How to Ramp Up VoC Programs with AI and Contact Center Automation?

With contact center call volumes showing no signs of subsiding and garnering complex requests that require a considerable amount of attention, contact center automation and contact center AI are major solutions to these rising complexities that businesses face today.

The importance of VoC programs

A successful VoC program shifts the focus to the customers’ needs, driving improvements in the brand, products, and service for an unbeatable customer experience. Through a VoC program, organizations gather, analyze, and act on customer feedback to foster a customer-centric culture.

In addition to building deeper relationships with customers, VoC serves the purpose of identifying opportunities for growth, optimized products, and revenue. With better performance visibility across all departments, the VoC methodology makes the overall processes more efficient for both the business and the customers.

A contact center is the most obvious choice to collect VoC information. While post-contact surveys are administered to calculate and evaluate Customer Satisfaction (CSAT) scores, Net Promoter Scores (NPS), and Customer Effort Scores (CES), the real gold mine is in AI-powered text and speech analytics. When AI works in conjunction with contact center automation, the surveys and analytics provide valuable insights into VoC analysis.

Analytics tools that implement AI analyze every contact from all available channels like phone, chat, email, etc., identifying areas to focus on from customers’ feedback. The results are packaged in a manner that is easily digestible and actionable.

8 effective methods to collect VoC data

A large part of an effective VoC strategy is reaching out to customers. A successful methodology generates all the insights that are needed to realize their preferences and problems.

1. Customer journey data

To better understand the buying behavior of customers, it is first important to observe their purchase intentions and buying journey, which is possible through AI-based analysis of the customer journey data that will ultimately, help identify the purchase intentions of customers. This will help define new campaigns to retain them.

Building a solid baseline of data and regularly testing new campaigns is crucial to know how best to maintain strong customer relationships.

2. Sentiment analysis

Sentiment analysis quantifies the emotions that a brand evokes. This type of analysis uses real-time text mining of every source of textual, unstructured data available and semantic models based on Natural Language Processing (NLP). The aggregate view of sentiments towards a brand, product, or service helps with a better understanding of the approach that is required by the company toward its customers.

3. Speech analytics from every customer touchpoint

AI is successfully expanding the scope of speech analytics to include data from every customer touchpoint, such as contact center conversations, text-based feedback, operational data, etc. This data is critical for consistently reliable sales and service responses across every channel and contextual conversations with customers.

There are cloud-based speech analytics platforms that rely on AI to eliminate the bottlenecks that prevent the successful launch of VoC programs across multiple geographies and languages. AI along with contact center automation allows organizations to ensure pro-customer strategies and successful VoC programs for an unparalleled customer experience.

4. Data-driven environment

In a data-driven environment, it is easy to deliver actionable insights for VoC programs and ensure top-notch CX. Contact center automation paired with AI has led contact centers to transform into strategic differentiators that drive significant improvements in customer satisfaction through enhanced productivity and efficiency.

Smart knowledge management capabilities facilitate the distribution of business-critical information among agents in real time. Chatbots and Interactive Voice Response (IVR) allow self-service to customers. IVRs also sort customers based on customer data and history enabling accurate routing to the relevant representative and saving time for both customers and agents.

Contact center automation reduces an agent’s cognitive load and the average Agent Handling Time (AHT) by automating workflow, daily tasks, agent training, reporting, and quality assurance. As a result, customers receive quicker responses and resolutions, and the need for repeat calls decreases.

Monitoring contact center performances drives workforce management, operational efficiency, and quality assurance.

5. Net Promoter Score (NPS) data

Net Promoter Score (NPS) is a popularly used metric that quantifies the level of loyalty customers have toward a brand or company. When this data is combined with AI-driven insights generated from real-time behavioral and operational data, it helps define the customer risk thresholds before they switch to a competitor. What would otherwise take weeks of analysis, contact center automation and AI-based analysis can provide the same conclusion within seconds.

6. AI-based service recovery strategy

Service Recovery attempts to save a customer relationship after a service breakdown. The goal is to correct the problems and exceed the customer’s expectations of the response they receive. AI-based techniques can tailor and personalize these service recovery responses. It is a highly effective method to maintain a good customer relationship even after a service error, improving retention rates and reducing costly customer churn.

7. Six Sigma-based customer onboarding

Six Sigma is a universally used quality management framework and methodology that eliminates variation in processes. Its core concept lies in the DMAIC (Design, Measure, Analyze, Improve, and Control) process.

VoC is essential to get DMAIC well acquainted with customers’ expectations and ensure that the process improvements meet their requirements. AI is widely used in this context to discern textual, unstructured content generated in any form by customer onboarding processes so that they can be improved.

A Six Sigma-based approach that is automated using AI troubleshoots customer onboarding by streamlining and improving initial customer experiences.

8. Promotional activities and campaigns

It is crucial to understand whether every upsell and cross-sell attempt is perceived as helpful. Upsell, cross-sell, promotions, and campaigns, greatly influence customers’ perception and loyalty to the brand, especially across mobile and e-commerce platforms.

Being able to leverage promotional activities to determine campaign and loyalty levels is the true essence of knowing the VoC and how best to serve them.

Through intelligent recommendations, Intelligent Virtual Assistants (or IVAs) help tremendously with cross-selling and upselling to potential prospects. A wide variety of contact center automation helps with processes, such as balance verification, order placements, and responses to initial inquiries are performed using chatbots and virtual agents that are infused with AI capabilities.

By capturing feedback from the right audience through the right channels, one can make meaningful improvements to their CX. Contact center solution, HGS Agent X can help build a strong VoC program with its AI capabilities and intelligent automation. Countless opportunities can be uncovered in contact center processes by investing in VoC technology and enhancing the experience across every interaction channel.

Learn more about how HGS Agent X can ramp up your VoC program.