Every company is becoming a data company. Data-Powered Apps delves into how product teams are infusing insights into applications and services to build products that will delight users and stand the test of time.
In philosophy, a “sine qua non” is something without which the phenomenon under consideration cannot exist. For modern apps, that “something” is data and analytics. No matter what a company does, a brilliant app concept alone is insufficient. You have to deftly integrate data and analytics into your product to succeed.
Whatever your audience, your users are getting more and more used to seeing data and analytics infused throughout apps, products, and services of all kinds. We’ll dig into ways companies can use data and analytics to succeed in the modern app marketplace and look at some now-extinct players that might have thrived with the right data in their platforms.
Sentiment analysis in customer messages
Yik Yak was an anonymous chat app that looked promising initially but failed because of problems that could have been resolved with data and analytics. What made Yik Yak popular was the exotic feature that enabled members to chat anonymously with others in the same geographic vicinity. Unfortunately, that feature was also the cause of the app’s demise: Yik Yak capitalized as a startup with about $75 million and grew to a value of $400 million before uncontrolled cyberbullying ruined its reputation. After Yik Yak’s name was spoiled as a result of abusive chat, the company could not sell ads on its platform, meaning it could no longer monetize its innovative concept.
How could Yik Yak have used data and analytics to avert disaster? Luma Health showed how message data can be analyzed for mood and meaning by using AI/ML methods on a data lake of chat messages. Yik Yak could have tagged message content with the originating IP address and then quickly blocked messages from that IP after abusive language was detected. This hindsight can now become foresight for other enterprising companies.
The benefits of leveraging collective data
Color Labs was another successful startup whose failure could have been avoided with the right analytics. Although the company’s investment in AI and convolutional neural networks (CNNs) may have been significant, in retrospect, an innovative use of these technologies on the right data could have given it a better shot at survival. The basic service model behind Color Labs’ app was that users would share images and then see images from other users who were posting pictures in the same vicinity (a media-based counterpart to Yik Yak’s concept). The app failed in part for reasons that new dating apps often fail: Needing to go live with a million users on day one! Color Labs’ users joined up only to find little or nothing posted in their vicinity, giving them little incentive to post and share. and leaving them feeling alone in an empty room. The company ultimately folded.
How could data insights have solved this problem for Color Labs? Leveraging the right collective datasets with CNNs could have identified images tagged to a geographical place already freely shared on the internet. Those images could be used to populate the app and get the user engagement ball rolling. Using CNNs in that way is expensive but justifiable if it means keeping the company afloat long enough to reach profitability. New dating app startups actually use a similar trick — purchasing a database of names and pictures and then filling in the blanks to create an artificial set of matches to temporarily satisfy new subscribers’ cravings for instant gratification (one such database is marketed as “50,000 profiles.”) The gamble is that new subscribers will remain hopeful long enough for a number of subscribers to join up and validate their existence. Color Labs could have benefited from existing media with a much lower cost in terms of ethical compromise as well.
Forecasting and modeling business costs
Shyp was an ingenious service app that failed for a number of reasons, but one of those reasons could have been fixed easily with data insights. The basic innovation of Shyp was to package an item for you and then ship it using a standard service like FedEx. The company’s shortcut, which turned out to be a business model error, was to charge a fixed rate of $5 for packaging. Whether the item to ship was a mountain bike or a keychain, the flat rate of $5 for packaging was a hole in Shyp’s hull, one that sank the company in short order.
Shyp’s mistake could have been resolved cleverly by using the wealth of existing data about object volume, weight, fragility, temperature sensitivity, and other factors to create an intelligent packaging price calculator. Such a database could even have included local variations in the price of packing materials such as foam peanuts, tape, boxes, and bubble wrap, and have presented the calculation at time of payment. Flat fees are attractive and can be used as loss leaders when trying to gather new customers or differentiate oneself in a crowded market, but if you aren’t Amazon, then you need to square the circle somehow. A data-driven algorithm for shipping prices (or whatever your service is) doesn’t just make good business sense — it can even be a selling point!
Social vs. personal networks: Sentiment analysis in data
“Path” fashioned itself an anti-Facebook: According to its founder, former Facebook developer Dave Morin, Path was a “personal network,” not a social network, where people could share “the story of their lives with their closest friends and family.” And for a moment it almost looked like Path might allow people to do just that. The startup boasted a whopping $500 million value with steadfast investor confidence that lasted all the way until it faded into obscurity, ultimately being purchased by a Korean tech firm and then removed from app stores. Path intended to enforce its mission to provide personal networks of true friends by limiting each user’s friend count to 50. The friend limit was perceived as detrimental to Path’s success at a time when Facebook users often had thousands of friends, but this alone did not account for the apparent irrelevance of the novel app. What was the missing piece? Data analysis.
Path could have sustained itself as a stalwart alternative to Facebook users disenchanted with the endless mill of likes and heart emojis. The key would have lain in sentiment analysis of user message content: By using natural language processing methods to distinguish close friends from distant acquaintances, Path could have offered its users an innovative platform for knowing who their “real friends” were.
Data analytics and the competitive future
We have seen that startup apps based on ingenious concepts and with funding levels over $100 million failed for a variety of reasons that could have been ameliorated or averted with savvy, transformative uses of data, analytics, and insights. One of the original e-hailing taxi companies failed for no other reason than the founding designers’ lack of awareness that Yellow cab drivers in New York at that time did not carry mobile phones!
Data is not only useful for calculating and forecasting the future, it’s a must-have for your app. Every company with a novel concept to unleash into the market must face the reality, as these companies did, that a good idea alone won’t guarantee an app’s success. Innovative use of data in concert with that idea is something that no modern app can survive without.
Jack Cieslak is a 10-year veteran of the tech world. He’s written for Amazon, CB Insights, and others, on topics ranging from ecommerce and VC investments to crazy product launches and top-secret startup projects.