Chris Chen's Profile
This is a really interesting business – thanks for sharing this! I think a crowdsourcing model like this will drive very interesting innovation, however I wonder what the competitive moat around these products will be. Once this company brings something to market, I imagine it will be easy for Big Beauty to replicate the products Volition introduces to the market. Given the capital base / resources of Big Beauty, they are likely able to perfect these products. It’s unclear that Volition can win on product.
One thing I think the Spotify model doesn’t solve for is people who have aspiration music preferences. For example, for some people the music they listen to today is not representative of the music they want to discover. These people want Spotify the ability to introduce new music that does not overlap with their listening history. I imagine their is other data available on customers social media accounts or other accounts that could suggest what some of these aspiration music categories could be – perhaps this will be an edge for a player in the music subscription business.
Really interesting questions you raise in this paper. Here you lay out a really interesting tension of leveraging data and customer feedback to be more innovative vs. preserving brand authority by being more discrete and “knowing”/developing the best product. It feels like as L’Oreal has ventured into some of these more innovative spaces such as adtech/martech they are risking their stronghold of being a collection of strong legacy beauty brands. Ultimately big data and machine learning capabilities still feel comoditized today – people have more or less similar data, at least no one has found a dominant data set within beauty – and the value of a brand still rests on it’s reputation and the augmented value they provide to the consumer. It certainly feels like L’Oreal may risk the credibility of their brand if they start blindly following tech trends and open their R&D up to the public rather than leverage their new data assets in a more private way that will allow them to maintain their brand authority.
Really interesting trend you’ve spotted here. I agree that it’s not obvious that these traditional companies are not very targeted in the technology capability of their newer acquisitions. There are benefits in better understanding consumer preferences and skin conditions / complexions, however I wonder if this will actually result in improved products or enhanced R&D processes. I think it may be easier for these traditional companies to roll out newer brands that communicate it’s new focus on product enhancement / efficacy through leveraging big data, however as a consumer it’s a little harder for me to believe that these traditional brands will be able to change the way they formulate / develop products with this influx of new data.
Fantastic article – as others have mentioned this is a really great way to frame up the security challenges in payments and the role of big data analysis in security. I think payments companies are highly advantaged in providing/partnering with others to provide dynamic fraud detection solutions given their large repository to historical transaction data. Payments processors have been utilities historically and players of scale have held an oligopoly in the space among large merchants, as the space becomes more competitive the ability for payment processors to aid in fraud detection and provide other KYC capabilities to their merchants may be a powerful differentiator. I agree with your concern that fraudsters are also innovators that will adapt to machine learning solutions. This is why in the long term I think it’s important for the broader payment processing industry to partner with one another as well as other financial institution players to share a data depository to improve the accuracy of big data solutions combating fraud. This will make it increasingly difficult innovative fraudsters.