Call Analytics

Call analytics is a process, in which you collect, analyze, and interpret data from customer calls to draw actionable insights. The primary data collected here are call duration, caller sentiment, resolution rate, and the content of the conversation. These can prove to be extremely valuable for your customer service teams and your businesses as a whole. Call analytics are widely used across industries like banking, healthcare, retail, and real estate to improve customer experience and increase agent performance.

Components of Call Analytics

Data Collection: The first step is to capture call data. Along with call recordings, you also need to capture the total number of calls received, the duration (both per call and average), call outcomes, and resolution times. This is the foundational data on which analytics will be performed.
Speech Analytics: Speech analytics has quickly become a norm in call analytics. Almost all contact centers use AI to analyze voice data from calls. This detects tone, sentiment and even specific keywords and phrases. This helps flag negative calls and outcomes. From here, you can dig deeper into what went wrong on a specific call. This is way more efficient than having agents manually tag negative calls.
Transcription and Text Analysis: Calls are transcribed into text in real time. With transcription data available, businesses can perform an in-depth analysis of customer interactions. This primarily helps monitor compliance adherence. Additionally, these transcripts can be used to spot keyword trends that provide additional insight into customer requests.
Customer Journey Tracking: In an omnichannel customer service setting, the call data is not isolated. The transcripts provide data on customer preferences, common complaints, and past issues. This updates a central database that can then be used to provide continuous support over chat, email, and social media.

Benefits of Analyzing Call Data

Better Customer Experience (CX): At its very core, call analytics are used to improve customer experience. By spotting trends in recurring issues, businesses can proactively resolve customer concerns. Additionally, call data collected from customer calls can be used to update caller preferences, so they can enjoy a more personalized experience when they try to contact you next!
Operational Efficiency: When you analyze call data, you can spot bottlenecks in your internal processes that may be holding you back. Long average handle times (AHTs) are a telltale sign that something is not going well. This could be incomplete training, a sub-par knowledge base, or even technical difficulties. Its up to you to go and find out what it could be.
Agent Optimization: Having access to real data is a goldmine for agent training and optimization. No matter how much you prepare, the customer will throw a curveball, and in these cases, you need to ensure that agents are prepared to handle these edge cases. Continuous coaching with real call data is the only path to improvement.
Actionable Insights Beyond Customer Service: Customer data from phone conversations is a valuable resource for all departments within the business. Sales and marketing especially can use these pain points to tailor their sales pitch and marketing campaigns. This can also inform product development, and highlight a need for process improvements.
Compliance and Risk Management: Analyzing calls can help ensure that compliance requirements are upheld for every single phone call. Several call tracking tools have a feature to flag compliance issues, which can then be dealt with quickly and swiftly.

Key Metrics in Call Analytics

Call Volume: Call volume is the first and most important statistic. You need to track phone calls for a specific timeframe to understand demand patterns. You can then adjust your workforce requirements to meet any surges.
Average Handle Time (AHT): It’s important to know your average handle time and have limits on how long a call can last. This helps you measure efficiency and pinpoint any delays. A sharp spike in AHT can be a sign of new issues cropping up, which require further investigation.
First Call Resolution (FCR): First Call Resolution (FCR) is the percentage of calls resolved during the first interaction. Most issues should not require a callback. This rate should be closely monitored.
Customer Sentiment: With sentiment analysis, you should have an understanding of what customer emotions look like during a call. Mostly this would be neutral, and happy. Anytime a customer is sad or frustrated, see what went wrong during that call.
Call Abandonment Rate: This is the number of calls that dropped before a resolution was achieved. A high abandonment rate is a sign of potential service gaps.

Challenges in Implementing Call Analytics

Data Overload: If you and your team handle, tens of thousands of calls a month, you are bound to experience data overload. You may struggle to find actionable insights. You would need an AI tool that can scan these calls, spot changes in key metrics, and provide actionable insights for you.
Integration with Existing Systems: Combining a call analytics solution with a CRM, a ticketing system, and other organization tools can be a technically complex and time-consuming ordeal. You also need to test all of these systems are working without having much downtime.
Privacy and Security: When looking for a call analytics provider, you need to be aware of the fact that they also have access to customer conversations. This means that their technology should be compliant with your market or industry. You need to validate this. Additionally, you may have to sign contracts with the provider to make sure that both parties will uphold their commitments to compliance.
Change in Management Style: Teams will have to get used to the new analytics tools and the insights they generate. With new data available, a new set of employee KPIs will emerge. The top-down implementation of this should be phased, so employees can get on board with this.

Optimizing Call Analytics

Most call analytics data is handled by an analytics tool, so optimization of analytics means optimizing the tool and then relaying the feedback to your team.

Use an AI AI-powered tool: For quicker and more accurate insights, automate the analysis of call data. Most large analytics providers already use AI in some form or other to assess call data.
Audit the Tool: Auditing the team is great, but also see the quality of reports that you are getting from your analytics solutions. The tool should continually serve a purpose in your business processes.
Run a Tight Ship: When it comes to selecting tools, see what integrations the tool offers and get those integrations up and running, so you can monitor call statistics from a single dashboard. The last thing you want is to use multiple tools for different call metrics. Make sure it’s all centralized and easy to access.
Invest in Training: Your team including managers and agents should know how to interpret analytics reports and get actionable from them. Conduct training sessions that outline data patterns and possible courses of action for them.

Conclusion

Call analytics is a powerful process for any business that wants to improve its customers’ experience during phone call interactions. Call analytics tools can help spot patterns in customer behavior, highlight service bottlenecks and also help uphold compliance. With their ability to crunch, analyze, and summarize call data, these tools can provide actionable insights for your business. No matter what industry you are in, if you offer customer support over the phone, you need to add call analytics to your processes.