Understanding your customer’s needs is imperative to running a successful business. It helps you tailor your product or service to potential customers and lets you enhance the experience for current ones.
With Loop’s Natural Language Processing capabilities, you can gain real time insight into customer sentiment. This means negative experiences can quickly be recovered and your team members can be held accountable for their interactions and duties.
In this tips and tools post we break down everything you need to know about the Loop® platform’s sentiment measurement capabilities and how you can leverage it to enhance the customer experience.
Why Measure Sentiment?
Before we delve into the specifics, it’s important to understand why sentiment measurement is critical.
Firstly, measuring customer sentiment allows you to understand the quality of customer service you are providing, especially over a long period of time. Knowing where you are excelling and where you are lacking can help you build effective strategies to enhance service in the future. In addition, with the Loop® platform, measurement is calculated in real-time so when escalated issues occur, adjustments can be made immediately to deter negative backlash.
Measuring customer sentiment also allows you to prioritize service. If a member of your team has multiple conversations come in at once and they are all “Happy” aside from two that are “Very Angry”, they know to tend to the upset customers first – to ensure their negative impression is recovered. If a team member is busy at the time that the customer sends a “Very Angry” message, a manager can also be notified to ensure they are prioritized.
Lastly, measuring sentiment gives managers visibility into team member performance. From your reporting’s page, if you’re seeing a consistent “Angry” sentiment correlating with a specific team member, you know where to focus attention to create improved skills around delivering an exceptional customer experience. Under-performing team members can then have one on ones with managers to discuss how their service delivery can improve. Similarly, managers can have visibility into top performers, which means during team meeting or huddles you can reward them or recognize them for their great work.
The Sentiment Detection Experience In Loop
The sentiment gathered from your customers is based on the analysis of all messages and texts between the customer and members of your team.
From the employees perspective, the sentiment is shown beside every one of the customers responses and is calculated to give a general sentiment in the sidebar of the conversation. For managers, sentiment is displayed as a flagged conversation in the manager inbox.
The conversation,depending on the guest responses, will be assigned one of the following sentiments:
- “Very Angry”
- “Very Happy”
The ideal scenario is to have every customer “Happy” or “Very Happy”. If a conversation ever reaches “Angry” or “Very Angry”, the conversation will then be flagged in the conversation as orange or red for team member and manager attention. This is to drive awareness of customer dissatisfaction and allows your team the chance to quickly recover the issue before anything negative ensues.
When the conversation has been tended to and the conversation is a more positive one, it will be marked as resolved and the team member can archive the conversation.
Steps to View Sentiment on The Loop® Platform
To view sentiment within Loop®, simply undertake the following:
- Login to your Loop platform portal using your valid Loop credentials.
- Once logged in, on the top left hand corner of the sidebar navigation menu click the white speech bubble icon that says “Conversations”.
- Once in “Conversations”, to see customer sentiment – go to the guests profile pane located on the right hand side of the page. To view their current and overall sentiment look under the label “Temperature”.
Sentiment can also be seen for each individual message a customer sends. This will be shown directly beside the customer comment as a face icon. A happy face that is green indicates a “Happy” response, a neutral face that is yellow indicates a “Neutral” response, a sad face that is red indicates a “Angry” response. With each response having a mood, you and your team can have visibility into where the customer turned negative and what caused such a change.