Monthly Archives: April 2019
Twitter shut down Dom Hoffman’s app Vine, giving away the short-form video goldmine to China’s TikTok. Now a year and half since Hoffman announced he’d reimagine the app as V2 then scrapped that name, his follow-up to Vine called Byte has finally sent out the first 100 invites to its closed beta. Byte will let users record or upload short, looped vertical videos to what’s currently a reverse-chronological feed.
the byte beta we’ve been running with friends and family *feels* exactly like the vine friends and family beta, down to the weird but appealing randomness of the videos. that’ll change as we expand, but it’s a pretty good sign pic.twitter.com/rBbQrNtTJ7
— dom hofmann (@dhof) April 22, 2019
It will be a long uphill climb for Byte given TikTok’s massive popularity. But if it differentiates by focusing less on lip syncing and teen non-sense so it’s less alienating to an older audience, there might be room for a homegrown competitor in short-form video entertainment.
Hoffman tells TechCrunch that he’s emboldened by the off-the-cuff nature of the beta community, which he believes proves the app is compelling even before lots of creative and funny video makers join. He says his top priority is doing right by creators so they’ll be lined up to give Byte a shot when it officially launches even if they could get more views elsewhere.
For now, Hoffman plans to keep running beta tests, adding and subtracting features for a trial by fire to see what works and what’s unnecessary. The current version is just camera recordings with no uploads, and just a feed with Likes and comments but no account following. Upcoming iterations from his seven-person team will test video uploads and profiles.
One reassuring point is that Hoffman is well aware that TikTok’s epic rise has changed the landscape. He admits that Byte can’t win with the exact same playbook Vine did when it faced an open field, and it must bring something unique. Hoffman tells me he’s a big fan of TikTok, and sees it as one evolutionary step past Vine, but not in the same direction as his new app
Does the world need Vine back if TikTok already has over 500 million active users? We’ll soon find out of Hoffman can take a Byte of that market.
Ruthless copying is common in tech. Just ask Snapchat. However, it’s typically more conceptual than literal. But car API startup Smartcar claims that its competitor Otonomo copy-and-pasted Smartcar’s API documentation, allegedly plagiarizing it extensively to the point of including the original’s typos and randomly generated strings of code. It’s published a series of side-by-side screenshots detailing the supposed theft of its intellectual property.
Smartcar CEO Sahas Katta says “We do have evidence of several of their employees systemically using our product with behavior indicating they wanted to copy our product in both form and function.” Now a spokesperson for the startup tells me “We’ve filed a cease-and-desist letter, delivered to Otonomo this morning, that contains documented aspects of different breaches and violations.”
The accusations are troubling given Otonomo is not some inconsequential upstart. The Israel-based company has raised over $ 50 million since its founding in 2015, and its investors include auto parts giant Aptiv (formerly Delphi) and prestigious VC firm Bessemer Ventures Partners. Otonomo CMO Lisa Joy provided this statement in response to the allegations, noting it will investigate but is confident it acted with integrity:
Otonomo prides itself on providing a completely unique offering backed by our own intellectual property and patents. We take Smartcar’s questions seriously and are conducting an investigation, but we remain confident that our rigorous standards of integrity remain uncompromised. If our investigation reveals any issues, we will immediately take the necessary steps to address them.
Both startups are trying to build an API layer that connects data from cars with app developers so they can build products that can locate, unlock, or harness data from vehicles. The 20-person Mountain View-based Smartcar has raised $ 12 million from Andreessen Horowitz and NEA. A major deciding factor in who’ll win this market is which platform offers the best documentation that makes it easiest for developers to integrate the APIs.
“A few days ago, we came across Otonomo’s publicly available API documentation. As we read through it, we quickly realized that something was off. It looked familiar. Oddly familiar. That’s because we wrote it” Smartcar explains in its blog post. “We didn’t just find a few vague similarities to Smartcar’s documentation. Otonomo’s docs are a systematically written rip-off of ours – from the overall structure, right down to code samples and even typos.”
The screenshot above comparing API documentation from Smartcar on the left and Otonomo on the right appears to show Otonomo used nearly identical formatting and the exact same randomly generated sample identifier (highlighted) as Smartcar. Further examples flag seemingly identical code strings and snippets.
Otonomo has pulled down their docs.otonomo.io documentation website, but TechCrunch has reviewed an Archive.org Wayback Machine showing this Otonomo site as of April 5, 2019 featured sections that are identical to the documentation Smartcar published in August 2018. That includes Smartcar’s typo “it will returned here”, and its randomly generated sample code placeholder “”4a1b01e5-0497-417c-a30e-6df6ba33ba46” which both appear in the Wayback Machine copy of Otonomo’s docs. The typo was fixed in this version of Otonomo’s docs that’s still publicly available, but that code string remains.
“It would be a one in a quintillion chance of them happening to land on the same randomly generated string” Smartcar’s Katta tells TechCrunch.
Yet curiously, Otonomo’s CMO told TechCrunch that “The materials that [Smartcar] put on their post are all publicly accessible documentation, It’s all public domain content.” But that’s not true, Katta argues, given the definition of ‘public domain’ is content belonging to the public that’s uncopyrightable. “I would sure hope not, considering . . . we have proper copyright notices at the bottom. Our product is our intellectual property. Just like Twilio’s API documentation or Stripe’s, it is published and publicly available — and it is proprietary.”
Otonomo’s Lisa Joy noted that her startup is currently fundraising for its Series C, which reportedly already includes $ 10 million from South Korean energy and telecom holdings giant SK. “We’re in the middle of raising a round right now. That round is not done” she told me. But if Otonomo gets a reputation for allegedly copying its API docs, that could hurt its standing with developers and potentially endanger that funding round.
To be successful in a fast paced, ever changing, and competitive market, it is imperative for brands to think beyond just CPC, CPA and ROAS in the short term. Instead they should be equally aware of their brands position in the attention amongst their prospective customers, AND increasing the customer lifetime value of their existing customers.
Read more at PPCHero.com
If you want to build a robot that can fall hundreds of feet and be no worse the wear, legs are pretty much out of the question. The obvious answer, then, is a complex web of cable-actuated rods. Obvious to Squishy Robotics, anyway, whose robots look delicate but are in fact among the most durable out there.
The startup has been operating more or less in stealth mode, emerging publicly today onstage at our Robotics + AI Sessions event in Berkeley, Calif. It began, co-founder and CEO Alice Agogino told me, as a project connected to NASA Ames a few years back.
“The original idea was to have a robot that could be dropped from a spacecraft and survive the fall,” said Agogino. “But I could tell this tech had earthly applications.”
Her reason for thinking so was learning that first responders were losing their lives due to poor situational awareness in areas they were being deployed. It’s hard to tell without actually being right there that a toxic gas is lying close to the ground, or that there is a downed electrical line hidden under a fallen tree, and so on.
Robots are well-suited to this type of reconnaissance, but it’s a bit of a Catch-22: You have to get close to deploy a robot, but you need the robot there to get close enough in the first place. Unless, of course, you can somehow deploy the robot from the air. This is already done, but it’s rather clumsy: picture a wheeled bot floating down under a parachute, missing its mark by a hundred feet due to high winds or getting tangled in its own cords.
“We interviewed a number of first responders,” said Agogino. “They told us they want us to deploy ground sensors before they get there, to know what they’re getting into; then when they get there they want something to walk in front of them.”
Squishy’s solution can’t quite be dropped from orbit, as the original plan was for exploring Saturn’s moon Titan, but they can fall from 600 feet, and likely much more than that, and function perfectly well afterwards. It’s all because of the unique “tensegrity structure,” which looks like a game of pick-up-sticks crossed with cat’s cradle. (Only use the freshest references for you, reader.)
If it looks familiar, you’re probably thinking of the structures famously studied by Buckminster Fuller, and they’re related but quite different. This one had to be engineered not just to withstand great force from dropping, but to shift in such a way that it can walk or crawl along the ground and even climb low obstacles. That’s a nontrivial shift away from the buckyball and other geodesic types.
“We looked at lots of different tensegrity structures — there are an infinite number,” Agogino said. “It has six compressive elements, which are the bars, and 24 other elements, which are the cables or wires. But they could be shot out of a cannon and still protect the payload. And they’re so compliant, you could throw them at children, basically.” (That’s not the mission, obviously. But there are in fact children’s toys with tensegrity-type designs.)
Inside the bars are wires that can be pulled or slackened to cause to move the various points of contact with the ground, changing the center of gravity and causing the robot to roll or spin in the desired direction. A big part of the engineering work was making the tiny motors to control the cables, and then essentially inventing a method of locomotion for this strange shape.
“On the one hand it’s a relatively simple structure, but it’s complicated to control,” said Agogino. “To get from A to B there are any number of solutions, so you can just play around — we even had kids do it. But to do it quickly and accurately, we used machine learning and AI techniques to come up with an optimum technique. First we just created lots of motions and observed them. And from those we found patterns, different gaits. For instance if it has to squeeze between rocks, it has to change its shape to be able to do that.”
The mobile version would be semi-autonomous, meaning it would be controlled more or less directly but figure out on its own the best way to accomplish “go forward” or “go around this wall.” The payload can be customized to have various sensors and cameras, depending on the needs of the client — one being deployed at a chemical spill needs a different loadout than one dropping into a radioactive area, for instance.
To be clear, these things aren’t going to win in an all-out race against a Spot or a wheeled robot on unbroken pavement. But for one thing, those are built specifically for certain environments and there’s room for more all-purpose, adaptable types. And for another, neither one of those can be dropped from a helicopter and survive. In fact, almost no robots at all can.
“No one can do what we do,” Agogino preened. At a recent industry demo day where robot makers showed off air-drop models, “we were the only vendor that was able to do a successful drop.”
And although the tests only went up to a few hundred feet, there’s no reason that Squishy’s bots shouldn’t be able to be dropped from 1,000, or for that matter 50,000 feet up. They hit terminal velocity after a relatively short distance, meaning they’re hitting the ground as hard as they ever will, and working just fine afterwards. That has plenty of parties interested in what Squishy is selling.
The company is still extremely small and has very little funding: mainly a $ 500,000 grant from NASA and $ 225,000 from the National Science Foundation’s SBIR fund. But they’re also working from UC Berkeley’s Skydeck accelerator, which has already put them in touch with a variety of resources and entrepreneurs, and the upcoming May 14 demo day will put their unique robotics in front of hundreds of VCs eager to back the latest academic spin-offs.
You can keep up with the latest from the company at its website, or of course this one.
Email is one of the most direct ways for organizations to reach their audiences on a 1:1 basis, which means that it is a rich source of information for research and new product development.
Read more at PPCHero.com
Microsoft has never been shy about being acquisitive, and today it announced it’s buying Express Logic, a San Diego company that has developed a real-time operating system (RTOS) aimed at controlling the growing number of IoT devices in the world.
The companies did not share the purchase price.
Express Logic is not some wide-eyed, pie-in-the-sky startup. It has been around for 23 years, building (in its own words) “industrial-grade RTOS and middleware software solutions for embedded and IoT developers.” The company boasts some 6.2 billion (yes, billion) devices running its systems. That number did not escape Sam George, director of Azure IoT at Microsoft, but as he wrote in a blog post announcing the deal, there is a reason for this popularity.
“This widespread popularity is driven by demand for technology to support resource constrained environments, especially those that require safety and security,” George wrote.
Holger Mueller, an analyst with Constellation Research, says that market share also gives Microsoft instant platform credibility. “This is a key acquisition for Microsoft: on the strategy side Microsoft is showing it is serious with investing heavily into IoT, and on the product side it’s a key step to get into the operating system code of the popular RTOS,” Mueller told TechCrunch.
The beauty of Express Logic’s approach is that it can work in low-power and low-resource environments and offers a proven solution for a range or products. “Manufacturers building products across a range of categories — from low-capacity sensors like lightbulbs and temperature gauges to air conditioners, medical devices and network appliances — leverage the size, safety and security benefits of Express Logic solutions to achieve faster time to market,” George wrote.
Writing in a blog post to his customers announcing the deal, Express Logic CEO William E. Lamie, expressed optimism that the company can grow even further as part of the Microsoft family. “Effective immediately, our ThreadX RTOS and supporting software technology, as well as our talented engineering staff join Microsoft. This complements Microsoft’s existing premier security offering in the microcontroller space,” he wrote.
Microsoft is getting an established company with a proven product that can help it scale its Azure IoT business. The acquisition is part of a $ 5 billion investment in IoT the company announced last April that includes a number of Azure pieces, such as Azure Sphere, Azure Digital Twins, Azure IoT Edge, Azure Maps and Azure IoT Central.
“With this acquisition, we will unlock access to billions of new connected endpoints, grow the number of devices that can seamlessly connect to Azure and enable new intelligent capabilities. Express Logic’s ThreadX RTOS joins Microsoft’s growing support for IoT devices and is complementary with Azure Sphere, our premier security offering in the microcontroller space,” George wrote.
When you incorporate machine learning techniques to speed up SEO recovery, the results can be amazing.
This is the third and last installment from our series on using Python to speed SEO traffic recovery. In part one, I explained how our unique approach, that we call “winners vs losers” helps us quickly narrow down the pages losing traffic to find the main reason for the drop. In part two, we improved on our initial approach to manually group pages using regular expressions, which is very useful when you have sites with thousands or millions of pages, which is typically the case with ecommerce sites. In part three, we will learn something really exciting. We will learn to automatically group pages using machine learning.
As mentioned before, you can find the code used in part one, two and three in this Google Colab notebook.
Let’s get started.
URL matching vs content matching
When we grouped pages manually in part two, we benefited from the fact the URLs groups had clear patterns (collections, products, and the others) but it is often the case where there are no patterns in the URL. For example, Yahoo Stores’ sites use a flat URL structure with no directory paths. Our manual approach wouldn’t work in this case.
Fortunately, it is possible to group pages by their contents because most page templates have different content structures. They serve different user needs, so that needs to be the case.
How can we organize pages by their content? We can use DOM element selectors for this. We will specifically use XPaths.
For example, I can use the presence of a big product image to know the page is a product detail page. I can grab the product image address in the document (its XPath) by right-clicking on it in Chrome and choosing “Inspect,” then right-clicking to copy the XPath.
We can identify other page groups by finding page elements that are unique to them. However, note that while this would allow us to group Yahoo Store-type sites, it would still be a manual process to create the groups.
A scientist’s bottom-up approach
In order to group pages automatically, we need to use a statistical approach. In other words, we need to find patterns in the data that we can use to cluster similar pages together because they share similar statistics. This is a perfect problem for machine learning algorithms.
BloomReach, a digital experience platform vendor, shared their machine learning solution to this problem. To summarize it, they first manually selected cleaned features from the HTML tags like class IDs, CSS style sheet names, and the others. Then, they automatically grouped pages based on the presence and variability of these features. In their tests, they achieved around 90% accuracy, which is pretty good.
When you give problems like this to scientists and engineers with no domain expertise, they will generally come up with complicated, bottom-up solutions. The scientist will say, “Here is the data I have, let me try different computer science ideas I know until I find a good solution.”
One of the reasons I advocate practitioners learn programming is that you can start solving problems using your domain expertise and find shortcuts like the one I will share next.
Hamlet’s observation and a simpler solution
For most ecommerce sites, most page templates include images (and input elements), and those generally change in quantity and size.
I decided to test the quantity and size of images, and the number of input elements as my features set. We were able to achieve 97.5% accuracy in our tests. This is a much simpler and effective approach for this specific problem. All of this is possible because I didn’t start with the data I could access, but with a simpler domain-level observation.
I am not trying to say my approach is superior, as they have tested theirs in millions of pages and I’ve only tested this on a few thousand. My point is that as a practitioner you should learn this stuff so you can contribute your own expertise and creativity.
Now let’s get to the fun part and get to code some machine learning code in Python!
Collecting training data
We need training data to build a model. This training data needs to come pre-labeled with “correct” answers so that the model can learn from the correct answers and make its own predictions on unseen data.
In our case, as discussed above, we’ll use our intuition that most product pages have one or more large images on the page, and most category type pages have many smaller images on the page.
What’s more, product pages typically have more form elements than category pages (for filling in quantity, color, and more).
Unfortunately, crawling a web page for this data requires knowledge of web browser automation, and image manipulation, which are outside the scope of this post. Feel free to study this GitHub gist we put together to learn more.
Here we load the raw data already collected.
Each row of the form_counts data frame above corresponds to a single URL and provides a count of both form elements, and input elements contained on that page.
Meanwhile, in the img_counts data frame, each row corresponds to a single image from a particular page. Each image has an associated file size, height, and width. Pages are more than likely to have multiple images on each page, and so there are many rows corresponding to each URL.
It is often the case that HTML documents don’t include explicit image dimensions. We are using a little trick to compensate for this. We are capturing the size of the image files, which would be proportional to the multiplication of the width and the length of the images.
We want our image counts and image file sizes to be treated as categorical features, not numerical ones. When a numerical feature, say new visitors, increases it generally implies improvement, but we don’t want bigger images to imply improvement. A common technique to do this is called one-hot encoding.
Most site pages can have an arbitrary number of images. We are going to further process our dataset by bucketing images into 50 groups. This technique is called “binning”.
Here is what our processed data set looks like.
Adding ground truth labels
As we already have correct labels from our manual regex approach, we can use them to create the correct labels to feed the model.
We also need to split our dataset randomly into a training set and a test set. This allows us to train the machine learning model on one set of data, and test it on another set that it’s never seen before. We do this to prevent our model from simply “memorizing” the training data and doing terribly on new, unseen data. You can check it out at the link given below:
Model training and grid search
Finally, the good stuff!
All the steps above, the data collection and preparation, are generally the hardest part to code. The machine learning code is generally quite simple.
We’re using the well-known Scikitlearn python library to train a number of popular models using a bunch of standard hyperparameters (settings for fine-tuning a model). Scikitlearn will run through all of them to find the best one, we simply need to feed in the X variables (our feature engineering parameters above) and the Y variables (the correct labels) to each model, and perform the .fit() function and voila!
After running the grid search, we find our winning model to be the Linear SVM (0.974) and Logistic regression (0.968) coming at a close second. Even with such high accuracy, a machine learning model will make mistakes. If it doesn’t make any mistakes, then there is definitely something wrong with the code.
In order to understand where the model performs best and worst, we will use another useful machine learning tool, the confusion matrix.
When looking at a confusion matrix, focus on the diagonal squares. The counts there are correct predictions and the counts outside are failures. In the confusion matrix above we can quickly see that the model does really well-labeling products, but terribly labeling pages that are not product or categories. Intuitively, we can assume that such pages would not have consistent image usage.
Here is the code to put together the confusion matrix:
Finally, here is the code to plot the model evaluation:
Resources to learn more
You might be thinking that this is a lot of work to just tell page groups, and you are right!
Mirko Obkircher commented in my article for part two that there is a much simpler approach, which is to have your client set up a Google Analytics data layer with the page group type. Very smart recommendation, Mirko!
I am using this example for illustration purposes. What if the issue requires a deeper exploratory investigation? If you already started the analysis using Python, your creativity and knowledge are the only limits.
If you want to jump onto the machine learning bandwagon, here are some resources I recommend to learn more:
- Attend a Pydata event I got motivated to learn data science after attending the event they host in New York.
- Hands-On Introduction To Scikit-learn (sklearn)
- Scikit Learn Cheat Sheet
- Efficiently Searching Optimal Tuning Parameters
- If you are starting from scratch and want to learn fast, I’ve heard good things about Data Camp.
Got any tips or queries? Share it in the comments.
Hamlet Batista is the CEO and founder of RankSense, an agile SEO platform for online retailers and manufacturers. He can be found on Twitter @hamletbatista.
The post Using Python to recover SEO site traffic (Part three) appeared first on Search Engine Watch.
Editors Note: This article is part of a series that explores the world of growth marketing for founders. If you’ve worked with an amazing growth marketing agency, nominate them to be featured in our shortlist of top growth marketing agencies in tech.
Startups often set themselves back a year by hiring the wrong growth marketer.
This post shares a framework my marketing agency uses to source and vet high-potential growth candidates.
With it, early-stage startups can identify and attract a great first growth hire.
It’ll also help you avoid unintentionally hiring candidates who lack broad competency. Some marketers master 1-2 channels, but aren’t experts at much else. When hiring your first growth marketer, you should aim for a generalist.
This post covers two key areas:
- How I find growth candidates.
- How I identify which candidates are legitimately talented.
Great marketers are often founders
One interesting way to find great marketers is to look for great potential founders.
Let me explain. Privately, most great marketers admit that their motive for getting hired was to gain a couple years’ experience they could use to start their own company.
Don’t let that scare you. Leverage it: You can sidestep the competitive landscape for marketing talent by recruiting past founders whose startups have recently failed.
Why do this? Because great founders and great growth marketers are often one and the same. They’re multi-disciplinary executors, they take ownership and they’re passionate about product.
You see, a marketing role with sufficient autonomy mimics the role of a founder: In both, you hustle to acquire users and optimize your product to retain them. You’re working across growth, brand, product and data.
As a result, struggling founders wanting a break from the startup roller coaster often find transitioning to a growth marketing role to be a natural segue.
How do we find these high-potential candidates?
To find past founders, you could theoretically monitor the alumni lists of incubators like Y Combinator and Techstars to see which companies never succeeded. Then you can reach out to their first-time founders.
There are thousands of promising founders who’ve left a mark on the web. Their failure is not necessarily indicative of incompetence. My agency’s co-founders and directors, including myself, all failed at founding past companies.
How do I attract candidates?
To get potential founders interested in the day-to-day of your marketing role, offer them both breadth and autonomy:
- Let them be involved in many things.
- Let them be fully in charge of a few things.
Remember, recreate the experience of being a founder.
Further, vet their enthusiasm for your product, market and its product-channel fit:
- Product and market: Do their interests line up with how your product impacts its users? For example, do they care more about connecting people through social networks, or about solving productivity problems through SaaS? And which does your product line up with?
- Product-channel fit: Are they excited to run the acquisition channels that typically succeed in your market?
The latter is a little-understood but critically important requirement: Hire marketers who are interested in the channels your company actually needs.
Let’s illustrate this with a comparison between two hypothetical companies:
- A B2B enterprise SaaS app.
- An e-commerce company that sells mattresses.
Broadly speaking, the enterprise app will most likely succeed through the following customer acquisition channels: sales, offline networking, Facebook desktop ads and Google Search.
In contrast, the e-commerce company will most likely succeed through Instagram ads, Facebook mobile ads, Pinterest ads and Google Shopping ads.
We can narrow it even further: In practice, most companies only get one or two of their potential channels to work profitably and at scale.
Meaning, most companies have to develop deep expertise in just a couple of channels.
There are enterprise marketers who can run cold outreach campaigns on autopilot. But, many have neither the expertise nor the interest to run, say, Pinterest ads. So if you’ve determined Pinterest is a high-leverage ad channel for your business, you’d be mistaken to assume that an enterprise marketer’s cold outreach skills seamlessly translate to Pinterest ads.
Some channels take a year or longer to master. And mastering one channel doesn’t necessarily make you any better at the next. Pinterest, for example, relies on creative design. Cold email outreach relies on copywriting and account-based marketing.
(How do you identify which ad channels are most likely to work for your company? Read my Extra Crunch article for a breakdown.)
To summarize: To attract the right marketers, identify those who are interested in not only your product but also how your product is sold.
The founder-first approach I’ve shared is just one of many ways my agency recruits great marketers. The point is to remind you that great candidates are sometimes a small career pivot away from being your perfect hire. You don’t have to look in the typical places when your budget is tight and you want to hire someone with high, senior potential.
This is especially relevant for early-stage, bootstrapping startups.
If you have the foresight to recognize these high-potential candidates, you can hopefully hire both better and cheaper. Plus, you empower someone to level up their career.
Speaking of which, here are other ways to hire talent whose potential hasn’t been fully realized:
- Find deep specialists (e.g. Facebook Ads experts) and offer them an opportunity to learn complementary skills with a more open-ended, strategic role. (You can help train them with my growth guide.)
- Poach experienced junior marketers from a company in your space by offering senior roles.
- Hire candidates from top growth marketing schools.
Vetting growth marketers
If you don’t yet have a growth candidate to vet, you can stop reading here. Bookmark this and return when you do!
Now that you have a candidate, how do you assess whether they’re legitimately talented?
At Bell Curve, we ask our most promising leads to incrementally complete three projects:
- Create Facebook and Instagram ads to send traffic to our site. This showcases their low-level, tactical skills.
- Walk us through a methodology for optimizing our site’s conversion rate. This showcases their process-driven approach to generating growth ideas. Process is everything.
- Ideate and prioritize customer acquisition strategies for our company. This showcases their ability to prioritize high-leverage projects and see the big picture.
We allow a week to complete these projects. And we pay them market wage.
Here’s what we’re looking for when we assess their work.
Level 1: Basics
First — putting their work aside — we assess the dynamics of working with them. Are they:
- Competent: Can they follow instructions and understand nuance?
- Reliable: Will they hit deadlines without excuses?
- Communicative: Will they proactively clarify unclear things?
- Kind: Do they have social skills?
If they follow our instructions and do a decent job, they’re competent. If they hit our deadline, they’re probably reliable. If they ask good questions, they’re communicative.
And if we like talking to them, they’re kind.
Level 2: Capabilities
A level higher, we use these projects to assess their ability to contribute to the company:
- Do they have a process for generating and prioritizing good ideas?
- Did their process result in multiple worthwhile ad and landing page ideas? We’re assessing their process more so than their output. A great process leads to generating quality ideas forever.
- Resources are always limited. One of the most important jobs of a growth marketer is to ensure growth resources are focused on the right opportunities. I’m looking for a candidate that has a process for identifying, evaluating and prioritizing growth opportunities.
- Can they execute on those ideas?
- Did they create ads and propose A/B tests thoughtfully? Did they identify the most compelling value propositions, write copy enticingly and target audiences that make sense?
- Have they achieved mastery of 1-2 acquisition channels (ideally, the channels your company is dependent on to scale)? I don’t expect anyone to be an expert in all channels, but deep knowledge of at least a couple of channels is key for an early-stage startup making their first growth hire.
If you don’t have the in-house expertise to assess their growth skills, you can pay an experienced marketer to assess their work. It’ll cost you a couple hundred bucks, and give you peace of mind. Look on Upwork for someone, or ask a marketer at a friend’s company.
- If you’re an early-stage company with a tight budget, there are creative ways to source high-potential growth talent.
- Assess that talent on their product fit and market fit for your company. Do they actually want to work on the channels needed for your business to succeed?
- Give them a week-long sample project. Assess their ability to generate ideas and prioritize them.
“We want your followers to focus on what you share, not how many likes your posts get. During this test, only the person who shares a post will see the total number of likes it gets.” That’s how Instagram describes a seemingly small design change test with massive potential impact on users’ well-being.
Hiding Like counts could reduce herd mentality, where people just Like what’s already got tons of Likes. It could reduce the sense of competition on Instagram, since users won’t compare their own counts with those of more popular friends or superstar creators. And it could encourage creators to post what feels most authentic rather than trying to rack up Likes for everyone to see.
The design change test was spotted by Jane Manchun Wong, the prolific reverse-engineering expert and frequent TechCrunch tipster who has spotted tons of Instagram features before they’re officially confirmed or launched. Wong discovered the design change test in Instagram’s Android code and was able to generate the screenshots above.
You can see on the left that the Instagram feed post lacks a Like count, but still shows a few faces and a name of other people who’ve Liked it. Users are alerted that only they will see their post’s Like counts, and anyone else won’t. Many users delete posts that don’t immediately get “enough” Likes or post to their fake “Finstagram” accounts if they don’t think they’ll be proud of the hearts they collect. Hiding Like counts might get users posting more because they’ll be less self-conscious.
Instagram confirmed to TechCrunch that this design is an internal prototype that’s not visible to the public yet. A spokesperson told us: “We’re not testing this at the moment, but exploring ways to reduce pressure on Instagram is something we’re always thinking about.” Other features we’ve reported on in the same phase, such as video calling, soundtracks for Stories and the app’s time well spent dashboard, all went on to receive official launches.
Meanwhile, Wong has also recently spotted several other Instagram prototypes lurking in its Android code. Those include chat thread stickers for Direct messages, augmented reality filters for Direct Video calls, simultaneous co-watching of recommended videos through Direct, karaoke-style lyrics that appear synced to soundtracks in Stories, emoji reactions to feed posts and a shopping bag for commerce.
It appears there’s no plan to hide follower counts on user profiles, which are the true measure of popularity, but also serve a purpose of distinguishing great content creators and assessing their worth to marketers. Hiding Likes could just put more of a spotlight on follower and comment counts. And even if users don’t see Like counts, they still massively impact the feed’s ranking algorithm, so creators will still have to battle for them to be seen.
The change matches a growing belief that Like counts can be counter-productive or even harmful to users’ psyches. Instagram co-founder Kevin Systrom told me back in 2016 that getting away from the pressure of Like counts was one impetus for Instagram launching Stories. Last month, Twitter began testing a design that hides retweet counts behind an extra tap to similarly discourage inauthentic competition and herd mentality. And Snapchat has never shown Like counts or even follower counts, which has made it feel less stressful but also less useful for influencers.
Narcissism, envy spiraling and low self-image can all stem from staring at Like counts. They’re a constant reminder of the status hierarchies that have emerged from social networks. For many users, at some point it stopped being fun and started to feel more like working in the heart mines. If Instagram rolls out the feature, it could put the emphasis back on sharing art and self-expression, not trying to win some popularity contest.
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