Mariam Hachem's Profile
On your latter point, that is what I thought as well. But according to a recent podcast by the CEO, he was suggesting that branded players are trying to get on their platform. Would be interesting to understand why they’re using Vention as a channel?
Ali, your article was an extremely enjoyable read! I am going to put forth an extreme perspective and suggest that open innovation might not be as impactful in SoundCloud’s situation given how entrenched Spotify and Apple Music have become in today’s industry. I think incumbents do have the advantage where music consumers are a little more sticky than in other industries.
What I do find fascinating however is that the solutions to the music industry often focus on the very tail end of the value chain, with folks like Spotify, Apple Music focusing on being music distributors. SoundCloud could have a competitive edge in that it nurtures indie artists, to help them attack a different part of the music industry: making music sound professional.
Another really interesting startup is Landr (https://www.landr.com/en/) who is tackling the music “mastery” (or music engineering to make your music professional sounding) using machine learning and artificial intelligence. This space, which represents a big painpoint for small indie artists who do not want to/ cannot afford to pay high label or music studio fees has remained largely undisrupted. SoundCloud, with its already semi established brand name and position in the market with its relationship to rising musicians could potentially address other parts along the value funnel of music making, aside from distribution, which is concentrated with competition from established players like Apple Music and Spotify.
I appreciate the nuance you brought to the answer Ti, teasing out customer preferences in the luxury segment vs non-luxury. I definitely agree that this idea of perfect shoe fit is a phenomena that only affects and/or will appeal to a select part of the population. However, you could argue that 3D printing will be revolutionary from a cost perspective, allowing cheaper models to be produced (customizable or not), furthering consumer habits of buying more shoes per year as shoes decrease in price!
Very interesting, Irene! As you pointed out, having a diverse set of clients giving ideas could lead to the production of too many products or worse, alienating customers whose ideas are not heard as mentioned by the author.
Piling on that, how would Glossier think about its product development process as they think of international expansion? Will they have to change their product line to suit the needs of customers in different geographical markets? What implications on scalabiltiy and resources required from Glossier would this have? Alternatively, if Glossier decides not to adopt the same crowdsourcing approach in the new geographies, i.e. offering the same products (which is what I think they did when they expanded to Canada), what does this signal to its clients who are outside their core geographical market?
I think the solution could be to continue to receive feedback (globally if possible) and to create dedicated “client” teams who can have more agency over final products, integrating them further into the community. This could be an rotating role (to give opportunity to many glossier fans) and different across geographies, creating an opportunity to engage customers further (giving it a competitive advantage over Sephora and others), while also abstracting the blame from Glossier, where the final decisions seemingly come from these groups.
Very cool perspective! Thinking about it from a different perspective, where you want to unleash the full power machine learning, I think having checks and balances in your system to ensure customer satisfaction and engagement levels are still high would work to ensure that your connection with customers is not lost. I think Glossier, whose leaders consider themselves a technology company, would be more amenable to this, given eventually they will want to converge towards machine learning for making sense of the massive amounts of customer data.
The bigger question for me relates to Jade’s initial question, is how to parse out the data and to understand which data is credible and should be acted upon? I.e. looking at customer surveys vs actual customer purchasing behavior. How do you assess credibility of these data sets and prioritize the data segments when making decisions? Do you still need human decision making skills for this or can machine learning solve this as well?
I am so happy someone spoke about this. I think Square, Amazon, and other companies with large access to data are prime to capitalize on this information and offer lending (and other banking) products to businesses (especially in small and medium business).
In terms of whether data is enough, I think this data is Square’s and Amazon’s competitive advantage over traditional banks. In fact, many banks across the US (and globally) are scrambling to figure out ways to gain access to this data and be able to expand the scope of their lending services.
However, the real hurdles I believe will be 2: (1) in changing the mindset of SMBs to take up these lending projects (which currently they will since they’re being offered at good rates) but the second and more important hurdle is (2) when the banking institutions catch up and are able to access this data (by buying it, by aggregating it through potential consortia with other banks, etc) and begin offering better lending products, how will that affect Square and Amazon’s positions in these financial products? Or will Square’s speed to market make the banking industry’s foray into this irrelevant?
Exciting world to be in regardless!!