6 min
Technology will continue to improve the business landscape and companies must innovate to stay ahead of the competition. As artificial intelligence (AI) and other technologies become more common, users are aware of the change, which makes it more important than ever for call centers to adapt quickly to meet their needs.
While user experience remains one of the top priorities, the challenge of creating a model centered on it lies in the lack of tools or access to the correct and isolated data. Although contact centers use more technologies to manage interactions with users, many find it difficult to extract information from the client’s voice (VoC) hidden in their own data.
Why Do Companies Want To Take Advantage Of Call Center Data To Improve User Participation?
According to a report, 85% of executives agreed that data and analytics are important for reporting changes in sales and marketing. However, 68% also admitted avoiding a major change initiative in their business.
This same report also revealed that when it comes to data sources, 39% of executives admitted to trusting a limited set of numbers that are the easiest to obtain, such as:
- Income figures.
- Social network data.
- Sales figures.
- Team feedback.
While these data are important, they lack the knowledge that can be found in real conversations with users that take place in the call center. This can put companies at risk of losing users in front of a competitor that offers a more attractive experience. The data can be used to drive organizational change, increase profits and improve the user experience.
How Can Companies Take Advantage Of User Data Collected In The Contact Center?
Organizations that use the knowledge extracted from their call center data will have access to unfiltered VoC, so they will know exactly what users want and, more importantly, what they do not want and how they want to be delivered.
However, there are so many ways to use analytics that contact center leaders often don’t know where to start when they want to implement it for the first time, or what to do once their first analytical project is up and running.
9 ways, grouped by three common organizational objectives, so that companies can use analytical and user data to improve their relationships:
Objective 1: Identify Places To Improve User Satisfaction
The happier the users, the more likely they are to remain loyal to an organization. Therefore, many companies classify user retention as a top priority.
1) Help contact Center Managers Increase First Contact Resolution (FCR)
The FCR is prioritized as the number one metric of the call center for a good reason: studies have shown that user satisfaction decreases by 15% every time you have to call back to solve your problem. The analyzes allow supervisors to not only track their volume of repeated calls, but also take them to the root causes so they can quickly improve their FCR and keep users happy.
2) Ensure That Call center Supervisors Perform Quality Management
Machine learning can be used to generate predictive assessment scores for 100% of the interactions, which makes it possible for supervisors to concentrate their time on the correct calls and use that quality knowledge to provide a complete training to the agents that most they need it.
3) Identify Agents That Receive High User Evaluations
Machine learning can also be used to identify agents that offer quality experiences to the user. This helps supervisors discover tactics they can use to train other agents, as well as to recognize the best for their performance.
4) Help Supervisors Improve Proactive Reach
By using machine learning to generate predictive Net Promoter Score (NPS) for each user interaction, regardless of whether you completed a survey, supervisors can see 100% of their promoters, detractors and liabilities and focus in them their efforts.
Objective 2: Respond Quickly To Problems That Negatively Affect User Engagement
Understanding how a user feels in relation to a company can be a challenge, but it is a good way to get information about the contact center, the product and the organization. This is because the analysis of feelings, a new technology that is part of the call center analysis, helps to understand what callers say and how they feel.
5) Allow The Organization To Detect Users At Risk Of Attrition And Respond Quickly
When a user feels frustrated, he often uses words that an analytical solution can classify as having a negative feeling. Call center data can be used to identify them and take measures to retain them, increasing the total lifetime value for users to identify them and take measures to retain them, increasing the total lifetime value for users.
So, how can call center analyzes be used to identify user feelings and allow the organization to respond quickly?
6) Find The Call Center Interactions That Lead To A Negative User Experience
Contact center managers can segment calls with negative feelings scores by agent, team or group and then proactively monitor interactions with negative scores within a specific team or group. You can use this segmentation of feelings to identify optimal opportunities for agent coaching; decide how to handle emerging problems; and predict the feeling of future calls.
7) Identify Opportunities To Improve User Participation Outside The Call Center
The entire organization can use sentiment analysis to recognize and rectify potential problems long before the indications they may receive from lagging KPIs, such as NPS or sales surveys. Thus, they can detect sentiment trends instantly, and then quickly adjust the affected business functions to replicate positive feelings or negative selection. And they can easily identify users who will be participatory.
Objective 3: Add Data And Perspectives Of Employees And Users On Multiple Technology Platforms
With users who interact with companies through more channels than ever, it is increasingly difficult to keep track of all the factors that affect the user experience. Advanced reports can be used to disaggregate that data by combining call center data with automatic call distribution (ACD), interactive voice recognition (IVR), quality monitoring, workforce management, CRM, human resources, Local software applications and even social networks.
By seamlessly compiling both customer voice (VoC) and employee voice (VoE) information from multiple systems and visually display integrated data, the analyzes allow call centers to comprehensively understand and continuously improve user experiences without having to increase budget or staff to do so.
How can analytics help improve user participation by collecting data from multiple channels?
8) Allow Call Center Managers To Perform Quality Monitoring Between Channels
By adding and analyzing data from multiple channels using sophisticated and visual tools, managers can evaluate agents from different internal and external perspectives; improve agent training in near real time; combine quality ratings of supervisors with comments from users and the agent itself; and highlight the best interactions and the best agents.
9) Facilitate The Organization To Obtain A More Comprehensive Understanding Of Agent Performance
Managers can easily send different communications to detractors versus promoters (or neutral customers) to maximize the impact of a message; create mathematical approaches to user and agent behavior to predict the factors that will offer the best results; and have more time to innovate in the overall user experience.
In essence, analytics automates tasks that would otherwise dominate the time of an agent, supervisor or organization, while offering significant information that can be used to continually improve the user experience, and that otherwise could be completely without discover.
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