{"id":1119,"date":"2021-10-30T08:42:06","date_gmt":"2021-10-30T08:42:06","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/30\/build-conversational-experiences-for-retail-order-management-using-amazon-lex\/"},"modified":"2021-10-30T08:42:06","modified_gmt":"2021-10-30T08:42:06","slug":"build-conversational-experiences-for-retail-order-management-using-amazon-lex","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/10\/30\/build-conversational-experiences-for-retail-order-management-using-amazon-lex\/","title":{"rendered":"Build conversational experiences for retail order management using Amazon Lex"},"content":{"rendered":"
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Retailers want to stay engaged with their customers as they move seamlessly between digital channels and physical storefronts. By delivering personalized and consistent experiences across a range of retail touchpoints, companies can drive brand loyalty. Customers contact retailers\u2019 customer support for reasons such as checking order status, updating shipping preferences, redeeming loyalty points, managing refunds, and finding a physical store. Product recommendations during these interactions are important towards optimizing operations and increasing conversions. Contextual and customized conversations help build relationships with customers and drive repeat business, reduce churn, and increase customer lifetime value.<\/p>\n

In this post, we review how you can use a pre-built solution with Amazon Lex to deliver rich customer service experiences for order management, such as getting order status, tracking a package, and canceling or modifying an order.<\/p>\n

Solution overview<\/strong><\/h2>\n

Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. Amazon Lex for retail offers pre-built solutions that help you enhance brand loyalty on an omnichannel customer journey. The pre-built bots are configured with intents, sample utterances, and slot types for retail order management use cases and are integrated with Amazon Connect<\/a> contact flows.<\/p>\n

Let us review a few sample conversations as we cover the different components of the order status pre-built solution:<\/p>\n

In the following sample conversation, the caller wants to check the order status. The agent gathers and validates the order ID. Using this information, the agent looks up the order status and updates the caller.<\/p>\n

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Agent: Thank you for calling Acme. How can I help you today?<\/p>\n

Caller: I want to check my order status<\/p>\n

Agent: Sure, I can help you with that. Please say or enter the 13-digit order number.<\/p>\n

Caller: 1234 56789 1234<\/p>\n

Agent: To confirm, I have your order number as 1234 56789 1234. Is that correct?<\/p>\n

Caller: Yes<\/p>\n

Agent: Ok. Give me a moment. Your order has been shipped. Do you want to know the delivery details?<\/p>\n

Caller: Yes<\/p>\n

Agent: Sure. Give me a moment. Your package has arrived at the final distribution facility, and it will be delivered on 11\/20\/2021. Caller: Thank you.<\/p>\n<\/blockquote>\n

In the following sample conversation, the caller wants to reschedule the order delivery. The agent captures the order ID and verifies the status of the order to determine eligibility for rescheduling. Next, the agent reschedules the delivery to the requested date.<\/p>\n

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Agent: Thank you for calling Acme. How can I help you today?<\/p>\n

Caller: I want to reschedule my delivery<\/p>\n

Agent: Sure. I can help with that. Please say or enter the 13-digit order number.<\/p>\n

Caller: 1234 56789 1234<\/p>\n

Agent: I have your order number as 1234 56789 1234. Is that correct?<\/p>\n

Caller: Yes<\/p>\n

Agent: Got it. Your order is still being processed, so it can be rescheduled. Can you tell me the date you want the order delivered on?<\/p>\n

Caller: November 20th<\/sup><\/p>\n

Agent: November 20, 2021. For verification, can I have the zip code of your billing address?<\/p>\n

Caller: 12345<\/p>\n

Agent: Thank you. Your delivery has been rescheduled. We will deliver your order on the new scheduled date of Nov 20th<\/sup> 2021. Anything else I can help you with?<\/p>\n<\/blockquote>\n

The RetailOrderManagementBot<\/code>\u00a0contains intents for common order management activities such as getting order status, tracking a package, canceling or modifying an order, returning an item, and rescheduling the delivery. It includes the following intents:<\/p>\n