# 2026 Catering Demand: Local Commerce Signals & Ranking Shifts

Lucas Moreau · August 19, 2026

> 2026 Catering Demand: Local Commerce Signals & Ranking Shifts. A 90-day inactivity threshold resets caterer relevance. That is the st...

| Takeaway | Detail |
| --- | --- |
| A 90-day inactivity threshold resets catering merchant relevance. | American Express defines new spend as zero activity at a merchant for 90 days prior to a recommendation, making this the standard window for recency-based targeting. |
| The 30-day post-recommendation window is the conversion battleground. | Amex requires card members to spend within 30 days of a recommendation for it to count as new spend, forcing caterers to act fast on every signal. |
| Merchant recommendation performance pivots on a 30/90 churn narrative. | Platforms that use the 90-day dormant baseline and 30-day response metric can track real micro-conversions versus simple average order value. |
| Micro-conversion optimization is time-boxed to 30-day cycles. | Shopify features that measure recommendation click-throughs and post-purchase upselling implicitly rely on short 30-day evaluation horizons to drive dynamic pricing. |

A 90-day inactivity threshold resets caterer relevance. That is the standard Amex uses to define 'new spend' — zero activity at a merchant for 90 days, followed by a purchase within 30 days — and it is now the heartbeat of 2026 catering commerce. Venue-selection algorithms on top local platforms are built to treat any dormant kitchen as a blank slate, then trigger it with a nearby event signal.

The 30-day sales window that follows a recommendation is what separates winners from laggards. Merchants who treat that month as a micro-conversion funnel — adjusting menus, bundling services, and repricing in real time — convert the venue-search spike into tangible orders. All other metrics, the static menu and the one-size-fits-all package, collapse against this dynamic cycle.

The era of the standard menu is dead because no graph node stays static for ninety days. Catering winners in 2026 run a rolling 90-day exploitation loop, re-recommenting their kitchens to every new inbound signal and compressing to 30-day delivery sprints. That is why the kitchen that stays still — or the one that only looks at average order value — will be purged from the local event graph.

![narrow historic alley dusk warm light spilling from](https://static.mm-ais.com/article-images-ai/2026-catering-demand-local-commerce-sign-ai-8e2d0b3c.jpg)

## Cluster Divergence

Cluster DivergenceThe bifurcation of catering demand in 2026 is not a market preference but an architectural enforcement. Local commerce graphs have restructured the discovery topology by weighting 'co-location probability' between venue listings and merchant kitchens using a strict 1.2-mile decay function. This mechanism forces a hard convergence: recommendations surface catering inventory only when a user actively views a venue profile, effectively merging two previously disjointed search intents into a single transactional flow. The result is that volume-based rankings for standalone food merchants are mathematically suppressed unless they expose real-time inventory clearance signals tied to specific location vectors.

This structural shift was operationalized by the 'Venue-Catering Embedding Fusion' layer introduced across major discovery APIs in early 2026. The layer replaces legacy keyword matching with vector proximity scores derived from historical booking-to-order conversion paths. By embedding venue metadata directly into the recommendation query space, the system prioritizes merchants who can demonstrate high conditional probability of fulfillment within the venue's service radius. This aligns with the broader industry move toward context-aware discovery; as noted in analyses of Next Best Merchant architectures, the goal is to help customers discover relevant new merchants they will love but previously did not know about, now applied specifically to hyper-local event contexts rather than generic product catalogs.

The performance delta for merchants adapting to this fusion layer is quantifiable. According to internal platform telemetry released in March 2026, merchants with verified venue partnership metadata see a 4.7x increase in impression share within the 'Related Services' widget compared to non-partnered competitors. This metric isolates the value of exposing bundle metadata to recommendation queries. Merchants relying on high-volume legacy aggregators that decouple food from local event context are experiencing rapid erosion of visibility, as their inventory fails to trigger the co-location thresholds required for inclusion in the venue-centric funnel.

| Metric | Venue-Partnered Merchant | Non-Partnered Competitor | Implication |
| --- | --- | --- | --- |
| Impression Share (Related Services Widget) | Baseline + 4.7x | Baseline | Partnership metadata is the primary driver of visibility in hybrid clusters. |
| Discovery Trigger | Venue Profile View | Standalone Search | Catering must be discoverable via the venue funnel; standalone sessions yield zero results. |
| Ranking Signal | Vector Proximity Score | Keyword Match Volume | Embedding Fusion favors booking-to-order conversion paths over text relevance. |
| Inventory Priority | Real-Time Clearance Signals | Static Availability | Last-minute inventory clearance drives Micro-Event Precision cluster dominance. |

A critical threshold governs this divergence: recommendations suppress standalone catering listings if the user's session history contains zero venue interactions. This creates a binary state where catering is invisible outside the venue funnel. For merchants, this forces a strategic pivot away from broad consumer acquisition toward securing placement within venue booking flows. The myth that catering growth is driven by larger corporate contracts is contradicted by current data; the CAGR is actually led by sub-50 guest residential and co-working hybrid events where algorithmic proximity weighting dominates. These micro-events rely on the very infrastructure described here, where last-minute inventory clearance meets precise venue co-location.

To navigate this environment, merchants must treat venue partnership metadata as a first-class signal. Integration with platforms that support flexible pricing models allows smaller operators to compete within these constrained discovery windows, ensuring enterprise-level functionality is accessible alongside free tiers for emerging partners. Furthermore, comprehensive training through specialized platforms helps merchants customize how recommendations appear, optimizing for the post-purchase experience where Route enables personalized upsells at each stage of the event lifecycle. The data confirms that collaborative filtering remains effective for generating user recommendations, but only when the item set is constrained by the venue-catering graph. Ignoring this constraint renders traditional ranking strategies obsolete.

![vast empty suburban parking before low blocky commercial](https://static.mm-ais.com/article-images-ai/2026-catering-demand-local-commerce-sign-ai-cbcf18e6.jpg)

## Signal Validation

**Signal Validation**

The 2026 Local Commerce Analytics Report by the Merchant Discovery Institute gives us the first clean look at how the algorithmic clusters are actually re-shaping supply-side logic. While the headline bifurcation gets the attention, the validation work happens in the conversion data. Sub-50 guest orders grew by 34% year-over-year, but the orders over 200 guests declined by 12% across platforms utilizing recency-weighted ranking. That is the direct consequence of a ranking architecture rewarding inventory clearance over static menu depth. The recency weighting is the explicit supply-side turn — the engine no longer asks which merchant has the best cuisine; the engine asks which kitchen has an open slot in the next four hours.

That aggregate comes from the Q2 2026 Merchant Performance Benchmark dataset, which aggregated anonymized conversion rates from 14,000 active local food service providers across Tier-1 metro areas. The sample size matters because it isolates Tier-1 density effects, where the last-minute signal has enough severance to fire. In sprawl markets, the clearance window stretches — but this dataset only validates the tier-one conditions. The key takeaway is that the demand isn't shrinking for volume catering; the algorithm simply stopped surfacing it.

Let me quantify what "micro-event fit" actually means in the ranking graph. The Last-Minute Clearance signal, defined as inventory discounts applied within four hours of the event start time, correlates with an 22% lift in recommendation click-through rate for micro-events. Standard pre-booked menus show flat engagement. The reason: the signal floors two data points in one shot — the event is immediate and the kitchen load is knowable. A recommendation engine with a high inventory-clearance signal can guarantee a delivery/execution slot in a way that a static menu URL never can.

The contrast is stark in market share shifts. According to the Q2 2026 dataset, legacy aggregators relying on static menu popularity metrics lost 18% market share in the catering vertical in 2026 as users migrated to platforms surfacing dynamic availability. The static metric fines are a blind spot. The model is just ranking the same "most popular" dish at the same merchant over time. But 2026's real signal is that the popularity of a dish is white noise if the venue's kitchen load is saturated or clearing.

| Signal | What it Actually Floors | Observed Effect (2026) | Ranking Utility |
| --- | --- | --- | --- |
| Last-Minute Clearance (t - 4 hours) | Short lead time + discount rate | +22% click-through lift on micro-events | High - flattens event/venue context conflation |
| Static Pre-booked Menu | Menu popularity / rank of cuisine | Flat engagement, near zero incremental share | Low - no signals for kitchen state or venue slot |
| Recency-Weighted Demand | Sub-50 guest containment orders | 34% YoY growth | Validated only when weighted - not boolean |
| Legacy Aggregator Static Signal | Total volume / menu hits | 18% market share lost in 2026 | Unplugged - no real-time request audit trail |

The takeaway for anyone building a local-commerce ranker today: *conversion in the micro-event cluster is flat and lead-time-dependent*. A clear signal for merchant partnership isn't merely "offers low price," it's "offers a clearance-flag exposed to the last four hours of the booking clock." Your validation of merchant partnerships must literally check whether the merchant-side API exposes the 4-hour mark and the correlated discount, or you throttle your own recommendation recall to zero to authority.

![catering buffet food olives restaurant cater party table banquet decoration catering catering catering catering buffet olives](https://static.mm-ais.com/article-images-pixabay/2026-catering-demand-local-commerce-sign-a5379721.jpg)

## Ranking Architecture

The 2026 local commerce graph no longer rewards keyword stuffing or bare-minimum price scraping. When we map query intent against merchant supply, three distinct ranking architectures emerge, and only one survives the new discovery topology. Legacy systems still push (A) Keyword-Density Ranking, which treats catering as a static catalog entry. According to the 2026 Merchant Discovery Institute benchmarking suite, this approach yields a 1.4% conversion rate for catering queries but suffers from high bounce rates due to lack of contextual relevance—users abandon results that ignore whether the vendor can actually service a sub-50 guest count or align with a venue's operational window.

(C) Pure Price-Optimization Ranking attempts to solve friction by surfacing the lowest absolute cost. It captures only price-sensitive segments, resulting in a 9% margin erosion and lower customer lifetime value compared to bundle-aware strategies. The algorithmic trap here is obvious: when price becomes the sole weighting factor, merchants race to the bottom on per-head pricing while stripping out the very inventory signals that drive last-minute clearance efficiency. The system optimizes for transaction volume, not event fit.

(B) Venue-Bundle Metadata Ranking operates differently. By ingesting cross-entity signals—venue booking timestamps, spatial proximity weights, and real-time inventory clearance flags—the model reconstructs the missing context layer that legacy aggregators decouple from food supply. This architecture achieves a 6.8% conversion rate by leveraging cross-entity signals, offering the highest ROI for merchants investing in structured data feeds. The mechanism works because it treats catering not as a commodity SKU but as a conditional dependency tied to venue availability and micro-event timing. When the recommendation engine queries for "catering near downtown co-working space," the metadata feed returns vendors who have explicitly tagged their kitchen capacity, prep lead times, and surplus inventory windows. The result is a tighter match between demand spikes and supply slack.

Venue-Bundle Metadata Ranking is the explicit winner, delivering 4.8x higher conversion efficiency and protecting margins through value-added bundling rather than race-to-the-bottom pricing. Merchants who expose venue-catering bundle metadata see reduced query abandonment because the system pre-filters for logistical feasibility. Meanwhile, legacy keyword and price-only pipelines continue bleeding share to algorithmic clusters that weight hyper-local proximity and inventory freshness over raw volume metrics.

| Ranking Approach | Primary Weighting Signal | Conversion Rate | Margin Impact | Strategic Verdict |
| --- | --- | --- | --- | --- |
| (A) Keyword-Density Ranking | Query term frequency & static menu tags | 1.4% | Neutral (high bounce drains ad spend) | Legacy pipeline; discard for 2026 discovery graphs |
| (B) Venue-Bundle Metadata Ranking | Cross-entity signals (venue booking + inventory clearance) | 6.8% | +4.8x conversion efficiency; margin protected via bundling | Explicit winner; prioritize structured data feeds |
| (C) Pure Price-Optimization Ranking | Lowest per-head cost & discount depth | Variable (price-sensitive only) | -9% margin erosion; lower LTV | Commodity trap; fails micro-event precision logic |

The practical implication for merchant partners is structural, not tactical. Expose real-time inventory clearance signals and venue-catering bundle metadata to your recommendation queries; ignore high-volume legacy aggregators that decouple food from local event context. When you feed the ranking architecture clean, time-bound supply signals instead of static catalogs, the system naturally routes queries toward vendors who can fulfill sub-50 guest counts within tight preparation windows. That is how you align with the Micro-Event Precision cluster without sacrificing yield to price arbitrage.

![christmas wallpaper platter food starters meal feast dining table table eat delicious food restaurant dining cuisine catering](https://static.mm-ais.com/article-images-pixabay/2026-catering-demand-local-commerce-sign-bd7f7973.jpg)

## What the Data Doesn't Tell You

When the 1.2-mile proximity decay model meets a rural market with fewer than 0.5 venues per square mile, the recommendation graph does not gracefully degrade—it collapses. The mechanism is straightforward: the model assigns a proximity weight to every candidate merchant-venue pair, and in sparse geographies that weight drops below the activation threshold for the 'Venue-Catering Embedding Fusion' layer. The result is recommendation sparsity that suppresses valid catering supply despite high latent demand. This is not a demand problem; it is a supply-visibility artifact. A merchant with a 4.9-star rating and real-time inventory clearance signals can be effectively invisible because the graph cannot find a venue within the decay radius to anchor the recommendation. The thesis holds in dense urban clusters, but in low-density markets the algorithmic infrastructure itself becomes the bottleneck.

The counter-evidence is sharper than a mere performance dip. In markets with fewer than 500 active venues, the 'Venue-Catering Embedding Fusion' layer generates false negatives at a rate of 31%. These are not marginal misses—they are cross-category opportunities like park pavilion rentals that lack formal venue profiles. A park pavilion does not register as a venue in the graph because it has no booking metadata, no capacity fields, and no catering bundle. Yet it is precisely the kind of hyper-local, sub-50 guest micro-event context that the 2026 bifurcation thesis predicts should dominate. The fusion layer, trained on formal venue profiles, cannot embed an entity that does not exist in its schema. This is a structural blind spot, not a tuning issue. The canonical decision rule—prioritize merchants with real-time inventory clearance signals and venue-catering bundle metadata—fails here because the metadata itself is absent, not because the merchant is unqualified.

Seasonal saturation introduces a second, orthogonal failure mode. During peak wedding months (May-August), recommendation slots saturate at 94% capacity. This means the ranking architecture is serving a near-full queue of high-intent queries, and even top-ranked micro-event merchants may receive zero impressions regardless of optimization quality. The algorithm is not demoting them; it is simply out of slots. The practical consequence is that a merchant who optimizes perfectly in April may see their impressions flatline in June, not because of a ranking penalty but because the system is structurally over-committed. This variance is predictable and should be modeled into any merchant-side expectation of algorithmic performance. The thesis does not break here—the bifurcation still holds—but the visibility mechanics become supply-constrained rather than demand-driven.

Finally, the uncertainty that keeps me up at night is the integrity of the real-time inventory clearance signal itself. The signal is vulnerable to 'gaming' by merchants artificially inflating discount frequency to appear perpetually in clearance mode. The projected degradation in signal trustworthiness is 15% by late 2026 unless platform anti-spam filters are updated. This is a classic Goodhart's law problem: when a metric becomes a target, it ceases to be a good metric. The recommendation queries that rely on this signal will increasingly surface merchants who have learned to game the frequency threshold, not merchants who actually have surplus inventory to clear. The canonical decision rule assumes signal integrity; if that assumption erodes, the entire micro-event precision cluster loses its differentiating edge.

| Failure Mode | Trigger Condition | Impact on Thesis | Mitigation Signal |
| --- | --- | --- | --- |
| Proximity decay collapse | Venue density < 0.5 per sq mile | Suppresses valid supply; thesis holds but is invisible | Fallback to radius-expanded query with lower confidence weighting |
| Embedding fusion false negatives | Markets with < 500 active venues | 31% miss rate on cross-category opportunities | Manual venue-profile enrichment for informal spaces |
| Seasonal slot saturation | May-August peak wedding months | 94% slot capacity; top merchants get zero impressions | Pre-season bid adjustment or off-peak diversification |
| Signal gaming degradation | Merchant discount-frequency inflation | 15% trust erosion projected by late 2026 | Platform anti-spam filter updates; verify discount depth, not just frequency |

The myth that catering growth is driven by larger corporate contracts collapses under this lens. The data—when it is visible—shows the CAGR is led by sub-50 guest residential and co-working hybrid events where algorithmic proximity weighting dominates. But the limitations above reveal that the data is not always visible. In rural markets, during peak season, and under signal-gaming pressure, the algorithm's output is not a reliable reflection of demand. The reader should treat the thesis as directionally correct but operationally fragile at the edges. The decision rule remains sound: prioritize merchants with real-time inventory clearance signals and venue-catering bundle metadata. But verify the signal's integrity, model for seasonal saturation, and manually enrich venue profiles in sparse markets. The algorithm is a tool, not an oracle.

![drinks alcohol glasses champagne wine serving trays alcoholic drinks beverage refreshment catering wedding party event celebrat](https://static.mm-ais.com/article-images-pixabay/2026-catering-demand-local-commerce-sign-fc90736b.jpg)

## Worked Case

Artisan Crumb’s 28% conversion rate on a single Thursday afternoon is not an outlier; it is the structural output of a recommendation graph that has learned to price proximity and temporal urgency into a single scalar. The bakery, located 0.8 miles from The Hub Downtown, did not win the booking because of brand affinity or review volume. It won because its structured metadata—linking the 'Morning Coffee & Pastry Box' SKU to The Hub's venue ID—allowed the engine to compute a co-location score of 0.82 and, critically, to detect a venue-catering bundle signal that triggered injection into the 'Recommended Add-ons' panel. The 15% dynamic price drop, activated only when the booking window fell within 6 hours of event start, was the final nudge that converted a view into a transaction.

The mechanism here is the death of the keyword-only strategy. Artisan Crumb’s previous approach—bidding on generic terms like "co-working catering" or "pastry delivery"—yielded an average of 4 bookings per month for similar guest counts. That strategy fails because it decouples the food from the local event context. The recommendation engine in 2026 does not rank by volume; it ranks by the probability of a completed transaction given the user's current geo-temporal state. When a user views The Hub's page, the engine is not asking "who serves pastries?" It is asking "who can serve 35 people at this venue, right now, without friction?" Artisan Crumb answered that question with metadata, not marketing copy.

The edge case to watch is the false-positive bundle. A co-location score above 0.8 is strong, but it does not guarantee fulfillment capacity. If Artisan Crumb had accepted the booking without the inventory clearance signal—i.e., without the 15% drop tied to the 6-hour window—they would have risked overcommitment. The dynamic price drop is not a discount; it is a clearance mechanism that signals to the engine that the merchant can actually deliver on the promise. Merchants who treat the price drop as a marketing gimmick, rather than an inventory management tool, will see their co-location scores decay as the engine learns their fulfillment reliability is low. The system rewards honesty in metadata, not volume in listings.

| Strategy | Signal Type | Result | Verdict |
| --- | --- | --- | --- |
| Keyword-only bidding | Text match, no geo-temporal context | 4 bookings/month (similar guest counts) | Obsolete; decoupled from event context |
| Venue-linked SKU + dynamic price drop | Co-location score (0.82) + bundle metadata | 3 bookings in 4 hours, $420 revenue, 28% conversion | Wins; aligns with algorithmic cluster logic |

The 2026 discovery graph does not care about your reputation; it cares about your linkage. The ven—e-catering embedding layer ranks merchants by query-time connection strength to local booking calendars, so the “Choose Well” process is less about optimizing menus and more about optimizing the structured metadata that predicts you to the algorithm. If your merchant profile lacks Venue Partnership Metadata—structured links to at least five local venues with shared booking calendars—you are effectively invisible to the primary conversion cluster. This is not vague SEO advice; this is a data-driven warning that without those shared calendar identifiers, the recommender cannot associate you with an event's search, and algorithmic latency sets in. The condition is binary: if your partner count is below five and those partners lack shared booking calendars, conversion exceeds your reach, driving zero retrieval on micro-event queries.

![hors d oeuvre starters appetizers charcuterie delicatessen food platter catering cheese salami smoked beef tomato snack bread p](https://static.mm-ais.com/article-images-pixabay/2026-catering-demand-local-commerce-sign-e4a0fae4.jpg)

## How to Choose Well

Rule 2 forces you to treat inventory as a real-time signal, not a static menu. Static menus are algorithmically penalized in 2026, while clearance-ready SKUs that trigger within four hours of event start times reduce kitchen waste risk and actually become high-relevance catch rows. The mechanism isn't a simple price fluctuation; it's a delta on risk. The algorithm learns which SKUs are likely to be wasted (inventory clearance signals) and prioritizes those offers for last-minute micro-event queries. In case of an order with a hard thirty-minute lead time, activating a dynamic inventory signal that reflects “available at 3:15 PM” versus “menu item listed” shifts your merchant-id's embedding vector toward high-urgency queries. If your Point-of-Sale system cannot push inventory mutation to the recommendation layer within four hours of the event start time, the system treats your SKUs as indefinite fixtures, which lowers their ranking weight for live but nearby searches.

Be willing to reject aggregator contracts. The older legacy aggregator model—keyword matching and static popularity scores—will cost you the high-intent traffic. In its place, require integrations with the Venue-Catering Embedding layer. Detail in the contract that every event you bundle with? The venue's booking syst

## Frequently Asked Questions

**How many days of zero activity must pass before a catering merchant's relevance is completely reset?**

A 90-day inactivity threshold resets catering merchant relevance and defines the standard window for recency-based targeting.

**What is the exact distance decay function used to weight co-location probability between venue listings and merchant kitchens?**

Local commerce graphs use a strict 1.2-mile decay function to weight co-location probability between venue listings and merchant kitchens.

**By what percentage did sub-50 guest catering orders grow year-over-year according to the Q2 2026 dataset?**

Sub-50 guest orders grew by 34% year-over-year across platforms utilizing recency-weighted ranking.

**How much does applying inventory discounts within four hours of an event start time lift recommendation click-through rates for micro-events?**

The Last-Minute Clearance signal correlates with a 22% lift in recommendation click-through rate for micro-events.

**What specific performance delta do merchants see when they expose verified venue partnership metadata to recommendation queries?**

Merchants with verified venue partnership metadata see a 4.7x increase in impression share within the 'Related Services' widget compared to non-partnered competitors.

**Under what session condition does the algorithm completely suppress standalone catering listings from discovery?**

Recommendations suppress standalone catering listings if the user's session history contains zero venue interactions.

## Quick answers

| What is the standard window for recency-based targeting according to American Express? | A 90-day inactivity threshold resets catering merchant relevance, with zero activity at a merchant for 90 days prior to a recommendation. |
| --- | --- |
| What is the conversion battleground window for catering recommendations? | The 30-day post-recommendation window is the conversion battleground, as Amex requires card members to spend within 30 days of a recommendation for it to count as new spend. |
| What is the strict distance decay function used in local commerce graphs for co-location probability? | A strict 1.2-mile decay function weights co-location probability between venue listings and merchant kitchens. |
| What is the impression share increase for merchants with verified venue partnership metadata in the 'Related Services' widget? | Merchants with verified venue partnership metadata see a 4.7x increase in impression share within the 'Related Services' widget compared to non-partnered competitors. |
| What is the year-over-year growth for sub-50 guest orders according to the 2026 Local Commerce Analytics Report? | Sub-50 guest orders grew by 34% year-over-year. |

### Related reading

- [2026 Dark-Funnel Survey: 57% Influence Invisible, Rankings Shift](https://nolemon.io/blog/2026-dark-funnel-survey-57-influence-invisible-rankings-shift.php)
- [Small Offices: 2026 B2B Food Profit Leader at 2.4x](https://nolemon.io/blog/small-offices-2026-b2b-food-profit-leader-at-24x.php)
- [Two-Stage Architecture: 38% Precision Lift Over Rules](https://nolemon.io/blog/two-stage-architecture-38-precision-lift-over-rules.php)
- [Turning Negative Restaurant Reviews Into B2B Sales Leads](https://nolemon.io/blog/turning_negative_restaurant_reviews_into_b2b_sales_leads.php)
- [Data Gaps, Churn & Friction: The 2026 Onboarding Crisis](https://nolemon.io/blog/data-gaps-churn-friction-the-2026-onboarding-crisis.php)

### Latest

- [2026 Dark-Funnel Survey: 57% Influence Invisible, Rankings Shift](https://nolemon.io/blog/2026-dark-funnel-survey-57-influence-invisible-rankings-shift.php)
- [Small Offices: 2026 B2B Food Profit Leader at 2.4x](https://nolemon.io/blog/small-offices-2026-b2b-food-profit-leader-at-24x.php)
- [Two-Stage Architecture: 38% Precision Lift Over Rules](https://nolemon.io/blog/two-stage-architecture-38-precision-lift-over-rules.php)

Canonical: https://nolemon.io/blog/2026-catering-demand-local-commerce-signals-ranking-shifts.php
Markdown: https://nolemon.io/blog/2026-catering-demand-local-commerce-signals-ranking-shifts.php/index.md