If your business has an aggregate star rating of 3 or less, you need to be concerned that shoppers might reject you out of hand. If, on the other hand you have a star rating greater than 4 most shoppers will accept you on face value. However we are seeing dramatically different standards within different age groups.
Several days ago I published surveys that clearly indicated that consumers perceive that a negative review corpus hurts a business more than positive reviews help them.
I asked the following questions:
If you found a [business] online with negative reviews from customers, how likely would you be to choose it?
If you found a [business] online with positive reviews from customers, how likely would you be to choose it?
The surveys, in asking the questions in a broad way, left the question of what negative and positive reviews meant to the survey taker.
To answer the question of exactly where a searcher drew the lines I asked these two follow up questions:
When searching online for a local business, at what point on a 5 star review scale would you decide to NOT consider the business?
When searching online for a local business, how many review stars on a 5 star scale do you need to consider the business?
Here are the overal results to the first question (sample size 2500 American internet adults ages 18 and up):
And the results to the second (sample size 2500 American internet adults ages 18 and up):
It’s a little easier to parse this data by consolidating some of the results. Essentially if a business is showing 3 stars or less, 82.8% would not do business with that business.
Like wise, about the same percentage, ~84.5% would need to see something greater than 4 stars before considering any given business.
The obvious conclusion from this, in a general sense, is that if a business has a star rating greater than 3 than they will not be rejected out of hand and if they have star rating greater than 4 then most folks would consider that business favorably.
That make all kinds of sense but the research really turned up some interesting results when you started looking at the data by age group. Continue reading