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A journalist at her desk

AP report: AI is an opportunity for local newsrooms

March 29, 2022/in AI, Digital Publishing, Radio, Television/by Zena

Groundbreaking report surveying hundreds of newsrooms points to the importance of AI.

By Tim Wolff
VP, TV & Digital Publishing Innovation, Futuri Media

What would you choose if someone else could do part of your job? What if, instead of a person, artificial intelligence could do it? And what if it could go beyond just replacing a mundane task to actually creating ways to make your product better?

Newsrooms across America have been wondering about that, and some have actively engaged AI — artificial intelligence. How much a newsroom uses AI has a lot to do with how many resources and how much time a newsroom has to experiment.

That’s according to a new report from the Associated Press, which was part of a project where they interviewed people from hundreds of newsrooms across the U.S. The goal was to learn what newsrooms are using and what newsrooms want, then to chart a plan for getting our newsrooms to that AI future.

Quoting from the AP’s press release:

“Key findings from the report include:

  • There is a significant gap between large and small news organizations in terms of how widely AI and automation technologies are used.
  • Despite some concerns about handing off human work to machines, there is nevertheless strong support among local newsrooms for automating tasks that could free journalists for deeper reporting, streamline production or enhance content monetization.
  • While interest is high, AI technologies are not in wide use at the local level because many news outlets lack the resources or time required to experiment.”

Diving deeper into the report itself, while survey respondents came up with a wide range of uses, there were some areas where nearly everyone agreed there was a need. Finding stories a local market would care about in social media was near the top of the list for newsrooms of every kind (the AP works with newspapers, TV and radio stations, and digital-only newsrooms).

This quote in the report really stood out to me:

“WTAE-TV News Director Jim Parsons said that sometimes events happen in the communities the Pennsylvania station serves, and the reporting team misses it on social media. ‘We don’t have a great system in place, other than keeping our eye on TweetDeck 24/7,’ he said. WTAE-TV relies on manual scouring of local websites and social media that AI may help solve. ‘It would help to have some ‘robotic eyeballs’ trying to find these events.'”

Sure, as the AP’s key findings note, there is some concern among newsrooms respondents about handing human work off to machines. But when resources are finite, aren’t they better spent creating compelling content that sets your brand apart? What’s the harm in letting technology do the busy work?

Reporters in every newsroom are spending a lot of time looking through social, trying to find stories. As anyone who’s spent a lot of time on social can tell you, most of it isn’t newsworthy. Our own research here at Futuri (conducted in partnership with SmithGeiger) shows that social media is the number one way newsrooms get stories today. It’s part of what makes AI-powered TopicPulse ContentAdvantage such an incredible tool. Futuri has been creating cutting-edge AI media tools for more than a decade, and the amount of data processed through a powerful AI is a true difference-maker for newsrooms.

The survey also showed automated writing and automated personalized experiences for users were also near the top of newrooms’ wish list, among about a dozen other key newsroom needs.

The adoption of AI in newsrooms varies, as has the AI knowledge within newsrooms. The AP will begin offering classes to close the knowledge gap next month. And as artificial intelligence in the newsroom is a topic on which I’m passionate, I’ll present the session What News Managers Need Now — spoiler alert! It’s AI — at NAB Show in April (learn more about the session and how to join me by clicking here).

I applaud the Associated Press for calling attention to the benefits of AI for local news. Used correctly, AI stands to change our industry — for the better, meaningfully.

Tim Wolff is Vice President of TV and Digital Publishing Innovation at Futuri. He has 20+ years of experience as a digital and broadcasting leader who’s led top-performing teams across the country at companies including Gannet, Belo, and Cox Media Group Ohio, which includes three daily newspapers, three radio stations, WHIO-TV, and more. Wolff, who holds a Master’s in Journalism from the University of Missouri, also makes a mean green chile stew.

 

https://futurimedia.com/wp-content/uploads/2022/03/APonAI_blog_032922.jpg 355 640 Zena https://futurimedia.com/wp-content/uploads/2020/04/futuri-logo-.png Zena2022-03-29 18:01:232022-03-29 18:03:57AP report: AI is an opportunity for local newsrooms
AI, big data, and the newsroom: It's Time

AI, big data, and your newsroom: It’s time

February 15, 2021/in AI, Digital Publishing, Television/by Zena

Adjusting strategies and tactics in newsrooms used to take months of work (and a lot of faith in limited info).  Now, AI and data tools can help you learn and make changes in minutes. It’s time for your newsroom to catch up.

By Tim Wolff, Vice President of TV and Digital Publishing Innovation, Futuri

It was the big moment in every newsroom: the day the sweeps ratings arrived. Early in my career, “diary markets” were prevalent. A small sample of viewers would be given books to write down what they watched each day.  At the end of the sweeps month, they would mail those books to the ratings company, who would compile them, turn them into ratings spreadsheets, and send them to the stations. Newsrooms wouldn’t find out how many people watched their newscasts until weeks or months after the newscast aired (assuming, of course, people religiously wrote down exactly what they watched and when they watched it, which was far from a sure thing).

That was why I couldn’t wait every day for the overnight ratings, driven by meters placed on some viewers’ televisions. As a news producer, I wanted to see what kind of impact the hundreds of little decisions I made had on the ratings. Getting a spreadsheet the next day that showed how the newscast performed in 15-minute increments was the best I could do in trying to figure out whether my strategy and tactics had worked.

In truth, it didn’t help much on an individual day. Seeing a tenth of a point change day over day in a quarter hour could be from so many factors, ranging from the quality of the newscast to the weather outside to the level of excitement in a baseball game on another channel. There were rare occasions when it would show a dramatic mistake—like country music interviews at 10pm that saw our second quarter drop 10 points—but those were rare. Having overnight ratings was valuable in long-term assessments, but not so much for all the decisions that go into a live television newscast.

Then I began to get really excited about minute-by-minute reports. These were detailed reports, showing each minute of the newscast with ratings increases or decreases, and included all the other channels, too. That way, I could see if my B-block tease kept viewers around or sent them flipping to the competition.  It gave some idea about which stories were retaining viewers, and which were turning them off. It wasn’t perfect, since many stories are shorter than a minute, or overlap into different minutes. It also wasn’t available until 3 days after the newscast aired, at which point I’d usually forgotten what was even in that newscast. Still, with a lot of tracking and work, I could use them to learn about what did or didn’t make viewers go away.

The digital metrics rush

When I first came to digital in the mid-2000s, the availability of metrics was like an adrenaline rush. I could see how well each individual story performed. It wasn’t in real-time then, with a lag of a few hours and a full report not ready until the next morning.  Still, it was amazing to me, as I could track all these individual stories to learn the trends of what performed well on our website (there were no apps or real social media then).

I moved back to broadcast for a while, and metrics tools improved. Eventually, we had a tool that showed real-time performance for individual stories on a monitor right above my desk. I’ll never forget how shocked I was when I glanced up and saw a relatively minor story getting 600 live page views (about 10 times our typical top story then). I asked one of our digital team members what was happening.  It was Facebook, she told me; the story had gone viral, and it appeared there wasn’t much rhyme or reason to it. 

Over time, as I returned to focusing on digital, the measurement tools got faster and helped us focus on which stories to spend our limited resources perfecting and promoting. But they were all really doing the same thing: tracking metrics. All these tools could tell us what our consumers were consuming, but the tools couldn’t tell us why, or what might work next. For that, we needed teams of smart people analyzing and tracking what worked so we could refine our strategies and help our producers figure out what they should post—and when they should post it. Given the mercurial nature of which stories take off on social and which ones fail, it was still a guessing game.

Data helped define strategy…slowly 

As we gathered more data on our social brands (and even on our talent’s social brands), we were able to continuously refine strategies, and help give everyone a better chance of creating winning social posts. But even with our team focused on the metrics, it was a ton of data to manage, especially as we tried to be competitive with posting 24/7. What I really wanted to be able to do was track data across our entire market, not just on our brands. Of course, there was no way our team was going to be able to effectively do that. Having people dive in to generate reports just on our pages and our competitors’ pages was already an onerous task—and accounted for just a slice of the social media usage among our consumers. More and more, the data was becoming available, but there was no way for us to track and analyze it all.

That’s where “big data” processing and machine learning really began to come into play.  We needed tools that could ingest data from all the millions of posts our consumers were seeing, then take that data and present it in a way we could learn from it and use it. The next game-changer was machine learning, the artificial intelligence that could spot the trends in all that data. As more and more data came into the machine, the AI could learn not only what was working, but what was likely to work. This created an amazing opportunity.

Futuri’s TopicPulse is at the forefront of this AI in the newsroom revolution. With years of data from more than a hundred thousand sources, the tool has insights from the performance of trillions of posts. With AI, TopicPulse uses those insights to be able to predict which posts are going to go viral—and which ones are about to lose all their momentum. This gives newsrooms actionable, immediate information that our digital producers and social teams can use to truly engage their consumers, drive traffic, and make sure that they are spending their time on the stories the community wants. 

Even with our great team using human eyeballs to pore over a (relatively) small data set, we would never be able to use data like that. Instead, our team could focus on writing better posts instead of staring and spreadsheets and looking for trends.

AI is coming…AI is here

AI has a growing presence in newsrooms in other ways, too. There are a number of projects that have studied how journalists write stories, and AI allows computers to take a set of facts and create stories that read as though journalists wrote them. These are common in financial reporting and sports (some video games like FIFA and Madden were early adopters). You’ve probably read these stories and not even realized they were written by computers.

Recommendation engines on websites have been powered by data and personalization for years (what did Lilly do before those AT&T commercials?). There are even AI-powered broadcast anchors on television, like China’s Xin Xiaowei.

For local newsrooms, these kinds of advances in AI are coming from many directions. The great benefit is that the data helps newsroom leaders make better, more informed decisions. They can also spend more time on creating great newscasts instead of slogging through limited and late-arriving data.

We have come a long way from waiting a month to find out whether anyone wrote down that they watched.

Tim Wolff is Vice President of TV and Digital Publishing Innovation at Futuri. He has 20+ years of experience as a digital and broadcasting leader who’s led top-performing teams across the country at companies including Gannet, Belo, and Cox Media Group Ohio, which includes three daily newspapers, three radio stations, WHIO-TV, and more. Wolff, who holds a Master’s in Journalism from the University of Missouri, also makes a mean green chile stew.

https://futurimedia.com/wp-content/uploads/2021/02/Blog_.png 355 640 Zena https://futurimedia.com/wp-content/uploads/2020/04/futuri-logo-.png Zena2021-02-15 20:02:232021-02-15 21:57:27AI, big data, and your newsroom: It’s time
The demo targeting you're probably getting wrong

The demo targeting you’re probably getting wrong

February 15, 2021/in AI, Digital Publishing, Television/by Zena

Every newsroom has a plan for targeting its key demographic groups. But the assumptions we make can do more harm than good. Here’s what newsrooms really need to focus on.

By Tim Wolff, VP, TV and Digital Publishing Innovation, Futuri

It’s a situation that probably sounds familiar to any television executive. At a TV station where I used to work, I was in a meeting where our management team was reviewing research, trying to figure out how to stop what we were told was a slide in viewing among a key demo: women 25-39.

(Note—while this meeting happened to be focused on this demo, I’ve found what’s true in this demo is true in any demo.)

We thought this demo was sliding because the consultant presenting the research quite gravely pointed out that favorability among women 25-39 had dropped nearly 2%. No one seemed to care that the entire research tracking study only had 500 participants, and only about 60 were in this demo… meaning exactly 1 person had rated us as less favorable than the previous tracking study.

Just because numbers seem alike doesn’t mean they are alike

As it happened, we were in the middle of making a decision about the hiring of an anchor. The first conclusion our team jumped to was that, if we are trying to attract women 25-39, we should hire a woman who is between the ages of 25 and 39. It may seem like a reasonable assumption, but it is, in fact a very large assumption—and assumptions are a poor proxy for data.

That was something I’d learned early in my news career. At another station where I’d worked, they had followed this exact approach when naming a chief meteorologist. She was quite good and very well-liked, but one of the managers told me they had really been surprised—her biggest fans were women 60+. “They love her because they remind them of their daughters or granddaughters,” this manager told me. She actually did not perform particularly well among the younger female demographic.

It was that experience that helped me understand the value of what one of the leaders said in the room. It was something along the lines of, “If we are going to try to target one demographic, shouldn’t we do some testing to see if this demo has a preference for a news anchor?”

Everyone agreed this was a great idea—even the VP who had to pay the consultant for another round of research. 

What the research showed

When it came back, it turned out that women in the DMA between 25-39 years old did appear to have a slight preference for one demo. The researchers had asked about trustworthy news among 6 different potential anchors, using a variety of pictures as representation: Females or males in the ages of 25-39, 40-55, and 55+. Each person in the research — again, women 25-39 —chose one as they’d most trust and want to watch in a local newscast: 

  • Male 40-55: 22%
  • Male 55+: 20%
  • Female 40-55: 18%
  • Female 25-39: 17%
  • Female 55+: 13%
  • Male 25-39: 10%

But even this research is misleading, as the margin of error was staggeringly high, and the margins so small as to hardly be definitive. But it did show us that our proxy for data, our assumption that people in a given demo have a strong preference to watch news delivered by someone in that same demo, was not a very good proxy at all.

The newsroom leader who was making the decision on hiring an anchor used all of this very wisely, deciding that what it really meant was just that we needed to hire the best anchor; someone who was both a strong journalist and a great communicator (and in this case happened to be a woman in her late 30s).

And while this particular case was focused on women 25-39, I’ve seen similar truths about men and women of all ages. If a sports radio station is targeting 25-49 year old males, it does not mean that the best anchors they could hire should be men 25-49.  There is simply very little correlation to how much they trust someone or want to watch someone and the coincidence of whether that person happens to be in their demo.

That’s not to say representation doesn’t matter—it does, and it is critical for all people in your DMA to see people they can connect with, or to see someone who looks like them and the people they know.  This is especially critical in traditionally under-represented communities.

But which demo your anchors happen to fit into is actually not very important in whether a viewer will choose to watch your newscast.  And there is a very simple reason for that: YOUR VIEWERS ARE NOT SHALLOW.

It’s about substance

News viewers care about substance. They care about their community, and they know their community is intertwined with people of every age, gender, lifestyle and hometown in your DMA. They live with and love people outside of their demo, and they care about news from every demo.

(Tip: To help your assignment desk and anchors quickly find content that suits your target audience demo, try Futuri’s TopicPulse. Its AI-powered feeds tell you what topics people in your target audience are seeing and sharing, and even clues you in to which ones aren’t yet getting a lot of news coverage.)

That’s why the best path for you, and the most critical way to move forward, is to focus on the representation in the substance of your content, not in the demo appearance of your talent.

Track what you’re doing

This was the biggest realization for me, and the way I found we could have the biggest impact on every demo. We started tracking our content topics, and we started tracking who we were interviewing…and made a discovery both startling and familiar. The overwhelming majority of our interviews were with officials, and, in our market at the time, the overwhelming majority of officials were white males over 50. 

This presented a few problems; first, it meant that we were getting an overabundance of one perspective; second, it meant that we were getting a lot of boring soundbites that just stated information.

So I started by using a proxy goal; have the numbers of people we interview align more closely with the breakdown of demographics in our DMA. It wasn’t a perfect proxy, but what it did do was open our content to having more perspectives that our viewers were used to in their lives and communities.

Most of us don’t spend our lives talking with “officials.” Most of us talk to people throughout our interwoven communities (social media bubbles in a pandemic will be a topic for another time), and want to hear the perspectives of real people affected by stories.

Over time—and it didn’t take very long—as we tracked who we interviewed, we made a dramatic difference in the perspectives of our stories, and in the quality of our news. It also made a big difference in our research; our ratings and our favorability scores went up with every demo.

If you’re in local news, and you’ve been tasked with raising ratings in a particular demo, I hope that you, too, will focus on your content and your community. Because that’s what your viewers will focus on.

Tim Wolff is Vice President of TV and Digital Publishing Innovation at Futuri. He has 20+ years of experience as a digital and broadcasting leader who’s led top-performing teams across the country at companies including Gannet, Belo, and Cox Media Group Ohio, which includes three daily newspapers, three radio stations, WHIO-TV, and more. Wolff, who holds a Master’s in Journalism from the University of Missouri, also makes a mean green chile stew.

 

https://futurimedia.com/wp-content/uploads/2021/02/Blog_demotargeting.png 355 640 Zena https://futurimedia.com/wp-content/uploads/2020/04/futuri-logo-.png Zena2021-02-15 20:02:082021-02-15 20:02:08The demo targeting you’re probably getting wrong
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