Math Problem - If we have 361 Reviews with a 4.3 Average how many...

[ryan.leslie]

EXCEPT...

I can tell you that at one point, one of the leaders in this space only counted reviews that were less than 24 months old toward the dealership's average. You'd probably need to know the "secret sauce" for each source's score to accurately give the number required to bump the average. If you have 100 1 star reviews from the previous management that are 23 months and 23 days old you may need 0 new reviews in the next week to see your average go up.

BUT...

That doesn't really matter. To Joe's point, the benefits of establishing a rock-solid process for review solicitation far outweigh the benefits of an average score bump alone.

To Joe's other point, I used to say often that "five words and five stars won't sell or service cars." The stars didn't matter much then and they REALLY don't matter now. Work to collect stories NOT stars and then consider taking the time to read the best stories that you collect in team meetings.

 

[joe.pistell]

If I was in charge of review scores, the algo would be sensitive to recent reviews and aware of the volume (or frequency) of reviews (overall and recently).

Luckily, I don't have that job, so your review scores are safe

 

[Jeff Kershner]

EXCEPT...

I can tell you that at one point, one of the leaders in this space only counted reviews that were less than 24 months old toward the dealership's average. You'd probably need to know the "secret sauce" for each source's score to accurately give the number required to bump the average. If you have 100 1 star reviews from the previous management that are 23 months and 23 days old you may need 0 new reviews in the next week to see your average go up.

BUT...

That doesn't really matter. To Joe's point, the benefits of establishing a rock-solid process for review solicitation far outweigh the benefits of an average score bump alone.

To Joe's other point, I used to say often that "five words and five stars won't sell or service cars." The stars didn't matter much then and they REALLY don't matter now. Work to collect stories NOT stars and then consider taking the time to read the best stories that you collect in team meetings.

Great advice Ryan. You're right, anymore I skip past the short reviews (especially on Amazon with smaller purchases.) I look at the NUMBER of reviews and then I'm looking for STORIES. So many dealers now have 100's of reviews and a 4+ star rating. The hard part is getting the stories, a customer to write something of real substance.

In order to get the "stories" I find it's a matter of volume mostly and teaching your team when to take the extra effort of go after the review due to how the purchase went down and the interaction they had with the customer. Like if they (the customer) had a story to begin with (prior shopping experience) and our sales team really stepped up and help out. That's where the stories are. If you have additional advice on that, I'm all open ears...



Since most reviews are short, go for a photo as well...

 

[Jeff Kershner]

If I was in charge of review scores, the algo would be sensitive to recent reviews and aware of the volume (or frequency) of reviews (overall and recently).

Luckily, I don't have that job, so your review scores are safe

I like that idea @joe.pistell - with so many dealers getting reviews anymore I would welcome a more stringent review score/algo.

 

[jscole86]

I like that idea @joe.pistell - with so many dealers getting reviews anymore I would welcome a more stringent review score/algo.

No way - we've got enough to manage on a daily basis!

 

[Jeff Kershner]

No way - we've got enough to manage on a daily basis!

I'm in no way asking for more the manage, just a more relevant score/algo. One that truly separates the Great dealers from the Good dealers.

@joe.pistell - how about some AI triggers that calculates actual "stories" into the final score?
Character count vs Positive words?

Was it DealerRater, or some other popular industry service look at different positive-negative keywords used in reviews and how they swayed the overall ratings? @ryan.leslie

 

[Dan Sayer]

I got 99 problems but an excel formula ain't one of them thanks to @jon.berna (and @Jason for confirming)!

Our group had a pretty good review game before I left for DI but over the last couple years they stopped asking for new reviews. Most of the GM's did reply and resolve a lot of the bad reviews that came in except there was no longer a flow of good reviews to offset. Beyond the obvious gaming (fake reviews or the head-em-off-at-a-qualifying-question-before-sending-them-to-your-review-links) that went on when "Reputation" was the new shiny nickel at NADA back in the day, I'm sure if anyone revealed their algorithm for Stars now the industry would see new types of cheating. I do know that Google games me within Local Guides. I leave a one-star review, 1 point, but if I add comments, more points, and if I add a picture, I'm practically Google royalty!

Issue I have now is if I fire DealerRater back up, that wasn't used for 2 years, would you point people to leave reviews on their program, in order to build it back up, knowing they're going to see your crappy 3.4? Oh, and if it is a straight math algo on DealerRater I would need 1,263 new 5-start reviews to get the current 3.4 to a 4.6 at one of the stores (according to Berna's formula).

We did fire up Reputation.com trials with FordDirect and Podium is wanting the same for our Kia stores. Anyone have an opinion on best method to increase review volume (for Service as well)? Prior to 2017 I had everyone handing out cards with our review page links, a couple automated emails asking for reviews, as well as we incentivized sales people with contests for a few months (which apparently a lot of salesperson's moms had great experiences at the stores).