AI CX Innovators

Wayfair’s Prasanna Chand on Using AI to Predict Customer Satisfaction

When only 20-25% of customers complete satisfaction surveys — and even those are primarily negative experiences — how can you truly understand your entire customer base? In this episode of AI CX Innovators, Prasanna Chand, Head of Data & Digital Transformation at Wayfair, reveals how they're using AI to predict customer satisfaction scores with 85% correlation to actual survey results, providing a complete picture beyond the inherently skewed feedback pool.
Prasanna takes Ashish through Wayfair's journey implementing AI across their customer experience operations, from identifying critical issues within days of launching their loyalty program to helping agents self-coach through personalized insights rather than generic examples. With ChatGPT's launch as the tipping point, he explains how Wayfair strategically separated which AI solutions to build versus buy, and why their partnership with Level AI has been transformative for users across the organization.
Topics Discussed:
How Wayfair's three-pronged approach to customer data analytics focuses on conversational insights, making business users more data-friendly without SQL knowledge, and creating an enterprise architecture that balances hyperscaler platforms with boutique vendor solutions.
The tactical advantage of AI-powered analytics that discovered loyalty program issues within days of launch, bypassing the months-long traditional data warehouse reporting cycle and uncovering specific functional problems hindering customer adoption.
Why AI-predicted customer satisfaction scores (achieving 85% correlation with actual surveys) solve the inherent bias problem when only 20-25% of customers complete surveys, but still don’t replace manual CSAT collection.
Wayfair's strategic bifurcation approach to AI implementation: building and extending homegrown systems for agent support while purchasing software for integration with third-party telephony, workforce management, and quality systems.
How connecting journey analytics with conversation data enables FCR analysis to identify and reduce multi-contact scenarios, allowing teams to immediately see negative sentiment pathways and make targeted improvements.
Three essential best practices for implementing AI transformation: educating stakeholders to manage resistance and expectations, selecting partners who can innovate at the market's pace, and identifying use cases with quick ROI through plug-and-play implementations.
The evolution from random sampling in quality assurance to holistic review capabilities, enabling personalized agent coaching with specific conversation examples rather than generic feedback, fundamentally changing how agents self-improve.
Leveraging AI for language translation and virtual training to overcome language barriers in agent development, creating training in one language and delivering it through human-like virtual instructors in multiple languages.
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AI Adoption in CX Teams with Andy Yasutake, Leader at Edgevana, ex-AirBnB, LinkedIn, & eBay

AI isn't replacing humans in customer experience — it's transforming them. In our very first episode of AI CX Innovators, Ashish Nagar, Founder & CEO of Level AI, dives deep with inaugural guest Andy Yasutake, SVP and Global Head of Strategic Growth & Ventures at Edgevana. 
As former architect of customer experience transformations at tech giants eBay, LinkedIn, and Airbnb and with over 25 years shaping how global brands interact with millions of customers, Andy presents his battle-tested strategies for leading multi-million-dollar AI initiatives, navigating organizational resistance, and implementing generative AI at enterprise scale. 
From turning the 2020 pandemic into an opportunity for Airbnb's technology transformation to personally helping Brian Chesky deliver his vision of "11-star experiences," Andy shares candid insights few technology leaders have experienced across three waves of digital disruption.
Topics Discussed:
The challenges of managing data due to rapid AI technology evolution and how companies must adapt their strategies from multi-year implementations to iterative approaches delivering value in days and weeks.
The process of determining when to build in-house vs. partner with AI vendors, including a framework for distinguishing between "core" business differentiators and "contextual" systems already solved elsewhere.
How successful companies develop integrated product-operations roadmaps rather than treating AI as a technology to be shipped over the fence, with monthly iteration checkpoints aligned to business seasonality.
Why Airbnb deliberately delayed customer-facing GenAI implementations despite being partners with OpenAI and Microsoft, focusing first on internal learning while competitors rushed to market.
The complexities of calculating true GenAI implementation costs, including unexpected compute expenses many companies failed to factor into early business cases.
How CX organizations can move from cost centers to strategic drivers by using rich customer data to demonstrate direct impact on executive-level metrics and brand differentiation.
The organizational structure shift that doubled AI adoption rates at LinkedIn and Airbnb by moving product teams under operational leadership rather than central technology organizations.
Andy's "Iron Man vs. dystopia" vision for AI's impact on contact centers, where technology augments human capabilities rather than replacing them, enabling agents to handle significantly more complex issues with higher quality.