5 Challenges Faced by Retail Companies

Earlier, we talked about common challenges for logistics companies and how AI can help address them. In this post, we’re going to look at similar, and often overlapping issues faced by retailers - from physical stores or e-commerce platforms.

Common issues range from handling angry customers to managing multichannel support and catering to customers’ channel preferences, which can vary by generation and other demographic factors. Like logistics companies, volume and seasonal peaks (hello Black Friday, end-of-january and summer sales) can cause major headaches in the retail sector.

Let’s stay away from the thought that AI can make every customer happy and slow the holiday season demand to a manageable pace. However, what it can do is help address the issue of scale and efficiency at the root of these challenges.

1. High Volume of Customer Inquiries

During peak shopping times or sales events, retailers may experience a surge in customer inquiries, which can overwhelm their customer support teams. Rightfully so. Sometimes these upticks in support cases are seasonal and predictable, other times they’re in response to unforeseen events or logistical logjams. Either way, increased volume can result in long wait times for customers and increased pressure on staff.

Therefore, retailers must be able to scale their customer support capabilities during high-demand periods without compromising on the quality of service. The question is how.

AI-powered automation, chatbots, and summary tools can help by increasing efficiency. By automating repetitive tasks like contact summaries and answering frequently asked questions like “How can I get a refund?” AI gives human agents more time to focus on complex tasks that require more human input.

2. Irate Customers

Hell hath no fury like a customer whose package is late or order is wrong…
Angry customers hurt your business. They stop being customers. They leave bad reviews. Beware, someone who has a negative experience is more likely to leave a review than a person who had a positive one. And customer support is often your last chance to make that customer happy.

The efficiency improvements AI delivers here give agents more time to handle these more specific cases because they’re not bogged down dealing with simpler issues. But AI can actually go a step further and use sentiment analysis to detect when a customer is getting upset. It can then offer solutions to the agents in real time, like free shipping and discount codes.

3. Multichannel Communication Management

In today’s open world, customers expect to be able to contact customer service across the channel of their choice, including email, phone, live chat, and social media. For instance, Millennials and Gen-Z do almost everything through text and mobile apps. However, older generations still want to be able to reach out over the phone. Managing these various channels efficiently while maintaining a consistent level of service can be difficult.
This is where Agent Assist tools can really make a difference. They work across channels for a seamless experience and easy switching.

4. Personalization of Customer Interactions

Customers expect personalized interactions that reflect an understanding of their purchase and support history. Delivering this level of personalization consistently across all customer interactions can be complex, especially for retailers with a large customer base across the world.

Summarizer and Knowledge Assist are a great way to provide a warm handover of the customer’s inquiry. These tools will put all relevant information and contact history at the agents’ fingertips which simplifies the interaction even for first-timers. This helps agents deliver tailored service and reduce the need for customers to repeat themselves if they get disconnected or have to transfer between agents. It’s also possible to take personalization one step further and use GenAI to deliver personalized offers based on purchase history and other factors.

5. Efficient Case Resolution

We all agree, it’s better for agents and customers alike when issues can be resolved quickly. On one hand, It’s very common to evaluate support agents based on the number of cases they resolve. On the other hand, customers don’t want to spend more time than they have to on the phone or in a chat trying to solve a problem.

When issues arise, everything is more efficient with Agent Assist tools. Responses that can be automated or handled by a chatbot are taken off your agents hands, and more complex issues benefit from AI support. But again…

AI is here to Support your Support Team, not replace it

From customer happiness to efficient resolution, AI Agent Assist tools are helping support teams tackle the most frustrating problems in the retail industry. While the human element remains at the heart of customer support, GenAI is empowering human agents to do better work by taking thankless tasks off their hands and working tirelessly behind the scenes to help them deliver delight.