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The Future of Big Data and Beauty: Data Driven Beauty
By: Emily Coleman, Jacqueline Chan, Courtney Das, Eileen Kim, Craig LaManna, Erica Roberson and Lisa Sequino
Posted: June 10, 2013
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Data will improve executional elements as well. Adjacent industries are using radio-frequency electromagnetic fields, also known as RFID, to transfer data, for the purposes of automatically identifying and tracking objects. Today, it is used to monitor the distribution of pharmaceuticals, assess food quality and temperature, and identify adverse reactions for hospital patients through implanted microchips. In 2020 there will be new ways to leverage this technology and integrate it with GPS and advanced data analytics. This will provide the manufacturer with real time information about the consumer, and most importantly, where and how products are used. (Gulbahace, 2010) For example, during a typical fragrance launch a beauty brand can spend an estimated 6% of projected retail sales on sampling. But the company is not always certain of the return it is getting on that investment. In 2020, this integrated tracking system will give businesses a way to capture data on where product was sampled, who received the sample and if they enjoyed it enough to purchase.
This technology will also improve consumer experience. According to NPD, by 2015, 1 in 6 people will be over 65 years old, and 14% more people will reach 100 years of age. As a result, the US anti-aging market is projected to skyrocket to $144 billion dollars a year.
In the future, brands will leverage algorithms so consumers can receive specific recommendations to prevent or reduce aging. (Grace, 2012) These algorithms will be based on three elements:
- Intrinsic biometrics that detect skin health, moisture, collagen and elastin levels.
- Extrinsic measurements that capture pollution, UVA and UVB rays, and climate conditions.
- Behavioral data that tracks behaviors that affect aging like stress, exercise, and sleep.
Once the technology captures this information, it will export a consumer’s profile of skin health and suggest hyper-personalized product and behavior recommendations. It will then calculate an aging index score and project the consumers’ future aging profile based on the three elements. With continuous data processing and regular input, the platform will empower the consumer with hyper-personalized recommendations to minimize the effects of aging. From product innovation all the way to consumer relationship management, the future of big data and beauty looks quite promising.
Beauty & Data Today
While 2020 holds groundbreaking possibilities, Big Data can affect the beauty industry right now. Today, 93% of beauty organizations do not have a data dedicated function. (Meerman Scott, 2009) Data is often generated and disseminated within the sales and finance teams; instead of cross-pollinating across the company. To solve for this, companies should create data centric beauty organizations. In this new data centric organization, data is at the core. It feeds into, and connects all elements of the company through a newly created function of data strategy. This new group receives data from all company sources including supply, finance, and sales. Data is analyzed and strategies are implemented across the company to affect everything from product development to execution.
While the new data focused organization will bring analytics across the company, finding data experts to lead these functions will be challenging. There are currently 40 universities in the U.S. with data science programs, and we predict that this will be the most popular major by 2020. However, at the same time, there will be a shortage of 200k data scientists in the workforce. Today, companies like American Express are buying smaller analytics companies to acquire talent in this area. The beauty industry needs to start recruiting, acquiring and developing data scientists now. ("Global powers of," 2010)
Many companies have a large amount of data, however only 12% of marketing experts claim to have access to actionable data. (Jones, 2012) In order to be effective, brands need to know what data to focus on, and what to push aside. They must start with the business objectives and eliminate irrelevant data to ensure that big data can go to work generating the desired results. For example, in 2010, a Singapore taxi company was looking to expand its customer base and increase taxi bookings. The company invested $8 million dollars in big data, ramping up its booking system and tracking its fleet of taxis through billions of data points including GPS, weather patterns, consumer usage and traffic trends. In two years, the company tripled its revenue, increasing taxi bookings by 251%. (O’Connor 2013)
Privacy is also a concern. Over 90% of internet users are worried about their privacy, and it is the top reason why non-users still avoid the internet. The current patchwork of privacy laws fails to provide comprehensive protection, which causes confusion, distrust, and skepticism. When it comes to preserving privacy, brands must invite consumers to share personal information in exchange for some type of value-add. Then, when speaking with them, brands must only use information which consumers have knowingly shared in order to build a loyal and trusting relationship. (Homes, 2010)
Beyond privacy, it is difficult to know exactly who your target consumer is. You need to go deeper to understand them, as inaccurate personalization could lead to missed opportunities. For example, when the credit agencies undervalued the probability of failure in the U.S. housing markets in the mid-2000s, they based their assumptions on data pulled from housing statistics during the boom years. It was a recipe for egregiously wrong predictions as there were many observations, but no variance to show how the housing system would function under different conditions. (David, 2012) This mistake demonstrates the importance of testing. You must ensure that you are constantly testing with a control group to capture real-time learnings and evolve your targeting.
Big Data is changing the beauty world as we know it. It is estimated to be worth 300 billion dollars a year to the health care industry alone, and the impact on the beauty industry is expected to be double. (Heussner, 2010). Big Data will increase operating margins by 60% in the retail sector, and will save over $150 billion in government spending through added efficiencies. The beauty industry must act today to capture the value that big data and associated analytics represent. If brands do not act on data now, they may not be ready in 2020. ("Big data issue," 2013)
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