Data Subscription Quotes

the data we see in this chapter shows racism isn’t a problem of outliers. It is pervasive. We’ve seen the same patterns repeated on three different sites, with different users and different experiences: men, women, free, subscription-only, casual, serious, “urban” demographics, and more “mainstream.” All told, the research set represents a large chunk of the young adults in this country, and the data uniformly shows non-blacks discount African American profiles. It’s not a problem caused by a small cluster of “ugly” black users or by a small group of unreformed racists throwing off an otherwise regular pattern.

But even when Facebook isn't deliberately exploiting its users, it is exploiting its users—its business model requires it. Even if you distance yourself from Facebook, you still live in the world that Facebook is shaping. Facebook, using our native narcissism and our desire to connect with other people, captured our attention and our behavioral data; it used this attention and data to manipulate our behavior, to the point that nearly half of America began relying on Facebook for news. Then, with the media both reliant on Facebook as a way of reaching readers and powerless against the platform's ability to suck up digital advertising revenue—it was like a paperboy who pocketed all the subscription money—Facebook bent the media's economic model to match its own practices: publications needed to capture attention quickly and consistently trigger high emotional responses to be seen at all. The result, in 2016, was an unending stream of Trump stories, both from the mainstream news and from the fringe outlets that were buoyed by Facebook's algorithm. What began as a way for Zuckerberg to harness collegiate misogyny and self-interest has become the fuel for our whole contemporary nightmare, for a world that fundamentally and systematically misrepresents human needs.

the new tariff system, prices were slashed across the board, including the one-time subscription charge (20-27%), wireless data and

These four factors—the access generation, light-switch reliability, delicious data, and the long tail—have led some of the world’s most successful companies and promising start-ups to shift their business models to a focus on subscriptions.

The facts are uncontroversial. Trump spent far less money on advertising than Clinton or his Republican opponents, yet he received a vastly greater volume of media coverage.20 The news business seemed strangely obsessed with this strange man, and lavished on him what may have been unprecedented levels of attention. The question is why. The answer will be apparent to anyone with eyes to see. Donald Trump is a peacock among the dull buzzards of American politics. The one discernible theme of his life has been the will to stand out: to attract all eyes in the room by being the loudest, most colorful, most aggressively intrusive person there. He has clearly succeeded to an astonishing degree. The data on media attention speaks to a world-class talent for self-promotion.21 Again, there can be no question that this allowed Trump to separate himself from his competitors in the Republican primaries. He appeared to be a very important person. Everyone on TV was talking about him.22 Who could say the same about Ted Cruz? Media people pumped the helium that elevated Donald Trump’s balloon, and they did so from naked self-interest. He represented high ratings and improved subscription numbers. Until the turn of the new millennium, the news media had controlled the information agenda. They could decide, on the basis of some elite standard, how much attention you deserved. In a fractured information environment, swept by massive waves of signal and noise, amid newspaper bankruptcies and many more TV news channels, every news provider approaches a story from the perspective of existential desperation. Trump understood the hunger, and knew how to feed the beast.

When you come down to it, alignment is about helping people understand what you want them to do. Most contributors will be motivated to ladder up to the top-line OKRs—assuming they know where to set the ladder. As our team got larger and more layered, we confronted new issues. One product manager was working on Premium, the enhanced subscription version of our app. Another focused on our API platform, to enable third parties like Fitbit to connect to MyFitnessPal and write data to it or applications on top of it. The third addressed our core login experience. All three had individual OKRs for what they hoped to accomplish—so far, so good. The problem was our shared engineering team, which got caught in the middle. The engineers weren’t aligned with the product managers’ objectives. They had their own infrastructure OKRs, to keep the plumbing going and the lights on. We assumed they could do it all—a big mistake. They got confused about what they should be working on, which could change without notice. (Sometimes it boiled down to which product manager yelled loudest.) As the engineers switched between projects from week to week, their efficiency dragged.

in IoT, there’s no such thing as a straightforward B-to-B or B-to-C market approach. You can have many different kinds of customers. The physical device is just an enabler. Your value lies in your IP, the usage data from your customer base, and your ability to trade information across multiple markets.

in the technology industry we would call The Life of Pablo a minimum viable product. That may sound like a pejorative term, but a minimum viable product is actually incredibly important. Only after it gets something out in the market can a business gather customer feedback and use this data to iterate and improve in a continuous deployment cycle. The MVP is a defining principle of cloud software development, and Kanye applied it to his music-writing process.

The data team would collect as much information as possible about potential voters, including age, race, ethnicity, voting history, and magazine subscriptions, among other things. Each person was given a score, ranging from zero to one hundred, in each of three categories: probability of voting, probability of voting for Hillary, and probability, if they were undecided, that they could be persuaded to vote for her. These scores determined which voters got contacted by the campaign and in which manner—a television spot, an ad on their favorite website, a knock on their door, or a piece of direct mail. “It’s a grayscale,” said a campaign aide familiar with the operation. “You start with the people who are the best targets and go down until you run out of resources.

customers were so used to that price point that they were skeptical of ClearFit’s value at $99/month. Ben says, “We don’t compete with job boards, we partner with them, but at the time it seemed reasonable to have a lower price point to garner attention.” Customers didn’t understand why they would pay a subscription fee for something that they would most likely use sporadically. “When a company needs to hire, they want to do it fast and they’re willing to invest at that moment in time,” says Ben. “Our customers are too small to have dedicated HR

As a result, the most important recommendation for organizations of all shapes and sizes moving forward is to anticipate worst case scenarios at a minimum. Even in cases where organizations cannot or will not make some of the operational changes recommended below, the exercise of focusing on nonsoftware areas of a given business can help identify under-realized or -appreciated assets within an organization. Particularly ones for whom the sale of software has been low effort, brainstorming about other potential revenue opportunities is unlikely to be time wasted. One vendor in the business intelligence and analytics space has privately acknowledged doing just this; based on current research and projecting current trends forward, it is in the process of building out a 10-year plan over which it assumes that the upfront licensing model will gradually approach zero revenue. In its place, the vendor plans to build out subscription and data-based revenue streams. Even if the plan ultimately proves to be unnecessary, the exercise has been enormously useful internally for the insight gained into its business.

The petition site bundles your name, email, and the insight about your interests (you just signed a petition about something you care about, remember?) and sells this information to data brokers, advertising agencies, subscription houses, and political campaigns.19 When you sign an online petition, the chances that you’ve just handed over your data for someone else to make money from them are higher than the chance that the intended legislative recipient of your petition will ever see it.

And when you finally discover your customers, it changes everything about your company. It affects every role. With a subscription model, suddenly your development team is spinning up new services based on usage data, as opposed in response to the loudest voices in the room.

Here’s a compelling quote from Scott Pezza of Blue Hill Research on the topic: If you currently sell products that collect some sort of data (or could be retrofitted to do so) and there is someone out in the world who would find that data valuable, IoT is a new revenue source for you. If you sell physical products that degrade or need to be serviced, IoT means you can offer remote monitoring services, or preventative maintenance services—new revenue streams. In the alternative, you can increase the attractiveness (and value) of those products by giving customers the ability to conduct that monitoring and maintenance themselves. If you sell services that could be expanded if you only had access to more data, it’s new money. And if you sell technology to help sense conditions, facilitate secure communications, conduct analysis, manage service provisioning and billing, or forecast and plan revenue—this market is going to need you.

Data has become an asset, and nobody has more customer information than a subscription business. Traditional companies are launching entire subscription offerings just for the data they provide.

Like many subscription models, Amazon Prime is a Trojan horse that is expanding the list of products consumers are willing to buy from Amazon and giving the eggheads in Seattle a mountain of customer data to sift through.

Ownership is dead. Access is the new imperative. International Data Corporation (IDC) predicts that by 2020, 50 percent of the world’s largest enterprises will see the majority of their business depend on their ability to create digitally enhanced products, services, and experiences. Focusing on services over products is also a sound business strategy. Zuora’s Subscription Economy Index, which you’ll find at the end of this book, shows that subscription-based companies are growing eight times faster than the S&P 500 and five times faster than US retail sales.

If you were in the railroad industry, would you be more interested in the business of laying the tracks or delivering the freight? One element is discrete and transactional (how many new rail lines do you really need?); the other represents ongoing value. A new management team at Cisco decided to go all-in on services, which by definition meant subscriptions. But how do you sell routers and switches on a subscription basis? By focusing on the data inside all that hardware—the freight, not the tracks. Cisco’s latest set of Catalyst hardware comes embedded with machine learning and an analytics software platform that helps companies solve huge inefficiencies by reducing network provisioning times, preventing security breaches, and minimizing operating expenses.