Today’s companies are constantly flooded with loads of valuable data, and proper analysis of such data is essential for better strategic decision-making and process improvements. Real-time analytics and elevated CX directly impacts higher brand preference, revenue and cost improvements, and lasting competitive advantages. According to a Harvard Business Review report, 44% of enterprises are acquiring new customers and increasing revenue as a result of adopting and integrating customer analytics, while 58% are seeing a significant increase in customer retention and loyalty.
To improve your contact center operation, consider the many ways analytics can be put to use:
Whichever metrics an organization has identified as critical to their business goals, the value of big data lies in how it’s analyzed and applied. Gathering and analyzing structured and unstructured data will keep your contact center operating at peak performance.
- Predict the success of a follow-up call: Understand customers’ buying history with predictive analytics and effectively determine customer intent before contacting them
- Analyze whole times to drive better customer experience: Are parties speaking over each other on a call? Use speech capabilities to find out
- Evaluate behavioral patterns to predict future insights: Tailor suggestions for better up-sales or problem resolutions with machine learning and predictive analysis
- Drive contract renewals with existing customers: Increase profit margins through the use of algorithms and historical customer data
- Identify monthly call response trends: Is there a particular month that’s busier than others? Once it’s determined, maximize resources at those times for a better chance of response
- Match agents to expected outcome: Predictive analytics isn’t just for customers; training and retention can be improved when it’s also applied to agents
- Help agents meet performance targets: Allow agents to view and determine whether their targets are being met in real time with predictive analytics
- Reduce customer churn: Identify historical customers who have canceled—and who might cancel next—through a combination of predictive, speech and text analytics
- Leverage data to create personalized and targeted offers: Build algorithms around customer purchase propensities and recommend unique products for each caller
- Fine-tune customer experiences based on segmented customer needs: Improve loyalty and brand relationships via personalized customer journeys based upon segmentation and prediction models
For a deeper dive into data analytics, download our complete Big data: Better data—Driving ROI with contact center advanced analytics whitepaper now.