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We already have True Fit / Fit Analytics- why should we move to StrutFit?

July 6, 2022
Ang Nayyar

We already have True Fit / Fit Analytics- why should we move to StrutFit?

You may already have a legacy sizing solution like True Fit or Fit Analytics, or you might be considering getting something like this. Well there’s a new footwear sizing solution on the block- StrutFit. We’ll dive into the differences between these legacy solutions and StrutFit, and why StrutFit is superior.

Let’s start with the customer experience. Have you ever gone into a shoe store, taken off your shoe, handed it to the retail staff, and said “I’m a US 10 in this Converse shoe, what size am I in this specific Adidas shoe”? 

Or, have you simply taken off your shoes and tried the shoe you want in different sizes to see what fits best?

True Fit and Fit Analytics represent a class of predecessors in the online sizing technology realm. They use a comparison method for deciding what size your customers should be wearing- akin to taking off a pair of shoes and asking for something similar.

The comparison method of simply select your size in an existing brand is more convenient than taking a scan of your foot, regardless of how quick that scan is. However, the trade-off for this convenience is customer confidence and accuracy. 

Ultimately, the purpose of a sizing platform is to do two things.

  1. Give the customer enough confidence to get over the sizing hurdle and make a purchase.
  2. Make sure the size is actually correct, such that they do not return their purchase.

Let’s start with the 2nd point: accuracy.

Why is the comparison method used by these legacy sizing solutions inaccurate?

The comparison method takes your size in one brand, and maps it to the sizes of another brand. Unless there's a perfect overlap between the brand size chart intervals, you cannot map one onto another. The same logic applies if using social proof (other people similar to you bought X) to map these instead of the size charts. There just isn't enough information to arrive as a confident suggestion. 

Fig 1: An example of how True Fit and Fit Analytic's comparison method works vs StrutFit's precision method.

As you can see from Fig 1 above, knowing that you’re a 9 in Converse does not give the legacy solutions enough information to accurately map you onto other brands.

Fig 2: Legacy solutions uncertainty and inaccuracy vs StrutFit's confidence and accuracy

With StrutFit, the inaccuracy and uncertainty is removed. The customer has been through the scanning process, they know the foot measurements are their own, and the size served back to them is personalized for them.

Do the legacy solutions increase customer confidence?

The customer may not realize they’ve been given the wrong size until they receive their purchase. They may still be more likely to buy with a size suggestion. So the next question is, is the customer confidence being increased? That is, are they being empowered to select a size and make a purchase? 

Fig 3: Fit Analytics sizing suggestion page after selecting a Converse US 9.

This is Fit Analytics results page after selecting a Converse US 9 as the comparison shoe. It's good to see that Fit Analytics are being transparent about their confidence in the sizing suggestion. With the comparison method, they can only be 76% confident in their sizing suggestion, and tell the customer that there's a 3 in 4 chance that the shoe will fit. Is that enough to convince the customer to purchase? And if they do purchase, are you happy with 24% of them returning their purchase? Will they get just 1 size instead of getting both 9.5 and 10, and returning it with On Running's free returns policy?

Fig 4: True Fit's suggestion page on a Hoka One One Bondi 7 after selecting Converse US 9 the comparison shoe.

Does True Fit’s results screen inspire the user with confidence? I did not input a width on the shoe, so they have assumed I’m a regular width. As a customer- you’re still not sure what your width is. This width issue is also why these legacy comparison solutions do not work for retailers offering different width options- we'll do a deep dive into this in a future blog post.

True Fit have told me that I'm a 9.5 Regular, and I have to take their word for it. It would be nice if could compare between other sizes, would that make the "True Fit" pie chart move? Maybe I like my shoes to be a bit looser or tighter. In a store, I could try them on- and Fit Analytics gives me at least two options. With True Fit, the customer will just have to take their word for it.

How does StrutFit inspire confidence?

Great question! We've thought a lot about this, and are always improving. Firstly, the user has taken a photo of their foot. They can see their measurements- so they know the size they are getting is unique and personalized to them. We determine their length and width, and use this to suggest a size in their desired shoe.

We visualize how the shoe would feel for them through a heatmap, giving them an experience synonymous with their in-store shopping experience.

If they want to try on different sizes, they can do that and see how the fit would feel different.

Fig 5: StrutFit's results page. On the left, the user is recommended a 9 US Wide. On the right, the user has selected a 9 US X-Wide to see how it feels.

Here, StrutFit recommended a 9 US Wide for the customer. They have the option of going up half a size or down half a size, and can move between the various widths available. As they do so, they get a feel for how the shoe would feel- akin to trying it on in-store. StrutFit's measurement method and heatmap provides a lot more confidence- leading 41% of StrutFit users to convert to a purchase.

These numbers sound incredible- but don't just believe us. Contact us and we can show you on your own website with your own customers! 

Okay, the comparison method is inferior. So why do TrueFit and FitAnalytics use it?

This is probably due to the technology available when these companies were founded. True Fit was founded in 2010, and FitAnalytics was also founded in 2010. The technology that StrutFit uses underlying our Computer Vision code is called Deep Learning. It’s the same technology that allows Tesla’s cars to self-drive. Deep learning wasn’t mainstream and available until 2016. So for six years, these companies had to make do some other way- hence, the comparison method.

Why not switch to a measurement method after 6 years? It’s just a case of being too late into the game to switch tactics. To switch now, they would have to start from the beginning. None of their previous data will help them scan and measure feet. To their credit, True Fit actually tried to do this in-store (much easier than doing this online) but it looks like the project has been scrapped since.

StrutFit was founded in 2016, where we worked part-time on it till 2019- when the aforementioned Computer Vision and Deep Learning technology left the halls of the tech elite of the Facebooks, Teslas, and Googles, and really became available to startups. So, we were able to approach the problem with the latest technology- leading to the best footwear sizing platform. See this timeline of Machine Learning.

Fig 6: A timeline of Machine Learning. StrutFit is truly at the cutting edge of footwear sizing- the scientific frontier.

We're at the forefront of what technology can do already, and StrutFit will continue to apply the latest technology to the problems that retailers face. Keep an eye out on the updates we have coming out soon... 👀

Ready to see how StrutFit can boost your retail experiences?

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