Speech and Text Analytics: Leaving Significant Customer-Service Improvements on the Table?
A recent survey showed, 86% of buyers are willing to pay more for a great customer experience. A Walker Study found that at the end of 2020, customer experience will overtake price and product as the key brand differentiator. But how are businesses going to build great customer experiences? There is no magic wand. They must listen to their customer to learn. Speech and text analytics becomes the most critical tool towards that goal. But leveraging this technology is as much Science as Art. Let’s discuss why Speech and Text analytics delivers on that promise.
Speech and text analytics tools have become an integral part of exceptional customer experience. Enterprises have discovered that interactions with customers and prospects hold valuable business intelligence and customer satisfaction insights.
The Cisco Contact Center Global Survey 2020 found 90 percent of organizations consider customer journey data analytics an essential function of the contact center. Unfortunately, many organizations fail to take full advantage of these insights. They leave opportunities to improve customer service on the table.
Where Organizations Are Going Wrong with Speech and Text Analytics
Managing a contact center often feels like herding a bunch of cats. While enterprises focus on improving one area, another area goes haywire. As a result, leaders are in a constant state of damage control. For this reason, organizations must get analytics right. Here are some of the mistakes enterprises must avoid:
1. Lack of Integration Across Channels
Many organizations have invested in call centers that function in silos. As a result, there’s no easy way to integrate the data into a single truth source that can help leadership make decisions.
In some cases, organizations use ad hoc tools to solve specific problems instead of having a single integrated speech and text analytics platform. Even worse, teams across the organization don’t have access to the same data.
2. Failure to Link Insights to Actions
Often, organizations generate insight from analytics tools but fail to translate the insights into meaningful action. For example, some companies run speech and text analytics to calculate customer satisfaction and first call resolution (FCR) metrics. However, they fail to use the feedback to redesign or transform internal processes.
Most times, operations managers don’t know what to do with the analytics.
3. Making the Most of Speech and Text Analytics
Analytics give contact center managers and agents the boost they need to perform at their best. Organizations can use analytics to identify best practices, measure performance, guide agents, and improve coaching. In short, analytics helps organizations to provide the best possible customer experience. But how can organizations get analytics right?
4. Finding A Clear Vision and Strategy
Organizations must have a coherent and clear enterprise-wide vision for analytics. Leadership should link the vision to the overall business strategy and have a clear road map for implementing specific use cases. For example, an organization may want to offer more self-service options to reduce call center demand.
5. Creating an Agile Organization with Internal Analytics Capabilities
Coping with dramatic and sudden changes requires organizations to be agile and resilient. This is particularly true today as the world tries to respond to the changes caused by the COVID-19 pandemic.
To create an agile culture, organizations must build strong in-house talent in analytics that aligns with their strategic goals. Moreover, organizations need agile mechanisms that can help them take advantage of analytics-driven insights.
6. Investing in the Right Platforms and Data Sources
Getting the right speech and text analytics tools is a critical step toward getting analytics right. Organizations must create a comprehensive data strategy and ecosystem that supports the broader analytics strategy of the organization.
Also, organizations must invest in building data lakes. These data lakes act as a single source of all data on agents, customers, surveys, product performance, and other sources.
7. Creating the Right Partnerships
Very few organizations can meet all their analytics and data needs internally. For this reason, they must identify the conditions they can handle in-house and those they should outsource to experts.
8. Building a Culture of Objective Decision Making
Leading organizations make decisions based on data rather than instinct. Some decisions data can help include hiring, coaching, performance-based bonuses, and other initiatives that improve outcomes.
Call centers are a gold mine for enterprises that want to understand customer pain points. Unfortunately, many enterprises miss out on a clear opportunity by failing to use the data generated to differentiate themselves from the competition. Using speech and text analytics tools the right way can help enterprises smash down data silos and unlock a 360-degree view of the call center.
Find out more here: Speech and Text Analytics
Praphul loves to build what’s next. He has a passion for solving customer problems by leveraging design, technology and data science in a SaaSy way. Praphul brings over 20 years of experience in Customer Experience, AI and Analytics space and held leadership positions in many organizations like Microstrategy and Genesys where he built and launched many products in the market.