Review and sentiment analysis

What are shoppers saying, and which part of it decides the repeat?

Verified reviews are read against the products they were written about. What shoppers praise and complain about lands on a specific attribute rather than on a star rating, and the reasons people buy separate from the reasons they buy again.

Three of every four reviews

High protein frozen mealsReview and sentiment analysis

Share of reviews naming one attribute

75%

Texture, named in three of every four reviews. Protein is what got the product bought. Texture and taste are what decided whether it was bought again.

One brand · verified reviews · read against the products

What the reviews saidI · III

Theme · share of reviews · what it decides

ITexture consistency75%

One dot per hundredth of the review set

When it was right, shoppers compared the meal to premium alternatives. When it was not, they said so and did not come back.

Repeat driver

IIIngredient balance34%

A third of reviews raised balance directly, which turned a vague quality complaint into a specific recipe brief.

Formulation

IIIProtein contentTrigger, not retention

Validated as the reason people tried the product, and not as the reason they stayed. No share of mentions is shown because that is not what the finding is.

Purchase trigger

An average star rating folds all of this into one number. Read against the products, the reviews say which attribute earned the repeat.

Figures from one published engagement in high protein frozen meals. Reviews are connected to the products they were written about, so the same read runs on any category.

Why the read holds up

Reading reviews is easy. Reading them so the answer attaches to a product decision is the part that needs the attribute layer underneath.

Attached to the product

reviews joined to UPCs

Every review is connected to the product it was written about, and that product carries its attributes. A complaint lands on a texture or a sweetener rather than on a brand in general.

Counted, not sampled

the whole set, not a selection

Themes are counted across every review in the set rather than pulled from the ones someone had time to read. A share of mentions is a real proportion, which is what makes one theme rankable against another.

Separated from the rating

what bought, and what kept

A single average star rating folds trial and repeat into one number. The read separates what made someone try the product from what made them buy it a second time, and those are often different attributes.

The same read, in other categories

Three more engagements where reviews were read against the products they belong to.

Read against the productsThree more engagements

The largest pattern in tortilla chips

of about 4,000 reviews named flavor balance, the biggest of five patterns the category's own sales cut never separated.

Tortilla chip category →

Reviews read per brand

1,000+

across eight ready to drink coffee brands, which is what made the sentiment read comparable brand to brand rather than anecdotal.

Ready to drink coffee →

Weeks of reviews behind one read

104

two years of a salty snacks portfolio, so a shift in what shoppers said reads as a trend rather than a bad month.

Health forward positioning →

Figures as reported in the linked case studies

Harmonya Helps You Make Faster, More Confident Decisions

What shoppers say is only useful once it reaches the pack and the page. The words they already use are the words that test best.

Brand Messaging

Close the gap between what the brand claims and what shoppers actually say about the product.

See the use case