The Lego Ideas platform is a very efficient way for Lego to better understand its customer’s desires, and be able to respond to them more nimbly. I wonder, however, what the review process for a top voted Idea looks like – how does Lego weed out ideas that may be voted to the top by the crowd, but are not feasible to produce, for instance? I’m also curious about how they have designed the review system so that it does not become a bottleneck in the overall process.
Very interesting example – I enjoyed reading about this company. Your post certainly highlights a potential problem common to platforms that rely on crowd-sourced content – that of monitoring quality and relevance in an efficient and cost effective way. It does seem that while one option here would have been more manual content curation, this definitely seems to be at odds with efficiency of the platform, and its ability to push content out quickly. While companies like Facebook and Twitter do rely on manual curation – outsourced to contractors in the Philippines, for instance – as part of their content moderation to keep nudity out of posts, this approach would have proved ineffective for SketchFactor, since it seems the type of content people might be manually moderating would not lend itself to the task – ie moderating whether a guy was selling drugs on corner x is nothing like making the much clearer call of “nudity” or “no nudity.” More manual curation would create a huge bottleneck in the platform, and the quality control mechanism therefore needs to somehow be automated – either by equipping and relying on the crowds themselves to perform this quality control function, or by limiting what can be posted in the first place.
Thanks for sharing your thoughts on this topic – great to read this side of the argument as a complement to some of the posts that focus on Netflix and Amazon and their approach to content creation. I think pointing out the importance of the human element and judgment in artistic creation, as you allude to, is difficult to replace entirely with data and algorithms – both in artistic creation, and in reviewing art. I think this type of lesson can serve as a tale of caution and extend to the danger of over-reliance on data and algorithms in other fields too – no matter how much data we might have, our ability to properly interpret and use it will always need to be paired with the right amount of human judgment.
Fitbit’s push to get such wide adoption comes back to where its ultimate value capture model will be – in the large and ongoing/real-time biometric database it can potentially create, assuming it can solve customer retention issues, and beat out competitors. The consumer facing value creation and hardware value capture piece is just a drop in the ocean compared to what the potential value creation/capture could be with a large biometric dataset. As you point out, many others are interested in building up this kind of dataset, from smartphones, to the Apple watch, etc. Apple has even filed for patents around “mood-based advertising” (http://techcrunch.com/2014/01/23/apple-patent-explores-mood-based-ad-targeting/), giving us a preview of just one of the types of ways (scary or not – up to you to decide, but I certainly find that something about “mood-based advertising” doesn’t sit well with me) in which companies with large biometric datasets will look to capture value from advertisers.
The pending questions about Fitbit’s future, for the time being, come back to stickiness and beating competitors, as you discuss.
I think Netflix got the balance right between art and algorithm, when it comes to a show such as House of Cards, in particular. Amazon, for instance, which forayed into producing its own content using a heavily data driven approach, was far off the mark in achieving this balance between art and data, and all but one of its shows have flopped after a few episodes. A few questions come to mind regarding Netflix’s use of data, and the use of data in artistic content creation in general –
How do they balance data with human judgment in the creative process? I too, like Salem, worry about what the overemphasis of data will do to the artistic process. What will become of truly innovative art, if producing of new content depends on determining which existing ‘variables’ of content are most pleasing to the average viewer? A lot of art that later becomes popularized in our culture was not necessarily popular with the masses at the beginning. Take the film “Birman,” for instance. I doubt this type of film would have been produced using an algorithmic approach, as it wouldn’t prove “popular” enough when evaluated against an algorithm’s recipe for success. A film like Birman, however, does end up having a tremendous impact on popular culture, and artistic production, because of the opinion of just a few “expert” human reviewers, rather than the data from the masses. This “expert” opinion may not align with what the masses may have initially thought, but serves to influence and inform culture in an important way, as their “review” trickles down. I wonder what will happen to the more unusual/”out there” types of art if content generation were to come to rely too heavily on data and algorithms, at the expense of the human touch.
“When the BuzzFeed algorithm determines a piece of content on one of these sites to be “going viral” it triggers the story and flows it into rotation on a set of reserved units on BuzzFeed.Com
“Going viral” is a proprietary measure, but we can at least say that it takes into account total traffic to a story in relation to the portion of that traffic that comes from sharing platforms such as Twitter, Facebook, Stumbleupon, Digg, and any other external sources that. (Traffic driven to a story from a publisher’s home page is not counted as viral because that audience is already on the publisher’s site.)” This is what the Buzzed site says about its algorithm, and I have to agree with Donald and Cal that based on this, there is a problem at the data level, the algorithm being calibrated the wrong way.
The secondary problem with sites like this is that they believe that content is commoditized, when it really is not – high quality and original content is still the currency without which aggregator sites cannot exist. Facebook’s NewsFeed also faces similar challenges, in that its algorithm – calibrated to try and please such a large audience – ends up showing lots of BuzzFeed worthy content.
Just to clarify – this post has nothing to do with catering to finer tastes, but with the problem of anyone being able to understand what the “standards” of another reviewer are. Yelp cannot truly continue to satisfy the majority of people, because of reviews’ lack of relevance to the individual reading them, having to do with the reasons listed in the post. It’s not about the “1%” that you bring up – the 1% in the post is about the fact that the majority of people have to rely on an average only coming from ~1% of Yelp users, who actually regularly post reviews. Beyond this, there is the problem of positive social influence bias, which means reviewers are more prone to following positive reviews that have already been given with their own positive review, (and far more reluctant to follow a negative review with their own negative review) – leading to a herd mentality, and ~75% of reviews being between 3.5-4 stars. If this shockingly high % of restaurants have a 3.5-4 star review, something doesn’t add up, and this star rating ceases to be indicative of much. I disagree that Yelp is actually an average of average tastes, because the regular reviews only come from 1% of people. If one thinks about who this type of person is (i.e. the “regular Yelp reviewer”), I don’t think they represent the average person – the average person doesn’t spend time writing a review there, unless they’ve had an extremely poor or extremely good experience, perhaps prompting them to write a one-time review.
The example of UrbanDaddy simply has to do with the filtering interface shown in the post. Just as UrbanDaddy prompts users to input some additional criteria about themselves and their context, Yelp – or any other crowd-sourced review site such as a TripAdvisor etc – might benefit from adding this extra granularity in creating reviews, and for users in filtering to find reviews more relevant to them. For example, if I can select criteria that help define my taste when looking for reviews (such as “I’m a hipster”/”I love a quiet place”/etc etc), and filter through the reviews of State Bird Provisions and be matched with reviews by people who share one or more of those characteristics, I’m a lot better off than sifting through the 75% 3.5-4 star reviews, without any context or knowledge of these people’s standards relative to my own.
What a great and creative post! Really enjoyed reading it. I think, as Asaf and you point out, there could be an interesting service if a ride-sharing service partnered with a dating app, limiting rides to parties of two/the people on the ‘ride date.’ To echo some of the concerns about how conducive ride-sharing in general would be on its own for dating once the service gains more penetration, I think a great example to look at is ride-sharing that has existed for decades in certain countries. I’m thinking of Iran in particular, where ride sharing has been around for decades, to allow people to affordably get around in cities such as Tehran, which are amongst the most congested in the world. The service developed out of pure necessity for another method of transport, priced only slightly higher than bus and metro, thus making it a viable option for the majority of people. Today, with ride sharing being very mature and with high ridership, people actually view this form of getting around as a form of public transport. Ride sharing is an institutionalized service with taxis in Tehran, for instance, and it is price-controlled – because of the maturity of this service, utilization per taxi is very high: the majority of rides have the full number of passengers the taxi can physically fit. Due to this, people either tend to be quiet during the ride, or discuss topics such as the weather, and sports. I know this type of ride sharing has existed for a long time with taxis in many other countries too – seeing the type of behavior that comes with high utilization per car can be an interesting way to consider how we might predict riders’ behaviours shifting with US ride-sharing services such as UberPool as this space matures, and utilization/car goes up. A fun read on taxi ride sharing in Tehran, in case anyone is interested! http://www.theguardian.com/world/iran-blog/2015/may/05/tehran-iran-taxis-transport
Great example of how quality, and subsidizing users made a difference in user adoption, and ultimately beating out other platforms with similar functionality. I think another important strategy Instagram had when it launched was to generate a lot of hype around the app by getting influential people to be amongst the first ‘posters.,’ as well as getting influencers to spread the word about the app. I think getting the word out in this way made a huge difference, in addition to capitalizing on Facebook’s and Foursquare’s already existing networks by integrating with those platforms.
Very interesting post. As you mention, having a local strategy makes a lot of sense for rolling out this business, and achieving network effects is tremendously important. Reading about how SpotHero is getting over the “chicken and egg” inertia reminded me of stories I heard from friends who tried out Luxe in San Francisco over the summer. Unlike SpotHero’s success in getting over the initial ‘inertia,’ it seems they haven’t amassed enough supply of valet parkers, with many people complaining that their valet parker took so long to arrive, they may as well have parked themselves. Getting that piece right at the beginning is very important, as a shortage on one side of the market can damage the service’s reputation, and potentially create a negative feedback loop.
‘Terrifying’ indeed – that was also my reaction when I came across this company’s demo. Thank you for sharing your reactions so frankly and eloquently – this is certainly part of the discussion I hoped to elicit by writing about this topic.
In response to people like us who are concerned about the serious implications of such apps being launched to the public, this company (or others pursuing similar work) argue that the information they have built their search engines on is ‘already out there.’ While it’s true that my photo, linked to my name may be online already, by my own volition or through someone else posting this content to the Internet without my consent, what they are offering to the public should in no means be qualified as “out there already,” or be defensible through this argument. Yes, someone today who knows my name (information A), can search for a photo or some other information pertaining to me (information B) based on this. However, changing the order in which these actions can be executed – that is, someone can use “information B” to find “information A” – translates into a radically different action overall. I would encourage people to think in terms of the profound difference in the order of these paired “A” and “B” actions – and why this means their argument that “the information is already out there” is not really the salient point to be making here.
Senator Franken (D-Minn) has publicly raised concerns about the launch of FacialNetwork’s apps – if you interested, you can read more here, including his open letter to FacialNetwork CEO Kevin Tussy – http://www.franken.senate.gov/?p=press_release&id=2699
Amongt other things, Franken tells Tussy that his company “has a duty to act as a responsible corporate citizen in deploying this technology” even though there are currently no federal laws related to facial recognition. Technological innovation and deployment in such areas are sometimes increasingly outpacing society’s ability to full grasp and react to its ramifications for us, and it’s important to consider appropriate applications, or “winners,” which will not “diminish our humanity”, as you so aptly put it, and have the potential to lead us to a Brave New World, left unchecked.
Very interesting post – the value of “skipping the line” in the Starbucks case makes me think of the tremendous value that can be created and captured in retail (or other) experiences in general by eliminating or minimizing lines – value for the customer, whose experience greatly improves, and for the retailer’s – or other entity’s – operations, who can have resources and physical space freed up from lines. An example of a place it would be great to see the type of thing Starbucks has done, for example, would be in clothing retailers that typically have heavy foot traffic, and both long dressing room and checkout lines. It will be interesting how other industries seek to create value creation/capture models around the elimination of lines.
Great post on a very though provoking topic. The part that is indeed quite scary is consumers being able to opt-in or out of such services and data collection, or even being aware of the possibility to do so, as you point out. The collection of voice-and behavioral based information to infer moods such as anger, fear, happiness, etc… here makes me think of other companies we can see planning ahead to monetize biometric data for “mood-based” applications. Examples would include Apple and Microsoft, which have both filed for patents (Microsoft’s can be viewed here: http://1.usa.gov/1UQTISg and Apple’s here: http://1.usa.gov/1ek2oFJ ) for “mood-based advertising delivery” systems that rely on collecting biometric data (think of an iWatch a few generations down the road, for instance, and what this could mean for you as a consumer if advertisers were to know your mood and deliver ads based on it…) It will be interesting to see how consumers’ rights are protected when technologies like these are used for “mood collection/inference” for such ends as customer support, or marketing.
Very interesting topic to read about and consider – your post made me quite curious about this so-called ‘smart’ luggage, and what it can offer. Taking a look at features such as a built-in scale, and the 10,000 mAh battery, these are definitely features which add convenience to a traveler’s routine. I question, however, how unique these features are (certainly nothing that can be IP protected), and how quickly luggage manufacturers themselves will simply integrate these features, as you mention.
In terms of some of the other features they market to potential customers, such as providing flight reminders, tracking how far you have gone/”trip data”, I would have to agree with DIGITite that they seem to simply duplicate information we can record on a smartphone, which we know users of this luggage will have, since it is necessary to operate the luggage. The same can be said of the value of this type of data for those that BlueSmart seeks to capture value from, who already track this data through other means.
In terms of the value of not losing luggage, the ability to track does offer a value-add for a niche of customers, however I wonder if this niche will be enough for BlueSmart. Last year, only ~6 bags/1000 were lost worldwide in the airline industry, 95%+ of which were recovered to travelers, and only ~3 bags/1000 were lost in the US. Perhaps they can capture some value from the airlines themselves, which lose billions each year as a result of dealing with lost luggage?
Finally, this topic sparks my interest in the future of the “Internet of Things” in general. In a world when consumers are increasingly tracked left and right, and anonymity and privacy are fast dying, will there not be increasing value to “not being located,” to having our whereabouts be unknown at times? Will consumers not tire of being viewed as “data sets” by which they become the targets of optimized ads, and become more discerning about what they receive in exchange for access to a piece of their identities? Sandy Pentland’s work with the MIT Media Lab suggests a provocative line of thought which may become quite powerful if legislation on personal data rights catches up to the technology of a “connected world of objects” – “Big data and the “internet of things”—in which everyday objects can send and receive data—promise revolutionary change to management and society. But their success rests on an assumption: that all the data being generated by Internet companies and devices scattered across the planet belongs to the organizations collecting it. What if it doesn’t?
Alex “Sandy” Pentland, the Toshiba Professor of Media Arts and Sciences at MIT, suggests that companies don’t own the data, and that without rules defining who does, consumers will revolt, regulators will swoop down, and the internet of things will fail to reach its potential. To avoid this, Pentland has proposed a set of principles and practices to define the ownership of data and control its flow. He calls it the New Deal on Data.” (https://hbr.org/2014/11/with-big-data-comes-big-responsibility)
Thanks for posting on this topic and getting us talking about it!