Call center data can support businesses to increase revenue and retain healthy relationships with customers.
In the modern global marketplace, multinational companies are opting for more call center service establishments. In fact, during the Covid-19 pandemic, call center services saw a large spike.
Call centers connect customers with agents to resolve issues and provide support. And with that type of customer interaction comes valuable data.
Businesses can improve their performance by adopting a data-driven approach, but only 24% of organizations analyze this data to uncover meaningful insights.
Don’t make that mistake.
In this blog post, we will discuss the importance of call center data and how it can help businesses boost their revenue and sales.
What Is Call Center Data?
Every customer interaction holds important data and valuable insights.
Unlike in-person brick and mortar interactions, call center conversations are recorded. Thus, every call has data to mine and learn from.
Call center data can help organizations identify their strengths and weaknesses; improving customer service generally leads to greater sales.
Moreover, companies can improve their performance by evaluating this data.
Here’s how.
7 Reasons Why Call Center Data Is Important
1. Encourage Teams to Align on Strategy
Call center activities are generally different from other departments. Their data is kept separate.
So, marketing, finance, sales, or product teams don’t combine their outputs with call center data. They can lose out on valuable insights as a result.
Organizations should share call center data with all teams so managers can understand its influence on other streams.
2. Provide Deep Insights into the Customer
A part of collecting call center data is recording customer conversations that contain significant information.
Extracting intent and semantics from the call records can help reveal interesting findings about the customers.
However, inspecting large audio files to understand the hidden facts can be hard without a proper solution. More on that later.
With the right software, though, you can easily divide the data with multiple filters and fields to help provide deep insights into the clients.
3. Reduce Call Center Costs
The workforce in a call center varies based on company size and nature.
For instance, a fortune 100 company can have call center staff between 20,000 to 40,000. These employees are expected to take several calls a day with many tasks being repeated on every call.
Employing AI tools can reduce human intervention by automating major repetitive tasks. This can lower cost and workload for the call center.
4. Boost Service Delivery
Call center data is not only useful for gaining knowledge about the clients, it also highlights inefficiencies and reveals potential time saving tasks.
Additionally, it enables quicker delivery and enhances performance.
Using AI techniques, businesses can focus on areas that need improvement. Employee behavior can also be monitored and evaluated one-to-one.
5. Improve Customer Experience and Sales
AI-enabled analytics forecasts customer inclination using information about the demographics, profiles, and previous purchase patterns.
After the data is analyzed, new sales strategies can be developed to yield maximum profit.
New incentives and promotions can also be introduced to spike sales.
6. Predict Customer Churn
Customers are the real assets of an organization and losing the clients can lead to undesirable consequences.
Therefore, businesses need to stay up to date and alert to the customer trends and demands.
In case of an imbalance between customers’ expectations and company services, these needs can be identified and remedied by evaluating call center data.
7. Promote Better Decision-Making
Businesses can’t rely on weak methodologies or mere predictions to excel in the long run.
To optimize call center operations and meet set targets, companies need call center data analytics to be accessible to the entire organization.
Managers can weigh the performance and reform strategies to derive maximum output.
Effective processes can be adopted and implemented—processes like searching efficiently, or hiring the top talent.
Call Center Data Analytics
Call center analytics refers to the process of gathering and analyzing customer data to discover new findings and useful insights. It includes:
- Revenue estimation
- Customer satisfaction
- Customer retention
- Response time
- Customer effort score
There are several ways of acquiring call center data and performing analytics. It all depends on the tools you employ.
The different methods have various benefits and features to use when capturing call center data. Some popular approaches to analyzing call center data include the following:
- Predictive Analytics: This approach predicts client behavior and observes trends to improve customer experience and enhance agent efficiency. The sole focus is on identifying the most effective way of communicating with customers.
- Speech Analytics: This uses algorithms to assist in monitoring client calls in real-time for observing sentiments and analyzing tone. It allows you to collect data on customer experience.
- Text Analytics: This category of analytics is important for companies that interact with customers over email, social media, live chat, or other text-based modes of communication.
- Performance Analytics: Managers and supervisors use dashboards with real-time and previous records of all agents’ performance. It gathers data about possible incompetency, handle time, training events, etc.
- Cross-Channel Analytics: With numerous communication channels available, customers may contact via different platforms. A cross-channel analysis is essential for the workers to streamline customer interactions.
In sum, traditional call centers generate rich sources of unstructured customer data in the form of recorded voice calls. These huge data files can help keep organizational performance at peak and help with auditing and analysis.
AI and Call Center Data
With the escalating business needs, manually attending calls and capturing call center data is not as feasible.
Speech-analysis technology has brought advancements in call centers, but there are other techniques to dissect customer data.
As a result, AI tools are becoming common for call center data acquisition and analysis.
Automate Manual Processes Using Conversational AI
Conversational AI is a branch of artificial intelligence that allows customers to communicate with machines and applications just like they interact with humans.
Reports predict 70% of customer interactions will be with AI in 2022.
Human analysts would listen to customer conversation audio for several hours to analyze the customer data. This manual call listening is time-consuming and expensive. Conversational artificial intelligence automates the process and made it much simpler.
The most common form of conversational AI is the chatbot that allows AI-powered online chats. According to Zippia, over 75% of customers prefer active customer service and contacting via different support channels.
For example, tech giant Google Cloud offers Contact Center AI to yield Conversational AI features in different call center businesses and products.
The Agent Assist tool empowers human agents to identify intent and real-time support in calls and chats, for example. Also, CCAI helps contact center managers analyze sentiments during customer interactions.
To get here, though, your data needs a transcription service. This helps businesses benefit from better AI technology and access client data faster.
Call Center Data Transcription Matters
Manual transcription (100% human) is the process of converting an audio or video file into written text.
Professional human transcriptionists prepare high-quality text, then review their transcriptions.
Manual transcription is the most accurate method because the transcriptionists pay attention to little contextual variations and remove mistakes and errors.
However, it is quite expensive because the cost depends on the audio file length.
Machine transcription transforms an audio file into written text with the help of automated tools. Speech recognition is used for audio analysis and generating the text, which is then reviewed by humans.
Machine transcription is cheaper than the manual method, and it works well for low-requirement transcription. However, its accuracy and precision are not like manual transcription, as software tools alone are susceptible to errors.
Moreover, various factors, like speaker accents, speech delivery speed, audio sound level, and background noise, can make it difficult for tools to prepare transcriptions.
So, to address the errors and fill the gaps, proofreaders and reviewers are hired to edit and improve the transcriptions.
Make Full Use of Your Call Center Data
Speech analytics and conversational AI is evolving rapidly. Call centers are no exception. They deal with tons of audio data daily and use analytics to bolster their own methods.
Chatbots are the simplest AI models used by companies to ease the manual labor, enhance the performance, and decrease the expenditure. Leading companies like Amazon and Accenture regularly use chatbots and purchase call center data to help improve their systems.
Some other big names in the market, such as Spotify, Lyft, and Sephora also use chatbots to provide best customer services.
Integrating modern tools is essential to upgrade performance and keep up with the changing trends.
Contact us today to learn how we can help.
