How AI Can Improve Voice of The Customer Programs
What is Voice of the Customer Program?
Voice of the customer program or VOC is a process to capture in-depth customer feedback and expectations. It is a market research technique that produces a detailed set of customer wants and needs. They are organised into a hierarchical structure and prioritised in terms of relative importance and satisfaction with current alternatives. And, using Artificial Intelligence can widely benefit VOC programs. According to NTT’s 2020 Global Customer Experience Benchmarking Report, 77% of enterprises believe customer operations, including Voice of the Customer (VOC), will be positively affected by AI.
Why does your business need a VOC Program?
Here is how the Voice of Customer Program can help your business:
- Customer experience: Understand customer satisfaction level and motivate them for engaging with the website or customer service team. Moreover, you can increase their willingness to recommend your product/brand.
- Brand and reputation management: Monitor and manage your business and improve customer satisfaction and loyalty.
- Market intelligence: Understand consumers’ buying patterns and preferences; capture trends like social, political, and environmental impacts on buying behaviours. Also, discover what customers say about competitors.
- Product management: Gather feedback on new product ideas and features; understand price sensitivity; conduct surveys; determine when to retire certain products.
How can AI Improve VOC Programs?
Here are a few ways by which Artificial Intelligence can help in improving the Voice of the Customer Programs:
1. Analyse Customer Journey Data
AI-based analysis of customer journey data will help the businesses in knowing why some customers churn faster than others. It will help in defining new campaigns to keep those customers. It understands customer buying behaviour using foundational AI concepts of supervised and unsupervised machine learning algorithms. Further, it will help change those customers’ purchase intentions. It works by building a solid baseline of customer journey data to reduce churn stats. Moreover, it will continually test new campaigns to know how best to keep customer relationships fresh.
2. Customer Sentiment Analysis
Sentiment analysis extracts meaning from many sources of text, like surveys, reviews, public social media, and even articles on the Web. Then, it is rated based on the sentiment of the text. For example, -1 for negative sentiment and +1 for positive sentiment. This works by using natural language processing. Artificial Intelligence algorithms can perform real-time mining of every source of textual data available to analyse the customer sentiment levels. Further, using NLP enables an aggregate view of customers’ sentiments towards a given brand, product, or service.
When you’re able to understand your customers emotionally, you’re ready to provide a more robust customer experience. Thus, using sentiment analysis can improve customer experience and provide valuable insights.
3. Determine Risk Thresholds using NPS
AI-driven insights gained from real-time customer operational data are combined with Net Promoter Score (NPS) metric. It is used in customer experience programs. It measures the loyalty of customers to a company. NPS scores are measured with a single question survey and reported with a number from -100 to +100 and a higher score is desirable. This data is helping to define customer risk thresholds before they defect to a competitor.
Deep learning neural networks analyse customer behavioural and operational data. It makes it possible to discover which customers are the most and least likely to churn. Further, AI-based analysis can provide results in seconds, rather than taking days or weeks
4. Personalising Service Recovery Strategies
Service Recovery is the process of trying to save a customer relationship after a service breakdown. Certainly, the essence of an effective Service Recovery strategy is to correct the problems beyond what the customer expected to receive as a response. Thus, using AI-based techniques can personalise service recovery responses. Further, they are proving very effective in keeping customer relationships intact after a faulty service. Personalising strategies by each customer using AI will help to:
- Improve customer retention rates
- Reduce costs
- Prevent customer churn
- Provide seamless customer service
5. Using Speech Analytics
Artificial Intelligence is widely contributing in the field of Speech Analytics. According to a Gartner research, AI embedded in analytics will free up more than a third of data analysts in marketing organisations by 2022, enabling them to focus their time on business priorities instead of spending time on manual processes like:
- Lead scoring,
- Anomaly detection,
- Marketing performance management
AI is making it possible to include contact center conversations, text-based customer feedback, and operational data from every customer touchpoint. For example, Cloud-based Speech Analytics platforms, including Amazon Connect, are relying on AI. As a result, they are removing the roadblocks that get in the way of launching and fine-tuning VOC programs across multiple geographies and languages.
Moreover, these insights gained using AI are transforming call centers. Now, they have transitioned from being first-line service providers to becoming strategic differentiators. As a result, they drive significant improvements in customer satisfaction.
6. Influence Customer Loyalty by Identifying Opportunities
The COVID-19 pandemic is causing a change in customer buying behaviour and habitual uses of data that customers didn’t have the time to consider before. In such situations, businesses should be able to understand how up-sell and cross-sell, campaigns, and promotions can affect customers’ perception and loyalty to the brand. Especially, it can be across new channels, including e-commerce and mobile platforms.
According to research by GlobeNewswire, 61% of business executives with an innovation strategy say they are using AI to identify opportunities in data that they would otherwise miss. Thus, being able to monitor campaigns and loyalty levels by promotional activity is the essence of knowing the Voice of Customer. Further, it will tell you how best to serve your customers now and in the future.
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