In this joint webinar, Neocom and DataFeedWatch show you how to turn average product data into a scalable, AI-ready foundation that powers better campaigns, smarter shopping assistants, and higher on-site conversion.
Neocom’s Simon Hoveling and Martin from DataFeedWatch walk you through the full journey:
from raw product data → feed enrichment & optimization → AI-powered guided selling experiences in your shop and in-store.
Why clean, rich product data is the foundation for everything: Shopping campaigns, guided selling, AI agents, personalization — and why poor feeds silently kill your ROAS and conversion every day.
How Neocom uses AI-powered web scraping to extract missing attributes directly from PDPs:
Color, size, material
Technical specs (e.g., IP rating, motor type, range)
Product features and benefits
Result: structured, comparable data without manual cleanup.
Practical insights and examples on how to:
Automatically optimize titles (brand, type, attributes, model numbers)
Derive missing attributes from descriptions
Use margin, seasonality & bestseller flags via custom labels
Build clean, channel-specific feeds for Google, Meta, Idealo & Neocom
In other words: how to turn your feed into a true performance lever.
How Neocom uses structured product data to:
Translate technical specs into clear customer benefits (e.g., range instead of Wh)
Reduce uncertainty in complex product categories
Combine quiz + AI chat into a hybrid shopping assistant that answers any product question using your data
Your product data becomes your competitive advantage.
How retailers deploy Neocom advisors on in-store touchscreens — including live inventory per location, on-site product consulting, and the same data foundation used online.
E-commerce & performance teams who want to get more out of their product feeds
Brands & retailers with complex or consultation-heavy products
Teams evaluating guided selling, shopping quizzes or AI assistants
Anyone who understands that:
Great AI requires great data — and great feeds fuel great campaigns.
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