Review Velocity

Velocity beats volume because it is hard to fake and easy to read: it says the business is doing good work now. Specificity multiplies it — reviews that mention the service, the neighbourhood, the situation ("burst pipe at 2am, here by 3") are exactly the language assistants quote when justifying a recommendation.

Steadiness matters as much as rate, and this is where well-intentioned campaigns backfire. Forty reviews in a week after two silent years is a pattern that reads as procurement rather than performance, and platforms are explicitly tuned to detect it. A business earning six to ten reviews a month, every month, outperforms one that arrives in bursts even at a similar annual total.

The AI layer amplifies the specificity point in a way the ranking layer does not. A five-star review saying "great service" contributes to an average and nothing else. A review saying the crew arrived the same day, worked around a tile roof, and handled the insurance paperwork gives an assistant sentences it can repeat as reasons. When an assistant explains why it recommended a business, it is usually paraphrasing review text. Businesses whose reviews describe specifics get described specifically.

Responses are part of the corpus too, and they are entirely within your control. A reply that names the service and the neighbourhood adds retrievable text to the profile; a reply saying "thanks!" adds nothing. Responding to negative reviews plainly and without defensiveness matters more than the rating itself, because it is read as evidence about how the business behaves when something goes wrong.

The system is operational, not marketing: ask at the moment of delight, text the direct link, respond to everything within days. Businesses that systematize this outrun bigger competitors coasting on a stale count — and it feeds the local pack and the AI shortlist from the same effort.