Artificial Intelligence to improve Customer Experience and accelerate innovation.

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Artificial Intelligence, AI has got dozens of applications in the business world. AI is able to fill in the gaps in human decision-making where uncertainties have always existed. A Quote by Forbes says, “By 2025, an estimated 95% of customer interactions will be supported by AI Technology”. Good customer experiences help people feel good about purchases and a brand. This positive experience influences them to return back within the future. Integrating AI into customer service can drastically improve customer experience. It can help identify customer pain points, automate manual processes and speed up decision making and optimizing service delivery. AI helps to build customer intimacy and thereby customer experience. AI not only helps in delivering a better CX, it also helps employees to get better at their jobs, thereby improving employee experience as well. From chatbots to automation, Cognitive AI can leverage information from customer interactions and automate common responses. Thus, the use of a full-fledged customer experience management platform can transform customer experience across multiple channels. 

It is estimated that 58% of consumers will use AI to save time, and 56% said they’d use it to save money. The more time they save, the more likely they are to purchase a product, and the more money they save, they’re likely to return to your business to buy again. And AI ranks as one of the biggest game-changers for organizations.

AI Ranks as the Second Biggest Game-Changer for Organizations

AI Ranks Gartner Report Ombrulla

It was also found that improving the customer experience was the top motivation in organisations that had already deployed Artificial Intelligence/Machine Learning initiatives or planned to do so soon.

Customer Experience using AI

The marketing department is more likely than other departments to control the majority of the budget for enterprise-wide Customer Experience (CX) efforts. Defining personas, social listening, customer segmentation, user experience, and voice of the customer are all part of these efforts.

Voice of the Customer (VoC)

Speech analytics

Customer Journey Analytics

Text and social analytics

AI can be used to significantly extend and enrich an organization's voice of the customer (VoC) initiatives by utilising tools such as speech, text, and social analytics to extract insights from massive datasets. Speech analytics tools provide targeted coaching and feedback to contact- center agents to improve call-handling skills. It can also help in flagging early warnings of an emerging trend, categorize problems and thereby enrich VoC initiatives. Emotion detection in speech analytics offerings has come a long way in the last decade. AI models can now detect customer relief, anger or losing patience as a call drags on by analysing tone, pitch, keywords and talkover. This can help alert agents to alter their approach if a call takes a negative turn. Text and social analytics uses Natural Language understanding models to elicit insights from unstructured text sources such as open-ended questions in survey responses, email, live chat, or online postings. 

Businesses these days are adapting with modern digital technologies and AI for customer service and personalization. For example, Starbucks uses Reinforcement Learning to know their customers better to make personalized recommendations. They are making use of DeepBrew AI platform to suggest optimal product pairings based on the information like the weather, context of store and so on. North Face uses IBM’s AI based Watson to create a personalized online shopping experience. It helps customers refine product selection. Content recommendations from YouTube and Netflix boost customer engagement. Amazon's cross-sell and upsell opportunities are driven by similar products and "people who bought". 

Customer Journey Analytics tracks and analyzes how customers and prospects move through the mix of channels available to interact with organizations. It helps to identify critical paths, segment customers, enhance and organize the next best action strategies.

 

Moreover, organizations have increased customer trust with the adoption of AI systems. 69 percent of customers believe AI decisions are fair. AI interactions with customers have become commonplace. Organisations expanding deployments and employing levers of trust and human resemblance. However, as adoption grows, Customers expect more from their AI interactions.

 

Nowadays, the Automotive industries are using voice assistants to solve customer queries in cars and also make music recommendations. Traffic prediction used on online maps for navigation, alerts on safety concerns, quality prediction are also with the help of AI for improving CX. Not only that,  autonomous parking using AI and ML is also gaining popularity. Public sector is utilizing AI for identifying fraudulent behaviours among citizens using public data. The Banking and Insurance sector make use of the advancement in AI for detecting fraudulent transactions and for risk management which could help customers stay aware of new threats and fraud. This has helped in building a more transparent and trustworthy relationship with customers and thereby improving customer experience. Bank of America recently introduced a new App Linking feature for all of their mobile apps under the Bank of America umbrella (Bank of America, Merrill Lynch, Merrill Edge, and US Trust) that allows users to authenticate once, using a fingerprint scan or facial recognition, and switch between these apps without having to reauthenticate. Thus, with improved customer services, different sectors were able to improve customer experience. 

Common cases where AI is used for customer service and enhancing customer experience are as listed below.

 

  1. Chatbots & Virtual Assistants.

 

Virtual Assistants perform a lot of tasks these days from doing rudimentary tasks like answering common questions, addressing basic customer service issues to being able to perform more complex intelligent issue resolution and personalized commerce that is personalized. Amazon Alexa, Apple’s Siri are widely accepted voice activated chatbots that provide a wide variety of services to the customers. These devices are time saving and help companies keep up with customers' busy lives. Thus, they help to engage with customers, support employees and scale the first line of support to answer common questions of users and hence reduce traffic to other support channels. 

   2. Language Analysis.

Language analysis is a powerful tool that can help call center staff improve their communication with customers. With it, agents can detect if the customer they are talking to is happy or unhappy and adjust their tone and actions accordingly. A great language analysis example is Behavioural and Emotional Analytics Tool (BEAT) by Deloitte. 

 

   3. Object Detection.

Object Detection has been paving a glorious path in the Surveillance Industry, Automobile industry, and in grocery retails. For example, Self driving cars make use of computer vision and image processing to determine the distance between the car and moving objects to create alerts and guides to self-driving cars. Traffic tracking systems, Activity recognition, face recognition and sentiment analysis are all possible with object detection. Thus, Object detection helps the organizations to provide better services to its customers and improve customer service and experience. 

 

    4. Optical Character Recognition (OCR).

OCR is commonly used in document processing automation. It helps to process documents in a more digital and efficient way. Recent advances in OCR allow online businesses to easily capture customer data while maintaining reasonable standards of accuracy. OCR is the ultimate solution to all possible problems emerging as a result of poorly managed files and formats. They not only allow experts to save time spent on manual structuring of content but also increase their productivity by making data acquisition quicker, reliable, and accurate.

 

Conclusion 

The various challenges in the use of AI in CX are development challenges, data integration challenges, implementing challenges and management challenges. The major challenges include the lack of adequate volumes and quality of training data, data security, performance, solution price, scale, etc. Fairness, explainability, robustness, data lineage, and transparency, including disclosures, are all critical requirements that must be met right away.

The major recommendations to improve the customer experience using AI include closely aligning existing CX initiatives with AI initiatives to ensure efficiency, concentrating on areas where high volume, high value and customer pain points intersect and integrating ROI metrics into deployment strategies. It also makes a strategic planning assumption that by 2023, 30 percent of customer service organisations will use AI-enabled process orchestration and continuous intelligence to provide proactive customer service.