Seaborn is an extension of MatPlotLib.PyPlot that integrates statistical plotting into the module. Seaborn provides a hi

A few months before I went on a much-needed vacation to Mexico City in 2018, I tried Duolingo, the ubiquitous language-learning app, to gently push my Spanish skills into something resembling respectability. But I generally dislike phone games, which meant I didn’t love the app, which gamifies language education through exercises and achievements. I fell off, as many of us fledgling second-language learners do.

If one language used by modern developers has the best ecosystem for data analysis and visualization, it is most likely Python. Python has tools that are not only great for statistical observations, but are also great for making the language act more statistical in itself. Furthermore, in terms of visualization, few languages rival the amazing modules that the Python programming languages gives to Python-based scientists. That being said, these are some modules I really like — but there are a lot more, so it would certainly be interesting to know more about those in the responses!

Prior to Edgar, our engineers had to sift through a mountain of metadata and logs pulled from various Netflix microservices in order to understand a specific streaming failure experienced by any of our members. Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. The process started with manual pull of member account information that was part of the session. The next step was to put all puzzle pieces together and hope the resulting picture would help resolve the member issue. We needed to increase engineering productivity via distributed request tracing.

While guilt can be effective for nonprofits and charities, making people feel guilty about products, apps, and games can be less effective, says Raji Srinivasan, a professor of marketing at the University of Texas at Austin’s McCombs School of Business. “Guilt can encourage negative attitudes and even anger. For example, guilt appeals by commercial marketers can backfire because consumers may be turned off by feeling guilty,” she says. “I would venture that because Duolingo is a commercial marketer, the use of guilt appeals may backfire.”

The frequency of these emails and notifications can also be a turnoff. The 2014 review found that repeated attempts to induce guilt are likely to piss people off to the point where they become resistant to the message. Consumers can become angry with the product, form negative associations with it, rationalize their behavior, and in the end disregard the marketing message. This is partly what happened to me: Duolingo’s subtle escalation, culminating with the message that “these reminders don’t seem to be working,” frustrated and insulted me — and made me feel ashamed about my poor Spanish skills.

The Duolingo users I talked to for this piece told me that when notifications work on them, it is generally despite the guilt appeal, not because of it. It’s the fact that they’re getting reminders at all that they’re hooked — the negative emotional tenor didn’t matter much to them.

This is an example of a question our on-call engineers need to answer to help resolve a member issue — which is difficult when troubleshooting distributed systems. Investigating a video streaming failure consists of inspecting all aspects of a member account. In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. Now let’s look at how we designed the tracing infrastructure that powers Edgar.

If one language used by modern developers has the best ecosystem for data analysis and visualization, it is most likely Python. Python has tools that are not only great for statistical observations, but are also great for making the language act more statistical in itself. Furthermore, in terms of visualization, few languages rival the amazing modules that the Python programming languages gives to Python-based scientists. That being said, these are some modules I really like — but there are a lot more, so it would certainly be interesting to know more about those in the responses!

“They’re really annoying and don’t serve their purpose. They don’t put me in the mood to learn,” says Alicia Kennedy, a writer based in San Juan, Puerto Rico. “They make it feel like homework rather than an enjoyable way to pass the time. The manipulation makes it feel like work. It’s so fun when I do open it up, but the notifications don’t make me want to.”

“I don’t think I really care about Duo’s feelings,” says Melissa Kravitz Hoeffner, a journalist in Brooklyn. “Mostly, Duolingo is something I want to be doing [but] neglect and like to be reminded about.”

Duolingo did not take it well. After ignoring the app’s rude emails (“Learning Spanish requires daily practice. Practice now?” “We haven’t seen you in a while.” “Keep Duo happy!”) and failing to log into the game for about a month, the app sent me one last, passive-aggressive-as-hell notification.

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The more you simply ask people how they feel or what they think in a given situation, the better your model for them becomes. And that in turn means your guesses at how they’re thinking and feeling will be more accurate. So we have a bit of a paradox: If you want to be able to read people’s minds more effectively, you need to stop trying to read their minds and just ask instead.

A 2014 review of the existing research on guilt appeal found that successful guilt appeals stress an element of threat present if someone doesn’t act in a specific way and frame the individual as responsible for someone else’s misery — but, again, the threat needs to feel credible and trustworthy to the consumer. So, for example, Duolingo insisting that I’m making the Duo owl sad is the company’s attempt to create a stress threat. By not engaging with the app, I’m hurting Duo’s feelings. But that turns me off, because the owl is not real and does not actually have hurt feelings as it goes on to learn Spanish without me.

Well, jeez. I made that little green owl cry. I failed at making myself into a more educated citizen and traveler and didn’t even respond to Duo the Multilingual Owl’s attempts to get me back in the game. It legitimately made me feel kind of bad, like I was lazy and unable to keep up with my goals. Duo, the pathetic, almost menacing owl, is so liberal with its guilt trips that it’s even the subject of a meme.

Duolingo’s attempts to reengage me relied on what’s known as “guilt appeal” marketing tactics. It was a risk, and it failed. In many cases, the guilt appeal can work, especially when employed by nonprofits looking to elicit sympathy, and donations, for their cause. But it’s hazardous territory for most companies. When a nonprofit uses the guilt appeal, it’s probably for a good cause; morally and ethically, the side effect of making people feel bad is worth it. But when an advertisement tries to guilt consumers into buying something that ultimately exists to enrich shareholders, a variety of adverse effects could turn people off, from eliciting suspicion and cynicism to quite literally causing shame. In this case, consumers might feel bad not just about what they’ve done (“I didn’t complete my Spanish lesson”; “I made Duo the Owl sad”), but about who they are (“I’m a failure because I’m not keeping up with my language lessons”).