While there’s a lot of excitement about artificial intelligence (AI) and machine learning (ML), there seems to be significantly less understanding of their capabilities and how marketers can utilize them to potentially change the customer experience. Even so, many brands are rapidly embracing both. As the CEO and founder of a company that uses AI and ML to help brands gain insights from customer data and help marketers apply those insights along the path to purchase, I’ve seen firsthand the progress as brands move from manually coding predictive models trying to improve broad segment responses to using AI and ML to automatically tailor messages, content and offers to the individual.

source: https://www.forbes.com/sites/forbestechcouncil/2019/02/08/the-role-of-artificial-intelligence-and-machine-learning-in-driving-customer-experience/#3ab33ca1c13c

Last year, International Data Corporation estimated that “spending on cognitive and AI systems will reach $77.6 billion in 2022, more than three times the $24 billion forecast for 2018.” As companies invest in these technologies, it’s important to understand their applicability to the customer experience.

So what’s the difference between AI and ML as applied to customer experience? AI is essentially when a machine is programmed to conduct tasks that mimic the behavior of a person. Evolutionary ML is a type of AI application based on the idea that if offered enough data and rules, machines can iterate and learn for themselves. ML can then be used to test many kinds of marketing models and algorithms to find the one that fits best for each individual context.

AI can provide the speed and scale necessary to facilitate thousands of customer interactions, and ML can allow marketers to ensure that the products, services and communications remain relevant for each customer throughout those interactions. Combined, these two technologies can enable marketers to deliver a frictionless customer experience companywide, in real time and on a scale previously unimaginable.

Artificial Intelligence In Marketing

Customers live in an omnichannel world where they often expect immediacy and a personalized experience with brands — no matter which channel they connect from. However, many companies still force customers through linear engagement paths that are often irrelevant to their customers’ wants and indifferent to where the customer is in their journey.

Brands can no longer expect all customers to take the same path to purchase. The experience should be based on each individual customer’s intentions, needs, wants and preferences.

While humans may be able to achieve this level of personalization with a customer at a particular point in time, such granular personalization may be humanly impossible to scale across an entire customer base. Using AI, marketers can implement sophisticated capabilities like dialog systems and chatbots with natural language processing that work alongside larger data sources and can accommodate unusual questions.

Thousands of customers can simultaneously call in, text or email queries, and an AI communications system can provide a personalized interaction for each customer based on their own preferences and stage in the journey. This is where AI can become an invaluable technology for marketers.

With Gartner predicting that 85% of customer interactions with an enterprise will be managed without a human by 2020, brands would be wise to consider embracing AI now.

Machine Learning Models

I believe that ML systems are one of the most useful technologies on the market today for brands looking to optimize their digital transformation strategy for customer experience. ML can help brands scale personalization in the one-to-one world.

Machines using multiple models and algorithms to find the one that fits best for each individual context can allow marketers to adaptively tweak testing, continually evolve models and provide operational flexibility across business environments. They also can use regression techniques where ML allows them to predict the values of existing features and test them, identify which features resonate with their customers and, ultimately, optimize those aspects of the customer journey. Marketers can apply the same evolutionary search techniques to improve customer engagement and broader business processes.

Implementing For The Future

While I believe that AI and ML are both important tools for the future of customer experience, the way that companies implement and integrate them can be a critical part of how brands can differentiate themselves and deliver a frictionless customer experience.

The biggest question to ask before adopting this technology is: Do we have the data required to successfully implement an AI and ML strategy? Accurate, complete, timely and robust data can be critical for successfully training AI tools to perform tasks and to leverage ML.

As AI and ML capabilities evolve, there are a few foundational items to consider acting on:

• Getting the freshest, most complete and most accurate customer data

• Integrating AI and ML into the customer journey processes rather than treating them as a separate function

• Training people not only to deploy but to understand how best to utilize AI and ML

• Encouraging IT and marketing departments to work together to implement change and garner support from the top of the organization

I believe that brands that embrace AI and ML technologies will be best able to achieve personalization at scale in the always on, always connected and ever-demanding consumer market. Acting on the items mentioned above in harmony can provide the foundation for delivering superior customer experiences as part of a company’s digital transformation strategy.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Dale Renner is CEO and Founder of RedPoint Global, the leading provider of customer data platform and customer engagement technology.

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