When your support team starts getting more than 1000 requests a month, you may want to have a better understanding of the top reasons why your customers ask for help. This way, you can work on preventing common issues from happening.
For example, if a lot of customers are reaching out to cancel their order, you may want to work with the product team to make it easier for customers to cancel their order directly from your website.
The thing is, it's usually hard to know if it's normal to get 5% of requests about order cancellation, or not. Is there something wrong with your product? Or will there always be a significant number of customers willing to cancel an order through support?
At Gorgias, we recently spent some time analyzing the types of requests our customers are getting, and we thought it could be interesting to share the list of most common requests, so you can compare it with your own support data.
You can see the entire list of requests companies are getting on this spreadsheet.
Note: We collected the data from 10 companies in SF, NYC & Paris. These companies get 15,000 tickets per month on average, over email, text & chat.
If you want to compare these results with your own support organization, here's how you can do it.
You are probably already using tags to classify requests. A good place to start is to map your tags with the types of requests from our list.
Unfortunately, this will only work with a few tags, as some tags are too broad to be mapped with request types (the order tag can be associated with a request about the location of the order, or with a cancelation request, which are totally different).
To get more accurate results, you can also count tickets manually. Using your helpdesk shortcuts, it should only take half an hour to classify a sample of 100 requests.
We'd be curious to hear what you think of this benchmark, and if it's similar to what you experience with your own customers!
An interesting next step would be to tag tickets with the type of request. This way, you'll be able to run stats on a per request type basis. You could then see if your customers are happy with the way you treat order returns for example.