Cannes Lions

Digital Beauty Advisor

MILKLAB AD-TECH, Istanbul / UNILEVER / 2019

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Case Film
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Overview

Entries

Credits

Overview

Background

Situation:

Digital media has greatly changed cosmetics industry. Shoppers search about similar problems and experiences, check product reviews and follow product applications before purchasing. Nowadays reviews and recommendations are more important than ever, and offering appropriate suggestions to users’ needs has a positive impact on sales and brand loyalty.

Brief:

We have been asked to develop a digital, immersive and experience-oriented communication strategy by moving users’ favorite beauty content from online media to in-store sales points. While developing this strategy, we needed to have an approach that benefits user and highlights experience but not the product in addition to finding a way for users to consult expert opinion in sales points.

Objectives:

- To have important place in users’ shopping experiences

- To know shoppers better and provide personalized experiences for their needs

- To strengthen the relationship between shoppers and our brand

- To increase store sales by differentiating

Idea

In beauty and personal care products, one of the most important factors that affect the purchasing decision of shoppers is the recommendation. This recommendation can come from a friend, a random blog or a social media influencer.

What if this advice came from a digital beauty assistant with AI that produces product suggestions based on shoppers’ needs and analysis results? We believed that this would make exceptional changes and created the world's first AI-powered 3D digital beauty assistant.

Continuing to learn the problems and demands of shoppers, identifying them in every store to establish a constant communication, providing unbranded suggestions, the assistant aims to be a part of shopping experiences of users. We chose mirror-looking-screens to place the assistant, where we were sure the shoppers would look at! To talk to this assistant with a UX that requires minimal touch, it is enough for users to look in the mirror.

Strategy

Target Audience:

Women/men aged 18-35, high school/university graduates, paying attention to personal care, regularly purchasing personal care products, researching products before buying, reading product experiences, spending time in online media, following and being inspired by influencers.

Approach:

We knew our target audience wanted “experience, not advertising”. However, when we wanted to introduce a personal beauty assistant in their lives and to gain trust, we had to do more.

So, we haven’t only introduced our product invested for 3 years as completely unbranded, but also offered them the products of other brands if a product isn’t available in our product range but we think will meet users’ needs!

We have created this accessible assistant that has trusted recommendations and values expert opinion by combining transparency with micro-localization with content we optimize according to user's location, maximum personalized experience based on user analysis results, and continuity by identifying user at every point.

Execution

Implementation:Digital Beauty Assistant is designed as an AI-powered 3D character. Depth Camera for features such as gender-detection and face-analysis was strengthened with hardware such as skin analyzers, cream and perfume sampling, and was positioned to communicate through mirror-screen designed as full-length mirror.

Scale:10 biggest Gratis stores for the 1st phase. Infrastructure and central management panel were designed for expansion.

Brand Relevance:Many “contour-makeup” videos on internet were viewed millions of times. But it's all made according to content owner’s face-shape. These contents would be more valuable when presented in line with user's face. Our understanding of content based on this insight has turned into a hyper-personalized experience. Optimizing all suggestions according to shoppers’ conditions, the assistant increases the strength of the connection between brand and user with consistent recommendations.

Touch Points:The assistant initiates in-store communication and provides exclusive discount-codes from the mobile platform. By requesting GDPR permission from this page, she adds her users’ data to CRM and maintains communication with Dark Posts, SMS and retargeting on the web.

Materials, Style Elements, Design Choices:She presents hyper-personalized content with an unbranded design concept. User experience is prioritized, but not the product and brand.

Methods&Process:In 3D assistant is designed in Maya and face&body motions created with motion-capture. The assistant placed on the screens shaped as full-length mirror is controlled from central management panel. The software is modular, allowing us to optimize each assistant individually. With AI, the assistant customizes contents for each user based on preferences and shopping history.

Timeline

1st Year:Market Research&Character Design, RD&Prototyping, Testing&Developing Prototypes

2nd Year:Production final 10 products, installation of centralized management panel and data&CRM integrations, finding retail partner and legal&commercial agreement process, loyalty card integration.

3rd Year:Intellectual, design and software production of initial contents (30 interactive contents), focus group tests, revision process and integration, product launch, and placement.

Outcome

Business Impact: In 10 days after launch, 8% of the total project investment was generated through sales collected from DBA's recommendations. 35% of shoppers using personalized promotional codes were added to the customer database, allowing the assistant legally to contact them on digital platforms.

ResponseRate: 20% of daily visitors of 10 stores have met and interacted with DBA.

Impressions: In 10 days, 70,000 shoppers interacted! The assistant made skin analysis for 30,000 shoppers and tested creams suiting to skin types, and suggested make-up applications and hairstyles to 50,000 shoppers. 35,000 shoppers interacted with native content.

Change in Behavior:According to store managers’ reports, shoppers who met the assistant have checked whether it has a recommendation for that category before buying product.

Consumer Awareness: Shoppers became aware of products and practices that the assistant suggested, and were informed about whether products were suitable for them and which products could meet their needs.

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