Very interesting post and discussion! And exceptional title. The slow digital transformation of many consulting firms is particularly unforgiveable given some of the low hanging fruit in the internal management of such firms. For example, the staffing process at most firms still works mostly through a “central planning” approach, with a whole staffing/ professional development team allocating resources (and lots of side deals happening through informal networks. Firms like Google (Project Chameleon) have pioneered algorithm based staffing markets/ approaches where the need for a central staffing staff is reduced, and outcomes are theoretically more efficient (e.g., more information is shared, employees skills are more tightly matched to project needs, recommendation engines help inform rankings). Just one example of the many internal processes that would benefit from digital transformation at the big consultancies (helping them to walk the walk as well as talk the talk).
Very interesting post and discussion! While you mention the role management consultancies are currently playing in guiding firms through digital transformation, I think it is a little underplayed — my understanding is that this will be a big business area for many years to come, with much of it unaddressable by AI tools. As we saw in several of our cases there are deep change management issues associated with digital transformation (e.g., helping the Flashion pricing staff to accept alogorithmic output/ finding new staff that do). My hypothesis is that management teams will look to consultants to support them on these types of organizational change management efforts over the next several years. Hopefully the consultancies will be able to leverage the insights from these efforts to achieve transformational change within their own firms. Some very interesting points above regarding talent as well. Consultancies are in a scramble to hire/ acquihire data scientist types — while this may not eliminate the need for generalist hires (who will still often be needed to act as translators) it could certainly reduce that need. It will be very interesting to see how the industry evolves over the next several years and who comes out on top!
Very interesting post, Hans. Looking at some of these sports analytics case studies, two of the big differentiators between the teams that are using analytics well (vs. just burning money on analysts) seem to be 1) clearly, effectively communicating insights to the coaching staff/ players (who often are not deep experts), and 2) delivering actionable insights in real time. Seems like the Eagles are nailing both of these, as well as creating a culture of data-based decision making/ statistical familiarity among the players. It would be interesting to know how often the coaches follow the algorithmic guidance vs. when they override it based on feel/ intuition (e.g., a sense that the QB is lagging) and what the outcomes are in each scenario; also interesting would be to know whether they have tried to codify some of that feel/ intuition and incorporate it into the algorithms.
Great post Sean! Netflix’s creation of proprietary data (e.g., through the 1000 tags mentioned above) is an interesting complement to its customer generated data and opens up really interesting possibilities for machine learning enabled analyses — it is fascinating that they have used these data to identify and capitalize on the micro-genres and micro-segments mentioned. The outcomes for their creative activities (80% vs traditional 35%) are incredible! It would be very interesting to know how this compares to other viewing platforms trying to get into the content creation business (e.g., Amazon) and what the competitive response from traditional content providers will likely be. Given the amount of data Netflix has generated, and is generating everyday, is it possible that they have they built a sustainable moat in terms of their data and algorithms?
Super interesting Eliza — I have only had low-tech interactions with Sweetgreen (which maybe makes be bougie but not as millenial?), but I will now consider downloading the app. The applications for the ingredient add/ drop information are particularly compelling — in a sense Sweetgreen is using the app to crowdsource improvements to its salads while avoiding some of the pitfalls we discussed in class (e.g., people are not going to submit crazy combinations if they then have to pay for and eat them). The opportunity to right size quantities of supplies ordered is also interesting. I am skeptical on how blockchain tech could be of any practical use to Sweetgreen or its suppliers (other then potentially adding some buzz/ tech sophistication halo effect), but we will see if it helps them!
Very interesting post, Liza! I think this highlights a very effective extrinsic motivator, which we have not discussed much to date in class: the opportunity to speak on a level footing with/ rub shoulders with respected/ famous individuals. The example of early posts from Zuckerberg and Andreesen and how they got the flywheel spinning for the site is very compelling. It is interesting to think about the counterfactual — if D’Angelo did not have such access/ such a powerful network, would Quora be among the graveyard of other dead Q&A sites. Their other innovations (e.g., the upvoting) maybe could have carried them through.
Also interesting is their effort to navigate monetization — it seems like a recurring theme across posts is how do you cash in without angering all of your contributors? Trick journey to navigate!
Such an inspiring story! I now many people who want to contribute to the refugees’ plight, but who have struggled to find something actionable. This is very impressive. A couple of concerns:
1) Apps like Google Translate are not currently good enough to serve this function (per your post), but they are improving dramatically, quickly (e.g., able to live translate japanese character on your phone screen) — how much longer will humans need to be in the loop for translation (especially for basic text etc)?
2) Regarding your monetization model, I think it is likely the best route, but I’d advise to walk it very carefully. Thoughtful communication will need to be conducted with the translators (who to this point have viewed there time as a gift, but may either a) want some cut of the profits, or b) disengage if the organization starts to charge end users fees). Given the fact that many other organizations out to make a profit are able to get crowdsourcing support without paying contributors it seems likely that this app will be able to do the same. However, navigating the change thoughtfully will be critical to not harm the contributor base.
Such a great story — up there with Colbert trying to get a module of the space station after him (he eventually got a treadmill within said module). Both of your prescriptions are sound. The first as you’ve pointed out, risks creative possibilities missed; the second is a sound practice being used by several actors (e.g., Lay’s). To Eliza’s point, it is critical to communicate these non-democratic screens/ gates to the crowd upfront, so they do not feel betrayed when a) management unilaterally nix the “Boaty McBoatface” suggestions, or b) curate suggestions so that the crowd can’t even see the momentum behind such a suggestion. Such communication will likely cost some engagement, but at the same time may make engagement more deliberate.
Great post Juan! The push to become more of a discovery platform rather than a search and play tool seems like it could hold a lot of promise for Spotify — I would guess that they have the user base (and thus the data) to presumably build better algorithms than their direct competitors at this point (although that may change with Amazon’s Alexa and other smart speakers prioritizing their own streaming services). It would be interesting to explore whether Spotify could sell these algorithmic insights back to the labels to help them further optimize their music offering to what certain segments are demanding (at an even finer level than the traditional gross sales by song model e.g., Spotify knows when in a song players are hitting skip for instance). Scary to be up against Amazon and Apple in anything though!
Meant to say Zach 🙂
Very interesting post Jesse! The strategy around providing a video game simulation to pull people into the platform play is really interesting, especially since this is a non-traditional activity (i.e., relative to football, baseball few people have grown up doing it). It would be interesting to see how these participative marketing approaches have worked out for other platforms (I’d hypothesize they could create pretty sticky users). Also interesting that the simlulators become another way to deliver ad content and monetize users. I’m curious about perspectives on the terminal scale of the platform — could it be as big as football/ baseball one day? Star Wars suggests maybe!
Very interesting post Juan! The third dynamic of affecting platforms in the dating space (i.e., that their incentives are not necessarily aligned with user success) is extremely interesting and not one that I had thought about much before. On the multi-homing front, perhaps some of their plays around media will make users stickier/ less likely to multi-home. By being first, presumably they have better trained algorithms for matching as well that could reduce desire to multi-home. The acquisition by a Chinese firm is interesting — I wonder if there are/ will be any negative effects associated with Chinese LGBTQ rights track record.
Great post! What an interesting example of technology in search of a problem instead of sitting down and thinking about whether disruption is really needed/ desired. Feels like they could have benefited from some basic design thinking approaches (e.g., early prototype testing with customers — my hypothesis would be that customers actually value the experience of dealing with real fruit and vegetables (at essentially the same per drink price and a much lower initial capex). I wonder if they could have designed the experience to more closely replicate that (perhaps with less of the mess). Also an amazing example of how the financing ecosystem can pile on without a lot of hypothesis-testing and validation.
Very interesting post! In addition to Zelle it feels like there are several other start-ups trying to capture value in this space (e.g., Splitwise). Tough when you have to navigate competitive threats from both well-capitalized incumbents (e.g., the partnership of the big banks) and scrappy start-ups. I am particularly struck by the valuation expansion between the Braintree and Paypal acquisitions — hopefully the founders kept reasonable equity in the initial transaction — drives home the concept of value capture not just from the company perspective, but also from the entrepreneurs perspective.
Great post! Your thesis that Amazon will keep the GO tech in house is interestingly supported by their previous experience with KIVA, however I wonder if your time horizon is long enough — over the 10-15 year horizon it may start to make sense for Amazon to license out GO (and even maybe its warehousing robotics approach). If other companies start to invest in similar tech and there is a reasonable probability that they will independently develop it, then it may be better for Amazon to sell said companies watered-down versions of its products (maybe as a managed services approach) to gain license fees.
Your favorability ratings data is fascinating — in the midst of techlash it is really interesting to see that bad sentiments are so unevenly spread. I wonder if this is better marketing on Amazon’s part or just fundamental differences in the value created by Amazon relative to peers?