Google has a keyword search on every listing, and it works. Open the listing, hit the search icon next to the sort menu, and type a name or a word. Google returns only the reviews containing that exact string, highlighted in bold, and it understands basic operators like Wine OR Sommelier.

Three things it will not do. It matches the exact word you typed, in the language the review was written in, so a French review of your wine list does not come back for "wine". It gives relative dates, so you cannot tell when something started being mentioned. And its own AI topic summary ignores your search, so you cannot combine the two.

Reviewflowz fixes all three. It also gives you two things a keyword search cannot do at all: tags, which match a concept instead of a word, and attribution, which links a review to the person who earned it even when nobody is named.

Head over to your listing on Google Maps and hit the search icon, next to the sort menu.

Google filters the list down to reviews containing your keyword, and bolds it in the text.

It supports most of Google's advanced operators, and definitely the basic AND and OR. So to surface every review mentioning wine or a sommelier, search Wine OR Sommelier.

It is language specific. Google searches the original review text for your exact keyword. If the word only appears in the translated version, that review does not come back. You can work around it with "Wine" OR "Vin", but you have to know every language your customers write in, and spell each one correctly.

The dates are relative. "3 months ago" is not a date, so you cannot pin down when a problem was first mentioned or count mentions per month. That is a separate problem with its own article.

The topic summary ignores your search. Google's AI surfaces the topics mentioned most often, but it does not combine with keyword search, so you cannot drill into one.

Same search, on every platform at once, and every review carries its exact date.

Two keywords, 102 matching reviews, and the platform split underneath: 63 on Tripadvisor, 14 on Consumer Affairs, 11 on Smart Customer, 10 on Better Business Bureau, 4 on Booking. Google's search only ever looks at one listing.

The Date column is a real date, not "3 months ago", so you can sort the matches, count them by month, or find when something was first mentioned. Download takes the filtered set straight to Excel.

A keyword search finds the word you typed. A tag finds what the review is about.

You create a tag, switch Applied by AI on, and every review gets read against it. The tag lands when the review speaks to the concept, not when it happens to contain the string. So a wait time tag catches "we stood outside for forty minutes", which no keyword search would ever return.

It is language agnostic for the same reason. The review is read, not string matched, so a French review about the wine list lands under wine without you writing a single French keyword. That is the exact limitation of Google's search, gone.

Then tags behave like any other filter: filter the review list by one, chart it over time, or break it down by rating.

If you are searching by name, you are almost certainly looking for the reviews one of your people earned. Keyword search is a poor tool for that, because it only finds reviews that spell the name correctly, and it counts nothing.

Attribution does it properly. Every review is read against your team roster, nicknames included, so "Alex" matches Alessandro. A match is only kept above 95 percent certainty, and anything below goes to a queue for you to confirm, so you are checking the machine rather than doing the work.

It also catches what no name search can. If your bookings come from a connected system, a review from that customer is credited to the crew on that booking even when the review names nobody at all.

The result is a running count per person, not a list of search hits.