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    Artificial Intelligence is changing the way businesses interact with their customers. From hyper-personalized experiences to chatbots built on Large Language Models, AI is driving new investment in digital experiences. That same AI and LLM can also be used to automate your product data program. From data onboarding and validation to generating descriptions and validating images, AI can help generate content faster and at a higher quality level to improve product findability, search, and conversion rates.  

     In our second webinar in the Product Data Mastery series, we’re speaking with Madhu Konety from IceCream Labs to show exactly how AI and product data can work together for your business.

    Speakers:

    Dan O'Connor, Practice Director/Enablement Lead, Earley Information Science

    Madhu Konety, Founder & CEO, IceCream Labs

    Watch Now

    Earley Information Science Team
    Earley Information Science Team
    We're passionate about enterprise data and love discussing industry knowledge, best practices, and insights. We look forward to hearing from you! Comment below to join the conversation.

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    [RECORDED] Product Data Mastery - Reducing Returns to Increase Margin Through Better Product Data

    Improving product data quality will inevitably increase your sales. However, there are other benefits (beyond improved revenue) from investing in product data to sustain your margins while lowering costs. One poorly understood benefit of having complete, accurate, consistent product data is the reduction in costs of product returns. Managing logistics and resources needed to process returns, as well as the reduction in margins based on the costs of re-packaging or disposing of returned products, are getting more attention and analysis than in previous years. This is a B2C and a B2B issue, and keeping more of your already-sold product in your customer’s hands will lower costs and increase margins at a fraction of the cost of building new market share. This webinar will discuss how EIS can assist in all aspects of product data including increasing revenue and reducing the costs of returns. We will discuss how to frame the data problems and solutions tied to product returns, and ways to implement scalable and durable changes to improve margins and increase revenue.