How to Turn Every Customer Interaction into a Revenue-Generating Opportunity
Simplr’s fourth webinar in the ChatGPT Webinar Series took place on August 23, 2023 and featured Simplr’s Chief Marketing Officer, Daniel Rodriguez, and Hannah Armstrong, Simplr’s Account Based Marketing Manager. You can watch the webinar on demand here.
In case it wasn’t obvious already, the era of generative AI is just beginning. As we dive deeper into the possibilities presented by artificial intelligence, it made it us stop and think:
Why are so many retailers still falling short when it comes to embracing innovation—or innovating quickly—in order to truly replicate the in-store experience online?
The retail landscape is evolving at an unprecedented pace, driven by technological advancements and changing consumer behaviors. Amidst this transformation, the essence of customer interactions remains paramount. But we’re still not seeing it in practice.
What we are seeing is that retailers think they need a strategy to minimize customer interactions. These well-intentioned “deflection” strategies often push customer inquiries out of the chat channel, forcing the customer to pick up the phone, initiate a new email thread or just give up altogether. This leads to increased operational costs and, more crucially, dissatisfied customers. The critical juncture here is to recognize that minimizing interactions might not always equate to optimizing the customer journey.
Now, AI is empowering businesses to implement “engagement” strategies. Rather than shying away from interactions, businesses are now empowered to embrace them. Imagine a scenario where each customer interaction is seen as a potential avenue for revenue generation. Sounds revolutionary, doesn’t it? Yet, it’s a reality that is fast gaining traction among today’s leading retail brands.
Serve, Solve, Sell
But how do businesses achieve this transformation? Simply put, the approach should embody the mantra of “serve, solve, sell.”
In this business shift, serving the customer becomes the focal point. This means not only resolving their queries, but also anticipating their needs. By addressing concerns comprehensively, businesses not only build trust but also set the stage for upselling and cross-selling. This approach isn’t just about driving immediate revenue; it’s about cultivating long-term customer relationships built on genuine value.
AI is making all of this easier and more efficient than ever before, opening up so many doors for enterprise retailers across the globe.
Generative AI in Retail CX
Well this all sounds great in theory, but what does this look like in practice? With generative AI, complex and empathetic conversations can now be replicated through bots. Let’s take a look at some use cases for generative AI in retail CX:
- Personalized shopping recommendations: A great use case for leveraging AI-powered bots is for creating personalized shopping experiences. AI and LLM-powered chatbots can help retailers offer 24/7 tailored product recommendations to customers, enhancing their shopping journey and boosting conversions. For example, let’s say a customer has shown interest in purchasing a particular brand of running shoes and has previously bought fitness-related products, the AI recommendation engine can suggest complementary items such as sportswear, running accessories, or even fitness gadgets to enhance the overall shopping experience.
- Streamlined support: With their deep understanding of human language and vast knowledge base, large language models empower chatbots to handle a wide range of customer inquiries, from returns & exchanges to complex troubleshooting scenarios in a more natural and contextually relevant conversation. We recently helped one of our partners, Solo Stove, launch the first generative AI bot in the outdoor recreation retail space. Their resolution rate went from a 45% average resolution rate to a 75% average resolution rate, and their CSAT remains high, at 4.6. We’re seeing very early signs of success!
- Agent assist: LLMs can provide suggestions to supercharge your agents’ productivity. They can even summarize an entire conversation so agents receiving escalations can quickly get up to speed and context and start responding instantly. For example, if a customer asks about the status of their recent order, the AI chatbot can quickly access the order history, track the package, and provide the current shipping status to the human agent. This allows the agent to have the necessary information readily available, enabling them to respond faster and more accurately to the customer’s query. Additionally, the AI chatbot can suggest response templates or knowledge base articles to the agent, based on the context of the conversation and previous successful interactions. This assists the agent in crafting personalized and appropriate responses, ultimately leading to a more efficient and satisfactory customer support experience. Before generative AI, even with live agent backup, shoppers were still unable to get their questions completely answered. Generative AI bots are changing the game, enabling agents to be more efficient, leaving customers much much more satisfied.
- Technical troubleshooting: By leveraging generative AI in troubleshooting, retailers can provide an effective and efficient support mechanism, empowering customers to resolve product-related issues independently. This not only reduces the workload on customer support teams but also enables retailers to deliver a seamless and satisfactory CX, resulting in increased customer loyalty and advocacy. Imagine a customer who recently purchased a smart thermostat and is experiencing difficulties with its installation and setup. Instead of searching through lengthy manuals or contacting customer support, they can engage with a virtual assistant through a chat interface or voice command. This virtual assistant is built on generative AI, allowing it to understand and respond to natural language queries in real-time. The customer can simply describe their problem or ask questions such as, “How do I connect my smart thermostat to my Wi-Fi network?” or “What should I do if the thermostat is not detecting the temperature accurately?” The generative AI-powered virtual assistant analyzes the customer’s query, applies its knowledge of the product and troubleshooting steps, and generates accurate and personalized responses.
Customer service leaders should be thinking about all use cases as they look to innovate and improve customer satisfaction.
Revenue-Generating Customer Experiences
Listen in on the insights shared during Simplr’s webinar to further understand how AI is ushering in an era where every customer interaction is a potential opportunity for revenue generation. The AI genie is out of the bottle, and its magic is transforming the customer experience landscape like never before.