June 21, 2018

Instagram launches IGTV app for creators, 1-hour video uploads

Instagram  is ready to compete head-on with YouTube. Today at a flashy event in San Francisco, the company announced it will begin allowing users to upload videos up to one hour in length, up from the previous one-minute limit. And to house the new longer-form videos from content creators and the general public, Instagram is launching IGTV. Accessible from a button inside the Instagram homescreen, as well as a standalone app, IGTV will spotlight popular videos from Instagram celebrities.
The launch confirms TechCrunch’s scoops over the past month outlining the features and potential of IGTV that we said would arrive today, following the WSJ’s report that Instagram would offer videos up to an hour in length.
“It’s time for video to move forward, and evolve,” said Instagram CEO Kevin Systrom onstage at the event. “IGTV is for watching long-from videos from your favorite creators.” Just before he took the stage, Instagram’s business blog outed details of IGTV.

How IGTV Works

IGTV will let anyone be a creator, not just big-name celebrities. People will be able to upload vertical videos through Instagram’s app or the web. Everyone except smaller and new accounts will be able to upload hour-long videos immediately, with that option expanding to everyone eventually.
The IGTV app will be available globally on iOS and Androidsometime today, as well as in the Instagram app through a TV shaped button above Stories. “We made it a dedicated app so you can tap on it and enjoy video without all the distraction,” Systrom explained.

In IGTV’s dedicated app or its in-Instagram experience, viewers will be able to swipe through a variety of longer-form videos, or swipe up to visit a Browse tab of personally recommended videos, popular videos, creators they’re following and the option to continue watching previously started videos. Users will also get callouts from the IGTV button alerting them to new content.

IGTV will also let creators develop Instagram Channels full of their different videos that people can subscribe to. Creators will be able to put links in the description of their videos to drive traffic elsewhere.

No Commercials In IGTV…Yet

“There’s no ads in IGTV today,” says Systrom, but he says it’s “obviously a very reasonable place [for ads] to end up.” He explained that since creators are investing a lot of time into IGTV videos, he wants to make that sustainable by offering them a way to monetize in the future. Instagram isn’t paying any creators directly for IGTV videos either, like Facebook did to jump-start its flopped Facebook Watch video hub.

With 1 billion users on Instagram, IGTV could be popular with creators not only trying to earn money but grow their audience. Instagram is expected to build out a monetization option for IGTV creators, potentially including ad revenue shares. The big user base could also attract advertisers. eMarketer already expects Instagram to earn $5.48 billion in U.S. ad revenue in 2018. Facebook shareholders loved the sound of more premium ad inventory that businesses crave as they shift spend away from television. Facebook’s share price is up over 2.2 percent today to nearly $202.
Instagram has evolved far beyond the initial simplicity of just filtering and sharing photos. When it launched, mobile networks, screens and cameras weren’t ready for longer-form video, and neither were users. As more families cut the cord or teens ignore television altogether, though, Instagram has an opportunity to become the TV of mobile. YouTube may always have a wider breadth of content, but through curation of creators and publishers’ video content, Instagram could become the reliable place to watch something great on the small screen.

Written by: Josh Constine - TechCrunch
Images provided by TechCrunch
& Jakob Owens - Unsplash

June 19, 2018

Data Science, Machine Learning, & Deep Learning, Simplified!

Over the past few years, the internet has been inundated with thousands of articles proclaiming the new age of data and how it interacts with and drives artificial intelligence (AI). As a result, the three terms data science, machine learning, and deep learning have transitioned almost overnight from buzzwords to standard vocabulary, and have become synonymous with the direction that society is moving in. But how many can really enunciate the differences between those sacred terms?

Data Science

In the olden days, it was called statistics. But now it has morphed and grown, like Thanos’s chin, until it became ‘data science’. Today, top-flight universities offer degrees in it and everyone is calling it a career path that will never fail.
The first recorded reference to ‘data science’ was by Peter Nauer, a Danish computer pioneer, at the White Hart Tavern in 1960 when he used it to replace the term ‘computer science’. Actually, the White Hart Tavern part is not true, he was probably in his office talking to a grad student, but it still makes for a good story.
One of the more modern references to it was by C. F. Jeff Wu in his 1997 lecture at the University of Michigan “Statistics — Data Science?”. In Dr Wu’s universe, data science moved somewhat past traditional statistics, using the trio of data collection, modelling and analysis, and significantly, decision making.
Turing Award winner, Jim Gray, looked at it in 2007 as a ‘fourth paradigm of science’, augmenting the normal scientific methods by including the dimension of ‘data-driven analysis’. But it was not until 2012 when the Harvard Business Review published their article “Data Scientist: The Sexiest Job of the 21st Century” that things really began to take off.
In the end, it is hard to succinctly and completely describe what a data scientist does because it does cross over into the artificial intelligence area but it certainly starts with some hardcore statistical concepts and a really solid knowledge of the programing language Python which has many functions that make statistical analysis easier.
At the same time, it goes beyond statistics. Instead of just collecting and analyzing data using tried and true statistical methods, the data scientists ask the all-important question: What if?
What if we looked at the data from a different perspective? What if we extended the modeling that our test data has given us in a number of independent ways? What if we let a machine analyse the data with no rules to guide it? What will it show in terms of relationships?
In the end, it is the input generated by the data scientists that will feed the two tools they will use to use the data to make decisions; machine learning and deep learning.

Machine Learning

Wikipedia says it best:
“Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn . . . without being explicitly programmed.”
Much of the data intelligence work prior to this required extensive programming to help the computer account for every eventuality. And, of course, those efforts were doomed from the beginning because not every eventuality can ever be considered.
Machine learning is different. In this case, a framework is set up that feeds statistically relevant information into the computer and lets it make decisions, lets it learn, from that data, rather than from programmer instructions. In other words, machine learning lets the computer discover what is probably true and what is probably false on its own based on data provided.
The more data fed in and the higher the quality of that data, the more the machine will learn. And when it is done learning, data can be fed in and decisions are spat out by the machine.
A relatively simple example of a machine learning system is the spam filter attached to email inboxes. By looking at the various words that make up the email, and evaluating the probability of a given word or group of words being a danger, it is able to make a decision as to what should be filtered out and what should be left in.
Machine learning is still in elementary school. That is, as with the spam filter, we have all seen cases where legitimate emails are marked as spam and things that should be are not. But most of the time, it is close enough to the target. And as more and more spam filters start using true machine learning, that should improve.

Deep Learning

Of all the words encountered in this article, none is more forbidding, more laden with the unknown, more likely to send a terrifying chill down the length of one’s back, than deep learning. Is this indeed what will unleash SkyNet on this unsuspecting world? There are some people who would swear it will. But it will not. Or at least not yet. Not for a few years.
Again, Wikipedia nails it by calling deep learning:
“part of a broader family of machine learning based on learning data representations as opposed to task-specific algorithms.”
Deep learning is part of machine learning, but it specifically ignores anything that is specific, that is task oriented. It is not used to define a system that will tell one what city in each state is the capital. Or the largest. Or the most fun.
Other names for deep learning are deep neural networks, deep belief networks, and recurrent neural networks. And it has been applied to things as freaky weird as computer vision, speech recognition, natural language processing, audio recognition, social network filtering (something that is way overdue), etc. Broad things for a broad approach.
As noted above, deep learning is a subset of machine learning, a subset that is focused on two things.
The first is patterns rather than rules or facts. The machines are taught to look for patterns or even just portions of a pattern.
The second is mimicking the behaviour of neurons, particularly those in the neocortex of the brain.
What difference does this make? One of the hallmarks of neurons in the human brain is that none of them works alone. There is not, for example, a neuron that is responsible for recognising a dog versus a cat. Instead many neurons will work together, each one perhaps only responding to one very small part of the patterns the brain has for ‘dog’ and ‘cat’. But working together they are able to reach a consensus on whether it is a dog or cat.
That is what deep learning is working on. It requires tremendous computing power, plus an in-depth understanding of how the brain works, something that is still being studied and an arena where knowledge is constantly growing and changing.

In the End

Data science is based in statistics but data scientists go beyond just linear regressions. Remember? They are sexy. The new data science goes beyond analysis to prediction, and to look at data in ways that the traditional techniques do not. And the main reason for this is not a breakthrough in the mathematics but the adoption of powerful computers to quickly run analysis that would have been impractical in the past.
Machine learning is about using data to let the computer learn on its own. Sometimes this learning is directed, as when rules are included or other parameters are set which guide how the machine makes its decisions, or undirected when the machine digs into the data and see what it can find. It is all about separating intelligence from programming. In the machine learning world, the data is the teacher, not the coder.
Deep learning is a subset of machine learning. It is based on data, but it uses a particular algorithm type, one that acts similarly to the neurons in a human brain. Will it result in a positronic brain and the three laws of robotics? Hard to say. But it is the future.
Written by: David Shirey
View Article Here

June 15, 2018

Here is to why ICO changes Digital Marketing — and 10 cases to prove it

It wasn’t a while ago that blockchain was far from being a word-on-the-street technology. A few years ago, only a handful of tech enthusiasts talked about cryptocurrencies, fewer knew what ICO even is, and even less could explain what it’s really about.
Last year things changed. In 2017, investors raised 1.5B through ICOs, and public cryptocurrency market cap has jumped over $170B. Token sales become a regular investment practice and a topic of major debates.
Is ICO trustworthy? What about the long-term consequences? With all these bubble-or-not discussions, it’s easy to get a few things overlooked.
  1. The purpose of tokenization is not just about getting profits. Token sales give a possibility to reward users for their resources and time invested on the platform. What’s better, received tokens can be used right away on the platform, leading to long relationships with customers. Users become regular contributors to the platform, basically creating an enthusiastic community.
  2. Blockchain is not a separate industry. It’s not living on its own but constantly impacting other industries, changing market position and influencing customer demands. ICOs are entering every field — from dental services with solutions like Dentacoin to space exploration, as in the Spacechain case.
In 2018, ICO is surely not to be ignored in any field. Obviously, there are some fraud cases but they can’t undermine the success of existing crowdsale campaigns, launched in different industries. Marketing automation is no exception.

10 ICO Answers to Current Digital Marketing Issues

Let’s face it, there are problems in digital marketing. Geographical and financial limitations make cooperation difficult, SMEs owners are constantly pulled down by big corporations, and the question of data protection is now more pressing than it ever was. Creating automation platforms, developers and marketers jump through these hurdles again and again — all the time.
One way or another, tokenization solves these issues.
By purchasing and exchanging tokens, based on conditions set by smart contract, marketers and business owners cooperate towards marketing automation. Decentralized systems allow to create monopoly-free platforms, opened to small businesses and direct cooperation.
Seems too good to be true? Take a look at 10 successful crowdsale cases in digital marketing automation that prove otherwise.

1. Inda Hash

Currently raised: $42 000 000
IndaHash — the influencer marketing platform that unites 300 000 influencers in 1 078 campaigns for global brands like Ikea, Milka, Gillette etc. Its mission is to tokenize the entire influencer industry to simplify the cooperation between brands, influencers, and their followers. How so?
Inda Hash uses IndaCoin circulation as their cryptoeconomic model allowing influencers and worldwide corporations to cooperate on a global scale. This allows avoiding all pain in the neck with money transfers, currencies, and exchanges, while also increasing the engagement between users and companies. All the tokens are eventually spent inside the platform, with users trading IndaCoins on services.

2. Basic Attention Token

Currently raised: $35 000 000
Digital advertising is overrun by middlemen, trackers, and fraud — that’s the problem Basic Attention Token solves with distributed system and tokenization. Their mission is simple and global at the same time: to change advertising which is currently run by big corporations that are not shy to use spying methods and data stealing mechanisms. Digital ads are creeping people out, and it’s time to do something about it.

With blockchain and tokenization, Basic Attention Token proves it possible. Decentralized systems allow SMEs owners to take charge of their own ad campaigns, advertising providers, on the other hand, cut Google mediation and cooperate directly with businesses. To get financial support, the company launched crowdsale campaign, selling BAT — an Ethereum-based token. The sale was beyond successful finalizing in 35 million dollars raised in less than 30 seconds.

3. Jet8

Currently raised: $32 706 262
Co2Co Engagement, user-generated content optimization, data mining, and user data exchange — these are just a few digital marketing fields covered by Jet8, a full stack tokenized mobile engagement software. The application analyzes mobile behavior, collects insights, sets the campaign, and measures its efficiency. Basically, it creates a special ecosystem for mobile marketing and user-brand cooperation.
Why tokenization? Internal cryptocurrency simplifies the payment process and gives users and business owners the full freedom on all the collected insights. With distributed systems, it’s possible to control and manage the gathered data, not depending on platform’s creators.

4. Thrive

Currently raised: $26,000,000
ICOs is successfully adopted in all digital marketing fields, including advertising marketplaces. Thrive, a community-based advertising platform where business owners buy advertising space, and not just any advertising space, but reviewed and approved by consumers. The platform analyzes its users’ behavior to create comprehensive insights about campaign efficiency. It’s a win-win: advertisers finally have tested space for their ads, potential customers get paid for just browsing the Internet.
How does tokenization make this possible? Imagine doing a project like this with currencies in place. Decreasing the payment speed, losing profits during the exchange, and limiting the market geographically — these would be just a few of many possible complications if platform creators used anything other than tokenization. It’s blockchain that made the entire innovation possible.

5. Presearch

Currently raised: $16 000 000
From its foundation to first CNBC appearance, Presearch, a decentralized search engine, became a major sensation. With their ambitious goal to fight Google’s monopoly as a search engine, they sold 155m tokens and signed the deal with top crypto wallets.
What’s even more interesting, Presearch team fully described the token sale experience, with its ups and downsides. As they wrapped it up, the process of token sale might be challenging, especially when it comes to such large scale, yet it’s an amazing method to not just generate revenue but also to build an amazing enthusiastic community of project supporters.

6. PM7

Currently raised: $12 500 000
PM7 — is decentralized affiliate marketing solution, created to cut the distance between creators and brands, eliminating the need of marketing, advertising, and PR agencies. The platform enables direct cooperation, with no time and money spent on mediator’s assistance.
Started in 2016, PM7 has already sold 9 million tokens and united 1 312 307 users. Blockchain and tokenization enabled the creators to create a decentralized project marketplace where creators can make cutting-edge projects, brands — cut off the meditation, and investors — join the groundbreaking platform.

7. AdHive

Currently raised: $12 000 000
AI-powered influencer marketing system where advertisers and influencers get rewarded for their work with ADH tokens. They are also used for all the transactions and support any financial interaction. See how this works? The platform creators sell internal tokens for flat currencies, therefore, raising revenues with crowdsale.
The statistics of the platform is fairly impressive: 5.2k buyers, 25k people whitelisted, 104.3M of currently circulated supplies. With successful tokenization, the system became the leader of influencer marketing and native video advertising.

8. Wolk

Currently raised: $11,500,000
Wolk, a decentralized data exchange company, uses blockchain to break the data monopoly of big corporations. All experiences in the digital world are basically controlled by Google, Facebook, and Amazon, leaving small businesses dependable on those Big Brothers. With decentralized data exchange, information goes directly to SMEs owners, no mediation of big corporations involved.
In 2017, the platform started its token sale, and closed it in September, when 11 216 735 tokens were sold. As company told on their Medium page, they are working on improved SWARM DB proof of concept for download and a demo decentralized application. Considering that the sale took two months, this is an impressive achievement and another case of tokenization done right.

9. SocialMedia.market

Currently raised: $8 484 606
With tokenization, marketers move further and create not just a cooperation space but a free marketplace for influencers and brands. This way SocialMedia.market, the platform for influencer marketing, manages to connect advertisers and bloggers worldwide with no entry barrier for monetization. Basically, users get simply register on the platform, interest advertisers, and get paid for that.
As the platform proves, the tokenization is a great solution for building open markets and creating conditions for the free cooperation. With tokens in place, it’s easy to universalize the results of influencer campaigns and monitor their effects. Unlike currencies, tokens are more stable and therefore, more objective for metrics.

10. Lydian

Currently raised: $11 315 843
Lydian™ has developed first blockchain Digital Marketing Cloud — a technology stack, created to gather customer experiences and draw comprehensive insights which are later used by marketers for targeting potential clients throughout their entire purchase journey. Adding Artificial Intelligence to the mix, Lydian™ created MonaLisa, a technology for precise targeting of the consumer audience.
Even though DaVinci11™’s Marketing Cloud is already used by already Fortune 1000 companies worldwide, the creators decided to expand their area of expertise by focusing on blockchain innovation. By opening a token sale, the company supports the development of cutting-edge blockchain services, such as the Whisper Network protocol that fights the raising army of ad blockers. By making advertising more personalized and much less annoying, it’s possible to create a decentralized advertising space that won’t be blocked by customers.
To summarize
Tokenization is a strong method of generating profits for blockchain companies in any industry, marketing in particular. ICO success is not a myth or a bubble, it’s proven by real products with real revenue. It’s amazing how many possibilities crowsale hides for marketing automation, and how rewarding the investments turn out to be.
It’s not just about generating profits though. Revenue is just a part of what tokenization gives. There is much more to ICO than we are used to think. If previous cases are any indication, it’s clear that tokenization is a great way to build a strong community of enthusiastic supporters and oppose monopolies, especially in digital marketing where big companies hold the most resources. It’s a chance to erase geographical limits and build long-term cooperations as we’ve seen on influence marketing projects.
What is going to be the next sensational ICO product in digital marketing? So far, it’s apparent that Triggmine has a good chance of becoming next ‘killer project’ in token sales and blockchain overall. We already received numerous investor requests which make us believe that Triggmine crowdsale might be just around the corner.
Written and Shared from Triggmine
View Article and Follow Triggmine's Blog Here

June 12, 2018

11 Power Sales Words to Use in your Sales Emails

What you say on your sales emails is as crucial as what you’re actually selling. An email is significantly different from a call or a face-to-face meeting. With emails, you can easily be ignored.
Keep your email off the spam folder by catching your reader’s attention. Use sales language that is powerful and can convert your audience into buyers.
1. Benefits
When it comes to sales words, benefits trump specifications and features, almost always. Using ‘benefits’ shifts the focus from impersonal details that may or may not interest your readers. You only have a few seconds before they click the delete button.
So, appeal to their needs. Sell your product’s benefits by talking about how it can improve people’s lives.

2. Value

Engage Selling Solutions owner Colleen Francis once claimed in her book Nonstop Sales Boom that customers “…only care about value and achieving their objectives.”
So, on email, talk about value, not price. Value is a powerful word. Price is objective; value is variable per person. When you use the word, you reach out to each person’s sensibilities. They think and assess for themselves. They might even click on your link to learn more about what you offer.
As Warren Buffet, American business magnate and philanthropist, said: “Price is what you pay. Value is what you get.”

3. Show

As sales words go, ‘show’ is a positive interactive word that you can use to imply your willingness to communicate with your audience. It works better than ‘learn,’ which – regardless of its positive meaning – implies one-sidedness. When you say ‘show,’ you are reaching out and telling your reader that you’re there to help.
Instead of saying “learn more about our product,” say: “Let me show you how our product adds value to your life.” It makes people feel good and signals what could be the start of a good business relationship.

4. You

Sales is not about you, your quota or that sales team leaderboard you so want to top. It is about the client. Without their willingness to listen, you won’t be able to take further steps in making the sale. And, people always pay attention when you start talking about them.
Even on email, your choice of pronouns communicate what you value more. So, use ‘you’ instead than ‘I.’ Because, in the end, you want your reader to think about themselves and the value that your product brings into their life.

5. Because

Harvard University professor and social psychologist Ellen Langer once did a study on the impact of using ‘because’ in phrasing statements. She went out to see if people would let her cut in line using these lines, alternately:

“Excuse me, I have five pages. May I use the Xerox machine?”
“Excuse me, I have five pages. May I use the Xerox machine because I have to make some copies?”
“Excuse me, I have five pages. May I use the Xerox machine because I’m in a rush?”

The last two sentences that used ‘because’ gained around 90% affirmatives. People responded more positively when a reason was offered.
So, when you make a statement – let’s say a claim about your product – add in a ‘because’ and then explain why. People become more open when they try to understand.

6. And

Usually, sales people use ‘but’ when trying to counter an objection. This is a negative word because ‘but,’ as is, signals an opposition. Your prospect knows that you are about to argue with an opposing statement. This is likely to put them in a defensive.
Instead, when you need to append anything to an original statement, use ‘and.’ This is an inclusive word. When used, regardless of how you use it, you sound like you agree.
Take, for instance, these examples by Seamus Brown, a sales trainer:

“I see that you only have a budget of $50,000, but let me tell you why our system costs $100,000.”
“I see that you only have a budget of $50,000, and let me tell you why our system costs $100,000.”

Not only does the second statement sound more positive. It also acknowledges the client’s reservations and offers an explanation to counter the client’s statement, without sounding negative.

7. Power Words/ Emotions

When you sell, you not only appeal to a person’s needs and values. You also appeal to their emotions. Strong feelings, such as joy, fear and distress, come with certain predictable responses.
If your sales strategy requires appealing to these emotions, then you should use words that could evoke them. A good guide is Jon Morrow’s online article called “317 Power Words That’ll Instantly Make You a Better Writer.” The words he put together are known to evoke certain human emotions. Of course, choose your words wisely. Be clear about what you want to achieve first.

8. Free

Ali Abdullah, an entrepreneur, once claimed that: “Two of the most powerful words in the English language are ‘free’ and ‘sex.’ While the latter is a bit racy, the former presents an opportunity all brands should capitalize on.”
‘Sex’ has no place in your sales email – unless, of course, it’s what you’re selling and it’s legal. ‘Free,’ on the other hand, offers many possibilities.
Consider a University of Minnesota 2012 experiment wherein they made consumers choose between similarly-sized lotion bottles. One claimed to offer 50% more free lotion while the other was cheaper by 33%. People chose the bottle with the free content by about 73% of the time.
Dan Ariely, a behavioral scientist, claims that ‘Free’ is always the more interesting offer because it increases the value of the product.

9. Imagine

‘Imagine’ is one of the most powerful sales words you can use in your email. It invites your reader to experience your product vicariously through imagination. When they do this, they stop being passive listeners. They become active participants who, alongside you, are thinking about a better future because of your product.

10. Opportunity

In sales, you want your prospect to acknowledge a problem and then see your product as the solution for that problem. The thing is, ‘problem’ is a negative word. It puts people on the defensive and makes them less open to discussing possibilities.
An easy fix is to use the word ‘opportunity’ instead. Because, in truth, a problem is also an opportunity to achieve something better. You just have to make your prospect see it, and take action.
“Your problem is that you’re hungry.”

“You now have the opportunity to eat what you love.”

Check out the two statements above. They both describe the same situation. However, the second one is more inspiring. It makes you want to reach for the nearby fork and eat with gusto!

11. Their Name

People want to feel that you are speaking to them directly, and that they’re unique to you. You can communicate this simply by saying their names. This is a powerful move and it tells your audience that you’re paying attention.
You can easily do this over the phone or face-to-face. It is harder with sales emails, however, especially when you’re doing blast emailing. A work-around here is to use mailing programs that allow you to use variables in your email text. A simple use of the “first name” variable in the greeting section of your email can make a big difference.
Written by: Dan Sincavage
View Full Article Here

June 11, 2018

How Instagram’s Algorithm Works

Instagram  users were missing 70 percent of all posts and 50 percent of their friends’ posts before the app ditched the reverse chronological feed for an algorithm in July 2016. Despite backlash about confusing ordering, Instagram now says relevancy sorting has led to its 800 million-plus users seeing 90 percent of their friends’ posts and spending more time on the app.
Yet Instagram has never explained exactly how the algorithm chooses what to show you until today. The Facebook -owned company assembled a group of reporters at its under-construction new San Francisco office to take the lid off the Instagram feed ranking algorithm.

Instagram’s feed ranking criteria

Instagram relies on machine learning based on your past behavior to create a unique feed for everyone. Even if you follow the exact same accounts as someone else, you’ll get a personalized feed based on how you interact with those accounts.
Three main factors determine what you see in your Instagram feed:
  1. Interest: How much Instagram predicts you’ll care about a post, with higher ranking for what matters to you, determined by past behavior on similar content and potentially machine vision analyzing the actual content of the post.
  2. Recency: How recently the post was shared, with prioritization for timely posts over weeks-old ones.
  3. Relationship: How close you are to the person who shared it, with higher ranking for people you’ve interacted with a lot in the past on Instagram, such as by commenting on their posts or being tagged together in photos.
Beyond those core factors, three additional signals that influence rankings are:
  • Frequency: How often you open Instagram, as it will try to show you the best posts since your last visit.
  • Following: If you follow a lot of people, Instagram will be picking from a wider breadth of authors so you might see less of any specific person.
  • Usage: How long you spend on Instagram determines if you’re just seeing the best posts during short sessions, or it’s digging deeper into its catalog if you spend more total time browsing.

Instagram mythbusting

Instagram’s team also responded to many of the most common questions and conspiracy theories about how its feed works. TechCrunch can’t verify the accuracy of these claims, but this is what Instagram’s team told us:
  • Instagram is not at this time considering an option to see the old reverse chronological feed because it doesn’t want to add more complexity (users might forget what feed they’re set to), but it is listening to users who dislike the algorithm.
  • Instagram does not hide posts in the feed, and you’ll see everything posted by everyone you follow if you keep scrolling.
  • Feed ranking does not favor the photo or video format universally, but people’s feeds are tuned based on what kind of content they engage with, so if you never stop to watch videos you might see fewer of them.
  • Instagram’s feed doesn’t favor users who use Stories, Live, or other special features of the app.
  • Instagram doesn’t downrank users for posting too frequently or for other specific behaviors, but it might swap in other content in between someone’s if they rapid-fire separate posts.
  • Instagram doesn’t give extra feed presence to personal accounts or business accounts, so switching won’t help your reach.
  • Shadowbanning is not a real thing, and Instagram says it doesn’t hide people’s content for posting too many hashtags or taking other actions.
Today’s Instagram whiteboard session with reporters, its first, should go a long way to clearing up misunderstandings about how it works. When people feel confident that their posts will reach their favorite people, that they can reliably build a public audience, and that they’ll always see great content, they’ll open the app more often.
Yet on the horizon looms a problem similar to what Facebook’s algorithm experienced around 2015: competition reduces reach. As more users and businesses join Instagram and post more often, but feed browsing time stays stable per user, the average post will get drowned out and receive fewer views. People will inevitably complain that Instagram is trying to force them to buy ads, but it’s a natural and inevitable consequence of increasingly popular algorithmic feeds.
The more Instagram can disarm that problem by pushing excess content creation to Stories and educating users about how the feed operates, the less they’ll complain. Facebook is already uncool, so Instagram must stay in our good graces.
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