Review management + business intelligence · Built for Toast
ShiftLift is a restaurant review-management tool with a connected business intelligence layer. The only tool that connects guest sentiment with financial and operational performance. Find the visit behind the review, investigate the root cause, and take action — then watch how sales and sentiment change, with keeping guests coming back at the center.
Value from day one is the goal · Sample data shown · Capabilities labeled
The three questions
Review management tells you how guests feel. Business intelligence connects that feedback to sales and server performance. Together, they give managers a clearer next step for service, coaching, and retention.
Start with review management: guest ratings, comments, and issues tied to the visit behind them. Managers can see what needs attention, understand the experience, and prioritize follow-up designed to keep guests coming back.
Guest conversations and replies are in development; live outbound messaging is not yet validated.
A sales total is only the start. See sales trends, check averages, spend per guest, and what's selling — by period and team member. Spot a softer lunch shift or a drop in beverage sales before it gets lost in the weekly total.
Server A has a lower check average than Server B. Do guest comments point to rushed service, or do the checks suggest missed recommendations? Bring sentiment and selling behavior into the same review. A tip-percentage concern is another question to investigate — not proof of poor service.
Illustrative example · Check sample sizes; a pattern is not proof of cause.
Who built this
By restaurant owners, for restaurant owners. Zach’s turnaround experience meets Scott’s data-science perspective — connecting what guests feel to what managers can actually change.
From global franchises to independent restaurants
Co-founder, ShiftLift
Before co-founding The Feinstein Group with Christina in 2014, Zach built his career turning around Burger King operations. TFG’s story describes him taking the company’s worst district to its best, then traveling internationally to help turn around some of the system’s largest franchisees through Burger King’s SWOT Team.
He earned his Six Sigma through McKinsey, bringing a disciplined, root-cause approach to restaurant problems. He now applies that experience to independent operations: TFG’s five restaurants and bars include The Black Pearl, The Living Room, Sonder Social Club, and Highland House.
The data perspective
Co-founder & Data Scientist, ShiftLift
Scott brings a data science perspective to ShiftLift, combining industrial design, entrepreneurship, and digital analytics experience dating to 2009. His work spans measurement strategy, reporting, and optimization for Fortune 500 companies. Through his own company, he worked with Elias to develop a business intelligence platform for American Electric Power (AEP).
That experience inspired him to bring enterprise-level analytics within restaurant owners’ reach. COVID paused the idea. Meeting Zach supplied the missing connection: linking guest sentiment with restaurant performance.
At ShiftLift, that discipline meets the restaurant floor, revealing the sentiment and visit context behind a lower check average or weaker tip percentage. The aim is better-informed decisions, measurable results, and insights managers can act on.
Financials, product mix, guest reviews, and training records belong in the same investigation. Zach has seen the scale of a global franchise and the thin margins of a neighborhood restaurant. That is the thinking behind ShiftLift: trace a concern to the visit, understand what happened, take action, and measure the change.
The ShiftLift difference
A guest says service was slow but the server was wonderful. Was the delay on the floor or in the kitchen? Connect what the guest felt with operational evidence before choosing what to fix.
Shift · Guest relationships
The Shift side is designed for real-time guest conversations and offers that give guests a reason to return. Address the concern while the experience is fresh, then follow up with a personal next step.
Guest conversations and retention offers are in development; live outbound delivery is not yet validated.
Lift · Connected business intelligence
The Lift side connects guest sentiment and follow-up activity to POS checks, server performance, and the health of the business. Investigate the root cause, choose what to change, and watch how sales and sentiment move together.
Reporting depends on supported POS data; verified return visits and recovered revenue remain future capabilities.
The voice
See ratings, comments, and recurring issues with the actual visit attached. Prioritize guest concerns and prepare a response with the whole story in front of you. Guest replies remain in development.
The receipts
Connect sentiment with check averages, spend per guest, and what each server sells. Where accessible, supported POS data includes kitchen display times, an overlay could help compare kitchen delays with slow-service comments and praise for server friendliness. This is an illustrative reporting opportunity; kitchen-time reporting is not yet an established capability.
The ShiftLift layer
Give managers the context to address concerns and improve the next visit. Retention is the goal, not a checkbox: closing a case is not proof a guest returned. Verified return and recovered-revenue reporting are future capabilities.
Toast stays the source of truth for operations; OpenTable adds reservation context where a shared check identifier exists.
How it works
One issue, followed end to end — the way a manager actually works it.
A guest rates their visit through a location QR survey, with the check number and date.
“Wait was long, but the team recovered nicely.”
Survey response · 8:42 PM
ShiftLift locates the Toast record behind it — the table, the server, the items, the spend.
Table 12 · 4 guests · 55-minute wait · Server M. Okafor
Toast · check 2417
Open the filtered conversation, review the evidence, and give the team a clear next step.
Review pacing with the floor lead before Friday service.
Guest Conversations · open
ShiftLift uses the restaurant, check number, and visit date to locate the Toast record. Ambiguous or missing matches stay unresolved rather than being attached to a guessed visit.
Perfect Check · In development
Define your Perfect Check: an entrée, an appetizer, a dessert, and a drink, for example. The monitor in development compares eligible checks by worked role, helping managers explore selling habits alongside guest sentiment and check averages.
Server A · Perfect Checks
Preview42%
Share of eligible checks meeting the restaurant's recipe. Which categories are most often missing?
Server B · Perfect Checks
Preview61%
Compare the same worked role and recipe. Is there a recommendation technique worth sharing?
Peer comparison
Preview19 pts
An illustrative gap to explore alongside guest feedback, check averages, and sample sizes — not a revenue promise.
Illustrative preview, not restaurant results. Perfect Check's dedicated per-role monitor is in development. Comparisons require reliable samples; shared accounts are not automatically an individual's performance.
Integrations
ShiftLift reads from the systems your team already trusts and hands back a single, calm view. It complements the POS and reservation system — it does not replace either.
Remains the source for POS records: checks, items and modifiers, spend, and the people who worked them.
Adds reservation context where a shared check identifier exists. Coverage depends on the source records — not every visit has a reservation.
Supported integrations, not formal partnerships. Data freshness reflects each integration's latest successful synchronization.
Common questions
Our goal is value from day one: less time piecing together a review, a clearer coaching opportunity, or a guest concern worth following up. See the connected workflow in a demo and assess what it could be worth for your restaurant — first-day payback is not guaranteed.
Request a demo