I love this overview of how Heineken has been using open innovation. I believe they have done it the right way by only “innovating” on the margins and within more operational capacities. Large, well-established brands can certainly benefit from crowd-sourcing ideas, but they need to stay true to who they are to not alienate their customers and to avoid dilution of the brand. Through similar logic, I do not think that Heineken should experiment with open innovation with their smaller brands as it is a lot easier to these smaller more niche brands.
Betabrand needs to combine their open innovation with a more traditional form of commercializing products. It is wonderful that they can get great designs that people love at very steep discounts (just their revenue sharing with designers), but creating long term value in the clothing space is about having a strong, consistent brand and scale. I do not see how they can achieve success if they do not have flagship essentials that customers can consistently buy from the company. This is how you both achieve scale and brand loyalty. If a customer loves a product, they will come back to find that same product or something similar. When these customers become advocates and there are enough of them, more people will begin to buy the products. There are very few people (relative to the entire clothing market) that exclusively wear quirky, distinct clothing every day. A staple is necessary.
Nike can certainly address its 3rd pillar of 2x direct with 3D printing both in the short term and long term. We have seen the success of Nike ID, which has already increased the firm’s direct to consumer sales both through direct sales of Nike ID products and through indirect sales to customers attracted to the website by the ID products. I think that 3D printed shoes and apparel could have the same impact in the short term, while commanding a premium. In the long-term, as 3D printing costs decrease and the technology can be scaled, customized shoes will be able to be created to order for a larger market, which will not only increase direct sales, but will lower inventory costs, a huge consideration for manufacturers and retailers. Additionally, if Nike could incorporate biometric scanning or algorithms that use a few measurements to predict fit, it could further increase its direct to consumer sales.
Excellent post! Given we are discussing Chanel, and more broadly the impact of 3D printing technology on luxury clothing brands, cost is not as much of a concern as it is in other industries, but the quality of the products does come into question more. Certainly, 3D printing, especially when coupled with biometric scanning, can provide personalized and perfect-fitting clothes extremely quickly, but what this means for the quality of the materials and the value of craftsmanship to luxury brand customers is still a large question.
Thinking about apparel more generally, you mention the possibility of clothing brands becoming simply designers. This brings up several questions, including some about the logistics of delivering the materials necessary to print clothes to consumers or even retail stores. What is done with the waste? How are the materials prepared and stored to prevent damage? I think clothing companies provide more value than just design. In this world that you describe, traditional companies would also provide convenience and ease.
Great article! I think Avant and similar lenders fill an important need in the market that traditional lenders cannot primarily because of scope. The situations that Avant addresses are fringe cases in the lending market and because of this as well as heavy regulations, banks tend to focus their data science efforts on better pricing business within their current target consumer space as well as marginal expansions around this range. It is simply too expensive and inefficient to test and learn in this space and assume charge offs of ~15% that are at least over 3x that of traditional banks’ current books.
Given that the algorithms used to address Liberty Mutual’s challenges need to be built on Liberty’s data and will be key to the firm’s ability to compete long term, I believe that the capabilities need to be built in house rather than through acquiring a company that is not tailored to meet Liberty’s needs. These capabilities are built by acquiring and retaining top data science talent. Additionally, I think machine learning can best be leveraged by Liberty to enhance their underwriting rather than to simplify claims. However, the main question with regard to machine learning in this space is whether it is a sustainable source of competitive advantage as large insurers gather more and more data and models become more of a commodity.