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Growth vs. Customer Experience: A Dilemma?

ECXO

Map the Customer Journey What to Do: Identify every touchpoint a customer has with your business, from awareness to post-purchase support. product quality, service speed, user experience). Why It Works: Customers are more likely to trust and engage with brands that offer consistent experiences wherever they interact.

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How Mixbook used generative AI to offer personalized photo book experiences

AWS Machine Learning

It provided an improved user experience with operational efficiency. User experience Enhanced storytelling : Captures the users’ emotions and experiences, now beautifully expressed through captions that are heartfelt. She loves traveling and spending time with her family.

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Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation

AWS Machine Learning

You can use LangChain and specifically the LLMChain type of chain and pass as parameters the LLM, the prompt template you created earlier, and the question: question = "When can I visit the AWS M&E Customer Experience Center in New York City?" Let’s see if we can improve it by providing some context.

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The Transformative Power of Cross-Functional Teams in Upgrading CX

ECXO

Provide the Necessary Resources and Support: Ensure the team has access to the resources and support they need to succeed. Management’s support is also critical in navigating bureaucratic hurdles and obtaining necessary approvals. A strong team dynamic is crucial for fostering collaboration and innovation.

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The Ultimate Customer Feedback Loop Playbook

Thematic

." And that’s not in product development; customer feedback is important even in other departments—marketing, customer support, and more. Businesses can gain valuable insights from multiple sources—including support tickets, social media, and app store reviews. A dark mode would improve my experience."

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Enhancing LLM Capabilities with NeMo Guardrails on Amazon SageMaker JumpStart

AWS Machine Learning

For a retail chatbot like AnyCompany Pet Supplies AI assistant, guardrails help make sure that the AI collects the information needed to serve the customer, provides accurate product information, maintains a consistent brand voice, and integrates with the surrounding services supporting to perform actions on behalf of the user.

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Use Amazon Titan models for image generation, editing, and searching

AWS Machine Learning

The following code snippet uses one of the images generated in the previous example to create variant images: # Import an input image like this (only PNG/JPEG supported): with open(" ", "rb") as image_file: input_image = base64.b64encode(image_file.read()).decode("utf8") exclusive) to 10.0 read()) images = [ Image.open(io.BytesIO(base64.b64decode(base64_image)))

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