Tell us a little bit about your role at Momentive. How did you come to that company?
I am currently Senior Director of Engineering and Machine Learning at Momentive. As the machine learning engineering team leader, I envision every product and business function powered by machine learning. Prior to Momentive, he spent six years at Cisco Systems, Mobile for non-tech industries. He was also an entrepreneur focused on building solutions and data products.
How can Momentive’s AI-powered solutions help drive positive customer experiences?
Momentive’s AI-powered technology helps drive positive customer experiences by getting to the heart of what matters most to customers, making that information available and available throughout the decision-making process. .
AI capabilities like Automated Insights and SurveyMonkey Genius brought to market by Momentive empower survey creators to conduct more impactful and meaningful surveys. It also helps customers gather cost-effective answers from targeted users and surface relevant insights faster than ever before. Speed to delivery is essential, and our unique ability to balance technology and human experience drives greater value for our customers.
Having data is one thing, but having data is another. Assembling a team to translate accurate and comprehensive AI-driven insights is another. We effectively combine the two to help our clients make data-driven, more realistic, and less biased decisions.
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How are AI and ML being used in customer experience?
Intelligent technology is built on sound data that blends humanity and technology to deliver more personalized results, helping customers make better, more confident decisions. For example, the human loop methodology that Momentive has introduced in its latest open theme text analytics solution provides highly customized insights specific to each customer and their specific use cases. This type of hyper-personalization transforms the customer experience from a mechanical identity to one in which people feel seen and heard, building loyalty and trust.
Machine learning plays a key role in leveraging insights from millions of respondents and identifying what to focus on to deliver a better overall customer experience. Machine learning is also used to reliably amplify real customer voices by detecting and filtering out poor quality responses. With the help of ML, real and relevant customer voices can be heard and service providers can focus on using the right information to improve the overall customer experience.
Using AI and ML to embed feedback options directly into your website or chatbot, or even generate automated emails after specific customer touchpoints, to help your bots lack the context they need to solve their problems. You can also avoid getting stuck in a frustrating loop. With concepts like AI copilots and his AI assistant gaining popularity, it’s no surprise that his AI sidekick appears more frequently in everyday workflows.
Momentive recently conducted a consumer survey to assess the extent to which AI is playing a role in the customer experience. Can you give us some important points?
We asked 2,201 consumers about their experiences with AI in CX, focusing on likes, dislikes, and future interests. Key findings show that:
- Regardless of age, gender or income level, 90% of people prefer working with humans over chatbots. Respondents felt that humans better understand their needs (61%), provide more thorough explanations (53%), are less likely to become frustrated (52%), and (50%) say it gives them more choice.
- Chatbots are the most visible aspect of AI in the customer experience, but customers are also interested in applications. Of him, 52% of consumers are interested in AI to help them through products, websites, features and experiences, 47% are also interested in personalized deals and 42% are also interested in product recommendations.
- A majority (89%) believe AI will impact their lives in the next five years, but the technology is still largely misunderstood. He is only 18% “very confident” in what he knows when interacting with a chatbot, and who feels confident in finding AI-generated content He is only 14%. Fewer than half were confident they could identify AI-generated content at all.
The broad point is that AI cannot replace human interaction, but if used correctly and tailored to specific customer objectives and outcomes, AI can shed greater light on the customer experience. about it. The key is to think carefully about your approach.
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What are the challenges for companies looking to adopt AI to improve the customer experience?
Some of the biggest challenges companies face are embracing and being open to change, both internally and from their customers. AI technology exists and is constantly evolving. But it will take time for us humans to adapt to it. Bringing AI into the customer experience flow within the enterprise requires changing and breaking some of the existing approaches. This requires extensive employee training and education at all levels of the organization. Successful adoption of AI in CX workflows also depends on customer awareness and readiness to use technology.
Taking a more open approach to customer feedback when testing new AI can have a big impact if done right. Businesses that successfully engage with their customers and ask for their input at every step can adapt and scale their priorities in real time. For example, engaging with a customer through a quick survey after a customer service interaction can yield important insights. We help you incorporate this type of feedback into your customer’s experience and use it to inform investment and other business decisions.
Beyond CX, how do you think AI/ML can help capture the true emotions that influence business decisions?
CX is just one of the typical use cases that show the benefits of AI/ML. Other use cases include market research, brand tracking, and concept testing. With the help of AI/ML, capturing and understanding emotions can reveal valuable information. AI/ML can also help identify the right focus so you don’t get lost in the vast sea of digital data.
Our AI and ML work as a learning and building process that continuously improves through feedback loops over time and usage. they do not remain static. It’s intentional because it doesn’t fit human emotions either. These are powerful sets of information for making better business decisions.
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How do you see ethics applying to AI use cases like these?
AI ethics is a complex and rapidly evolving field. We understand and respect the complexity of this space. The customer experience use case discussed here focuses on customer feedback data.
Adopting an ethical and comprehensive approach to the data collection process will help ensure that the data collection process is representative of the population and reduce opportunities for bias and discrimination that can contaminate the results. Our team puts ethics first, but this is not something she can do in one step. It should be done at every stage of the data collection process.
Currently, many applied ML/AI features/products rely on open-source or paid large-scale language models as the foundation for understanding text/images/videos, etc. It is important to understand and audit algorithms before adopting them. It’s also a good time to fine-tune your algorithms with a wide variety of data for your specific use case.
Again, AI ethics is an important topic today. It’s important to collect data along the way and build an effective feedback loop to keep learning and improving.
Thank you Jin! It was a lot of fun. We look forward to seeing you again soon at AiThority.com.
[To share your insights with us, please write to sghosh@martechseries.com]
