Can’t I just compare my sizes with another brand?

Where size/fit recommendation solutions are concerned, what retailers and solution providers have both found is that Precision Is Everything.

One of the biggest issues is that shoppers don’t know their measurements. At least, not accurately. But, because they are actually keen to get a recommendation, they estimate their measurements. Historically, the results for online fit solutions that depend on shoppers knowing their measurements were predictably underwhelming, ranging from minimal positive impact on returns rates to a negative impacts on return rates (where they would have been better off, on average, taking a look at their existing wardrobe). Of course, precise results depended on each shopper – and, for any given retailer, on the nature of its patrons.

This is an example by which – in just one step – a lack of precision results in a flawed recommendation. Where there are several steps, the potential for inaccuracies is cumulative, and the chances of a good outcome diminish correspondingly. And this is the flaw in the approach by which a shopper is advised to buy a particular size in one brand because a particular size in another brands fits well.

Different brands have different sizing standards

Most brands have their own sizing standards, tailored to meet the nature and expectation of the audience to which it markets itself. This clearly means that a size small in one brand may not be the same as a size small in another brand; the size that fits well in one brand may not necessarily fit well in another brand.

Variation in style and cuts

In addition to the reality that different brands have different target markets, brands may also employ different styles and cuts of clothing, even within the same size range; a particular size may fit well in one style but not in another. Advising shoppers to buy a particular size based on another brand's fit experience does not account for these variations in style and cut.

Body shape and proportions

Body shape and proportions vary widely among individuals. Even if two individuals wear the same size in different brands, the fit can still differ due to differences in their body shape and proportions. Advising shoppers to buy a particular size based on another brand's fit experience does not account for these individual differences.

Lack of consistency across products and seasons

Even within the same brand, sizing and fit can vary between products and seasons; a size that fits well in one product or season may not fit well in another. Advising shoppers to buy a particular size based on their fit experience with another brand does not account for these variations.

Notably, these possible sources of inaccuracy are not mutually exclusive. It’s entirely possible for two or more of the conditions above to co-exist, ensuring that any final recommendation – “Choose Size A in Brand B because Size C in Brand D fitted you” – can become far from accurate.

And… why risk reminding your shopper that another brand’s clothes fit them really well?

On a slightly different note, there is another extremely good reason for not using a comparison with another brand’s garments: it reminds the shopper that another brand makes clothes that fit them well, and may serve to send them away from your store to their store. Virtual fitting rooms built on such comparison technology risk doing another brand’s marketing for them. (The shopper will forget that there may be little consistency between the products, seasons, etc, even at that other brand!).

Conclusion

The secret to an effective, accurate AI fitting room is accuracy at every step, and keeping those steps to a minimum to eliminate cumulative errors. The Swan virtual fitting room precisely measures your shoppers to within 1cm accuracy and depends solely on the measurements and other attributes of your garments. This ensures maximum precision, and enables Swan to make highly accurate, confidence-giving size recommendations to your shoppers.

Explore our site to find out more.