I’ve been reading Fred Kofman’s book, Conscious Business. Written in 2006, the book summarizes Kofman’s experiences as a management consultant to some of the great leaders in technology and other industries. In the book, Kofman lists 12 questions Gallup used to identify great managers in one of the largest management surveys conducted.
As I read this list of 12 questions, I started answering them for each of the different roles I’ve had. When I worked for great managers and answered the questions, I found I answered yes to almost all of them. The converse is also true.
This list incorporates questions about communication clarity, mission, shared values, respect, community and teamwork.
Do I know what is expected of me at work?
Do I have the materials and equipment I need to do my work right?
At work, do I have the opportunity to do what I do best every day?
In the last seven days, have I received recognition or praise for doing good work?
Does my supervisor, or someone at work, seem to care about me as a person?
Is there someone at work who encourages my development?
At work, do my opinions seem to count?
Does the mission/purpose of my company make me feel my job is important?
Are my co-workers committed to doing high-quality work?
Do I have a best friend at work?
In the last six months, has someone at work talked to me about my progress?
This last year, have I had opportunities at work to learn and grow?
I love this post from the Buffer co-founders Joel and Leo, (open.buffer.com), I have personally experienced consistent doubling in team sizes in nearly every tech startup i have either founded or guided and its a dynamic that if managed well will ensure your success, failure is messy and painful. So read on, regards Bradley Birchall …
The Buffer team is more than 65 people right now, which means our startup has more than doubled in size this year. It’s been an incredibly exciting adventure!
The Buffer team is more than 65 people right now, which means our startup has more than doubled in size this year.
It’s been an incredibly exciting adventure!
There are a lot of big factors for this growth, as well as many changes for all of us that have gone along with it.
We recently sat down for a video chat with Buffer: Open’s Content Crafter, Courtney, to talk about why and how we’ve grown so much this year. She asked us some great and tough questions about things like the challenges of growth and scaling our culture, how big Buffer could possibly become and lots more. We wanted to share it all with you here!
In this post we’d love to highlight just a few of the things we talked about in the video and invite you to share any thoughts this brings up for you!
When we noticed that from one retreat to the next we had almost the same amount of people, that didn’t feel too ideal.
We realized that there’s so much more that we want to do; so much opportunity. We weren’t moving as fast as we wanted to, and that was a big trigger point.
Luckily, we were also growing in revenue, and had started to hit profitability.
We decided to reinvest that, thinking that ideally we should keep growing and make use of that money to provide a better product and better customer service.
As a result, we have a different situation leading up to our upcoming retreat in Hawaii in January, where we’ll almost have doubled from one retreat to the next!
How has it changed the way we work?
As we began to ramp up and grow again, we realized we had stretched our existing structure as far as we could.
We’ve never had a lot of hierarchy, especially during our self-management period. We were a small enough group that we organized naturally, for the most part, without breaking into too many specialized teams.
So when we hit 10-15 people in the product and engineering group, that was 15 people on one team. That becomes really inefficient—people are jumping from one thing to another.
The product has grown so much at 4-5 years in, and it has a much wider span. It’s hard to be able to effectively jump into all its different areas.
And ideally, you don’t want to have to split your brain between them. For people to be able to work and focus, we’ve learned that areas needs to be separate so someone can give one all their attention.
We realized that we needed to split into multiple teams—ideally, we’d have 5 people per team. So at a team size of 15 in product and engineering, that’s 3 teams.
We knew we needed more than that to handle each element of Buffer, maybe 7 or 8 teams total.
So that meant we would need to be a product and engineering team of 35 or 40! That’s what triggered this wave of growth.
The system we have now, we think, works. And yet we’re growing so fast that as soon as we hit the point where things works, we might grow to the next point and it’ll all break again.
That’s just going to be how it works now. It’s a challenge, but it’s also exciting.
How big could Buffer become?
In terms of vision, our feeling is that there’s a lot of opportunity.
We want to continue helping small businesses to have the voice they deserve to have and get more reach through social media. There are a lot of different spaces we could move into, and much more we could do to help customers with social media publishing.
The culture we’ve established and movements we’ve ended up being part of, like transparency and growing as a distributed team— we believe this is a purpose of Buffer, too, to spread these movements.
The more we can grow, the more we can show that this kind of work can scale. That’s part of the motivation for going further.
Nothing grows forever, and that’s not a good aim to have. But right now for Buffer, we think we’re far from our limit. Our growth may not always be this fast, but we will be on a pretty fast trajectory from now on.
We’ve now moved to this new structure, so we’re building up to that. Once we hit it, we probably won’t need to double every few months again—until we need a whole new structure, which could happen every few years.
How does our culture evolve as we grow?
We’ve recently started to send out periodic surveys to get a feel for how teammates are feeling at buffer, and recently the rate of growth has got quite a few people worried about the culture changing.
That’s on people’s minds, and it’s really important to talk about and think about and make changes around.
Culture evolves. Every new person we add evolves the culture—that’s why diversity is so important, because we want the culture to evolve in a diverse way.
At the same time, there is this underlying idea that you’ll have culture whether you like it or not—it’s down to whether you decide to shape it.
That’s something we’ve always believed in, and why we put our values into words when we were just 10 people. We believe we should be very deliberate about what kind of company we want to build and how we want it to feel.
The two of us used to talk about culture together. On Fridays, we would go to a coffeeshop and work on culture, make changes. Things like pair calls, the salary formula, all these things we introduced through that weekly meeting.
Motivating employees seems like it should be easy. And it is — in theory. But while the concept of motivation may be straightforward, motivating employees in real-life situations is far more challenging.
Motivating employees seems like it should be easy. And it is — in theory. But while the concept of motivation may be straightforward, motivating employees in real-life situations is far more challenging. As leaders, we’re asked to understand what motivates each individual on our team and manage them accordingly. What a challenging ask of leaders, particularly those with large or dispersed teams and those who are already overwhelmed by their own workloads.
Leaders are also encouraged to rely on the carrot versus stick approach for motivation, where the carrot is a reward for compliance and the stick is a consequence for noncompliance. But when our sole task as leaders becomes compliance, trying to compel others to do something, chances are we’re the only ones who will be motivated.
Why not consider another way to motivate employees? I’d like to suggest a new dialogue that embraces the key concept that motivation is less about employees doing great work and more about employees feeling great about their work. The better employees feel about their work, the more motivated they remain over time. When we step away from the traditional carrot or stick to motivate employees, we can engage in a new and meaningful dialogue about the work instead. Here’s how:
Share context and provide relevance. There is no stronger motivation for employees than an understanding that their work matters and is relevant to someone or something other than a financial statement. To motivate your employees, start by sharing context about the work you’re asking them to do. What are we doing as an organization and as a team? Why are we doing it? Who benefits from our work and how? What does success look like for our team and for each employee? What role does each employee play in delivering on that promise? Employees are motivated when their work has relevance.
Anticipate roadblocks to enable progress. When you ask anything significant of team members, they will undoubtedly encounter roadblocks and challenges along the path to success. Recognize that challenges can materially impact motivation. Be proactive in identifying and addressing them. What might make an employee’s work difficult or cumbersome? What can you do to ease the burden? What roadblocks might surface? How can you knock them down? How can you remain engaged just enough to see trouble coming and pave the way for success? Employees are motivated when they can make progress without unnecessary interruption and undue burdens.
Recognize contributions and show appreciation. As tempting as it is to try to influence employee satisfaction with the use of carrots and sticks, it isn’t necessary for sustained motivation. Far more powerful is your commitment to recognizing and acknowledging contributions so that employees feel appreciated and valued. Leaders consistently underestimate the power of acknowledgment to bring forth employees’ best efforts. What milestones have been achieved? What unexpected or exceptional results have been realized? Who has gone beyond the call of duty to help a colleague or meet a deadline? Who has provided great service or support to a customer in crisis? Who “walked the talk” on your values in a way that sets an example for others and warrants recognition? Employees are motivated when they feel appreciated and recognized for their contributions.
This is an awesome overview of AI and related terms and technologies by Tim Appenzeller, www.sciencemag.org
Just what do people mean by artificial intelligence (AI)? The term has never had clear boundaries. When it was introduced in 1956, it was taken broadly to mean making a machine behave in ways that would be called intelligent if seen in a human.
Big data has met its match. In field after field, the ability to collect data has exploded—in biology, with its burgeoning databases of genomes and proteins; in astronomy, with the petabytes flowing from sky surveys; in social science, tapping millions of posts and tweets that ricochet around the internet. The flood of data can overwhelm human insight and analysis, but the computing advances that helped deliver it have also conjured powerful new tools for making sense of it all.
In a revolution that extends across much of science, researchers are unleashing artificial intelligence (AI), often in the form of artificial neural networks, on the data torrents. Unlike earlier attempts at AI, such “deep learning” systems don’t need to be programmed with a human expert’s knowledge. Instead, they learn on their own, often from large training data sets, until they can see patterns and spot anomalies in data sets that are far larger and messier than human beings can cope with.
AI isn’t just transforming science; it is speaking to you in your smartphone, taking to the road in driverless cars, and unsettling futurists who worry it will lead to mass unemployment. For scientists, prospects are mostly bright: AI promises to supercharge the process of discovery.
Unlike a graduate student or a postdoc, however, neural networks can’t explain their thinking: The computations that lead to an outcome are hidden. So their rise has spawned a field some call “AI neuroscience”: an effort to open up the black box of neural networks, building confidence in the insights that they yield.
An important recent advance in AI has been machine learning, which shows up in technologies from spellcheck to self-driving cars and is often carried out by computer systems called neural networks. Any discussion of AI is likely to include other terms as well.
ALGORITHM A set of step-by-step instructions. Computer algorithms can be simple (if it’s 3 p.m., send a reminder) or complex (identify pedestrians).
BACKPROPAGATION The way many neural nets learn. They find the difference between their output and the desired output, then adjust the calculations in reverse order of execution.
BLACK BOX A description of some deep learning systems. They take an input and provide an output, but the calculations that occur in between are not easy for humans to interpret.
DEEP LEARNING How a neural network with multiple layers becomes sensitive to progressively more abstract patterns. In parsing a photo, layers might respond first to edges, then paws, then dogs.
EXPERT SYSTEM A form of AI that attempts to replicate a human’s expertise in an area, such as medical diagnosis. It combines a knowledge base with a set of hand-coded rules for applying that knowledge. Machine-learning techniques are increasingly replacing hand coding.
GENERATIVE ADVERSARIAL NETWORKS A pair of jointly trained neural networks that generates realistic new data and improves through competition. One net creates new examples (fake Picassos, say) as the other tries to detect the fakes.
MACHINE LEARNING The use of algorithms that find patterns in data without explicit instruction. A system might learn how to associate features of inputs such as images with outputs such as labels.
NATURAL LANGUAGE PROCESSING A computer’s attempt to “understand” spoken or written language. It must parse vocabulary, grammar, and intent, and allow for variation in language use. The process often involves machine learning.
NEURAL NETWORK A highly abstracted and simplified model of the human brain used in machine learning. A set of units receives pieces of an input (pixels in a photo, say), performs simple computations on them, and passes them on to the next layer of units. The final layer represents the answer.
NEUROMORPHIC CHIP A computer chip designed to act as a neural network. It can be analog, digital, or a combination.
PERCEPTRON An early type of neural network, developed in the 1950s. It received great hype but was then shown to have limitations, suppressing interest in neural nets for years.
REINFORCEMENT LEARNING A type of machine learning in which the algorithm learns by acting toward an abstract goal, such as “earn a high video game score” or “manage a factory efficiently.” During training, each effort is evaluated based on its contribution toward the goal.
STRONG AI AI that is as smart and well-rounded as a human. Some say it’s impossible. Current AI is weak, or narrow. It can play chess or drive but not both, and lacks common sense.
SUPERVISED LEARNING A type of machine learning in which the algorithm compares its outputs with the correct outputs during training. In unsupervised learning, the algorithm merely looks for patterns in a set of data.
TENSORFLOW A collection of software tools developed by Google for use in deep learning. It is open source, meaning anyone can use or improve it. Similar projects include Torch and Theano.
TRANSFER LEARNING A technique in machine learning in which an algorithm learns to perform one task, such as recognizing cars, and builds on that knowledge when learning a different but related task, such as recognizing cats.
TURING TEST A test of AI’s ability to pass as human. In Alan Turing’s original conception, an AI would be judged by its ability to converse through written text.
This might sound contradicting — and it’s scary for us to admit. But, despite building a product that helps people succeed on social media, we have committed a good number of social media mistakes ourselves.
This might sound contradicting — and it’s scary for us to admit.
As we have been producing a lot of content on our blogs and podcast, we had many things to share. So we shared — a lot. Also, to fill our Buffer queue, we might have included content which was good, but perhaps not the best.
Limiting our Facebook posts to just one or two per day forced us to share only the best content. These quality posts resonated with our Facebook fans, and the Facebook algorithm surfaced them to more people.
Small business owners and solo social media managers usually don’t have the time to create or find enough high-quality content to post five times a day on Facebook or tweet 10 times a day on Twitter. By reducing the number of times you post each day, you can focus on the quality of the posts rather than the quantity of posts.
We weren’t getting the results we want for the time and effort we put into Snapchat and most of the users on Snapchat aren’t our target audience. Whereas Instagram provides several advantages such as better discoverability, analytics (including audience insights), and audience targeting through ads.
Every additional platform your business is active on means additional time and effort required to create great tailored content for that platform and engage with your fans on that platform.
Take stock of your social media profiles and consider which channels are performing for your business and which are not. By moving away from social media platforms that might not suit your business or not be performing well, you can double down on those that are.
Mistake 3: Posting the same content across platforms
Tailored content for each platform increases reach and engagement
To help solve this problem, we recently improved our product so that you can customize your posts for each social media platform and share them to your profiles all at once.
We would love for you to give it a try if you haven’t had a chance to! Just compose a post on your Buffer dashboard or through the Buffer extension to try this new feature.
Mistake 4: Using only landscape videos and images
Square videos have higher average views and engagement
We were used to posting landscape videos and images because that was the ideal image size for most social media platforms such as Facebook and Twitter.
1,024 pixels by 512 pixels.
But that might not be true anymore. As square videos and images are no longer cropped on Facebook and Twitter, they take up more real estate on someone’s feed — 78% more, in fact.
Once we discovered these findings, we started posting more square videos and images on our Facebook Page and Twitter profile. It could be worth experimenting with square videos and images to see if they perform better for you, too.
Do you have any experience with vertical multimedia? How do they perform compared with landscape or square multimedia?
Mistake 5: Sharing only our own content
Curated content helped grow our Facebook fan base
We used to shy away from curated content because we thought it wouldn’t contribute to our bottom-line: traffic, signups, and revenue. It even felt counter-intuitive. Do we really want to send traffic to someone else’s website than our own?
Then, we realized that might have been a short-sighted thinking. While we were marketing to our fans, we weren’t growing our fan base much. So we were marketing to mostly the same people who could potentially be annoyed by too much Buffer content.
When we experimented with posting also content from other sources such as TechCrunch and Wired, our Page’s reach, engagement, and fans grew significantly.
Five out of our recent top 10 Facebook posts are curated from others. In total, they reached over 1.7 million people, most of whom are (or were) not our Facebook Page fans. (For context, we have about 93,000 Facebook Page fans.)
Posting quality content from others increased our brand awareness and following on Facebook. These pieces of content reached people who may not have heard of Buffer before and converted some of them into our Facebook Page fans. Now, we can share Buffer content to a bigger engaged audience.
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