Beauty Trends - 2021
02 Jun 2021
The global beauty industry (encompassing skin care, color cosmetics, hair care, fragrances, and personal care) has been shaken by the COVID-19 crisis. With months of lockdown, retail businesses closed and international travel ban, consumer’s purchase and usage behaviour has witnessed a dramatic change leading to fall in sales across many beauty segments.
Beauty sales declined as much as 30% in the first half of the year, according to a McKinsey report and even major brands took a blow. With more than a year under pandemic, brands are now working towards better ways to deal with the humongous shift in consumer values and expectations.
In this effort, brands are adopting new technologies at a faster speed to redefine personalisation. Some companies such as L’Oréal offers AI powered at-home devices, which can measure user conditions, like the emergence of dark spots or surrounding environmental concerns, on a daily basis. L’Oréal’s Perso device accounts for this data to dispense custom-formulated makeup every day. Another company Atolla uses AI capabilities to customize facial serums for consumers by using data collected through quizzes and tests measuring oil, moisture, and pH levels.
As per a CB Insights report, Johnson & Johnson, has invested in new engineered preservatives that could be used in items like haircare or body care products. The company invested in Curie Co, a startup that makes biomaterials to replace preservatives in everyday beauty and personal care products, through its JLABS incubator.
Another apparent trend is BigTechs offering retail channel for beauty products. Amazon launched a private label beauty brand called Belei in 2019 and recently invested in India-based D2C beauty site MyGlamm. China-based tech giant Alibaba offers livestreaming and AR features which it has used to attract luxury beauty brands to its e-commerce platform.
Virtual try-on tech leverages augmented reality to allow shoppers to test how different beauty products will look without actual trial. Remarkably, virtual try-on can also help brands personalize the beauty shopping experience, enhancing product discovery and making tailored recommendations about foundation shades, skincare products, and more.
In December 2020, Google launched an AR-powered cosmetics try on tool in Google Search, partnering with brands like L’Oréal, Estée Lauder, MAC Cosmetics, and more to let users try on searched-for makeup products using front-facing mobile phone cameras.
Going forward, we expect to see beauty brands and tech giants alike turn to virtual try-on to gather shopper data and make more personalized product recommendations.
The auto industry has been facing the heat to move digital more than ever as the pandemic has brought upon new challenges and deepened the need to shift toward digital solutions. Auto dealers have been slow to adopt digital car-buying solutions, but with lockdowns closing dealership doors, the pandemic accelerated the shift to omni-channel auto retail.
Online car buying has taken off in a big way during the pandemic. According to Publicis Sapient, many digitally enabled OEMs are seeing increased, higher quality leads that are 30 percent more likely to buy and a two to four-fold surge in website traffic compared with pre-COVID-19. These online tools are, in some instances, responsible for more than 20 percent of new leads during the second quarter of 2020.
More recently, a number of digitally focused disruptors such as Carvana, Carmax and Tesla have entered the market, offering unique, omni-channel experiences like flexible return policies, virtual auctions, home deliveries, online negotiation and virtual trade-in valuations. These digital leaders recognized a shift in customer expectations and focused on creating seamless user experiences across the entire shopping journey.
Online used car seller Vroom noticed a considerable growth in demand as a result of the pandemic, with people turning to digital methods for purchasing cars. Similar to its competitor Carvana, Vroom offers no-haggle pricing and a no-questions-asked return policy. Another Used car marketplace Shift Technologies went public via SPAC in October 2020. Shift allows users to buy, sell and finance cars online. The company offers a "buy it now" option that allows a buyer to purchase a vehicle online without a test drive. Similarly, Cazoo, a UK based company, sells refurbished cars online, delivers them to customers' homes within 48 hours, and offers a seven-day free returns policy.
Then there are digital platforms that help the dealerships move their businesses online. Take for example, Modal which makes software for car dealerships to move the entire buying process online. Another company, Digital Motors builds a car-buying platform for auto retailers, dealerships, brands and manufacturers.
The new car ownership model of subscription offers ease and convenience to customers like never before. Switzerland-based Carvolution offers car subscriptions where Customers pay a monthly price for a vehicle and are free to switch cars as they like.
We believe that, the winners in this industry will be defined by how quickly they adapt to technological innovations. The dealers and OEMs who adjust can thrive, while those reluctant to change will fall further behind.
Credits : Akhil Handa,Aparna Anand
We are living in the midst of a revolution. Supervised learning, a branch of Machine learning allows engineers to develop models that can train themselves. In turn, these models are helping solve crisis management problems before disaster strikes.
Technologists have long modeled data to harness machine learning for disaster relief. After the Chernobyl crisis, scientists analyzed satellite imagery and weather data to track the flow of radiation from the reactor. Today’s algorithms far outpace their predecessors in analytic and predictive powers. Machine learning models are able to deliver more granular predictions. NASA has developed the Landslide Hazard Assessment for Situational Awareness (LHASA) Model. Data from the Global Precipitation Measurement (GPM) is fed into LHASA in three-hour intervals. If a landslide-prone area is experiencing heavy rain, LHASA then issues a warning. Analysts then channel that information to the appropriate agencies, providing near-real-time risk assessments.
Roofing material is a major risk factor in resilience to natural disasters. So, a model that can predict it is also one that can predict which buildings are most at risk during an emergency. In Guatemala, models are identifying “soft-story” buildings–those most likely to collapse during an earthquake. “Forecast funding” can mitigate damage by providing the most vulnerable with cash assistance to prepare for disaster. Bangladesh and Nepal are nations that are already implementing this strategy.
Natural disasters, such as earthquakes, hurricanes and floods affect large areas and millions of people, but responding to such disasters is a massive logistical challenge. Crisis responders, including governments, NGOs, and UN organizations, need fast access to comprehensive and accurate assessments in the aftermath of disasters to plan how best to allocate limited resources. To help mitigate the impact of such disasters, Google in partnership with the United Nations World Food Program (WFP) Innovation Accelerator has created "Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks", which details a machine learning (ML) approach to automatically process satellite data to generate building damage assessments. As per Google this work has the potential to drastically reduce the time and effort required for crisis workers to produce damage assessment reports. In turn, this would reduce the turnaround times needed to deliver timely disaster aid to the most severely affected areas, while increasing the overall coverage of such critical services. The World Food Programme was awarded the 2020 Nobel Peace Prize and they thanked Google and its team of engineers in pioneering the development of artificial intelligence to revolutionise humanitarian operations.
The application of machine learning techniques to satellite imagery is revolutionizing disaster relief. Crisis maps and image comparisons are helping relief organizations to deliver aid with precision.
Credits : Akhil Handa Prithwijit Ghosh