Fit is the single biggest reason apparel returns exist. Shoppers cannot try the garment on, the size charts are inconsistent across brands, and the photo only shows one body type. A static size guide is a defensive document, not a recommendation. A chatbot can ask one or two follow-up questions and turn the same data into a real piece of advice.

SleekAI reads the WooCommerce product, the size_chart postmeta, the fit_notes field where merchandisers leave 'runs small in the shoulders' style guidance, and the historical return_reasons aggregated by SKU. The bot asks the shopper a small number of relevant questions (usual size in a reference brand, fit preference, height) and proposes a size with a confidence note. When confidence is low it says so, which builds trust the way a knowledgeable shop assistant would.

Display conditions show the widget on product pages with the fitting-room flag set, hide it on accessories and final-sale items, and pass the current product id into the conversation. Logs capture the recommendation, the size eventually ordered, and the eventual return outcome, so the model's recommendations get measurably better over time.

Apparel returns are not a margin problem, they are a margin disaster. The industry average sits around twenty to thirty percent of online orders for ready-to-wear and far higher for tailored pieces, and every return carries handling, restocking, and a meaningful share that ends up in landfill. Cutting the rate even a few points compounds across millions in revenue.

A chatbot is not the only lever but it is the cheapest one to deploy. It surfaces information your team already produced: the size chart the production manager finalized, the fit notes the merchandiser typed into the product backend, the return reasons your customer-care team has been logging for years. None of that data was ever exposed to the customer at the moment they were choosing a size.

The chatbot puts it in their hands as a conversation rather than a wall of tables. The second-order benefit is calibration. Once you log recommendations against orders and returns you can see which products the bot reads correctly and which ones it overshoots.

That data feeds product development, not just chatbot tuning. A particular dress that keeps returning small in the bust is a design note for next season, not just a fit-bot hedge. Treated this way the chatbot becomes a feedback loop between the customer and the people who make the clothes, and the size chart finally stops being a static document.