Perfume Recommender

Client Parfumado
Front-end development partner Ask Phill

Engaging customers with personalized perfume recommendations

We developed and integrated Parfumado’s Artificial Intelligence driven fragrance recommender. The quiz on the perfume subscription platform helps customers to find perfumes that fit their lifestyle and personal preferences.The conversational interface and recommendation solution enables Parfumado to increase customer engagement and conversion, optimise their marketing spend and build extensive customer insight.

 
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About Parfumado

The Amsterdam-based startup is revolutionising the way customers explore and buy perfumes. With their €14,95 a month subscription model, they offer a wide variety of designer fragrances. Conveniently delivered at home.

 
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Key benefits

CUSTOMER ENGAGEMENT

The conversational digital quiz invites customers to reveal their preferences in exchange for exciting, unexpected, yet spot-on perfume recommendations. Customers are engaged with Parfumado to explore their fragrance offering.

CUSTOMER INSIGHTS

Feedback generated by involved Parfumado subscribers is used to continually optimise the recommendation while learning about customer segments and the qualities of perfume that matter to them. The solution develops an elaborate understanding of customer behaviour and desire.

MARKETING & SALES

The data-driven customer insights, fragrance insights and personalised match help Parfumado target their subscribers with inspiring content and relevant offers. The perfume recommender optimises media spend, enables acting on customer demand, and increases conversion.

Technology

FEATURES

•    Customer-centric quiz that emphasises lifestyle

•    84% customer satisfaction rating on recommendations

•    Utilises buying behaviour and customer feedback

•    Business Intelligence & Marketing automation integration

•    Continuously improving (AI)

LIFESTYLE-BASED RECOMMENDERS

Scent, style, taste and other forms of essential characteristics are hard to quantify in data. They are difficult to understand and even harder to make actionable for personalisation. Our approach to developing lifestyle-based Artificial Intelligence-driven recommenders promises to be fruitful for matching customers to inherently-emotional products such as fashion, beauty and lifestyle.

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