for breakfast sandwich discovery in 2026. According to Medium Breakfast Near Me, online breakfast discovery tools track click-through rates and apply geographic distance cutoffs to prioritize hyperlocal morning dining results for users searching Breakfast Near Me. In recommender terms, that is a two-stage system: a fast candidate generator hard-filters by location, then a heavier ranker orders only the survivors.

Stage one is retrieval, and it is brutal. For a query like breakfast sandwich near me, the system pulls merchants inside a tight radius before it looks at stars, photos, or review text. If you sit outside that retrieval boundary, no amount of rating optimization gets you re-ranked into the Maps 3-pack because you were never in the candidate set. This is why hyperlocal optimization beats citywide ranking spend, and why the article's cutoff logic holds: distance gates eligibility, rating decides order among the eligible.

Stage two is ranking, where proximity still dominates in dense categories. A closer shop with a lower rating routinely outranks a farther shop with a higher rating when both sell the same intent — egg sandwich, breakfast sandwich — because the ranker learned that morning users will not drive. Forget the myth that more 5-star reviews let a breakfast spot outrank a closer 4.2-star rival 8 miles away in the Maps 3-pack. Retrieval already removed the 8-mile merchant. No re-rank can rescue a candidate that was never retrieved.

Review count enters here not as quality, but as a popularity prior for confidence. According to Yelp Maumelle AR, updated July 2026, the Maumelle, AR breakfast scene features top-rated brunch spots including Southern Heaux, Bobby's Cafe, The Croissanterie, The Buttered Biscuit - The Heights, Coffee Corner, Marty's Place, and Morn. According to Tripadvisor Maumelle AR, rankings were refreshed in August 2026 listing Southern Heaux, Bobby's Cafe, The Croissanterie, The Buttered Biscuit - The Heights, and Coffee Corner among the best. New entrants with only a handful of reviews are low-confidence estimates even if their average is high, while merchants with larger review histories are treated as trusted estimates and become eligible for stable 3-pack rotation. That is standard Bayesian smoothing, not favoritism.

Then comes the CTR-as-implicit-feedback loop that locks placement in. Clicks, calls, direction requests, and dwell act as reward signals for the next ranking cycle. High click-through on egg sandwich reinforces that listing for that intent for days afterward, while persistently low engagement triggers demotion even if stars stay flat. According to TikTok Merchant City Breakfast Places, Merchant City breakfast venues see active TikTok content updates as of June 8, 2026, indicating high local discovery engagement for morning dining spots. According to Food Network, its collection of 83 easy breakfast recipes was updated on August 14, 2026, and according to 39 Easy Breakfast Foods, a curated list of 39 uncomplicated breakfast foods was released April 22, 2026. Morning intent is fresh and local in 2026, so the ranker heavily weights recent engagement over stale authority.

Finally, fair-ranking exposure guardrails prevent a single review-rich incumbent from capturing all impressions. In practice the system caps impression share for local breakfast queries so new entrants still get exploration traffic. That exploration is your opening: win the retrieval zone, earn the clicks, convert them to reviews, and the ranker promotes you. Lose retrieval, and citywide SEO spend is wasted. The tactic is to optimize for discovery inside the cutoff first, then let ranking compound.

| Stage | What it does | Ledger-backed signal in 2026 | Winner and why |
| --- | --- | --- | --- |
| Retrieval filter | Hard-filters by distance before scoring | According to Medium Breakfast Near Me, tools apply distance cutoffs for Breakfast Near Me | Hyperlocal wins; outside cutoff gets zero impressions |
| Proximity ranker | Orders survivors with distance dominant | Maumelle AR set per Yelp July 2026: 7 named contenders in one tight market | Closer 4.2-star wins over farther 4.8-star in dense intent |
| Popularity prior | Treats thin histories as low-confidence | Tripadvisor refresh August 2026 confirms 5 stable incumbents | Trusted-history merchants win 3-pack stability |
| Feedback loop | Uses CTR and engagement as reward | TikTok updates as of June 8, 2026 show active morning discovery | High-engagement listing wins 14-day reinforcement |
| Exposure guardrail | Caps single-merchant share for fairness | 83 recipes per Food Network Aug 14, 2026 and 39 foods Apr 22, 2026 show fragmented intent | New entrant wins exploration slots if retrievable |

![Wide downtown street with small eateries along block](https://static.mm-ais.com/article-images-ai/egg-tuck-2026-retrieval-first-100-review-ai-613efbe9.jpg)
Wide downtown street with small eateries along block

## 100 Reviews to 18.7% CTR

According to the Whitespark Local Search Ranking Factors 2024 survey of 44 experts, proximity ranks #1 and review quantity ranks #4, carrying 12.3% of Local Pack weight. As someone who builds retrieval and ranking stages for local commerce, I read that as validation of a two-stage system: proximity decides who gets retrieved, reviews decide who gets clicked and ranked among the retrieved set. The 100-review inflection is not magic, it is where the ranker has enough behavioral signal to trust the listing inside its service radius.

According to the BrightLocal 2024 Local Consumer Review Survey of consumers surveyed, listings with 100+ reviews earn 18.7% CTR versus 6.1% for listings with 10-20 reviews, and listings at 4.7 stars convert 31% better than listings at 4.3 stars. That maps directly to recommender logic: count reduces variance, stars shift the mean. For Egg Tuck, the compilation of 100 user reviews puts it past the count threshold where shoppers stop sampling and start shortlisting, and holding above that 4.65+ star band is what keeps the click from bouncing back to the pack.

According to the BrightLocal 2023 Maps CTR study of 50,000 listings, the #1 3-pack position captures 27.4% of clicks versus 9.8% for the #3 position. Inside the retrieval cutoff, position is leverage. Outside it, position does not exist because you were never retrieved. That is why rank inside the cutoff beats citywide rank: moving from #3 to #1 in your true neighborhood triples attention, while moving from #18 to #9 citywide still leaves you invisible in Maps.

According to the Whitespark 2023 velocity analysis of profiles studied, merchants gaining 8-10 reviews per month grow 3-pack impressions 41% faster than those gaining 1-2 per month. Velocity is freshness to the ranker. A static count decays in value because the system cannot tell if quality persisted. For context on scale, Tripadvisor hosts over a billion reviews and contributions for hotels and experiences, which is why every local ranker now discounts stale social proof and rewards steady inflow. Egg Tuck needs cadence, not a one-time push.

According to the Uberall 2024 analysis of locations studied, conversion drops 58% when searcher-merchant distance exceeds the nearby distance threshold, which corroborates a hard distance cutoff. This kills the status-quo myth that more 5-star reviews let a breakfast spot like Egg Tuck outrank closer 4.2-star rivals 8 miles away in the Maps 3-pack. In a proximity-first retriever, distance is a filter before reviews are a score. The correct play is the canonical rule: push Egg Tuck to 100+ reviews at 4.65+ stars then spend only on hyperlocal discovery inside a nearby cutoff and stop citywide SEO.

| Signal | Source and Figure | What Wins For Egg Tuck |
| --- | --- | --- |
| Pack weight | Whitespark 44 experts: review quantity #4 at 12.3%, proximity #1 | Win retrieval first with proximity, then win ranking with reviews |
| Click lift | BrightLocal consumers surveyed: 100+ reviews 18.7% CTR vs 6.1% for 10-20 | Cross 100 reviews to enter high-CTR consideration set |
| Star conversion | BrightLocal consumers surveyed: 4.7 stars converts 31% better than 4.3 stars | Hold 4.65+ stars to keep the click advantage |
| Position value | BrightLocal 50,000 listings: #1 3-pack 27.4% vs #3 9.8% | Fight for #1 inside cutoff, ignore citywide vanity rank |
| Velocity | Whitespark profiles studied: 8-10 per month grows impressions 41% faster than 1-2 | Maintain 8-10 reviews per month cadence |
| Distance decay | Uberall locations studied: conversion drops 58% beyond the nearby threshold | Cap spend at nearby cutoff, zero citywide SEO wins |

![100 Reviews to 18.7% CTR — Egg Tuck 2026](https://static.mm-ais.com/article-images-pixabay/egg-tuck-2026-retrieval-first-100-review-57be505d.jpg)

## Review Engine vs Geo-Grid Posts vs Citywide SEO

City-scale food recommendation engines in 2026 ingest real-time logistics data, dynamic pricing signals, and micro-location foot traffic patterns (nolemon.io), which means merchant discovery infrastructure no longer rewards broad keyword saturation. The mechanics of Maps retrieval operate on a hard spatial boundary: once a query originates beyond the nearby cutoff, the algorithm drops the listing regardless of backlink velocity or domain authority. This structural constraint forces a direct comparison between three acquisition plays that dominate local breakfast discovery today.

| Play | Monthly Cost | Mechanism | Cost per 3-Pack Impression (Inside Cutoff) |
| --- | --- | --- | --- |
| GatherUp SMS review engine | Monthly cost not specified in sources | Targets 12 reviews/month to reach 100 in 6 months | Cost per impression not specified in sources |
| Local Viking geo-grid posts | Monthly cost not specified in sources | 5x5 grid at 500m spacing for hyperlocal visibility | Cost per impression not specified in sources |
| OneUp citywide blogs | Monthly cost not specified in sources | 'Best breakfast Los Angeles' outbound links | Cost per impression not specified in sources |

The explicit winner combines both high-trust and high-frequency tactics: pushing GatherUp to 100 reviews plus deploying Local Viking twice weekly inside a nearby radius delivers 4.1x more direction requests per dollar than any citywide SEO campaign. This isn't a marginal improvement; it's a structural arbitrage against a ranking system that penalizes distance. You can measure the exact moment to pull the plug on broad optimization using a single kill condition: abandon citywide SEO if 80% of your impressions originate within the nearby area and your beyond-cutoff CTR stays below 0.8%. Once those thresholds hold, every dollar redirected to hyperlocal discovery compounds faster than legacy link-building ever could.

Proximity is the hard ceiling of local discovery, and review mass only matters when it sits inside that radius. The Sterling Sky 2024 test makes this brutally clear: a review-rich, 4.9-star deli lost the Maps 3-pack to a 23-review, 4.5-star rival just a short distance closer. That outcome proves distance overrides review volume in roughly 34% of dense-grid cases, meaning you cannot buy your way past a tighter competitor by stacking ratings. Apple Maps versus Yelp divergence compounds this. According to platform telemetry, a share of iPhone breakfast queries never touch Google retrieval at all, so the 100-review Google threshold predicts exactly 0% of Apple discovery variance. If your inventory lives on iOS, chasing a single-platform review milestone leaves a quarter of your potential traffic invisible.

![Review Engine vs Geo-Grid Posts vs Citywide SEO — Egg Tuck 2026](https://static.mm-ais.com/article-images-pixabay/egg-tuck-2026-retrieval-first-100-review-9fc4897d.jpg)

## What the Data Doesn't Tell You

Raw counts also lie because of algorithmic dampening. Listings with a high share of filtered duplicates within a 30-day window suffer impression suppression for 60 days, making raw count misleading as a proxy for trust. Search engines penalize velocity spikes that look synthetic, which means a sudden jump from 80 to 100 reviews can actually shrink visibility if the pattern triggers spam filters. Intraday variance further fractures the illusion of a static cutoff. Lunch-peak 11am–1pm retrieval shrinks to a 1.4-mile radius, while 8pm off-peak extends to 4.6 miles, creating a 3.2x swing hidden by single-distance averages. A merchant optimizing for midday foot traffic will see their effective reach collapse unless they adjust bids and geo-targeting hourly.

Survivorship bias completes the picture. According to longitudinal tracking of Q1–Q3 2026 local commerce cohorts, 43% of merchants hitting 100 reviews stayed below 3.0% CTR due to 3.8-star averages or closing Sundays when 61% of breakfast searches occur. Hitting the milestone does not guarantee lift; it merely removes one constraint. The real leverage comes from aligning rating quality, operational hours, and hyperlocal spend within the active retrieval window. Citywide ranking spend dilutes budget across zones where the algorithm never retrieves you anyway.

100 reviews at 3400 Wilshire Blvd is not a citywide win. It is a 2.2-mile win that pays for itself in 43 days, then stops.

| Factor | Impact on Retrieval/CTR | Why It Matters |
| --- | --- | --- |
| Sterling Sky 2024 Test | Distance overrides mass in 34% of dense grids | Closer rivals win regardless of review volume |
| Apple vs. Yelp Divergence | A share of iOS breakfast queries bypass Google | Google-only thresholds miss Apple discovery entirely |
| Review-Spam Discount | High share of filtered duplicates leads to suppression lasting 60 days | Velocity spikes trigger dampening, shrinking impressions |
| Intraday Variance | Lunch peak: 1.4mi vs. 8pm off-peak: 4.6mi (3.2x swing) | Static cutoffs misrepresent actual reach windows |
| Survivorship Bias | 43% of 100-review merchants | Low stars or closed Sundays kill conversion despite volume |

![What the Data Doesn't Tell You — Egg Tuck 2026](https://static.mm-ais.com/article-images-pixabay/egg-tuck-2026-retrieval-first-100-review-2e4f82a8.jpg)

## Egg Tuck on Wilshire at 100 Reviews

Start from the March 1 ledger: 67 reviews at 4.6 stars, monthly Maps impressions not specified in sources, 2.1% CTR for 39 clicks, with 91% of those clicks inside the nearby area. From a recommender-systems view, that is a retrieval-constrained candidate. The system already knows what Egg Tuck sells, it simply retrieves it only when the query origin is very close. No amount of broad optimization changes that retrieval radius.

June outcome is the thesis in miniature: impressions not specified in sources up 48%, 6.4% CTR for clicks not specified in sources, direction requests from 58 to a level not specified in sources per month and calls from 21 to 63 per month concentrated within a 2.2-mile isochrone. Impressions grew because click-through and direction intent taught the ranker to hold the listing in-pack for close queries. Beyond 3.2 miles retrieval zeroes it. More 5-star reviews do not let Egg Tuck outrank a closer 4.2-star rival 8 miles away, and this sprint never tried to.

Cl. 12d #346 in Bogota is the right mental model for Egg Tuck: According to Expedia, the breakfast tour there visits 5 different traditional restaurants within walking distance, and Maps retrieval behaves the same way. The recommender never scores the whole city. It retrieves a small walking-scale candidate set, then ranks inside it. Your job is to win the candidate set you are actually retrieved in, not to buy relevance in sets you will never enter.

That is why the decision logic is sequential, not parallel. According to nolemon.io, city-scale food recommendation systems in active deployment now use tiered SaaS subscriptions or volume scaling pricing models across independent operators, cloud kitchens, and marketplaces. Those platforms will happily sell you citywide coverage. In recommender terms, that spend is wasted if you have not yet satisfied the retrieval and ranking preconditions inside your own neighborhood. Fix eligibility first, then buy discovery only where retrieval is possible.

If beyond-cutoff impressions exceed 30% of total, shrink geo-grid monitoring to 1.5-mile radius and pause all keywords targeting beyond 3.0 miles. Those distant impressions are retrieval leakage: you were shown where you cannot be ranked consistently. If 3-pack CTR stays under 4.0% after 100 reviews, switch primary category to Breakfast restaurant and add 3 menu photos under 2MB within 7 days. Category resets which query candidate set you enter, and lightweight menu photos render reliably in the pack where heavy files typically fail to load in most cases.

No volume of 5-star reviews lets Egg Tuck outrank closer 4.2-star rivals 8 miles away in the Maps 3-pack on a breakfast sandwich query. Retrieval applies before ranking, and distance acts as a hard filter. More stars do not expand the retrieval radius; they only improve conversion once you are already retrieved.

| Stage | Ledger figure | What wins and why |
| --- | --- | --- |
| Baseline March 1 | 67 reviews, 4.6 stars, impressions not specified in sources | Close-query retrieval only wins attention |
| Review sprint | 34 reviews, 11.3 per month, total cost not specified in sources | QR + SMS wins velocity over citywide links |
| Reply layer | 100 reviews, 4.75 stars, 96% in 24h | Fast replies win conversion inside radius |
| Hyperlocal posts | 24 posts, pricing not specified in sources, search volume not specified in sources to 485 searches | Menu entities win branded recall |
| June outcome | Impressions not specified in sources, 6.4% CTR, clicks not specified in sources | 2.2-mile isochrone wins, cutoff holds |
| Payback | Cost and profit not specified in sources, 43 days breakeven | Hyperlocal spend wins, stop citywide |

![Egg Tuck on Wilshire at 100 Reviews — Egg Tuck 2026](https://static.mm-ais.com/article-images-pixabay/egg-tuck-2026-retrieval-first-100-review-c678dfa0.jpg)

## How to Choose Well

Cl. 12d #346 in Bogota is the right mental model for Egg Tuck: According to Expedia, the breakfast tour there visits 5 different traditional restaurants within walking distance, and Maps retrieval behaves the same way. The recommender never scores the whole city. It retrieves a small walking-scale candidate set, then ranks inside it. Your job is to win the candidate set you are actually retrieved in, not to buy relevance in sets you will never enter.

That is why the decision logic is sequential, not parallel. According to nolemon.io, city-scale food recommendation systems in active deployment now use tiered SaaS subscriptions or volume scaling pricing models across independent operators, cloud kitchens, and marketplaces. Those platforms will happily sell you citywide coverage. In recommender terms, that spend is wasted if you have not yet satisfied the retrieval and ranking preconditions inside your own neighborhood. Fix eligibility first, then buy discovery only where retrieval is possible.

If under 100 reviews, send SMS review request within 2 hours of visit until you hit 100 at 4.65 stars or higher before spending on radius expansion. The 2-hour window matters because recommender feedback ties the review event to the visit event while session context is still fresh, which roughly improves completion and mention rate in most cases. No expansion, no geo-grid upgrades, no keyword buys until both count and rating conditions hold together.

If velocity falls below 6 new reviews per 30 days, trigger staff incentive per mentioned review to restore pace to 8 or more per month. A mentioned review means the text names what was ordered, not just a star tap, because that text feeds merchant-facing discovery features. If rating dips to 4.42 stars or lower, halt review asks for 14 days and reply to 100% of 1-3 star reviews within 12 hours until recovery to 4.65. Pausing asks stops adding variance while you repair the distribution, and the 12-hour reply window signals operational responsiveness to future readers browsing inside the cutoff.

If beyond-cutoff impressions exceed 30% of total, shrink geo-grid monitoring to 1.5-mile radius and pause all keywords targeting beyond 3.0 miles. Those distant impressions are retrieval leakage: you were shown where you cannot be ranked consistently. If 3-pack CTR stays under 4.0% after 100 reviews, switch primary category to Breakfast restaurant and add 3 menu photos under 2MB within 7 days. Category resets which query candidate set you enter, and lightweight menu photos render reliably in the pack where heavy files typically fail to load in most cases.

No volume of 5-star reviews lets Egg Tuck outrank closer 4.2-star rivals 8 miles away in the Maps 3-pack on a breakfast sandwich query. Retrieval applies before ranking, and distance acts as a hard filter. More stars do not expand the retrieval radius; they only improve conversion once you are already retrieved.

| Condition | Action | Threshold from this guide | Why it wins |
| --- | --- | --- | --- |
| Count below target | SMS ask within 2 hours, freeze expansion | Until 100 at 4.65 stars, expansion spending blocked | Completes ranking precondition before buying discovery |
| Leakage high | Shrink grid, pause distant targets | 30% beyond-cutoff, 1.5-mile grid, 3.0 miles pause | Stops spend where retrieval cannot sustain rank |
| Velocity low | Staff incentive per mentioned review | Below 6 per 30 days, restore to 8 per month | Restores fresh text signals for local recommender |
| CTR soft after threshold | Change category plus photos | Under 4.0%, Breakfast restaurant, 3 photos under 2MB in 7 days | Reassigns candidate set and fixes pack rendering |
| Rating stressed | Halt asks, reply to low stars | At 4.42 or lower, halt 14 days, 100% of 1-3 stars in 12 hours to 4.65 | Stabilizes distribution before resuming volume |
| Reference cluster | Optimize for walking-scale set | 5 venues within walking distance at Cl. 12d #346 per Expedia | Proves discovery is won in micro-radius, not citywide |

## What to do next

| Step | Action | Why it matters |
| --- | --- | --- |
| 1 | Prioritize availability, logistics, and pricing signals around $2.28 rather than chasing broad totals that retrieval filters out. | Optimize for eligibility inside the core service area; small transactional details feed retrieval logic alongside distance. |
| 2 | Spend only on hyperlocal discovery inside a 3.0-mile cutoff and stop citywide SEO immediately. | 62% of discovery sessions never get past the proximity filter, so distance gates visibility long before ratings matter. |
| 3 | Push Egg Tuck to 100+ reviews at 4.65+ stars to secure ranking position among retrieved candidates. | Proximity outranks stars; once inside the candidate pool, high ratings decide order in the Maps 3-pack. |
| 4 | Align dynamic pricing and real-time availability with morning foot traffic and courier supply patterns. | Graph-based systems ingest logistics signals and micro-location demand to determine if a venue is eligible to appear. |
| 5 | Verify block-level geography matches neighborhood cravings to ensure graph database mapping captures local demand. | Eligibility is shaped by logistics signals and proximity; misalignment prevents entry into the ranked set entirely. |

## Frequently Asked Questions

**What percentage of discovery sessions never pass the proximity filter before ratings are considered?**

62% of discovery sessions never get past the proximity filter, according to platform infrastructure reporting.

**How does a breakfast sandwich price point factor into the initial retrieval stage?**

The $2.28 breakfast price signal shows how small transactional details feed that same retrieval logic alongside distance.

**Why might a 4.2-star shop consistently outrank a 4.8-star competitor eight miles away in the Maps 3-pack?**

A closer shop with a lower rating routinely outranks a farther shop with a higher rating when both sell the same intent because the ranker learned that morning users will not drive.

**At what review count threshold do shoppers stop sampling and start shortlisting for a local breakfast spot?**

The compilation of 100 user reviews puts it past the count threshold where shoppers stop sampling and start shortlisting.

**How much higher is the click-through rate for listings with over 100 reviews compared to those with 10-20 reviews?**

Listings with 100+ reviews earn 18.7% CTR versus 6.1% for listings with 10-20 reviews.

**What monthly review velocity is required to grow 3-pack impressions 41% faster than slower-growing merchants?**

Merchants gaining 8-10 reviews per month grow 3-pack impressions 41% faster than those gaining 1-2 per month.

## Quick answers

| Why does retrieval matter first for Egg Tuck in 2026? | Google Maps never scores Egg Tuck if it never retrieves it. |
| --- | --- |
| How much do proximity filters affect discovery sessions? | Systems apply a proximity cutoff seen in 62% of discovery sessions, so distance decides candidacy before ratings matter. |
| What happens to venues outside the service radius? | Only venues inside the service radius are scored for relevance, a constraint tied to 62% of demand shaped by courier supply and foot traffic. |
| How does proximity affect ranking among eligible breakfast shops? | A closer shop with a lower rating routinely outranks a farther shop with a higher rating when both sell the same intent — egg sandwich, breakfast sandwich — because the ranker learned that morning users will not drive. |
| How does CTR influence future placement for egg sandwich intent? | High click-through on egg sandwich reinforces that listing for that intent for days afterward, while persistently low engagement triggers demotion even if stars stay flat. |

### Related reading

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