What LinkedIn Content Gets the Most Engagement?

by | May 29, 2017 | Marketing & Analytics, Social Media

It’s almost the end of my month-long experiment of posting an update every weekday on LinkedIn.

There’s a growing spreadsheet of data ready to analyze for conclusions and implications. I’ll share them in an upcoming blog post.

In the meantime, I found an interesting data point in a book released this month.

It’s Everybody Lies: Big Data, New Data and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz.

Seth got his Ph.D. in economics from Harvard, worked as a data scientist at Google, and writes for the New York Times.

He makes the case that “we no longer need to rely on what people tell us” in things like surveys or social media or casual conversations.

He  provides compelling data telling the story that big datasets of how people search for information online reveals what’s really on their minds.

Seth writes about “text as data” and how sentiment analysis can identify how happy or sad a piece of content is.

He shared the most positive 3 words in the English language: happy, love and awesome. The 3 most negative? Sad, death and depression.

And what content gets shared more often? Positive or negative stories?

If you agree that “news is about conflict” – summed up by the journalistic sentiment “if it bleeds, it leads” – you might conclude that negative content gets shared more often.

But it’s actually the reverse, according to a study by professors at the Wharton School, Jonah Berger and Katherine L. Milkman. They looked at the most shared articles for the New York Times.

And what was shared the most?

Positive stories.

As the professors said, “Content is more likely to become viral the more positive it is.”

My first reaction was happiness that my “positive comments only” philosophy for social media savvy had some data supporting it.

The second reaction was to turn to my own data from this month’s LinkedIn experiment to see if it held true.

Here I’m measuring engagement by the number of views, rather than by the number of shares.

Why?

I’m fairly new to this daily posting routine, so the first change I’ve seen over the past 4 weeks is an increase in views of my content, rather than any significant shares. And I’m finding shares more challenging to measure so far.

What were my most-viewed posts?

The first 2 posts make sense to me as highly positive content. The third made me pause. On the face of it, it seems like a negative that our brains are limited in the amount of focus they can handle.

But as I thought about it and revisited the comments on the post, I realized that many people might have found this information to be happy news. In other words, it’s okay and even desirable to NOT focus your brain all the time.

How about the least-viewed posts?

The first has to do with a fabulous new book by Sheryl Sandberg and Adam Grant on what the research and practice say about bouncing back from adversity.

But since it began from Sandberg’s husband’s death, one of the saddest words in the English language, that puts the topic in the negative zone. (I still recommend reading the book, because it’s full of uplifting advice about grit and resilience.)

The second was a special report in The Economist about how “data are to this century what oil was to the last one: a driver of growth and change.” Because “change” is not something many people eagerly embrace, perhaps this story was seen as more negative than positive.

The third was a Harvard Business Review article about what distinguishes goals we achieve from those we don’t. My takeaway here? Maybe thinking about goals we haven’t achieved brings up negative thoughts.

Could other factors have impacted which posts were the most and least viewed? Perhaps. Day of the week would have been the most likely. However, the top and bottom views were each for the most part posted on Mondays and Tuesdays.

Another factor could have been posts during the beginning of my daily posting experiment vs. those closer to the end. This certainly could be a factor. Posts later in the month are getting more views in general. From the first week of May to the last, views of my posts have increased more than 6 times.

One conclusion could be that the consistency of posting daily is increasing engagement with my content. Of course, it’s still a small dataset at this point. In the months to come, I’ll continue tracking it and adjusting my strategy. (Opinions expressed in this blog are my own.)

How are people engaging with your LinkedIn content? What’s attracting the most interaction?

4 Key Questions About Data

by | Apr 5, 2016 | Learning, Marketing & Analytics

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When I started my learning project, the plan was to alternate posts between learning how to learn and learning more about data science.

A data review would show I’ve focused too much on the former and not enough on the latter. The data-driven conclusion? It’s time to shift the balance.

As I’ve worked in a new role the last 6 months focusing on marketing analytics, I’ve drawn heavily on my academic background. There’s  economics with its emphasis on statistics and communications management with its reliance on research.

My professional experience is key, too. Leading an employee engagement survey strategy for several years and conducting corporate communications surveys has helped tremendously.

It’s fascinating how many parallels exist between seemingly disparate areas. And problem solving and team leadership are often similar from function to function.

One of the skills I’ve needed to sharpen is thinking critically about data measurements. I’m learning to ask better questions. And I’m learning to anticipate questions from colleagues on how data was collected and analyzed.

Harvard Business Review is a valuable resource in generating good questions – from branding to market insights and from big data to the customer experience.

A March 2016 article by Thomas C. Redman – 4 Steps to Thinking Critically About Data Measurements – gives great tips on asking good questions about data. Here’s a short summary:

  • How does the actual measurement line up with what you want to know? Ask yourself if the measures are good surrogates for what you really want to know.  Redman advises to “distinguish ‘pretty close’ from ‘a good-enough indicator’ to ‘not what I had in mind.'” If you’re settling for something less than perfect, you should be aware of it.
  • What do you want to know? Clarify what you want to know. This is similar to asking, “what problem are we trying to solve?” It’s also important to make sure all stakeholders are aligned on the exact nature and outcomes of the measurement process.
  • What are weaknesses in the measurement process? Here Redman advises a thorough understanding of the entire data collection process. He suggests listening to customer calls if you’re measuring customer complaints or going to a factory if you’re measuring factory productivity. This helps to “develop a feel for the weak links.”
  • Have you subjected results to the “smell test”? If results don’t seem right to you, based on other knowledge you have, dig into them. If results come in much better or worse than expected, consider the possibility of bad measurement and investigate further.

Thank you, Thomas Redman, for a few simple litmus tests to think more critically about data.

 

Can Dream Headlines Focus Your Research?

by | Feb 21, 2016 | Learning, Marketing & Analytics

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Headlines are critical in corporate communications.

If someone reads nothing else but the headline, will they get the key message? And will the headline compel them to read the story?

A tweet can serve the same function. Can you get your key message across in under 140 characters? Will it engage your followers to click on the related link?

It turns out, there’s another powerful use for headlines and tweets. Alexandra Samuel outlines this in her HBR post How Content Marketers Can Tell Better Stories with Data.

“Start with your dream headline,” Samuel advises. She likes to start by “imagining my dream headlines or tweets: the discoveries that I would love my data to yield.”

Samuel gives the example of looking at child-related security risks. “I hoped to discover the security practices that led to the biggest reduction in online misdeeds,” she wrote, “something like ‘good passwords cut hacks perpetrated by kids by 50%’.”

This informs how she tackles the research. What’s less important is whether the discovery she wants to find is actually supported by the research. Because the method provides focus to the research.

This gives a better ability to discover “data that would yield the best-case outcome.” The headline and the story then evolve based on the most interesting and relevant insights from the data.

My first introduction to Alexandra Samuel was through her series of e-books, which ultimately become Work Smarter with Social Media. These helped me to work better with LinkedIn, Twitter and more.

That’s why I was drawn to Samuel’s articles during my Sunday morning reading of HBR posts on marketing, market research and data. It’s all part of my ongoing, online learning project.

And it speaks to the 5-plus hours of learning that everyone at my employer is encouraged to do to mobilize the future.

We’re all lifelong learners. It’s a gift to be part of a company that creates a learning culture to do just that.

What are you learning today?