by Caroline Leach | 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?
by Caroline Leach | Aug 14, 2016 | Corporate Communications, Marketing & Analytics
To my surprise and delight, “communication” topped the list of key skills for data scientists in a CEB Market Insights blog post I read this week.
The post covered the top 10 skills for data scientists and 2 strategies for hiring them. Yet “communication” felt like a lone outlier among a list of highly quantitative skills, like managing structured data, mathematics, data mining and statistical modeling.
But indeed, the Business Broadway study the post cited showed that “communications” recurred the most frequently across a variety of data science roles.
When Thomas Davenport and D.J. Patil named “Data Scientist” the sexiest job of the 21st century in Harvard Business Review, they cited an enduring need “for data scientists to communicate in language that all their stakeholders understand – and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or – ideally – both.”
As a communicator who pivoted into marketing analytics, it’s heartening to to see data showing there’s a role and need for effective communication and storytelling skills.
And having led communications, the field is dramatically improved by data that demonstrates what works and what doesn’t, and helps predict how various audiences might respond to different communications strategies.
Beyond enabling data-driven decisions, clear communications about data can literally be a matter of life or death. Two fascinating examples crossed my path this morning in an article by Dr. Jenny Grant Rankin called Over-the-Counter Data: the heroics of well-displayed information.
The first example was an early use of data visualization in the summer of 1854. In London, 500 people died of mysterious causes in a 10-day period. A Dr. John Snow made his data user-friendly. He took a neighborhood map and noted the exact locations where people had died.
This pointed toward a local water pump that was the culprit in the spread of cholera. With this clearly displayed data, Dr. Snow was able to convince authorities to remove the pump’s handle in order to stop the outbreak.
Another example took a much more ominous turn. The night before the Space Shuttle Challenger launched in January 1986, NASA engineers and their supervisors looked at charts and data on the rocket’s O-ring function. This is what keeps hot gasses contained. Based on what they saw, the launch was cleared for takeoff.
But the available data was not displayed clearly. It showed failed launches, but not successful launches. And this led decision makers to overlook a critical piece of information – the O-rings worked properly only when the temperature was above 66 degrees. The day of the Challenger launch was 30 degrees below that. It was “so cold it does not even fit on the graph.” It’s still heart wrenching to recall the tragedy that occurred that day.
While thankfully the work of data scientists is rarely a life or death matter, these examples underscore the need for clarity in communicating data. For what cannot be understood cannot be implemented.
by Caroline Leach | Aug 6, 2016 | Marketing & Analytics
Data is the raw material of the information age.
So says Alec Ross in his book The Industries of the Future.
An expert on innovation, Ross draws parallels between land being the raw material of the agricultural age and iron being the raw material of the he industrial age.
Essentially, big data will touch every aspects of our lives. “Big data,” he says, “is transitioning from a tool primarily for targeted advertising to an instrument with profound applications for diverse corporate sectors and for addressing chronic societal problems.”
Here are a few of his predictions:
- During the next decade, big data will enable people to converse in not just one another language but dozens. While I won’t give up on my Spanish studies anytime soon, it’s good to know that data-based help is on the way.
- As the world’s population grows, so does the need for more food. “Precision agriculture” enabled by big data will help solve this problem.
- Smarter financial systems can be powered by big data. It was surprising, and even a little shocking, to read how antiquated many banking systems still are today.
An important caution is to understand the limits of big data and the critical interplay between machine and mind. This comes in the form of spurious correlations that may result from ever larger and bigger data sets. “Not all the trends it finds are rooted in reality,” he says.
The solution? Including error bars with data analysis predictions. Error bars are “visual representations of how likely a prediction is to be an error rooted in spurious correlation.”
In addition to peering into the future of big data, Ross gives two great tips for “the most important job you will ever have.” How does he define that? Parenting.
What can parents do to help their children be ready to embrace the future?
Ross frames it in terms of languages. The first language is globalism. “Ironically,” he writes, “in a world growing more virtual, it has never been more important to get as many ink stamps in your passport as possible.”
And even though big data may eventually make the need to learn other languages obsolete, it’s wise to learn another language beyond English. The most practical choices, not surprisingly, are Spanish and Mandarin.
The other language to learn is technology. “If big data, genomics, cyber, and robotics are among the high-growth industries of the future,” Ross says, “then the people who will make their livings in these industries need to be fluent in the coding languages behind them.”
Other benefits come with understanding technology. Ross cites fellow pundits who tout the ability to better see patterns and to think in new and different ways. Studying technology is a valuable way to sharpen your critical thinking skills.
One of Ross’ points that I was happiest to see came in the introduction. Because his book explores competitiveness, he delves into the driving force behind competitive countries and businesses being the development of people.
He takes it a critical step further. “And there is no greater indicator of an innovative culture than the empowerment of women. Fully integrating and empowering women economically and politically is the most important step that a country or company can take to strengthen its competitiveness.”
Well said, Alec Ross.
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