5 Battle-tested Methods to Analyze Call Center Data in Salesforce
2 min
Businesses are always looking for new ways to improve their call center performance. However, you cannot improve your metrics unless you monitor and analyze the data related to your KPIs. Call Center Analytics gives businesses the ability to monitor and improve their call times, efficiency, performance, and even customer satisfaction.
How to Use Your Call Center Data in Salesforce
Call Centre Analytics refers to a variety of tools that companies can use across call center channels to ensure that operations remain at their peak performance. Management only has access to limited information and is often alerted to issues during high-priority instances such as system outages or complaints. Analytics will bridge the gap and highlight opportunities for improvement.
1. Use Salesforce CRM Call Center Reports
Call Center reports analyze the phone calls handled by the team. It’s available in most editions, including Lightning Experience, Salesforce Classic (not in all orgs), Enterprise, Unlimited, etc. The My Team’s Calls This Week/Today feature provides information about calls handled by agents during the past week or day, including associated records and the results of each call. You can use this information to measure how effective agents are at closing sales or resolving queries and provide assistance to agents who fall below the benchmarks.
2. Tracking Call Center KPIs
How well is your call center really performing? KPIs or Key performance indicators are the targets you should track to achieve strategic business outcomes. These KPIs support your strategy and help teams focus on what is important, e.g., the number of sales to close or queries to resolve.
Most call centers share similar KPIs, including:
- First Call Resolution: How often are your caller’s queries resolved on the first call, with no follow-up calls or emails required? Boosting your first call resolution rates will go a long way toward improving customer satisfaction.
- Average Time Spent in Queue: This metric measures how long customers have to remain on hold in the queue before they are helped and reveals a lot of the effectiveness of your agents and your call center. How quickly, on average, do agents respond to different channels, e.g., live chat, email, social media, or phone calls? If the wait times are too long across the board, your call center may be understaffed. If wait times for specific agents are longer than average, there is a clear performance management issue.
- Average Handle Time: This metric measures the average time spent on a call (including the time spent talking and on hold). There should be a balance between low handle times and effective customer service. Cutting handling time down will boost productivity but may impact customer service as clients feel rushed or dismissed.
- Average Abandonment Rates: By measuring how often customers abandon a call, you can determine whether or not your call center is operating as efficiently as you’d like it to. You can reduce abandonment rates by addressing long handling and queuing times and implementing easy-to-navigate IVR systems. A callback system that enables customers to leave a message can also be effective.
- CSAT scores: Customer satisfaction scores demonstrate how satisfied customers are with your company or the service they have received from the agent. Simple surveys and feedback portals will help you keep an eye on your CSAT score and identify whether additional staff or training may be required.
You should also measure your staff metrics, including agent absenteeism and turnover rates. Retention is always of concern in a call center, and identifying worrying trends of absenteeism and turnover is the first step to making improvements.
3. Call Centre Speech Analytics
Call Centre Speech Analytics is relatively new to the field, and Salesforce was one of the first companies to utilize the technology. Calls are monitored in real-time, enabling companies to unearth inefficiencies in their process or develop systems for call center agents to improve outcomes. You can capture the content of calls and link transcripts to the customer profile or make recommendations for the Next Best Actions or Replies to speed up customer queries and reduce handling times.
4. Use Predictive Analytics
Predictive Analytics will review past performance in areas like call volume, service level, handling times, and customer satisfaction levels - and then apply past solutions to upcoming problems. This can include determining how many agents are required to man the phones on holidays, determining how a new product launch impacts call volumes, or how changes to fees and services impact CSAT scores. Predictive analytics uses artificial intelligence (AI) to analyze call center data and apply the logic from past solutions. Predictive analytics can even identify churn risk or customer issues before they actually occur based on trends in your data.
5. Examine Customer Self-Service Analytics
Self-service centers enable customers to update their information, set up appointments, or check on the status of their orders. These portals can reduce case volumes considerably, but not all customers find them helpful or user-friendly. Keep an eye on how well these channels are working for your customers and your employees by evaluating case deflection scores, common searches, and new trends in customer requests.
Conclusion
Analyzing your call center data is the first step toward improving your call center’s service levels, sales, and overall effectiveness. By using these tried and true methods of analysis, you’ll have all the data you need to make improvements.
