Can excellent customer service compensate for wrinkled clothes?

CM Almy is a family-run company based in Armonk, New York. The company was founded in 1892 and is one of the largest and oldest producers of clerical clothing in the US. One of their clients is St. Patrick’s Cathedral, the seat of the archbishop of the Roman Catholic Archdiocese of New York.

We wanted to see how customers talk about this clerical clothing company. To analyze this, we took 4 711 reviews about CM Almy posted on TrustPilot and ran them through Gavagai Explorer. 

Here are 4 things we found out!

The topic “customer service” is the most talked-about topic. It occurs in 34% of the reviews and is often mentioned together with “excellent” and “great”. The average grade is higher than the overall average: 4,84 / 5 compared to 4,71 / 5.

The average grade for “materials” is 4,55 / 5, which is lower than the overall average of 4,71 / 5. Some reviewers mention they have problems with wrinkles.

“Wrong” is often mentioned together with “sizes” and “address”. The topic has an average grade of 4,54 / 5, which is lower than the overall average (4,71). The topic is only mentioned in 1% of the reviews.

Reviewers who talk about “quality” seem to give a higher grade than the overall average: 4,84 / 5 compared to 4,71 / 5. The topic is mentioned in 29% of the reviews, which is more than average. It is often mentioned together with “good” and “excellent”.

What does this mean for CM Almy?

Our analysis shows that some of the topics that are talked about most are “customer service” and “quality”. While these topics are a positive driver for the grade, there are also some topics that seem to have a negative impact. For example, some customers seem to be dissatisfied with the clothing material – they did not expect the clothes to be ironed. 

In our analysis, we only looked at reviews posted between 2018 and 2019. To see how important topics vary over time, we suggest repeating an analysis like this in a couple of months for more information.

Want to try this kind of analysis on your own data?

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