top of page

You're in! This is going to be epic! 🥳


Stay ahead of the curve and sign-up for monthly updates on fashion trends, industry news + retail tips

  • Writer's pictureAnna-Louise McDougall

Cindy or Naomi? How to know which eComm models will boost ROI using retail analytics

Squeezing a whole collection shoot into two days? Calling in samples, photographers, hair and makeup, and spending your precious budget on 3 pairs of styling shoes worth your pay packet? Whether you shoot twice a day, twice a week, or twice a year, it pays to stack up your model stats.

fashion shoot with photographer checking model shots on computer

Product imagery isn’t usually considered the most creative aspect of the brand, but - no matter what kind of retail store you run - it’s what your online customers engage with the most.

It’s the final stop in your efforts to have them click ‘Add to Cart’. Truthfully, there are plenty of opportunities to get creative and not only remain commercial but boost your AOV, amongst other business vitals.

So, before you start staring down the barrel of a talent agency invoice and wondering how you let your budget blow this far, it’s prudent to be clued in on determining the right eCommerce model for your online store. There are often overlooked tweaks you can make to help you easily break even on that agency fee and start scaling.

And while there’s never going to be a one-size-fits-all formula for your model choice, there are steps you can take to execute best practices for your brand.

Here’s how to make your eCommerce product models really werk for your online retail store.

Choosing your eCommerce model(s)

A good place to start. There is no doubt using models for your eCommerce site is worth every penny for their time, especially if your data tells you that on-model imagery translates into better sales. Plus, there’s not much point in low-balling the fee for the risk of having to reshoot… and there’s only so much retouching you can afford, right?

Ticking these checkboxes should put you in a pretty good position on shoot day:

1. Does the model tell your story?

Every eCommerce platform is selling a unique story to its consumer about who they are, or want to be, after making a purchase.

For example, Net-a-Porter has long served the fashion-forward set with cash to splash using partially headless models, high-end styling, and a typical runway body type.

web screenshot of Net-a-Porter website depicting model wearing Valentino black sweater

The intention is to spark the same ‘I gotta have it’ response that one might get flipping through Vogue. The difference? The customer can act on the emotion immediately.

Patagonia’s reputation precedes the clothing, so it’s no wonder they play into their outdoorsy tomboy aesthetic, with models who look like you might find them at the base of a mountain. With smiling faces, little to no styling, and natural hair and makeup, the customer is able to discover the product without distraction, which suits the technical, long-wearing nature of the garments.

web screenshot of Patagonia website showing woman wearing mustard yellow Patagonia raincoat

Even though both retail sites lead with ghost or flat imagery, the story is told by showing how the clothing fits and functions on a model best fit to represent the customer, and more importantly, how to wear it.

2. Does your choice match historical data?

Everyone on the online team (and every team) has that one model who just blows their socks off wearing the brand. But, does that model resonate with your audience as well?

It’s just like when the buyer might want to invest like crazy in particular styles because they’re #obsessed. Will the audience be #obsessed too?

There’s only one way to find out if your users are engaging with one model over another, or if they don’t see their (immediate or aspirational) selves in the image. Historical data! To know if you should go ahead with your top preference, you can consider looking at that data two-fold.

Retail Analytics

Firstly, a robust retail analytics portal like, say, Style Arcade allows you to filter by the model during any given period. You can choose as many filters as you want, but make sure they remain consistent as you flip through the analytics of different models.

The model choice won't be the be-all and end-all for a product selling, it could have come down to a particular promotion, best-selling style, or colour. However, the key is to look for patterns. For example, in the units sold vs the returns rate.

In the example below, we have Cindy vs Naomi. Each with 6 styles at full price in the same period.

screenshot of Style Arcade app depicting statistics comparing models Cindy and Naomi

From 6 products it appears Cindy sells an average of 29 units per product, with an average return rate of around 2%. Meanwhile, Naomi sells an average of 55 units per product with an average 8% return rate. This generally tells us that the way both Cindy and Naomi wear the clothes, and how they’ve been shot is accurate to the size the customer predicted they would need. Naomi seems to be the winner here if you want to go by average units sold.

You can go deeper; if we look at the first 6 weeks of the product dropping on site, you might get a better read of how the audience responded to that model from the jump.