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Cloud telephony services are a crucial part of every business today. Even though there are many features the system provides, cloud telephony services like analytics provides businesses with deep, data-driven insights into their communication performance, customer interactions, and team efficiency.
Every modern business proposes to put forward their time as fruitful, whether it is in a quick enquiry message or in a prolonged call. Also earlier these same companies invested in a traditional phone system as it was their mode for communication. But with the emerging popularity of cloud telephony and call analytics, this perception as a mere mode of communication has changed.
The times when we used to keep checklists of missed calls is now a thing of the past. With the help of advanced cloud telephony services like analytics we can now sort out information from the massive number of voices and messages into insightful data. This data proves to be beneficial in driving revenue, cutting expenses that are not necessary and ultimately thereby to redefine the customer experience (CX). So for a professional who looks after the next generation communication platforms, understanding this analytics is not optional, but a strategic imperative.
In this blog, we will explore how call data analytics moves from its basic form to further embrace the future developments like sentimental analysis and predictive modeling that are required to understand and make use of the potential given by these data insights.
What is a Cloud Telephony Service called Analytics?
Cloud telephony analytics is the process of collecting and analyzing the data generated from a cloud based call management system. Yes, traditional landlines also provide us with reports like the number of calls made to and fro, but with cloud telephony services we get indepth and insightful details that are useful for our business growth and reducing unnecessary expenses. This process is more proactive rather than reactive reporting.
Main Data Sources: Calls, Messages, and CRM Interactions
The analytical insights of a good platform lies in its ability to categorize and sort diverse data sources.The factors for these insights includes:
- Voice Data: records of call details, duration of the call, wait times and transfers and call analytics.
- Messaging Data: sms or mms data, chat interactions and social media chats if the platform supports it.
- Contextual CRM Data: data like customer tickets, purchase history and agent notes which are integrated via API.
- Network & Performance Data: data of call quality and disturbances felt in the network.
What are the key business insights we receive from call data analysis?
The key notable value of a voice call data analysis lies not in the report but with the measurable results that it creates in the business. With the help of analytical data every business look forward to achieve three major factors:
- To improve their customer experience
- To boost their sales
- To optimize operations
Customer experience is the most important factor that every business strives to improve. With the help of call data analytics you get every minute details regarding the pain points of customers, their queries and frustrations, the areas to be improved and details of customer churn.
- Call volume and peak hour analysis: identify the peak hours when the business gets more customer queries and adjust their staff and shift times according to it.
- Sentiment analysis: with the help of AI, the business can verify the details regarding how the agent and customer is conversing and if needed they can use it to train their staff.
- Customer journey mapping: this data is helpful to analyze a customers journey from the initial conversation, to agent transfers and to ultimately reaching resolution. This data also gives an in-depth analysis to understand where they need to improve their process or product.
Call data is like a treasure for every sales team. It links the cloud telephony platform to revenue generation.
- Lead source and campaign schedule: the analytics data allows businesses to decide where and which marketing channels they should invest.
- Call to conversion rate: track the conversion rate by each agent from the calls they received.
- Script analysis: speech to text engines analyze the scripts used by agents to solve recurring queries and then it prepares a common script that can be used effectively in such situations.
Enhancing operational efficiency is also an aim of every successful business. In order to achieve it, a high level data analysis of call handling is helpful.
- Average handle time and first call resolution: this data helps the management understand the details of call like the duration and whether the agent was able to handle the call efficiently the first time itself.
- Quality assurance: businesses can improve their service by scoring every call and integrating the data received to understand where they need improvement.
AI, ML and Next GEN Features
The future of advanced analytics in cloud telephony is powered by AI and ML. Rather than giving technologies like a simple dashboard these features allow you to interpret and guide human interaction in real time.
The vast amount of data will be scattered without a structure. The next gen technologies help us to sort this into manageable and insightful reports. Furthermore they can help with:
- Automatic topic detection: with the help of AI, the system can go through all the data regarding recurring queries and can notify us about the issue beforehand.
- Call transcription and compliance: all data is converted from speech to text but at the same time the security data like card details and passwords are masked while transcription.
- Deep keyword analysis: beyond basic sentiment and tone analysis, this helps to track high value keywords from the recurring calls.
Predictive modelling is the end goal of data analytics. By providing and analyzing all historical call data, customer interactions and agent behaviour metrics into ML algorithms, systems can accurately see through future events. With this data the businesses can reduce customer churn by risk scoring agent performances and issues and also optimize their workforce according to call volume.
Furthermore, with live agent guidance and real time coaching if an agent forgets like in a call scenario, he or she forgets an important legal disclosure then the system can give notification reminders. Likewise, the system can also suggest next best steps to proceed by evaluating the call data interactions.
Implementing an Analytics driven Cloud Telephony Strategy
Adopting advanced cloud telephony analytics requires a strategic and structural plan of action. Not each and every employee can access every data and at the same time critical data requires anonymization which will further improve the trust in your business security and compliance.
In order to use the power of call data analytics in fullness, you need to integrate it with a suitable or your existing crm. Also in order to justify such an investment you should be keen on key performance index factors like customer value, operational cost, sales efficiency, service quality etc..
Conclusion
The future of communication definitely relies on data analytics. Also the transformation of cloud telephony services from a utility to an intelligent data engine marks one of the most important milestones in the field of communication.
By adopting future generation tools like AI and ML, organizations can quit their guessing game and start understanding what the customers actually want. Ready to transform your growth strategy?
Visit our website https://bonvoice.com/
Frequently Asked Questions:
Why do we need cloud telephony analytics?
It is an important tool for businesses to optimize their operations, boost their sales, improve customer experience and reduce unwanted expenses.
Can analytics reduce missed calls?
Yes, the data insights from these calls helps us to give information on call volume thereby necessitating an on-call personnel or arranging shift times according to it.
What is called sentiment analysis?
This tool uses artificial intelligence to analyze the calls and evaluate tone and emotions of both the agent and customer during a conversation. It allows the agents to flag customers who create unnecessary escalations or train them on how to deal with unhappy customers.
What is meant by call to conversion rate?
It provides the key metrics of a sales person by analyzing how well he or she has handled customers during the call duration thereby leading them to buy the product or service.