Customer’s expectations are the guard rails that guide how their relationships progress with any business. The pandemic has made the predictable unpredictable, erasing marketing personas of the past and re-writing them in real-time. Old guard rails and expectations are changing fast. Having an accurate outside-in view from the customer’s perspective is the value VoC programs deliver, with the best ones providing data to guide strategy.
It’s Time to Bring New Energy and Insights to Customer Relationships
Pure e-commerce orders have grown 110% since January, and e-commerce revenue has increased by 96%. These figures are based on the COVID-19 Commerce Insight dashboard created by Emarsys in cooperation with GoodData. With 306 million Americans under stay-at-home orders, it’s intuitive that e-commerce will flourish. What’s not clear is how the habitual shopping and buying patterns being formed today will shift customer relationships, ending some, and creating new ones in the future.
Gaining insight and intelligence to energize existing customer relationships calls for greater use of AI in VoC programs. The following are ten ways AI can improve Voice of the Customer programs:
1. Knowing what causes some customers to churn faster than others using an AI-based analysis of customer journey data helps define new campaigns to keep them. Using the foundational AI concepts of supervised and unsupervised machine learning algorithms to understand better how customers’ buying behavior this year is changing their purchase intentions is key. Reducing churn starts by building a solid baseline of customer journey data and then getting in the practice of continually testing new campaigns to know how best to keep customer relationships fresh.
p class=”color-body light-text”>Source: https://www.pointillist.com/blog/reduce-churn-customer-journey-analytics/
2. Using algorithms to perform real-time text mining of every source of textual, unstructured data available to analyze the sentiment levels of customers. Using Natural Language Processing (NLP) to build semantic models of all available unstructured text enables an aggregate view of customers’ sentiments towards a given brand, product, or service being measured. Being able to quantify what emotions a given brand evokes is possible using sentiment analysis. By continually teaching prediction models with customer data, marketing teams are able to better understand what will most and least delight customers and how to avoid making them indifferent or angry – all invaluable insights given how fast customers are changing today. For more information on this topic, there’s a useful blog post from Qualtrics available, How Sentiment Analysis Can Be Used to Improve Customer Experience. This graphic illustrates what sentiment analysis looks like:
3. AI is making it possible to expand the scope of speech analytics to include contact center conversations, text-based customer feedback, and operational data from every customer touchpoint. Providing consistent, reliable sales and service responses across every channel in real-time and keeping every customer conversation in context is critical for any business to hold on to customers today. CMOs I’ve spoken with who actively use speech analytics say it’s showing them the need to have a much broader strategic framework for excelling at customer experiences. The concepts they’re talking about are best illustrated as a framework. BMC’s Autonomous Digital Enterprise (ADE) reflects how CMOs are envisioning AI and Machine Learning (ML) providing the intelligence needed to stay customer-centric and excel at VoC Programs for a transcendent customer experience. What’s noteworthy about the framework is how it is designed to enable greater agility and time-to-market across every function of an organization. It offers a way to deliver an unparalleled customer experience, provide a positive employee experience to so, and support an organization’s ongoing digital evolution. The following graphic illustrates the ADE framework:
4. Cloud-based Speech Analytics platforms, including Amazon Connect, are relying on AI to remove the roadblocks that get in the way of launching and fine-tuning VoC programs across multiple geographies and languages. Amazon Connect combines Amazon Transcribe to perform real-time speech recognition and create a high-quality text transcription of each call into text. Amazon Comprehend is used for analyzing every interaction and detecting the sentiment of the caller. Amazon Connect can also identify keywords and phrases in the conversation. And, when combined with Amazon Translate conversations can be delivered in the agent’s preferred language. The following is a diagram of the Amazon Connect platform:
5. Insights gained using AI are leading to call centers being transformed from being first-line service providers to becoming strategic differentiators that drive significant improvements in customer satisfaction and financial performance. Companies have already applied advanced analytics to reduce average handle time by up to 40%, increase self-service containment rates by 5 to 20%, cut employee costs by up to $5 million, and boost the conversion rate on service-to-sales calls by nearly 50% all while improving customer satisfaction and employee engagement.
Source: How advanced analytics can help contact centers put the customer first, McKinsey & Company, February 1, 2019
6. Machine learning algorithms are making it possible to provide a real-time multidimensional view of caller and agent-based attitudinal performance, intonation, sentiment, and the relative changes in each, all in the single integrated dashboard. Combining supervised and unsupervised machine learning algorithms to find patterns in existing data and create entirely new linguistic and attitudinal models is delivering the intelligence organizations need to bring new energy and insights into customer relationships. The following Tableau example of what’s possible when machine learning techniques are applied to VoC data to find out what needs to improve in real-time and what’s going well. Source: VoiceBase Voice of the Customer Dashboard.
Source: VoiceBase Voice of the Customer Dashboard.
7. Combining AI-driven insights gained from real-time customer behavioral and operational data with Net Promoter Score (NPS) data is helping to define customer risk thresholds before they defect to a competitor. NPS is one of the most commonly used metrics for quantifying the level of loyalty customers have to a given company they buy from. Qualtrics’ recent blog post, What is Net Promoter Score (NPS)? Definition & Examples provides an excellent explanation. When NPS, customer, behavioral and operational data are analyzed using deep learning neural networks, it’s possible to discover which customers are the most and least likely to churn. What’s noteworthy about this technique is can otherwise take weeks of analysis to arrive at the same conclusions AI-based analysis can provide in seconds.
8. Personalizing service recovery strategies by each customer using AI improves retention rates and reduces the high cost of customer churn. Service Recovery is the strategy of attempting to save a customer relationship after a service breakdown has happened. The essence of an effective Service Recovery strategy is to correct the problems beyond what the customer expected to receive as a response. AI-based techniques for tailoring or personalizing service recovery responses are proving very effective in keeping customer relationships intact after a service error.
9. Troubleshooting customer onboarding to streamline and improve initial customer experiences using a Six Sigma-based approach to quality, automated using AI. Six Sigma is a well-known and universally used quality management framework and methodology used for removing variation in processes. A core concept of Six Sigma is the DMAIC (Design, Measure, Analyze, Improve, and Control) process. Voice of the Customer is integral to getting DMAIC grounded in customers’ expectations, ensuring the process improvements meet their requirements. AI is being used extensively to interpret all forms of textual, unstructured content customer onboarding processes create so they can be improved.
10. Understand how upsell, cross-sell, campaigns, and promotions, influence customers’ perception and loyalty to the brand, especially across new channels, including e-commerce and mobile platforms. The pandemic is leading to entirely new buying behaviors and habitual uses of data that customers didn’t have the time to consider before. Knowing how every upsell and cross-sell attempt is perceived as helpful or not, is key. Also, being able to monitor campaign and loyalty levels by promotional activity is the essence of knowing the Voice of Customer and how best to serve them now and in the future.
2020 Global Customer Experience Benchmarking Report, NTT, (42 pp., PDF, opt-in)
How advanced analytics can help contact centers put the customer first, McKinsey & Company, February 1, 2019
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