Uncovering Customer Desires and Preferred Aesthetics For Data-Driven Marketing

Detailed consumer insights are helping pitch highly effective online offers for fashion ecommerce

Photo by Gabriel Gonzalez on Unsplash

Ecommerce merchants are a decade into the age of the customer and the indispensable importance of knowing what products customers like best is now situation-normal.

How we got to the age of the customer. Source: Forrester Research, Inc.

The next thing is to know why. To put the question more precisely, what product attributes are most desirable to individual consumers?

Future-savvy merchants are now using SaaS solutions on their websites to analyze consumers and get the answers to these questions.

The websites with the latest tools can automatically figure out exactly which product attributes attract the most attention. For apparel and beauty brands using these technologies revenue per visitor has increased on average by 6.6x.

Style-conscious shoppers are picky about brands, colors, sizes, materials and fits. With this in mind website personalization solutions can now be optimized to track preferences and create customer profiles that can be used to personalize discounts and make product recommendations.

These tools are empowering merchants of all sizes to collect valuable consumer insights and engage website visitors with marketing efforts that are driven by real-time, 1-to1 personalized data.

How data-driven marketing came to be. Source: Thunder Experience Cloud.

While the age of the customer began around 10 years ago, online data-driven marketing has been around for over 20 years.

And now, in 2022 and beyond, data-driven marketing featuring personalization is table stakes. Everyone has a solution in some form or another and customers — especially younger ones — expect see it every time they shop online.

What’s important to know nowadays, is that some personalization solutions are much more advanced than others.

Read on as we break down how cutting-edge data-driven marketing can be fully realized with the Rosetta.ai personalization solution, which features:

(1) Automated preference analysis on the backend
(2) Frontend personalized recommenders and discounts
(3) The X factor: a unique customer experience

Automated preference analysis on the backend

The market research required to figure out what customers like can now be done with machine learning. The AI can track onsite behavior like clicks and scrolling to figure out which products customers like and even which product attributes are most desirable.

Then the AI factors in demographic and transactional data to complete preference profiles that can accurately predict what customers want to see, in which context and at what time.

The key for the apparel and beauty industry is image-based AI. Computer vision can analyze the product images that a shopper views and determine why the shopper likes the image. It may be the length of the sleeve, the material or the pattern, but the AI will figure it out.

This gives merchants better control of their inventory planning, product development, promotional campaigns and more.

For example, popular trends and current stock can be compared for specific attributes like length, design, color, pattern and material:

Preference profile for length.

With easy-to-access data on hand that shows what’s being viewed vs what’s currently in stock, fashion ecommerce merchants can plan ahead based on up- to-the-minute data instead of leaving it to hunches and best guesses.

A preference profile on the back end begins to accumulate within 3–5 clicks of a user’s first arrival on your website. Browsing behavior and viewed items are tracked. Once the profile is created, preferred attributes populate recommendation boxes with products that match the profile.

Backend preference analysis and frontend personalized recommendations.

The preference analysis continues and return visitors get recommendations and discounts that are even more in touch with what they like because the AI has been analyzing them longer.

Front-end personalized recommenders and discounts

Personalization for branded apparel and beauty has come a long way in recent years. Gone are the days of putting a customers first name in an abandoned cart email and calling it a day.

The effort is much more proactive now and extremely effective, with onsite solutions significantly reducing bounce rate and abandoned carts. Sites optimized for fashion are achieving increases of:

  • 2x average order value
  • 3.3x conversion rate

Attracting new site visitors with a cash offer or discount in exchange for an email address is a tried and true tactic that works.

Homepage discount pop-ups encourage more click-throughs.

Discount pop-ups can also be offered to returning customers once a preference profile begins to build. In the example below, “Tania Lee” sees a personalized discount buy-one-get-two pop-up on moisturizer that fits her spending habits and general preferences for color and more.

Further along the customer journey on the product page, personalized recommendations have been shown to dramatically increase average order value.

Personalized product recommendations on the product page.

Seeing a recommender box with colors and styles that match a consumer’s desires and aesthetic preferences is extremely effective on fashion ecommerce product pages, where the intention to buy is strong and the credit card is already halfway out of the wallet.

The X-factor: a unique customer experience

The importance of a unique CX for online shopping has never been higher. Shoppers want to feel like the website shopping experience is tailored especially for them.

How a shopping experience goes down is as important for most consumers as the product itself. The L’Oréal Luxe Group has made this idea central to their marketing of the Shu Uemura cosmetics brand.

via/https://www.loreal.com/en/loreal-luxe/

The idea is to make the online experience like a personalized relationship that makes the shopper feel personally catered to.

The results speak for themselves, in less than a year Shu Uemura was able to achieve increases of 140% for conversion rate, 109% for average order value and 149% for revenue.

Conclusion

Apparel and beauty brands have entered a new era of ecommerce where they can build intimate relationships with customers via personalization tools optimized for providing unique customer experiences.

With the right tech-stack, fashion brands can now pursue truly data-driven marketing that makes real-time 1-to1 personalized connections with their online consumers. This is what customers want, this is the new standard of excellence.

Brands that adapt to this new reality and adopt the latest technology are poised to grow quickly because they can provide the CX consumers crave and products consumers want — in the moment.

About Rosetta.ai

Rosetta.ai offers fashion-optimized, image-based AI personalization for unique onsite shopping experiences, authentic email marketing campaigns and preference analysis insights.

On average, our clients double their order value and triple their conversion rate because shoppers on their websites are more engaged by the industry-leading accuracy of our personalized recommendations.

Rosetta.ai has been featured in Forbes Top 25 ML startups and Analytics Insights Top 10 companies.

Sign up for a free 14-day trial today and start growing your business on day one!

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Our mission is to empower merchants. Our technology lets merchants understand fashion-savvy customers and create unique shopping experiences that grow their businesses. 1000+ ecommerce shops using Rosetta.ai saw a 2x increase in AOV and 3.3x increase in conversion rate.

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Ian Mckinnon

Ian Mckinnon

Content Strategist @Rosetta.ai

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