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Solution · Customer serviceThe system does the asking, after every visit, so the profile starts telling the truth about the firm
Google reviews collected after every visit
After a visit, a robot sends a thank-you with a Google review link: to everyone, by written rules, no more often than you set. New reviews land as Teams alerts, replies stop waiting for weeks, and the review count starts matching the count of happy customers.
Executive summary
Happy customers do not write reviews by themselves, because nobody asks at the right moment; the unhappy do, so the profile shows a distorted image, and new customers choose by that image.
After every visit the robot sends a thank-you with the review link, to everyone, by rules: who, when, no more often than a set interval; new reviews arrive as alerts, and replies have a rhythm.
The review count grows from single digits to dozens a year, the average starts reflecting reality, and the profile turns from a liability into the cheapest customer acquisition channel there is.
the visit calendar or transaction list; the SMS gateway; the Google profile; alerts and a report in Microsoft Teams
Business problem
A profile that tells an untruth about you
For a new customer the first contact with a firm is not your website or your reception, but the profile on the map: the stars, the review count, and what others wrote. The choice between two firms is settled there in a dozen seconds, before anyone calls. It is the firm’s most important storefront, and the only one whose content you do not write.
The trouble is that its authors are not random. A happy customer leaves, says thank you and disappears: he will not write a review, because he has no reason, no moment and no link at hand. The unhappy one has all three. As a result, a firm with hundreds of successful visits a year collects a handful of reviews with the bad ones over-represented, and its profile tells an untruth: it shows a worse firm than the real one.
Asking manually fails for the same reason every memory-based process fails: reception asks when it remembers and when the customer looks pleased, that is, rarely and selectively. And the replies to reviews, which Google and readers visibly reward, wait for weeks, because nobody checks the profile daily.
The arithmetic is plain: the difference between 11 and 60 reviews is, to a customer’s eye, the difference between an accidental firm and a confident one. That difference converts into phone calls, and it is produced not by the quality of the work, but by whether somebody asks systematically.
How it works today
Below is what the work looks like before anything is automated.
- PersonReception asks for a review when it remembers and the customer looks pleased: rarely
- Risk of errorThe happy leave in silence; without a link at hand nobody returns to it
- Risk of errorThe unhappy one has a reason, a moment and a keyboard; he writes on Sunday at 11:40 p.m.
- WaitingA new review hangs unanswered for weeks, because nobody checks the profile
- Risk of errorThe average and the count distort the firm’s image against it
- Risk of errorNew customers choose the competitor by the stars, before anyone calls
Why the current process costs more than it appears
The bill that never shows up in a budget.
- Every customer who chose a competitor by the stars costs the full value of a relationship that never happened.
- A profile worse than reality is a tax on every working day, paid in calls that never come.
- An unanswered review tells readers more than the review itself: that the firm does not care.
- Asking manually, selectively and on holidays does not change the numbers; only consistency does.
Cost of inaction
The first row is a cautious model: it takes only a dozen or so customers a year settling their choice by the stars for the sum to assemble. You will learn your own scale by comparing your profile with three competitors from the map; it is a five-minute exercise and it usually stings.
The second row is the heart of the matter: hundreds of happy customers a year are hundreds of review chances, of which, without a system, a handful materialise. The system does not create satisfaction; it stops wasting it.
A model organisation with realistic proportions – the numbers exist so you can run the same maths on your own data; they are not a client result.
A services firm with customer visits: around 420 visits a month, a Google profile with 11 reviews after three years, competitors with several dozen, Microsoft 365.
Verbal review requests, on special occasions; a review link exists in no process; replies sporadic.
New reviews noticed by accident, sometimes weeks later; the bad ones hurt twice, hanging unanswered.
The owner knows the profile undersells the firm, but no process changes it.
After a visit the robot sends a text with a thank-you and the review link: to all customers by the rules, with a per-person frequency cap and the exclusions you set; a new review arrives as a Teams alert with a suggested responder; the monthly report shows growth, the average and reply speed.
In the modelled case the firm collects dozens of reviews a year instead of a few, and replies appear within a day. Model numbers, not the firm’s records; nobody controls the reviews’ content, because customers write them.
Proposed solution
We start with rules that are honest and yours: the request goes to all customers after a visit, with no filtering into happy and unhappy, because both Google’s policies and plain decency require it. You set the limits: no more than once per half-year per person, exclusions for sensitive situations, the timing after the visit.
The message itself is short and human: a thank-you for the visit and a link that opens the review window with one tap. It is the absence of that link at the right moment, not an absence of satisfaction, that has been holding the reviews back. Next to the link the customer also finds your direct contact in case something went wrong: not to talk him out of a review, but so the problem reaches you faster than it reaches the internet.
Every new review arrives as a Teams alert with the visit’s full context and a suggested responder; good ones get a thank-you within a day, hard ones go to the owner with the full history before anyone replies in the heat of the moment. The monthly report shows review growth, the average and reply speed: for the first time you can watch the profile catch up with reality.
UiPath Orchestrator: the post-visit send queue, frequency caps, retries and an audit trail; UiPath Integration Service connectors for Microsoft Teams and Outlook 365; the SMS gateway you already use
Send rules with caps and exclusions, message wording in your tone, new-review alerts with visit context, a reply rhythm and the monthly report
The visit calendar or transaction list as the trigger; the Google profile via its interface; the SMS gateway via API
How the automated process works
- AutomationA finished visit triggers, after a set delay, a thank-you with the review link
- SystemThe request goes to everyone by the rules: a per-person cap, exclusions, no filtering
- SystemA customer with a problem finds your direct contact in the message
- AutomationA new review arrives as a Teams alert with the visit’s context
- PersonGood reviews get a thank-you within a day; hard ones go to the owner with history
- PersonThe monthly report: growth, the average, reply speed
Human-in-the-loop model
Automation handles
- Post-visit sends by the rules, with caps and a trail for every message
- New-review alerts with context and the reply rhythm watching
- The monthly report: growth, average, reply speed
People decide
- The reply content; templates help, but a human signs
- The conversations with customers who used the direct contact
- The rules: send timing, caps, exclusions and their changes
Before and after
Systems and integrations
The stack is short on purpose: one engine, one execution layer, one place where a person decides.
Inputs
- finished visits from the calendar or transactions
- send rules, caps and exclusions
- new reviews from the profile
- thank-you and reply templates
Automation layer
- UiPath Orchestrator
- UiPath Robots
- UiPath Integration Service
- UiPath Action Center
Target systems
- sent requests with a trail and caps
- review alerts with context
- the monthly report in Microsoft Teams
Human touchpoints: review alerts in Microsoft Teams; replies on a rhythm; the monthly report; a rules review quarterly
Technologies used
the send queue, per-person caps, retries, a trail for every request
Aalerts, reply reminders, reports
Areading new reviews and the posting link; scope per the platform’s policies
Athank-yous with the link; we work with the gateway you already use
Bthe send trigger; via Bookings, export or API
BIllustrative economic model
Numbers you can check against your own data.
The model promises no purely positive reviews and does not influence their content; it only assumes that customers asked systematically write in proportion to their real experiences. Volumes and values belong to the scenario; you will see your own numbers on the profile after a quarter.
Run the maths on your data
An illustrative estimate based on your inputs. It models freed capacity, not promised savings.
Business benefits
- The review count starts matching the count of happy customers
- New customers see on the map the firm as it really is
- Every review gets a reply on a rhythm that shows publicly
- Problems reach you through the direct channel before they reach the internet
- The cheapest acquisition channel starts working without marketing hours
The management view
- Map reputation stops being the work of chance and of the unhappy’s over-representation
- Every visit works twice: once as revenue, once as a brick of credibility
- A growing profile lowers the cost of every other marketing channel
Board-level KPIs
new reviews monthly · the average rating · reply speed · problems reported directly · request effectiveness
Security and governance
The automation has exactly the permissions it needs. Not one more.
- Sends only to customers with a recorded visit and contact consent; the frequency caps are hard
- No filtering of recipients by satisfaction; the process complies with the platform’s policies
- Nobody controls or moderates the reviews’ content; customers write them
- Every request has a trail: to whom, when, after which visit
- Data stays in your Microsoft 365 tenant and SMS gateway; the robots run in the EU region of UiPath Automation Cloud
Why now
Maps have become the first place local services are chosen; the profile settles comparisons before the phone rings.
Competitors already ask systematically; the review gap grows with every month of delay.
Customers are used to review requests after every service; asked well, they simply write.
Relevant executive roles
The profile finally shows the firm he built, and brings customers instead of scaring them off
No need to remember to ask or catch the moment; the system asks tactfully and always
Gets a thank-you, an easy way to share his view, and a fast channel when something went wrong
Common questions and objections
An unhappy customer needs no invitation; he writes by himself. It is the happy who need the link and the moment. Systematic asking shifts the proportions towards reality, and the direct contact in the message means some problems reach you instead of the profile. Practice is unambiguous: averages rise after such roll-outs, they do not fall.
No, and we do not want to: selective asking breaks Google’s policies and is simply dishonest towards readers. The process sends to everyone by the rules. Honesty works in your favour here: a credible profile with a few critical reviews and replies to them persuades better than a suspicious wall of pure praise.
A reply is two sentences, and the system serves it on a tray: an alert with the visit’s context and a draft thank-you to approve. Two minutes a day suffice, and the reply speed shows publicly, building trust just as the stars do.
When this is not the right solution
- A firm with no visits or transactions tied to an identifiable customer: there is nothing to trigger
- Expecting influence over the reviews’ content or removal of critical ones: we do not do that, and nobody honest does
- No customer contact consents: first order in the consents, then the sends
A question for the next management meeting
How many customers chose a competitor this month after comparing profiles, and how many of those comparisons were lost not by our work but by our silence?
Implementation approach
Scope without ambiguity, before anything is signed.
We deliver
- Written send rules: timing, caps, exclusions
- Thank-you messages with the link, in your tone
- New-review alerts with visit context and a reply rhythm
- A direct problem channel in every message
- The monthly report and two weeks of parallel running
We need from you
- Access to the visit calendar or transaction list as the trigger
- Access to the Google profile and the SMS gateway
- Approved message wording and send rules
Stages
Discovery
The profile today, the competition, visit volumes, contact consents
Rules
Timing, caps, exclusions, message wording, the reply rhythm
Build
Visit triggers, sends, alerts, the report
Parallel run
Two weeks on part of the customers; timing and wording tuned
Go-live
Full traffic; a profile and rules review after the quarter
A quick win. The smallest effort in the customer service catalogue: a visit trigger, a message and alerts; the effect shows on the profile after the first quarter.
The competitor has 60 reviews and 4.7. You have 11, and the memory of that one Sunday at 11:40 p.m. Customers see only the numbers.
Send us your profile link and your monthly visit count. We return a comparison with the map competition and a rules design after which the profile starts catching up with your actual work.
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