When it comes to customer service quality assessment, there is the option of reviewing customer feedback. This does not always give a valid overview, because most customers do not reply to surveys. Analyzing the actual contacts, whether they are calls, emails, or chats, is much more efficient – when done right. The current process for many companies is sampling contacts for quality assurance. Automated contact assessment for chat customer service with the help of AI-based technology can take a company from sampling to reviewing 100% of contacts.
In this post, we will share how Feelingstream’s AI-based analytics platform can help reach and review all of the contacts for chat customer service. As a result, you can have enhanced customer service quality, improved NPS, and your monitoring can be more efficient. All Zendesk chats can now be automatically monitored and assessed with Feelingstream. Read more about our product.
Customer service quality assessment as it is today – based on sampling
Currently, most companies review a few different metrics for chat handling – average waiting time, chat handling time, and chats per day for example. These together with a survey rating from a customer give some data for the companies to look at, but do not show the chat quality. To get an actual overview of the chat customer service, a quality manager should read the chats. Samples will give some idea about the quality level, but as we’ve found from our experience, this data does not always scale.
A better solution would be to sort out the relevant chats to review and use those for customer service quality assessment. Agents may be doing well during most contacts, but finding the areas for improvement based on real customer contacts will help make their work better. Above all, that is what quality assurance is about – finding ways to improve.
Going from reviewing samples to 100% visibility
If an agent has 100 or even 200 chats per day, sampling only a few chats will not give a quality manager a true picture of customer service quality. Instead, when using Feelingstream’s technology, the technology can be taught to look for certain keywords to find problem areas and chats. The automated customer service quality reports are then generated using the words your agents use or AI bases the reports on pre-selected negative chat conversations. This way, the technology assesses and reviews all of the chats, not just samples. As a result, you may find that 90% of chats are perfectly okay and can focus on the 10% that need improvement.
How would you like your brand to be perceived in chat?
Great customer service quality goes hand in hand with great attitudes from agents and words that are chosen well. Therefore, if you want your chat customer service to do great in their interactions, it would be good to have standards for your service. For example, when it comes to apologizing – there are many options to do that.
Some examples for you:
- sorry for the inconvenience caused
- I am sorry about the inconvenience
- I am sorry about the delay
- sorry to see that
- It just depends how busy it is, sorry
- I am really sorry about the delay
- I’m sorry for the missing items
- I do apologize again for this
If you look at these samples from real customer interactions, what would you like to be the way your brand is seen by the customers? You can set standards for chat openings, closings, apologies, and ways that customers are told about certain things. By setting such standards, you can ensure improved customer service quality. With the help of Feelingstream’s automated analysis, you can have monitoring in place to always check that those standards are met. You can even check for poor grammar use by using filters to look for repetitive grammatical mistakes.
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|Define your vision for customer experience||Set up automated reports and get notifications if something does not comply with agreed rules||Take your actions – talk with agents, add|
a training, update instructions
|Track long term trends and reports|
From improving chat customer service to service standards through automated chat quality assessment for a better business
With Feelingstream, you can have an overview of not just a few random samples but 100% of your chats. Feelingstream’s platform has pre-selected filters for topic and sentiment which can be added or modified to suit your company’s specific needs. This leads to greater transparency and visibility to support data-driven managerial decisions. Automating customer service quality assessment is an easier and more effective way to help quality managers track agent performance, provide training where needed and modify coaching targets. This will increase customer satisfaction scores and NPS, and achieve better business results overall.
All Zendesk chat conversations can now be automatically analyzed in the Feelingstream platform – make sure to contact us for a demo.