What happened to response time?
It went from a 72-hour average to under two minutes, across 51 locations and five brands, at a 97.8% response rate.
That is the short answer, and the size of the change is easy to misread. A drop from three days to two minutes sounds like the automation is simply faster at typing. It is not. The 72 hours were never spent writing replies — they were spent finding out a review existed.
Where did the 72 hours actually go?
Before any automation, review data across the whole network was compiled, organised and analysed by hand in Excel. Store managers and regional leads pulled reviews from Google and other platforms, entered them into spreadsheets, and calculated performance formulas manually.
That consumed 15 to 20 hours per week of management time — people whose job was operational decisions, doing data entry instead.
The response delay was a symptom of that. A review posted on Tuesday afternoon might not appear in anyone's spreadsheet until Thursday. By the time it was visible, it was three days old. Nobody was slow; the review was simply invisible until someone went looking.
This is the part worth understanding before you assess any vendor's response-time claim: if your bottleneck is discovery rather than writing, a tool that only drafts replies faster will not move your average at all.
Why does this get worse as you add locations?
Manual review compilation scales linearly with locations. Management capacity does not.
Inspired Cannabis grew from 40 stores to 51 during the period, and the data discrepancies grew with it. The specific failures were predictable ones:
- No centralised view of how any individual store was performing
- No way to compare one location against another
- Weekly and monthly trends invisible without hours of digging
- Five separate brands impossible to track consistently by hand
- Response rate itself unmeasurable — there was no way to know what share of reviews got a reply
That last one matters more than it sounds. A network that cannot measure its response rate cannot tell whether a given location is neglected. The stores that most needed attention were the hardest to identify.
What replaced the spreadsheets?
A centralised review analytics platform, with the manual spreadsheet workflow removed entirely rather than sped up:
- Automated daily sync. Reviews across all 51 stores and 5 brands pulled, matched and indexed every day, with no manual entry.
- Six report views. Executive, Weekly, Monthly, All Reviews, Needs Attention and Store History, filterable by brand, store, date range and rating threshold.
- Brand-consistent automated replies with response-rate tracking, and a Needs Attention queue that flags unresponded or low-rated reviews for a human.
- Cross-location benchmarking — including a volume-adjusted rating change view that separates stores genuinely improving from those flattered by low review volume.
Thirty-plus metrics in total. The point of that number is not comprehensiveness for its own sake — it is that leadership can ask a question about any slice of the network and get an answer without asking someone to build a spreadsheet.
What did automation not change?
The network held a 4.57 star average through the deployment. Automating responses changed response time and response rate, which are the two things it controls. It did not manufacture better ratings, and any vendor telling you a response tool will lift your average is selling you something else.
What the reporting layer does contribute to rating is indirect and slower: when you can see which locations are declining, you can act before the trend sets. That is a management outcome, not an automation one.
What should you measure first?
Three things are worth taking from this, whether or not you ever work with us:
- Measure your discovery lag, not your reply speed. Time from review posted to review seen by a human is the number that governs everything else.
- Find out your response rate first. If you cannot state it, that is the finding. Coverage gaps hide in networks where nobody is measuring.
- Count the management hours, not just the reviews. The 15 to 20 hours per week is what funded this build. Response time was the visible win; the reclaimed senior time was the return.
The figures in this article come from a single deployment across one 51-location network, documented in our Inspired Cannabis case study. One deployment is not a benchmark — your numbers will differ with platform mix, review volume and how your locations are managed. It is real, and it is specific, which is more than most claims in this category offer.
Frequently Asked Questions
Manually, the practical average across a large network is measured in days rather than hours. Before automating, Inspired Cannabis averaged 72 hours to respond to a review across its 51 locations — not because anyone was slow, but because reviews were compiled by hand from Google and other platforms into spreadsheets before anyone could act on them. After deployment the same network responds in under two minutes, at a 97.8% response rate.
Response rate and recency are visible signals on a business profile, and unanswered negative reviews sit permanently at the top of what a prospective customer reads. For a multi-location network the compounding problem is inconsistency: a well-run store and a neglected one look the same to a customer if neither replies. Automating the response layer makes coverage uniform across every location rather than dependent on which manager has time.
At Inspired Cannabis it consumed 15 to 20 hours per week of management time. Store managers and regional leads were pulling reviews from multiple platforms, entering them into Excel and calculating performance formulas by hand. The burden grew with the network — the discrepancies worsened as store count went from 40 to 51 — because manual compilation scales linearly with locations while management capacity does not.
They can if the system is built for the brand rather than bolted on generically. The Inspired deployment generates brand-consistent replies across five separate brand identities, and routes anything that needs judgment to a person: a "Needs Attention" view flags unresponded and low-rated reviews for human follow-up. The automation handles volume and consistency; it does not attempt to handle apologies that matter.
Not directly, and it would be dishonest to claim otherwise. Automation changes response time and response rate, which are the two things it controls. Inspired Cannabis maintained a 4.57 star average across the network through the deployment. What the reporting layer adds is the ability to see which locations are genuinely improving versus which look better only because they have low review volume.
Running reviews across multiple locations by hand?
We will look at your discovery lag and response rate before proposing anything. If a spreadsheet is genuinely working for you, we will say so.
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