Ian Mckinnon
Ian Mckinnon 25 May 2022

What We Learned From Fashion Ecommerce Marketers About Consumer Insights, Product Design and Marketing Strategy

In our last round of UX interviews (December 2021) with our more data-savvy clients (in the fashion ecommerce biz) one thing kept coming up: Detailed consumer insights from first-party customer data are becoming more and more valuable for new product design and sustainable marketing strategy. So to better understand our clients we had to unravel this this common thread. For starters, why now? And how does our platform contribute? If you're on a marketing team in ecommerce retail, and you're already thinking this sounds familiar, read on, you're not alone.

First-Party Data Matters More Now. Why?

Market research and competitor analysis still matter but unique first-party data from actual customers will matter more in the future for two reasons:

1. Advertising via Third Party Data is Done

The long predicted cookie-less future has arrived. Third-party data and even second-party data have lost their worth and ecommerce marketing teams are working harder to capitalize on the traffic they already for better ROAS have by cross selling more via personalized recommendations.

2. Personalized Experiences are Expected

Digital-native young people have known nothing but personalized online shopping. Amazon and Netflix use machine learning to figure out what individual shoppers like, and now social shopping presents pleasantly familiar items being used by their peers in a steady stream of user-generated content.

This headline from 2020 on the topic sums up the situation and the Gen-Z attitude:

Consumer Insights and Product Design

Imagine a product development team in the early days of a new product: the idea generation and design stage. The ultimate goal of course is too identify a specific product that hits its mark with a specific audience. But when it comes to actually designing the product, where to the ideas come from?

User-Centred Design in the Fashion Industry

Our clients in the fashion industry clued us in to a well-known term from the design world: UCD (user-centered design).

The UCD method:

“Calls for involving users throughout the design process via a variety of research and design techniques so as to create highly usable and accessible products.” Source: International Design Foundation

Sounds like a good plan!

Another tidbit from our interviews about defining user requirements stood out:

How a product is viewed, valued, and compared with other products based on features, price, season, etc., all factors in to fashion design.

These insights add to product development process, giving product designers, the product manager and indeed everyone on the product team plenty more to consider as they figure out the “use context.”


What our Customers Need for Product Design

What our most data-savvy ecommerce teams crave most are the consumer insights about shoppers that are already coming back often to browse their way along the brand's customer journey.

These are the VIP customers, 20 percent from the old 80/20 rule in sales that tells us that most of your profit comes from your VIPs that love your brand and the online experience it provides. As it turns out, what ecommerce teams really want to know is what their individual VIP customers like as they browse, in real time, so their designs and their offerings can be on the cutting edge.

Real Time Preference Analytics

With our clients’ needs in mind our Data Science team tweaked the Preference Analytics feature so that the preferred product tags for each shopper are weighted with a percentage that shows the probabliity of a shopper clicking on an item with an attribute described by that tag.

It creates anonymous, GDPR-compliant shopper profiles from first-party data to figure out which product attributes shoppers like in the images they're browsing.

It could be length design, color, pattern, material or any other customized attribute they need for analyzing real time attribute preferences from their first-party data.

In the ever-changing fashion industry these kinds of detailed, attribute-level consumer insights give designers direction based on customer data and reassures them that they’re developing the right products, all the time.

Consumer Insights and Marketing Strategy

Former CDO of L’Oréal, Lubomira Rochet, spearheaded many marketing campaigns during her time with the company and emphasized the importance of customer centricity and building 1-to-1 relationships.

Unique consumer insights are the key. They’re essential to developing a marketing strategy that reaches out to shoppers one at a time.


In a 2018 interview with Commonwealth, Rochet explained,

“Digital technology has changed the way we market. An important goal is having the ability to build rich, personalized consumer relationships and to create content that engages consumers and makes them happy to share it.” Source: Commonwealth

But what is it about personalized content that's so engaging?

Consumer Insights and Delightful Surprises

If done well, consumer insights can inform personalized recommendations that are extremely memorable. A case in point is the delightfully unplanned purchasing decision, as described by Hubspot Marketing Blogger, Amanda Zantal-Wiener:

“Those who know me are aware of my borderline obsession with hip hop, which is also the motivation for a lot of my online shopping behavior. Clearly, Amazon has taken notice… the purpose of my most recent visit to Amazon was to check out its personalization features for this article. But then, I discovered that Rapper’s Delight: The Hip Hop Cookbook was in my recommended books. Did I buy something I don’t need? Sure. But I also was left delighted by the fact that it was brought to my attention with very little effort… the best part about it, for the user, is the resulting discovery of new things that we like — whether it’s a book, a tool, or an article.” Source: Hubspot, These 9 Brands Take Personalized Marketing to a New Level


Personalizing a shopping experience with consumer insights and delighting shoppers with familiar recommendations is a proven marketing strategy that Amazon has used for years to keep shoppers coming back for more. And as the Hip Hop cookbook above exemplifies, sharing the delight with others.

Moving from Marketing Strategy to Tactics

Tracking data about transactions and about which products are selling is still part of the game and the Platform offers plenty of handy metrics at a glance.

But the really valuable contribution the Platform provides — the thing that fuels customer-centric marketing strategy — is Preference Analytics. And the tool that drives more cross-sells and increased sales is the personalized recommender.

Personalized Recommendations

Outside of the marketplaces, big branded websites use the same marketing strategy but often create their own inhouse recommendation systems, employing small armies of backend engineers who specialize in data science and machine learning.

Lately however, some brands have been taking the no-code SaaS approach and outsourcing the backend work to automated systems that can be easily added as plugins to their existing websites. A marketing team without coding experience can use these marketing tools to gain a deeper understanding of consumer insights from their site visitors.

Korean Fashion Multi-Brand Website, Codibook

Codibook compiles it's consumer insights automatically with their subscription to the Rosetta AI Personalization Experience Platform.

The in-page carousel recommender plugin cross-sells items that match attributes previously viewed as the shopper browses product images. This tool increased average order value for Codibook by 38%.

The Shu Uemura Website in Taiwan

Shu Uemura also put their marketing strategy to work with Personalized Product Recommendations. They customized the look of the recommender to match Shu Uemura look optimized it for mobile devices on their Add to Cart pages. Revenue increased 149% in 2021.

They also used the Hesitant Customer Promotions plugin to engage shoppers at just the right time (before they intend to bounce) with discount popups that are personalized to their individual preferences.

And to retain customers who get away without making a purchase they mine their consumer insights from their own first-party data and then send personalized messages via email or SMS using the Personalized Omnichannel Marketing plugin.

For the full results breakdown of Shu Uemura’s foray into personalization check out the case study.

Final Thoughts

So according to our clients, consumer insights are central to ecommerce success nowadays for product development and marketing strategy.

  • First-party data is key to maximizing ROAS and personalization has become common online and shoppers expect to see it.
  • This is especially true of the digital native Generation Z and the Millennials, who are willing and able to pay more for online experiences that reach out to them as individuals.
  • The more data-driven ecommerce teams eagerly track transactional metrics, but the real value is in the consumer insights on product attributes from first-party data.

For fashion ecommerce teams looking to complete their market research with real-time consumer insights — and the conversion tools to put a customer-centric marketing strategy into action — the time is now.

About Rosetta AI

The Rosetta AI Personalization Experience Platform discovers shopper preferences from your first-party data and provides 1-to-1 product recommendations, hesitant customer promotions and omnichannel marketing.

Our AI algorithms are written especially for apparel, beauty and accessories ecommerce merchants and we serve them passionately because their success is our success.

On average, our clients double their order value and triple their conversion rate because their shoppers are more genuinely engaged by the experiences our platform provides. has been featured in Forbes Top 25 ML startups and Analytics Insights Top 10 companies.

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