By Robin Gomez
Director, Customer Care Innovation, Radial
The retail industry admittedly is slower to adopt technology that can impact customer experience than other sectors; with good reason. Perhaps nowhere else does customer experience weigh so heavily on business success and competitive differentiation. Crafting customer experience strategies that improve the shopping journey involves multiple factors; technology facilitates much of it. In my work with retailers over the last 20 years, I have seen the benefits and disadvantages of adopting emerging tech. The current rush to adopt generative AI will eventually reveal some of those pros and cons. But for retailers that have adopted previous versions of AI in the last decade, it’s important to understand that generative AI can be adopted safely now – even while we wait out the unknowns.
AI is Not New to Retailers
Artificial intelligence has been around for some time; it sat quietly in the background where developers programmed it and had to continually refine it for it to have impact. Much of AI has been used in the backend to automate processes and systems. In customer care, retailers have used AI for self-service offerings, such as interactive virtual response systems (IVRs). In eCommerce, we have seen AI power recommendation engines. We haven’t seen AI used as effectively in customer facing endeavors.
Generative AI is changing that.
Let’s take bots, for example. We all know that conversational AI bots have been an iffy proposition for most customer care departments. When they work well, they’re great. More often, they do not. Customers get frustrated with bots that feel like robots, are limited in their interaction capacity, and do not provide the needed solution. Many customers just want to get to an agent and must wade through the bot interaction to do so. This is not a great experience.
Generative AI has enabled conversational AI to leapfrog in its development and close the gap between a very limited engagement capacity and a more human-like contextual interaction. In fact, in my experience, the nature of the conversation is night and day different when it comes to quality.
I’ve also seen generative AI make a real difference in supporting agents with contextual real-time assistance and in summarizing and aggregating interaction data. I believe this is one of the highest value propositions for gen AI in the contact center, as it can support agents in delivering a faster, more seamless resolution. There are numerous use cases for generative AI in customer care, all of which focus on automating self-service, assisting agents, improving resolution rates and handle times, accessing precise data faster, and lifting rote transactional tasks from human agents to virtual agents. The possibilities will continue to be developed, but to date, gen AI has proven its ability to improve the agent and customer experience when it is implemented well.
Generative AI Needs Human Management
Contrary to perception, generative AI is not ready to go out of the box. It needs to be programmed to represent the brand voice, to be confined to the areas you want it to operate in, and it needs guardrails. Generative AI has been trained on large language models (aka massive amounts of internet data) which means it draws on a much larger volume of data for its responses. This data is unknown in origin and unvetted to the retailer, which is why parameters must be established to outline how generative AI is to perform.
Radial Customer Care serves as a business process outsourcer for leading retailers. We have actively and strategically adopted generative AI to provide leading edge customer service for our retail clients. I have learned that unprogrammed generative AI can be very much like a child who does not have the adult context to know appropriate behavior or the implications of its actions. For example, generative AI will share data without restraint if it is not programmed to restrain itself. In our testing, we found that generative AI bots in their eagerness to be helpful will readily reveal data around customer identity and information that must be restricted under privacy law. Again, reinforcing the fact that generative AI is a technological tool, and not another species, as it is often colloquially assumed. Human management is absolutely essential.
How to Safely Adopt Generative AI
One of the wisest ways to safely adopt generative AI is to partner with a vendor that has the expertise, investment, and that can support your AI initiatives as AI continues to evolve. Generative AI is in its infancy, but even at this point it has the ability to improve your customer experience. A vendor can offer the technical expertise and best practices which will save you from having to source these internally. They can also help you identify where to pilot gen AI and the best way to expand usage across your customer care.
If you prefer to not invest in developing your own generative AI solutions, consider partnering with a 3PL like Radial that will bring the full investment and lessons learned from a network of retailers to your customer care.
Generative AI requires investment, oversight, strategy, and supervision. But the payoff of getting it to perform the tasks and duties that you want it to do are worth it. Radial Customer Care clients are experiencing 3X containment rates by using gen AI bots.
With the right strategy and partner, adopting and implementing generative AI in your customer care can be done safely, effectively, and for maximum results.
Robin Gomez has been in the industry for 25 years, beginning with catalog-based fulfillment and customer service through the growth and evolution of eCommerce. He has worked in Human Resources, Training, Quality, Operations, Consumer Insight, Project Management, BI, CI and is currently the Director of Customer Care Innovation for Radial. He is focused on engaging and partnering with leading and emerging technology providers and curating a robust solution stack for Radial’s eCommerce focused clients and internal agents and users. Robin has a B.S. in Psychology with an M.S. in Industrial/Organizational Psychology. You can follow Robin on LinkedIn.
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