10 Competitor Insights From Review Data, Plus a BONUS Calculation to Increase Sales

WATCH the 15-minute case study or READ it below.

What if you could access your competitor's data for quick and reliable? market research?

I investigated the #1 best-selling mattress on Amazon and uncovered:

???? Which sizes and styles people ACTUALLY like most and least.

?? Estimated weekly sales, by size and style.

Imagine what you could learn from your competitors’ review data!?

Competitor review data could help you:

?? Launch a new product feature?

?? Enter a new product category

?? Conduct due diligence research before an acquisition

?? And MANY MORE scenarios

Today, I’m sharing one of my most valuable secrets. Reliable competitor data is available right now. You can analyze it yourself.

Yep. Millions of real customer data points are publicly accessible online. They’re called reviews.

In this case study, we’ll look at a real competitor product and reveal:

?? Which versions of a product are most and least popular

?? Which versions of a product are selling the best and worst

This case study is for MARKETERS, PRODUCT DEVELOPERS, and all LEADERS who want to conduct reliable market research, fast.

My secret approach is not:

? Copying competitors?

? Using intellectual property

It is:

?? Data analysis of public info

?? Strategic market research

?? Go undercover with me in this competitor research case study.

FINDING A COMPETITOR PRODUCT

For this case study, pretend you are about to launch your own mattress brand. ????

From your initial research, you learned that mattresses can be high-margin products. ?? We all spend about one third of our lives sleeping, so mattresses are high impact and high consideration items.?

Before jumping into the mattress industry, you first want to know:

? How the market is responding to available options

? How to differentiate from existing options

? Ways to save time and money

Your first step is to find the right competitor product to evaluate.

With this backdrop, I’ll take you to Amazon, where I entered the search term “mattress”.

While looking at the search results, I searched for a competitor mattress that met my 3 criteria:

1. Is it a top seller??

?? We want to see what a successful competitor can teach us.?

2. Does it have multiple variants?

?? Having multiple versions of a product gives more to compare and learn from.

3. Does it have a lot of reviews?

?? More data points will make our findings more reliable.

The Zinus Green Team Memory Foam Mattress has the “best seller” tag for the search term “mattress”.

I confirmed this is indeed the #1 best selling mattress on Amazon.com.

Clicking into the Zinus mattress listing, I see it has many sizes from Twin to California King. It also has several styles (or depths) from 6 inches to 12 inches. This product meets requirements #1 and #2.

This Zinus mattress also has 139,224 total ratings and 46,957 of them have review text, so it meets the #3 requirement. (You can find how many of the ratings have review text by clicking on the rating total -> scroll down, then click “see all reviews”.)

You may wonder why there are so many ratings without review text. In October of 2019, Amazon launched one-click ratings. A customer can click a star rating and publish it to the product listing without review text. 66% of all ratings on this mattress do not have a review due to this Amazon feature.

Now that we have a great competitor mattress identified, we want to access all available review data.

ACCESSING COMPETITOR REVIEW DATA

REVIEWS.ai tracks and analyzes product review data across dozens of the top ecommerce platforms, from Amazon to Walmart.com and BestBuy.com.

On the PRODUCTS tab of the REVIEWS.ai dashboard, I added the Zinus Green Tea Mattress for tracking. This part is super simple. I just filled out the product info, then copied and pasted the Amazon.com product listing URL into the tool.

Yep, it was that easy to start collecting competitor review data. Product data can also be uploaded in bulk when tracking many products.?

NOTE: Amazon ONLY makes the most recent 5,000 reviews discoverable. So although we won’t get all 46,957 ratings with reviews, this is still a large enough data sample to be reliable. For interested readers, REVIEWS.ai will soon launch a beta feature that tracks ratings without reviews.

CONDUCTING THE ANALYSIS

With the review data now collected from the Amazon listing for the Zinus Green Tea Memory Foam Mattress, we go to Graphs & Charts, under the REPORTS tab.

Here, we see total listing stats and trending star rating info for ONLY the most recent 5,000 reviews. With the high sales volume on this listing, that data takes us back to 4/2021 for about 18 months of review history.

Interestingly, the mattress has been averaging 3.5-stars for months even though the displayed star average is 4.39 across the 139,179 total ratings. That tells us a couple things:?

1. It is possible that reviewers in the last 18 months are less satisfied than in years past.

2. It is possible that less satisfied customers are taking the time to leave text with their review while more satisfied customers are ONLY leaving a star rating. This would account for the higher average of 4.39 while the most recent 5,000 reviews are averaging 3.5-stars.

Because I was curious about these two points, I added the same mattress found on Amazon (CA) to see how it compared. With a lower volume of sales and reviews than Amazon (US), we can see review history back to 8/2017.

The star rating trend line is gradually declining from 4.65-stars in 9/2017 to 3.94-stars in 11/2022. (That’s an average decline of 0.018-stars per month.) I also looked at the volume of reviews received per month on the Rating Distribution graph.

Based on these two graphs, it does look like customer satisfaction of this mattress is steadily declining. Why? There is no significant change in star rating or review pace coinciding with the 10/2019 introduction of ratings without reviews. Since this is the exact same product offered in both countries, we can assume the same trend is true in the US.

?? Discovery 1:

?? Customer satisfaction is trending down

• This is likely (in part) due to the long length of time this mattress has been sold. The longer this model exists, the more years customers have slept on it. Even the most expensive mattresses wear out over time.
• It would be insightful to compare this downward trend rate (-0.018-stars per month) with other mattress models.

Back to our Amazon (US) report for the Zinus mattress, I wanted to export all collected data points for the 5,000 most recent reviews. To do this, I clicked “Downloads .CSV”.

On the Downloads page, I selected the “Zinus mattress”, “Amazon (US)” and “reviews” then clicked “create”. (You can also export all Q&A’s for a separate analysis.)

I then simply downloaded the nice and organized .CSV file with all possible data points for each review. This is what it looked like:

There ended up being 5,020 reviews collected. 41 of 49 possible data points were able to be collected for each review of the Zinus mattress. Not all platforms provide all of them, but the REVIEWS.ai export includes them all. This is helpful for a couple reasons:

• If using REVIEWS.ai for ongoing reports, you can use the API to push new review data to your preferred data tool such as Tableau or Power Bi.
• When exporting data for a wide variety of products across many websites, you will have all applicable data available in a single report.

Now that review data is exported for the Zinus Green Tea Memory Foam Mattress, I selected only the necessary columns for this analysis, which left me with these:

The goal from here was to:

• Measure key stats for the entire data set (aka, the parent listing)
• Measure key stats for each unique variant (aka, each child variants)
• Compare all the stats and see what we learn

Key stats for the parent listing:

There are 3 primary items we want to know most: 1. Total # of reviews, 2. Review pace (# of reviews received per week), 3. Raw average star rating.? To get these items, I built this basic table:

Date of first review: Sorted the data set by date to find the oldest review, which was 4/26/21

Date of most recent review: Sorted the data set by date to find the newest review, which was 11/1/2022

Raw Average Star Rating: (=Average(ALL ratings)) Note this is the raw average rating and excludes 149 non-verified reviews I filtered out.?

Side note: Amazon launched an algorithmic calculation for their displayed star ratings in 2015, which weighs recent reviews, helpful voted reviews and other characteristics more.

Total # of reviews: The 149 non-verified reviews were filtered out for this as well, leaving 4871 verified reviews from the 5020 exported from the Amazon (US) listing. (We only want to use verified reviews so our sales projections are more realistic.)

Review Pace: =F2/((D2-C2)/7) Number of new reviews received per week

Projected Sales Pace: =G1/0.02 This is a projected weekly sales pace based on 2% of customers leaving a review.?

% Listing Share: =F1/4871 This is the percentage representation as a share of the parent listing. We’ll use this later, but for now it’s 100% since we’re looking at the totals for the parent listing.

Estimated Review Rate: 2% was just entered by me. (I’ve seen review rates from 0.5% to 7% across various ecommerce sites. 2% is a reasonable estimate for Amazon.) We’ll use this later.

Key stats for the child sku variants:

With the above stats ready for the parent listing, we are ready to record individual child sku variant data. To do this, I built the below table. Data for columns C-F were entered by sorting all data first by the variant combination indicated in columns A and B.?

I entered the first and most recent review received for that variant in columns C and D respectively.?

Column E was calculated with a basic (=Average(All ratings for that variant)).?

Column F was calculated with a basic (=Count(All ratings for that variant)).

Columns G-I were than calculated automatically using the following formulas I built:

Review Pace: =F1/((D2-C2)/7)This is the total reviews / number of weeks this variant has been available.

Projected Sales Pace: =G1/0.02 This is a projected weekly sales pace based on 2% of customers leaving a review.

% Listing Share: =F1/4871 This is the percentage of reviews for this variant compared to the whole parent listing.

I filled out all the data for all child variants in columns C-F. Here’s the finished table:

From this data, we quickly learn:

?? Discoveries 2-4:

?? Top category sales: 3,077 units/week

• This is likely the high cap of sales potential. (Measure sales potential)
• Use this, and perhaps a similar analysis for a lower ranked mattress to estimate sales potential before you enter the industry.

?? 11 of 32 possible variants not needed

• No review data was available for pink highlighted variants, meaning Zinus likely identified these size and depth combinations were not profitable. (Avoid investing in duds)
• Narrow Twin, Twin XL and Short Queen are specialty mattress sizes. It makes sense they are only offered in select depths.
• California King is only offered in the 12” depth. This makes sense as well. A luxury size mattress would not be sought after in budget depths of 10” or lower.

?? Remaining 21 of 32 variants have value

• Zinus must find value in offering all available variants as they have all been available the past 18 months at least. It’s likely they carry little stock in the fringe sizes, but make them available in order to showcase a wide variety of options. (Narrowed focus of your research)

Next, I added some basic conditional formatting by color scale to highlight quantities and ratings in columns E-I. This visually highlights trends.

I then sorted the data by projected sales. After all, we want to follow the money. ??

From this data, we quickly learn:

?? Discoveries 5-7:

?? ?60% of all sales for the entire parent listing are coming from 3 child variants:

• 12 inch Queen (37.36%)
• 12 inch Full (11.41%)
• 12 inch King (10.94%)

?? Top 3 sellers are 12” larger sizes

• It appears the thicker depths are selling best, and in the larger sizes. Prioritize launching a thicker mattress for your first product. (Guide your product offering)

?? Detailed listing % by variant

• Use the % listing share in column I to inform demand planning when ordering your inventory. This can help you avoid cash sitting in child variant inventory that will not sell as fast and to ensure you have sufficient stock in the most popular sizes. (Inform inventory order quantities)
• Imagine you are launching your 12 inch mattress and have enough cash to order about 6,000 units. Rather than order 2,000 in Queen, 2,000 in Full and 2,000 in King, this info would help you order smarter. Looking at the listing share in column I, order 3,700 units in Queen, 1,100 units in Full and 1,000 units in King instead.

Next, I wanted to compare the child variants by star rating and sorted them accordingly:

From this data, we quickly learn:

?? Discoveries 8-10:

?? 7 variants are rated better than the listing leader (12” Queen) at 3.71-stars.

• Child variants rated higher than the 12 inch Queen are lifting ratings up, strengthening the listing and increasing sales. Even if these variants aren’t top sellers, they are positively impacting the parent listing.?
• In your research to launch your first mattress, note that twin mattresses of all depths are rated higher. (These lift up the rating, and sales)

?? 8 variants are 0.5-stars lower than the listing leader (12” Queen) at 3.71-stars.

• Child variants rated 0.5-stars lower than the 12 inch Queen are pulling ratings down, damaging the listing and decreasing sales.?
• It is worth considering NOT offering some of these child variants. The lowest 3 rated variants are large sizes (king and queen) with lower depths (6 and 10 inches). It is likely those shopping for larger sizes do not want a thinner depth. (These pull down the rating, and sales)

?? The 8 inch short queen is rated 0.79-stars higher than the 8 inch queen

• There could be a valuable opportunity to offer the short queen as a specialty size. Both of these variants are the same mattress, but the short queen is 5 inches shorter so it fits in RVs. There are TONS of 8 inch queen mattresses on the market and this Zinus option is rated 3.23-stars, signaling it’s below average compared to the many alternatives. However, the 8” short queen is rated 4.02, signaling it is one of the better options among fewer alternatives. Offering a high-quality short queen may be a specialty niche consumers will pay more for, giving you a higher profit margin. (Could be a specialty size opportunity)

?? BONUS CALCULATION:

There is still one more calculation to share. Think of it as a bonus for reading to the end. ?? I wanted to calculate the projected impact on the star-rating and total sales by removing the lowest rated child variants.

First, remember that this listing is selling a projected 3077 units per week as it is now.

There are 13 total variants rated lower than the listing leader (12 inch Queen) at 3.71-stars. These child variants collectively were bringing in 27 reviews/week and a projected? 1,348 sales/week.

Let’s assume we are working with ALL reviews for the Zinus mattress. (In reality we only have the most recent 5,000 of 139,224 ratings…so yes, put on your imagination cap for this one! ??)

By discontinuing the lowest rated 13 child variations, the overall parent product listing would lose 1,348 sales/week (56%).

?? Removing low rated variants:

Previous sales pace: 3,077 units/week

(Removed skus) ? ? ? ? ? - 1,348 lost units/week

New sales pace: 1,729 units/week

(56% decrease)

On the surface, this seems not just bad, but horrible! But remember, by discontinuing those 13 variants that now aren’t selling, we also remove 2,095 reviews that averaged 3.31-stars collectively. Taking them away would impact the listing as follows:

?? Removing low rated variants:

Previous star rating: 3.59-stars

(Removed reviews) ? - 2095 @ 3.31-stars

New star rating: 3.79-stars

(0.2-star increase)

You may ask: “So what? There is still a 56% in units sold by removing 13 skus!” Well hold on. Amazon rounds displayed star ratings by half stars, so this 0.2-star rating lift would get the listing from a displayed 3 ? stars to 4 stars.?

From the research I’ve conducted managing thousands of product listings over the past decade, I’ve observed each half-star increase doubles sales. (Yes, doubles, like a 100% increase.)

That means our remaining 1767 projected weekly sales from the left over units would double to 3534.

?? Removing low rated variants:

Impacted sales pace: 1,729 units/week

(0.2-star increase) ? ? X 2? review boost

New sales pace: 3,458 units/week

(12% increase)

Although this is theoretical, the principle is true. Discontinuing low rated variants on a parent product listing can lift sales for the remaining units and, in some cases, increase overall profitability. ?? Imagining that, and you’d decrease your sku count at the same time. Nifty. ??

CONCLUSION

In this case study, we easily identified 10 valuable insights about a best-selling competitor mattress. If you were really about to launch a new mattress brand, these insights would be directly applicable to you.?

Conducting a similar analysis of a phone case listing could tell you which colors and patterns are selling best and rated the highest. You could follow the same pattern with apparel and learn which sizes and styles are working well for competitors. The applications are only limited by your creativity.

Fuel your market and product development research with review data.

Find ways to increase your profitability.

Which competitor products would you like to analyze?

Get free REVIEWS.ai access to analyze any 3 competitor products

I hope you enjoyed this case study. For more insightful tips and tricks about review data, follow me and follow REVIEWS.ai on LinkedIn. Also check out my book, The Review Cycle, and learn about my proven four-step model to mastering your online reviews.

?? BONUS GIFT:

LIKE and COMMENT “Send me the file” if you’d like the excel file with all my competitor calculations from this case study. No strings attached.