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With the aesthetics business on the upswing, satisfying consumer requirements and making aesthetic treatments more accessible is a top priority for Allergan Aesthetics, which will be accomplished through its customer loyalty program, Allē Flash.
Fremont, CA: "Our Allē Flash Members lead busy lives, and we know aesthetic regimens are not one-size-fits-all. We believe there is further opportunity to support them with personalized offers delivered to them when they are in the position to redeem." comments Jasson Gilmore, Senior Vice President, Global Digital and Consumer Marketing, Allergan Data Labs. Allergan Aesthetics, an AbbVie business, has announced the availability of an innovative, unique, and industry-first experience through its consumer loyalty program, Allē: Allē Flash. This innovative in-office offer distribution system uses cutting-edge technology to give highly tailored offers to the platform's 3.7 million active loyalty program members in real-time while they are visiting a partner dermatologist, plastic surgeon, or med spa.
According to internal data, 65 percent of JUVDERM Collection of Fillers visits in 2021 occurred on the same day or within 30 days of a BOTOX COSMETIC (onabotulinumtoxinA) treatment. Allē Flash's mission is to surprise and excite people with customized offers while they are in their doctor's office. The offer could be for the treatment they are scheduled for, which they can redeem on the spot, or it could be for another Allergan Aesthetics treatment in the future.
"Since launching Allē Flash in late 2020, our providers have driven its utilization in office, significantly exceeding our expectations. We are eager to take our 'much loved' loyalty program another step further with Allē Flash, adds Jasson Gilmore.
Allē Flash is committed to fostering strong consumer connections with the Allergan Aesthetics family of brands. Allē enhances and democratizes customer access to aesthetic items and treatments through smart technological solutions. Allē Flash predictive model employs an ensemble method that combines supervised and unsupervised learnings, as well as collaborative filtering, propensity models, the team's own flavor of market basket analysis, and other techniques.
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