AI for Driving Quality Customer Interactions in Retail Distributor Models – with Joshua Haddock of Herbalife and Phil Gray of Interactions

Riya Pahuja

Riya covers B2B applications of machine learning for Emerj - across North America and the EU. She has previously worked with the Times of India Group, and as a journalist covering data analytics and AI. She resides in Toronto.

AI for Driving Quality Customer Interactions in  Retail Distributor Models_1x

This interview analysis is sponsored by Interactions and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

While AI is making significant strides in various fields and disciplines across the global economy, many AI-driven systems still fall short of delivering genuine empathy and human connection. Despite the expanse of the AI hype cycle, the truism remains there is no one-stop technology solution for all customer interaction needs.

Despite initial optimism about AI applications for empathy-based workflows, a recent article from MIT Sloan, based on the research done by CCW Digital on CX trends, highlights a concerning trend where AI is actually contributing to a decline in quality interactions. The study reveals that only 7% of consumers feel their experiences have improved, while 55% believe they have worsened. 

The decline is attributed to the need for more genuine human connection, with many consumers expressing frustration over the difficulty in reaching live agents. 

KPMG, in its report, “Artificial Intelligence and the orchestrated customer experience,” highlights a notable decline in empathy, with a 4% drop, marking it as one of the most significant year-over-year declines alongside expectations. The report points out that technology, including AI, still struggles to replicate human empathy. 

However, the report also suggests that AI holds the potential to foster empathy within companies by analyzing customer data to gain insights into customer emotions and needs. The absence of empathy and the human touch in AI-driven interactions is seen as a significant pain point, the pit of an emotional “uncanny valley” leading to increased consumer dissatisfaction and regression in customer experience.

Emerj Senior Editor Matthew DeMello recently had a conversation with Joshua Haddock, Director of Contact Center Technology at Herbalife, and Phil Gray, Chief Product Officer of Interactions, to discuss leveraging AI to enhance customer and distributor experiences by balancing efficiency, personalization, and regional adaptability while addressing the complexities of global operations and evolving technology.

Interactions provides customer engagement solutions through its IVA and Herbalife, which is a global health and wellness company that offers a range of nutrition products, including meal replacement shakes, supplements, and personal care items.

The following analysis examines three critical insights from their conversation:

  • Prioritize quick escalation in self-service failure: Measure and minimize the time it takes for self-service systems to recognize failure and redirect customers to live agents, ensuring a seamless and authentic customer experience.
  • Evaluate tools beyond features — focus on usability and reporting instead: Prioritize agent-facing tools that are easy to administer and deliver actionable reports, ensuring usability and strategic insights for better decision-making.
  • Enhance customer experience with AI-assisted wait times: Engage customers during hold times with AI tools to address simple queries, retain attention, and reduce live agent call durations. 

Guest: Joshua Haddock, Director of Contact Center Technology at Herbalife, Herbalife

Expertise: Vendor Management, Business Analysis, IT Service Management, Contact Center Strategy

Brief Recognition: Joshua has been with Herbalife for over 14 years. In his current role, he is responsible for all contact center technology globally, covering 98 markets and 40 locations. He also leads a high-performing digital transformation team of engineers, developers, analysts, and product specialists. 

Guest: Phil Gray, Chief Product Officer, Interactions

Brief Recognition: Phil Gray is the Chief Product Officer at Interactions, where he leads the company’s product development and innovation efforts in conversational AI. With extensive experience in AI and customer experience technologies, Phil focuses on driving technological advancements that enhance human-like interactions in customer service.

Expertise: Conversational AI, customer experience innovation, natural language processing, AI-driven product development, customer service automation

Prioritize Quick Escalation in Self-Service Failure

Joshua opens the conversation by highlighting metrics for evaluating customer experiences. He talks of how, in the 1990s, the focus was on basic resolution metrics, such as whether a customer’s issue was resolved. By the 2000s, metrics like Net Promoter Score (NPS) emerged, reflecting whether customers would recommend the company. 

While these were steps forward, more is needed in today’s context, where customer interactions often involve automation and AI. He points out the risk of robotic empathy in heavily scripted interactions, which may solve problems but feel inauthentic. To build trust and satisfaction, Joshua instead insists the focus should shift to maintaining authentic empathy, whether through human agents or automated systems.

He also introduces the concept of speed of failure to address the challenges of automation. When self-service tools fail to meet customer needs, the failure should be recognized and resolved quickly by redirecting customers to a live agent.

Prolonged attempts to force self-service can increase frustration, particularly for tech-savvy customers who already know their issue is too complex for automated solutions. Speed and efficiency in identifying and addressing these failures are critical for reducing customer effort and friction:

“When you talk about that friction, you’re talking about how self-service is great when it works. But when you have a savvy customer who knows their issue is too complicated, this solution is not able to help them. They definitely need to speak to someone. The question becomes: How quickly can you do that? And can you measure that metric for the interactions that fail and go to a live agent or live interaction? How long did it take them to figure that out? How long did our AI or self-service figure that out and get them over?

—Joshua Haddock, Director of Contact Center Technology at Herbalife

The ultimate goal, he says, is to balance efficiency with authentic, frictionless experiences that make customers feel valued, regardless of whether they interact with a machine or a person.

Evaluate Tools Beyond Features — Focus on Usability and Reporting

According to Phil, agent-facing tools are becoming a top priority in customer experience, with industry surveys showing that about 75% of professionals emphasize these solutions. Innovative tools are emerging to enhance empathy in customer interactions, using approaches like AI-generated response suggestions and features that allow agents to refine messages for clearer, more effective communication.

In response, Joshua explains that while stability, redundancy, and compliance are foundational requirements, two often-overlooked factors are ease of administration and actionable reporting. Many platforms prioritize releasing flashy features but neglect these fundamentals, which can significantly impact an organization’s success.  

Using an analogy, Joshua compares Linux and Windows: while Linux may be more robust, it is not an ideal software for widespread adoption due to its complexity. Similarly, a product must be user-friendly, provide clear, actionable reports, and demonstrate trends and performance to executive stakeholders in a way that informs strategic decisions.  

Enhance Customer Experience with AI-Assisted Wait Times

Joshua then outlines Herbalife’s focus on deploying internal-facing AI agents while cautiously exploring customer-facing AI use cases. 

He and his team identified 20+ distributor-facing use cases, prioritizing natural language processing over generative AI due to challenges like supporting over 30 languages, regional dialects, and cultural nuances across 98 markets. These complexities necessitate careful preparation before broader customer-facing AI implementation.  

For now, their goal is to handle low-effort transactional tasks through self-service AI, particularly during times when agents are unavailable. The strategy includes offering AI assistance while customers are on hold, rather than before, ensuring customers retain their place in line for live agents. 

During hold times, AI can address simple queries like account balances or shipment tracking, reducing the subsequent call’s length and allowing live agents to focus on more complex issues, improving overall efficiency.

Joshua shares with Emerj another advantage of engaging customers with AI while they wait: attention retention. Instead of listening to hold music or static messages, customers interact with AI, making wait times feel shorter. Additionally, this multi-modal approach enables simultaneous interaction across multiple channels, enhancing the overall experience and boosting efficiency for both customers and agents.

Phil acknowledges that AI holds significant potential for personalized upselling once fully operational. By tracking customer interactions—such as identifying a customer’s fifth call in a year—AI can enable tailored upselling strategies. Operating across diverse markets and regions adds complexity, but this diversity also enriches customer interactions, offering unique opportunities for personalization and growth.

In response, Joshua highlights the challenge of balancing consistent distributor experiences across Herbalife’s global markets while adapting to regional differences like regulations and product offerings. Managing diverse languages requires geographically dispersed contact centers, as no single location can support all needs. 

The goal is to standardize processes to reduce complexity while maintaining flexibility to meet unique regional requirements—a demanding yet rewarding task in his career.

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