Customer Experience Tools
Artificial intelligence (AI) is a constellation of technologies that enable computers to mimic human cognitive function. Machine learning, technology that uses algorithms that learn and improve based on experience, is a major subfield of AI.
AI-based technologies and applications, like voice recognition, computer vision and process optimizations, are quickly finding their way into a wide number of industries. Businesses want to harness the vast amount of data available to improve their customer experience. AI applications can directly affect customer metrics, such as Net Promoter Score®, retention and share of wallet. AI can also reduce costs and simplify processes.
How companies use artificial intelligence
- Customer service optimization. Companies use AI applications to understand industry trends, manage their workforce, address problems, power chatbots and personalize content to enable self-service.
- Offering enhancement and extension. Natural language processing and pattern recognition can help companies design the right propositions for the right times.
- Marketing mix optimization. AI can enable companies to identify the marketing elements that work best, so they can spend more on winning approaches and steer away from losing ones.
- Fraud and anomaly detection. Companies deploy AI to identify and prevent security breaches, tapping the technology’s ability to learn and adapt to previous patterns.
Key considerations with artificial intelligence
Before integrating AI into a company’s day-to-day activities, leaders should:
- Define the business opportunity. Not all tasks can or should be replaced by AI. Companies must identify the right customer episodes, such as those with high repetition, strong data, regular patterns and, ideally, low mistake costs. AI does not have to replace a human to be effective; it can be very valuable in helping humans do their jobs faster and more accurately.
- Design with the goal in mind. AI exists to enhance human decision making. Companies get the most out of the technology when they know the applications and decisions they want to improve.
- Invest in the process. Modern AI or machine learning algorithms still require vast amounts of data and large manual efforts to train and tune them. Test-and-learn strategies can help companies figure out which approaches work best for their businesses.
- Determine the data sources. Data from customer interactions across episodes, combined with powerful internal operational or financial data and various external data sources, can generate valuable insights.
- Raise awareness. Discuss how employees and customers can interact with AI-based interfaces. Explain what it takes to understand the output and how to alter the approach, if necessary.
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