The evidence
The research behind Slad
Every claim Slad makes, and every default in our revenue calculator, is built on published research and established industry data. Here is the evidence, the sources, and exactly where we stay conservative.
Last reviewed May 2026.
How to read this page
- We tag every source by type: academic (peer-reviewed), research firm (established analysts and surveys), and industry data (trade and platform reporting, directional).
- Where a study gives a range, our calculator uses the conservative end.
- None of this is a guarantee. The calculator is directional until it is calibrated against your own point-of-sale, and nothing here is financial advice.
Reviews and reputation move revenue
Your public rating is not vanity. For an independent restaurant it is a measurable driver of revenue, and the way you handle reviews changes the rating itself.
+5 to 9%
revenue for each one-star rise in rating, concentrated among independent restaurants.
Luca, Harvard Business School →92%
of consumers read reviews of a local business before a first visit, and 95% trust a business more when it has many reviews.
BrightLocal 2025 →+0.12 star
average rating rise, and 12% more reviews, once a business starts replying to reviews.
Proserpio and Zervas, Marketing Science →How Slad uses it: Reputation Shield steers happy guests to public reviews, routes unhappy ones to private feedback before they post, and replies to every review in the reviewer's language. The calculator's reputation line prices a rating climb at the conservative 5% per star, the low end of the Luca range.
An active Google listing wins the local search
Most diners still start on Google, and a fresh, complete profile is what earns the click, the call, and the visit.
76%
of people who search for something nearby on their phone visit a business within a day.
Think with Google →+70% / +50%
more likely to attract location visits, and to lead to a purchase, when the Google Business Profile is complete (Google's own data).
Google Business Profile →How Slad uses it: Slad keeps your Google profile active on autopilot: posts on a cadence, a reply to every review, fresh photos pulled in for you, and your Instagram turned into Google posts, so the listing stays complete and alive without you touching it.
And AI is now where diners ask
A fast-growing share of diners now ask an AI assistant where to eat, and there is published research on how to be the answer it gives.
45%
of consumers now ask AI for local business recommendations, up from 6% a year earlier, ahead of Yelp and TripAdvisor.
BrightLocal →up to +40%
gain in visibility inside AI answers from optimizing content for generative engines, in a Princeton-led study (KDD 2024).
GEO, Princeton / KDD 2024 →-25%
forecast drop in traditional search volume by 2026 as AI chatbots and assistants become answer engines.
Gartner, Feb 2024 →How Slad uses it: Slad features your food on the @searchslad channel and the getslad eats guide, structured pages an AI assistant can read and cite, so you surface in the answers ChatGPT, Claude, Gemini, and Perplexity give. The calculator's "found on Google and AI search" line stays a flat, conservative count of new diners, not a multiplier.
Diners eat with their eyes
People choose food by looking at it. A clear photo sells the dish, and motion (the steam, the drip, the sizzle) sells it harder. The pattern holds across menus and social feeds.
+44%
more monthly sales for menu items that carry a photo, on DoorDash, where 38% of diners say they look at the photos when choosing.
DoorDash merchant data →Video beats static at every level
Across follower tiers, video stories and posts reach and engage more people than still images.
Rival IQ →Short-form video leads
In food and beverage, short-form video is the top-performing format for reach and engagement.
Dash Social →How Slad uses it: Slad turns one dish photo into a polished food video with the steam, drip, and sizzle a still cannot show, and posts both your photos and videos to every channel, so your best dishes are the first thing a diner sees.
Ordering from the table lifts the check
When guests order from their phone, they browse longer, add the drink and the dessert, and do not wait on a server to do it.
+10 to 20%
average-check lift commonly reported from self-ordering (kiosk, tablet, or QR), with higher figures when the menu is built for upsells. This is trade and platform reporting, so we treat it as directional, not proof.
How Slad uses it: the calculator's table-ordering line uses a conservative 12% check-uplift default, near the bottom of the reported range, and tells you to calibrate it against your own point-of-sale after a short pilot.
Why a tap, not a scan
Slad uses NFC stickers, so a guest taps the table once and the menu opens. A QR code takes several steps: open the camera, aim, wait, then tap the link, and every extra step loses people. The independent research that exists is on contactless and mobile-payment adoption, which finds ease of use and speed are what make people prefer a tap to extra steps. There is no independent study on tap-to-open-menu versus scan-to-open-menu specifically, and the much higher conversion rates that NFC vendors claim, we treat as directional. The step-count difference itself is the real, simple reason Slad uses a tap.
Memberships and loyalty lift visits and spend
Regulars are worth more than their next visit. A club or membership turns an occasional guest into recurring, predictable revenue.
~20% more
visits and spend per visit reported among loyalty members versus non-members.
Paytronix →+8 to 12%
higher average order value commonly seen in a loyalty program's first year.
Restaurant Business →How Slad uses it: guests join memberships and clubs right from the table. The calculator models that as recurring monthly fee revenue from the regulars you set, and a modest 1.25x repeat multiplier captures the extra value of return visits.
How the revenue calculator uses this
Each line in the revenue calculator maps to the research above, and where a study gives a range we take the cautious end.
| Calculator line | What it uses | Grounded in |
|---|---|---|
| Reputation lift | 5% revenue per star, the low end | Luca (5 to 9%) |
| Found on Google and AI search | a flat count of new diners, not a multiplier | Google, BrightLocal, Princeton GEO, Gartner |
| Guest sharing | Stories reach, then conservative action and conversion | Rival IQ, Stackla / Nosto |
| Table ordering | 12% check uplift, near the bottom of the range | Industry reporting (10 to 20%) |
| Memberships | your own adoption and price inputs | Paytronix, Restaurant Business |
| Repeat and LTV | a modest 1.25x multiplier | Loyalty repeat-visit research |
What we do not claim
- That you will get these exact numbers. The calculator is a directional planning tool until it is calibrated against your real point-of-sale.
- That every figure is peer-reviewed. We label which sources are academic and which are industry or platform data, and we keep the industry figures conservative.
- That this is financial advice. It is evidence to help you reason about the decision, nothing more.
All sources
- Academic Michael Luca, Reviews, Reputation, and Revenue: The Case of Yelp.com, Harvard Business School. Read the paper →
- Academic Proserpio and Zervas, Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews, Marketing Science (2017). Read the paper →
- Research firm BrightLocal, Local Consumer Review Survey 2025. View the survey →
- Research firm Think with Google, local and "near me" search behavior. Think with Google →
- Research firm Gartner, Gartner Predicts Search Engine Volume Will Drop 25% by 2026 (Feb 2024). Read the release →
- Research firm Stackla (Visual UGC by Nosto), consumer authenticity survey. View the survey →
- Research firm Rival IQ, Instagram Stories Benchmark Report. View the report →
- Research firm Dash Social, Food and Beverage Industry Benchmarks. View the benchmarks →
- Research firm Sprout Social, Instagram algorithm and the priority of private shares ("sends per reach"). Read the analysis →
- Academic Current Psychology (Springer, 2025), information sharing on short-form video platforms (dual-systems theory). Read the paper →
- Academic Aggarwal et al., GEO: Generative Engine Optimization, KDD 2024 (Princeton). Read the paper →
- Academic Frontiers in Psychology, contactless and mobile-payment adoption (ease of use and speed). Read the paper →
- Research firm BrightLocal, AI and local business recommendations. View the study →
- Platform data Google, Business Profile performance and completeness. View the data →
- Platform data DoorDash, merchant guidance on menu photos (April to June 2022 study). View the data →
- Design note NFC tap versus QR scan for menus: no independent study exists, and the menu-conversion figures are vendor-reported, so we treat them as directional. The closest independent evidence is the contactless-payment research above. We rely on the step-count difference, not the vendor percentages.
- Industry data Paytronix, loyalty program effectiveness. View the data →
- Industry data Restaurant Business, restaurant loyalty program economics. Read the report →
- Industry data Self-ordering check-size lift: trade and platform reporting (kiosk, tablet, and QR ordering). Treated as directional in our calculator.
We calibrate these numbers to your real POS data on the call.