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Real personalised beauty: ‘Technology is the only way to be able to help customers at scale’

By Kacey Culliney

- Last updated on GMT

Beauty 4.0 Podcast by CosmeticsDesign-Europe speaks to Beauty Matching Engine about personalisation via tech

Related tags beauty 4.0 beauty tech Personalisation personalised beauty ecommerce omnichannel Artificial intelligence machine learning

Technology is required to provide beauty consumers true personalised offerings at scale, and brands and retailers need to be smart about tailoring the shopper experience because consumer demands are certainly there, says Nidhima Kohli, founder of Beauty Matching Engine.

In CosmeticsDesign-Europe’s sixth Beauty 4.0 Podcast​ – a digital series looking at how technology and innovation will shape beauty’s future – we catch up with Nidhima Kohli, founder of predictive shopping technology Beauty Matching Engine. 

Nidhima Kohli, founder of Beauty Matching Engine
Nidhima Kohli, founder of Beauty Matching Engine

In this 29-minute podcast, Kohli talks about the true potential of personalisation in a digital and even omni-channel beauty world, providing insight on the need for enriched data, machine-learning and a tailored approach in fulfilling all of this.

Beauty + data must always consider the end customer

At the end of 2019, Kohli launched Beauty Matching Engine – a tech startup that used artificial intelligence and big data to offer beauty brands and retailers highly personalised white-label retail models and optimise business​. Part of the L’Oréal Open Innovation accelerator program, the B2B business already worked with a number of players in the market, including Douglas, By Terry and The French Pharmacy.

Kohli said what was important to remember, no matter the beauty business, was that when working with data “you have to think about the end customer”​ where needs differed every time – price versus brand sensitivity being just one example.

And as digital continued to boom, fuelled by the ongoing COVID-19 crisis, she said beauty businesses had to go beyond spending big on social media advertising.

“Now, people have realised they’ve spent all this money to get traffic to their website but what they really need to now invest in is customer return rate and, of course, optimising the sales conversation rates on their website.”

Using Beauty Matching Engine enabled this, quickly, because it centred on pre-populated, relevant data pooled from Kohli’s earlier consumer-facing business venture My Beauty Matches.

Importantly, this data – used alongside environmental and brand-specific data – got smarter over time, she said.

Beauty brands and retailers have different personalisation challenges

Beauty Matching Engine worked with both individual brands and retailers, but Kohli said there were different challenges for each when it came to personalisation. Retailers, for example, faced the challenge of consumer “choice paralysis”​ and difficulties in prompting return purchases to enable personalisation. Brands needed help in building the consumer journey and upselling other products that worked as part of a personalised beauty routine.

“Technology is the only way to be able to help customers at scale because there’s new products launching all the time, there’s new trends, new ingredients launching all the time and the best way to do that is, I would not say use only technology, but use technology to assist and enable the customer shopping experience.”

“…I just want to make beauty shopping a really quick, easy and pain-free experience for them, and enjoyable.”

Beauty 4.0 Podcast – more insight on tech-driven personalised beauty offerings

For more insight on the future challenges and opportunities for beauty brands and retailers in truly personalising offerings, listen to our 29-minute podcast above or access our podcasts by subscribing via Apple Podcasts​ or finding us on Spotify​.

This podcast was recorded on March 9, 2021.

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