Michael Edwards' work

Developing a unique classification system to match and recommend fragrances was the guiding philosophy behind Michael’s work at its inception over 30 years ago. Changes in consumer behaviour (now buying online and in-store) drives the business to develop and deliver innovative solutions. API technology offers retailers the option of pre-formatted or customised products. Through developing technology, matching and recommending fragrances to their customers is more accessible than ever before.

Perfume Legends explores the evolution of modern French fragrances through the eyes and words of their creators. Michael Edwards,
Author of Perfume Legends

Database

Updated daily, refreshed twice a week, the Database feeds the suite of Fragrances of the World digital products. Subscribers to fragrancesoftheworld.info can perform detailed searches and apply visualisation tools to the results, while the dashboard shows a snapshot of recent updates with links to reporting and printing features. The Database offers limitless applications to media, marketing analysts, training designers, brand developers or researchers within the fragrance industry.

Match my Fragrance

An easy to use digital tool designed to maximise online sales. Matching from the database and recommending from the brand’s stock inventory Match My Fragrance is also visually engaging, offering customisable branding. The tool organically creates sales opportunities and empowers the buyer with knowledge and expertise about their fragrance preferences.

Retail iPad App

Designed to enhance customer engagement, this powerful tool allows sales staff to match and recommend fragrances depending on the individual’s needs. Options include a mood-profiling tool, an interactive Fragrance Wheel and the ability to match and recommend by brand, fragrance family or ingredient.

Match It

Designed to overcome the challenge today’s retailers face of making quick and accurate customer recommendations. This simple, single brand digital tool allows consultants to match the individual’s known preference from the database and make a recommendation from the stock list. Results are returned quickly and easily with minimal operator input.