13th of June 2024
How Ami contains 50% of the incoming conversations for delivery-related queries
The issues typically faced by customers of retail companies are difficult for Ami to solve: Ami cannot make a delivery arrive faster, or refund customers who are unhappy with their purchase. This means that to serve retail customers, Ami will always have to escalate some number of queries. However, Ami can still assist customers in a wide variety of ways, increasing containment and lightening the workload of live agents.
A number of years ago, Ami partnered with well-known retail company. Here, Ami deals with complex queries about late deliveries, returns and refunds and product questions. Ami contains between 45% and 50% of chats, even though retail-related queries are often impossible for an AI to solve. Despite 15% of escalated chats being necessary escalations, where the client has requested Ami escalate straight away because of the seriousness of the issue, Ami still contains around half of chats.
In this case study, we will go through the strategies Ami employs to increase the containment rate to improve both customer satisfaction and agent efficiency.
Deflection
Ami has different ways of deflecting when a customer asks to speak to an agent. When a customer asks to speak with an agent in their first question, Ami will prompt the customer to state the topic of their query, so that Ami can try to help.
Since Ami has to escalate a customer if it doesn’t understand their query, Ami tries to understand the customer’s question even when the customer doesn’t provide a lot of information.
By prompting the customer to elaborate, Ami has an opportunity to give the customer advice or guide them to the correct section of the retailer’s website, without the need for a live agent. Hence, Ami’s deflection strategies help lighten the load of live agents.
When escalation is necessary
Because of the nature of customer queries in retail, some customers have to be escalated to a live agent. If a customer has been waiting for a refund for a considerable amount of time, or if they have a serious food quality complaint, it is important for Ami to recognise these queries and escalate appropriately.
Ami can distinguish between more and less serious issues. For example, Ami can recognise when a customer has to wait for their refund a bit longer, and when the customer has already been waiting for a while.
When Ami has to escalate a customer, Ami can gather useful information to escalate the customer to the correct agent to help with their issue.
Rather than escalating each customer to a random agent with no additional context, Ami can gather useful information and escalate the customer to the correct agent. For example, Ami can ask for the customer’s name and email address
This not saves the agent time by already having useful information about the customer available and improves the customer’s experience.
Customer satisfaction
Key to improving containment is keeping the customer happy throughout their interaction with Ami. Often, issues that retail customers have can be quite frustrating, such as late deliveries, incorrect refunds and product quality issues. Ami has several strategies for preventing customers from getting frustrated.
Ami often needs to ask the customer questions to be able to give them the correct advice. A customer asking about a late delivery might get frustrated at being asked questions before getting advice. Ami keeps the customer from getting frustrated by letting the customer know that it will help the customer resolve their query, before asking questions to gather necessary information.
Here, Ami tells the customer that it will help them track their delivery before asking the relevant questions.
Ami can also keep context throughout the conversation. This way, Ami avoids asking questions the customer has already answered, which can make the customer feel like they are not being listened to.
In the above examples, Ami responds differently depending on whether it has already received relevant information from the customer. Ami employs these strategies to improve the customer’s experience with the client.
Keeping Ami’s knowledge up-to-date
Ami will only ever provide content from the client’s website, or content given to Ami by the client. For this reason, it’s important that Ami’s knowledge is up to date. If Ami doesn’t have the information available to answer a customer’s question, Ami will have to escalate. Ami reads the website and will notice when content on the website changes. This is then manually reviewed to ensure the information Ami provides is up to date.
Conclusion
In this case study, we have introduced various strategies for increasing automation rates. Despite the inherent limitations of certain conversations needing a live agent, Ami's ability to handle 50% of incoming delivery-related queries for retail companies showcases its effectiveness in managing customer service tasks. While Ami cannot speed up deliveries or process refunds directly, it enhances efficiency and satisfaction by managing a significant portion of customer interactions. This reduces the workload for agents either by fully automating the conversation, or by partially automating the process, leaving agents to only handle a fraction of the interaction with the customer.
Author
Juliette van Steensel
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