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Navigating the e-commerce world can be tricky, but it doesn't have to be a headache. With SmarterX, retailers are unlocked to find new ways to grow and get the most out of their online platforms and customer experience.
Imagine running an online store and not knowing details about the products you're selling. Sounds like a disaster, right? That's the problem many retailers face when they expand into third-party marketplaces. They end up with messy or incomplete data, inefficient return processes, inventory pileup – this all translates into lost money. Into the multi-millions of dollars.
The lack of complete, usable data holds retailers back from growing their online marketplaces as much as they could. Take Macy's, for example—they’re diving into expanding their marketplace, and are seeing their e-commerce ads perform well – to the tune of profit margins of 30% to 40%.
Today, most data is handled with manual tracking or piecing together different, disparate solutions. This method isn’t just a headache; it’s costly, slows everything down, and presents an enormous hurdle to scale.
But it doesn't have to be that way.
smarter-1 takes messy, incomplete, disparate data and turns it into smart decisions. It removes any question about what is in your product catalog and what to do with it. Because smarter-1 comprises trillions of chemistry handling, product data, and transportation decisions and includes 231 unique embedding models, 100+ fine-tuned GPT models and 2,200+ ground truth data sources, it’s a completely unique, cutting-edge AI model designed to boost operational efficiency with minimal inputs.
Said in a different way, smarter-1 unlocks the operational efficiency required to effectively and quickly build and scale a marketplace. It enables quick product registration, accurate categorization, and efficient management of returns.
Data Input: Retailers input basic product information (UPC, product name, description, etc.) into smarter-1 (via API or platform).
Data Organization: The system categorizes and assigns customized decisions based on the data. You can write natural language rules to produce your customized decisions. In the example below, we’re trying to determine if the product contains light bulbs, which impacts its transportation requirements.
Final results: smarter-1 data is incorporated to the retailer’s system (via API or flat file), translating the output to business-specific actions. In our example, this data and determination supports inventory management, store operations, and ensures regulatory compliance.
Using smarter-1, retailers dramatically reduce losses and unlock opportunities to support additional revenue streams.
Interested in learning more? I'd love to connect.