Customer service is crossing a new horizon. While live agents aren’t going anywhere, AI radically changed the playbook. We’re in the first inning of a game that promises to alter the way we handle customer interactions and how we mine insights from them. For players in ecommerce, this isn’t just theory; it’s a change happening now.
AI’s Role in Customer Experiences
AI is an evolving ecosystem. Consider conversational AI’s strides in recent years. Thanks to advances in natural language processing, intelligent virtual agents (IVAs) have grown from simple keyword matching to understanding the subtleties and context behind entire conversations.
For retailers, this is hugely important. Businesses leverage IVAs to automate customer interactions beyond simple order tracking and inventory checks. Imagine an IVA assisting in product discovery by asking insightful questions about customer needs and preferences and offering personalized suggestions. Or upselling or cross-selling products during a customer interaction, essentially mimicking the skills of an experienced sales rep. These capabilities are particularly valuable during peak shopping seasons when human agents are swamped and the risk of customer churn is high.
Moreover, IVAs capture meaningful data that allows businesses to analyze customer behavior, map out customer journeys and predict future trends. Retailers gain actionable insights that can drive targeted marketing campaigns or inform inventory decisions.
With conversational AI, retailers are profiting from increased efficiency and data collection, and customers benefit from seamless, responsive customer experiences. But generative AI is set to take things to a whole new level.
Unpacking the Benefits of Generative AI
Generative AI uses the information it’s been trained on to create new data or patterns. What sets it apart is its ability to make or predict new outcomes. For example, it can write text, compose music or generate images.
There will be several transformative effects once generative AI is fully implemented in retail. One compelling use case is the development of advanced product recommendation engines. Imagine a customer who has just purchased a high-end DSLR camera. A generative AI-driven recommendation engine wouldn’t just suggest related items like lenses or camera bags. Instead, it could generate an entire “photography adventure package” tailored to that individual. It may include a selection of outdoor equipment, travel books and even photography classes. All are items the customer didn’t explicitly search for but are highly relevant to their interests and recent purchases.
While this example may come to fruition in the future, most retailers are not quite at that stage yet. Experts indicate that much of generative AI’s value comes from how businesses embed it into everyday tools. For leaders ready to adopt generative AI but unsure where to start, there’s a perfect testing ground: your employees. By starting with internal people and operations, you’ll learn lessons and develop processes that can lead to immediate, impactful results when directed toward customers.
Tools for the Team
Integrating generative AI isn’t just about the customer. It’s also about creating automated tools that make employees’ lives easier. According to McKinsey, an integral part of its business value is based on helping automate 60-70% of the activities that take up most of your customer service agents’ time.
Your internal tools can be an excellent starting point for generative AI integration. If your company hasn’t dipped its toes into the waters yet, consider starting with employee-facing applications. This enables your team to become comfortable with the technology. It also provides an opportunity to work out any bugs and gauge its efficacy in a more controlled environment. Think of this trial phase as a blueprint, helping you identify best practices and possible pitfalls. It will help you make the eventual customer-facing rollout far smoother and more effective.
Your Playbook: Maximizing Generative AI
So, how do businesses ensure they’re capitalizing on what generative AI offers? Here are some steps to consider:
Spot Internal Roadblocks: Identify areas where employees frequently face cumbersome tasks or inefficient processes. Focusing generative AI on automating these points can result in immediate employee efficiency and job satisfaction gains.
Seamless Internal Integration: Generative AI tools should integrate smoothly into the various internal systems your customer team already uses, be it project management software or internal communications platforms. A seamless experience boosts employee engagement and productivity.
Tailor to Organizational Culture: Generative AI solutions should align with your company’s internal culture and ethos. Customize both the functionality and the “personality” of the tools to ensure they resonate with your employees and facilitate a natural workflow.
Employee-driven Improvement: Establish a system where employees can provide feedback about generative AI’s effectiveness and suggest enhancements. This fine tunes the tools and makes employees feel involved in the process, increasing adoption rates.
Ensure Internal Security and Compliance: While automating tasks for your customer team, focus on safeguarding sensitive company data. Stay abreast of any regulatory changes concerning AI and data protection.
By consolidating your approach into these six essential steps, you can adjust your strategy for integrating generative AI into customer service. This will make the most of its capabilities without overwhelming your team or customers.
A New Mindset
This process is less about replacement and more about augmentation. By strategically embracing generative AI, you’re not just playing the game but changing it. And in customer service for ecommerce, where the pace never slows and the stakes keep rising, that’s precisely where you want to be.
Rebecca Jones is general manager of Mosaicx