Ranking Score Explained

G'day, thanks for your interest in how we calculate an experience's ranking score. It's at the core of Rankers so I'm pleased you're curious.

The ranking score percentage is used to compare and sort experiences in ranking tables. It is not necessarily a direct measurement of the quality of a particular experience as rated by its customers. I've found it a useful tool to allow me to find the best experiences with confidence. But I've also found it important to read the customer reviews before making any final judgements!

We calculate an experience's ranking score using a multi-factor data model instead of a raw data average (mean). This model takes into account several important questions. For instance - is there a trusted body of reviews? What is the age of a review and is the review from a credible source?

Below you'll find details around some of the important factors that went into calculating the ranking score for Lake Alexandrina.

If you have any questions or comments about our ranking score calculation please get in touch at info@rankers.co.nz. We don't believe this is perfect or complete so we're always interested in ways we might make improvements.

Cymen Crick's avatar

Cymen Crick

Rankers Owner

Lake Alexandrina

Valid Reviews

32 Valid Reviews

The Lake Alexandrina experience has a total of 35 reviews. There are 32 valid reviews that are included when calculating the ranking score and 3 invalid reviews that are excluded from the calculation. Reviews can be excluded only when a reviewer is not verified or after an investigation by our team determines the reviewer is not genuine.

Below is the distribution of ratings for the 32 valid reviews:

Rating Count Percentage
10/10 4
13%
9/10 3
9%
8/10 8
25%
7/10 6
19%
6/10 4
13%
5/10 4
13%
4/10 2
6%
3/10 0
0%
2/10 0
0%
1/10 1
3%

70.63% Average

The raw data average (mean) for all the Lake Alexandrina valid reviews is 70.63% and is based on 32 valid reviews. This value is not used to calculate the ranking score and it only provided here as a comparison to the weighted average.

Face-to-Face Reviews

6 Face-to-Face Reviews

The Rankers team meets with travellers while they’re in New Zealand and conducts face-to-face surveys. These reviews, in our opinion, are the most trusted in the industry and represent a critical control sample. To our knowledge, we are the only travel review website in the world that has gone to this extent.

More about face-to-face reviews

Within the 32 valid reviews, the experience has 6 face-to-face reviews collected during interviews by our team.

Below is the distribution of ratings for the 6 face-to-face reviews:

Rating Count Percentage
10/10 1
17%
9/10 0
0%
8/10 3
50%
7/10 1
17%
6/10 1
17%
5/10 0
0%
4/10 0
0%
3/10 0
0%
2/10 0
0%
1/10 0
0%

78.33% Average

The raw data average (mean) for all the Lake Alexandrina face-to-face reviews is 78.33% and is based on 6 face-to-face reviews. This value is not used to calculate the ranking score and it only provided here for comparison purposes.

Weighted Average

75.99%

Rankers calculates a weighted mean as a base average on which we can improve. Individual review's ratings are given a weight based on several factors. The weight of a review determines the overall impact it'll have on the final weighted average.

Recent reviews have more weight as they are more relevant and reflect the experience as it currently operates. Over time reviews become less relevant and loose their impact on the ranking score.

Low rating reviews carry slightly less weight. This dampens the effect of very low ratings for every experience across the board. This is especially important when the experience has few reviews overall and a single negative rating can grossly mischaracterise an experience. Consistent poor reviews will still result in the experience receiving a comparitively low ranking score.

Credible sources provide reviews that can be trusted. If we have verified a reviewer is genuine via a face-to-face meeting then the review carries additional weight.

Reviewer Rating Age Relative Weight
Caolan Harvey 8/10 747 days 100%
Ian 8/10 992 days 65%
lilie 6/10 1630 days 9%
TC 5/10 1782 days 6%
Sascha 9/10 1782 days 8%
Megan Belanger 7/10 2116 days 6%
kiwikath 5/10 2116 days 5%
Ryan 7/10 2239 days 6%
Rachel 1/10 2331 days 2%
Laura Clay 9/10 2422 days 6%
Sanne Heil 4/10 2543 days 3%
Jenny Jaye 7/10 2607 days 5%
Christine Allgaier 8/10 2817 days 4%
Megan Jurgensmeyer 8/10 2911 days 4%
Saskia Ruttor 9/10 2914 days 4%
Ulrica Vilen-Letts 7/10 2937 days 4%
E Janssen 5/10 3095 days 3%
Sally Smith 7/10 3487 days 2%
Ruben Sanchez 6/10 3606 days 2%
Sidney Stokkers 5/10 3668 days 2%
Sandra Frischmann 8/10 3911 days 1%
Mara 6/10 3916 days 1%
Andrea Morello 7/10 3916 days 1%
Kerri 8/10 4095 days 1%
Raquel 8/10 4286 days 0%
Phil & Laura 4/10 4307 days 0%
Philip Gibbons 6/10 4307 days 0%
rangi 10/10 4369 days 0%
Knorr Bracher 10/10 4373 days 0%
Monika & Roger 8/10 4686 days 1%
Stephane 10/10 4735 days 1%
Mary Hamilton 10/10 4765 days 1%

Adjustments

No Adjustment

Several adjustments to the weighted average may be added to improve relevancy and credibility. Lake Alexandrina does not meet the criteria for any of these adjustments to apply.

Balancing Adjustment

4.28% Adjustment

Every experience's review score is adjusted to balance out the disproportional number of negative reviews that are contributed.

You won't be surprised to learn that disgruntled folk are more likely to leave a review than happy ones. They are motivated to share their experience and warn others. We consider this a good thing and it's why reading the reviews is important. However we've learned it can misrepresent the experience in a more overall sense.

We apply a balancing adjustment to counteract this effect and ensure the ranking score is a more fair representation of the experience. This adjustment is applied equally to all experiences.

Final Ranking Score

80%

The final ranking score once any adjustments, ratings, and rounding has been applied. This value is recalculated each day and a short rolling average is applied. Therefore it may not be precisely accurate based on the other values presented.