AIN

How a Small Publisher Used First-Party Data To Scale Its Reach 50x

by Media

Last month Penske Media, which owns Hollywood Reporter, Billboard and Vibe, announced a new data services division called Atlas Data Studio that creates first-party data segments for marketers to target ads to specific customers.

Unlike third-party data, which is information collected by an entity that does not have a direct relationship with the user, first-party data is information collected directly from your customers. The Atlas Studio takes data points like subscriptions, membership data and virtual event sign-ups to develop information around known users.

The tidal wave of data privacy regulation (CASL, GDPR, California Data Privacy and a slew of others) combined with major tech platforms like Apple and Google abandoning third-party cookies lead many to predict the decline of third-party data and power coming back to publishers who can use that first-party data to sell high-value audiences and scale their reach beyond their own websites and communities.

While Penske joins a list of heavy hitters such as The New York Times, The Washington Post, Forbes and Bloomberg in building out first-party data solutions, the opportunity is open to publishers of all sizes, provided they make the not-insurmountable investment in a tech stack that both organizes the data and makes it actionable.

“With the demise of the third-party cookie, resources are going to shrivel up and disappear,” says AnnMarie Wills, CEO and president at first-party data specialists Leverage Lab. “Organizations with deep, rich, organized and accessible first-party data will be in the catbird’s seat.”

Not Just Retargeting
Legal publisher ALM in 2019 introduced Audience First, an advertising platform that targets decision makers and influencers through first-party data and self-reported demographic data. They then use advanced ad technology to drive those messages to audience segments on both ALM channels and beyond, including social media and other websites.

ALM is quick to point out that this is different from retargeting. “Retargeting allows for an anonymous user to be followed based on cookies,’” says Matt Weiner, president of marketing services at ALM. “If I am identifying a specific individual and targeting that individual, you can see where the value starts to increase.”

How Aviation International News Scaled Its Reach 50X
Scale has always been a challenge for B2B media, which typically serves high value but niche audiences. Today’s digitally-focused marketers are demanding both scale and ROI without any wasted spending.

“First-party data is not new for B2B publishers,” says David Leach, COO of Aviation International News (AIN), which covers the aviation sector. “We’ve always tracked subscriptions and demographics with our print product. That is the same first-party data that we’re talking today but the tech stack and complexity have changed.”

With a traditional mix of print, websites and newsletters, AIN faces similar challenges to much of the B2B industry when it comes to serving digital marketers looking for reach and ROI.

“We could offer print but that includes many of the demographics they aren’t interested in specifically, and the ROI is difficult to show,” says Leach. “We could offer digital display or newsletter placement, and there is some demonstrable ROI but still a lot of unknown traffic. We could isolate our audience in CRM and target with direct email, but that could burn out our list. We could target content on our website but doing that at scale doesn’t work—it cuts our traffic and inventory too thin.”

Despite knowing more about its audience than ever before, AIN’s ability to productize this information at scale—the key part—was limited.

To jump that hurdle, AIN realized it needed to add a Customer Data Platform to the mix. Guided by Leverage Lab, AIN tapped Lytics as its CDP to an integrated tech stack that included HubSpot as digital CRM and Computer Fulfillment as print CRM.

“This brings together all our siloes of data,” says Leach. “Now what we can do is track that behavior pattern in our CRM—we have opens and clicks but also website behaviors like white paper downloads and webinar sign ups. It gives a much more robust look at our audience and brings all behaviors and activities into one profile.”

If AIN sold an advertiser on the magazines, it could target 5,600 names. With the addition of behavioral interest data, third-party lists and another 4,300 names from its other media brands, AIN can now offer a targeted audience on its own properties of more than 15,000.

AIN can then target its own readers and lookalike demographics with offsite display advertising on other websites and social media channels and drive those eyeballs back to its own brands. “We can increase our inventory by 50 times in terms of what we can offer a client,” says Leach.

Selling Audience, Not Product
AIN has shifted to selling audience, not just selling product. “That can be a hard thing for our sales staff to get their heads around but it’s incredibly powerful, especially with what marketers are asking for,” says Leach.  “This allows us to target audience at scale. In the old days, our ability to reach this audience on our own channels at scale would have been nearly impossible.”

Like ALM, Leach stresses that this approach is not retargeting or programmatic advertising.

“These are folks that we’ve identified with first-party data that we’ve collected forever—they’re a pilot for this company, flying out of this location, flying this type of aircraft and one day they might be interested in retrofitting that aircraft with a $500,000 avionics overhaul,” he adds.  “That’s who our advertisers want to reach. We’re just starting on this journey, but the results so far are very encouraging. Some of our clients are all about this while others are still doing all print. Either way, it’s still a great story to tell.”

Business network concept. Group of businessperson. AI (Artificial Intelligence).

‘The Core Job of Journalists Isn’t Going Away’ – ALM’s New AI Content Tool Shows Human Plus Machine is the Way Forward

by Uncategorized

Last month, legal publisher ALM introduced Legal Radar, a “first-of-its-kind website and app” that uses artificial intelligence and natural language generation to offer faster and more personalized user experiences.

Legal Radar puts the reader in charge, allowing users to select the news they would like to see from a list of relevant industries, practice areas, law firms, companies, and geographic regions, then scrapes information from federal case database PACER to generate automated summaries (usually between 50-80 words) of key details about cases as well as pulling in original ALM content from other channels.

“The newsfeed is filled with short, easy-to-digest news briefs that are intended to be scanned, kind of like the experience you would have on a social media app like Twitter or a news app like Flipboard,” says Vanessa Blum,  head of newsroom innovation for ALM’s Global Newsroom. “It’s a very mobile friendly experience and responds to that habit we know our users have which is responding to short news snippets while they are on the go.”

Legal Radar represents a significant shift in the way that content is both generated and consumed. Connectiv spoke with Blum about the realities of building an AI-driven content product, how the customer content experience is changing in B2B media and what the rise of AI really means for editors and journalists.

Connectiv: Vanessa, how does the AI component of Legal Radar work?

Vanessa Blum: We start with a stream of raw data from the federal court system via PACER (Public Access to Court Electronic Records). We apply some data processing on the back end in order to normalize, structure and clean up that data.  Then it’s converted into short summaries using natural language generation (NLG) technology from a platform called Automated Insights.

It goes in as structured data and it comes out as a readable summary. Then, as the final step, we have editors review the summary for accuracy and to make any edits that are necessary.

Connectiv: The release refers to a “first-of-a-kind website and app.” Can you talk more about what makes this first of a kind and how this offers a new customer experience?

Blum: I’ll talk about two things. First is that user experience. There’s never been a legal news product, certainly not a free legal news product, that is so easy to use on mobile, that can be personalized by user selection and is so seamless to digest information and respond to it. We think we nailed that UX in a way that hasn’t been done in legal media

The second part, which we are really excited about, is the way we are using technology and data processing to generate content for Legal Radar. It’s not the tech in itself, it’s that using technology allows us to be exponentially faster in delivering news to readers and also to deliver news across a wide array of topics and interest areas. I’m really excited about what the technology allows us to do, not only the tech in itself.

Connectiv: Talk about the interaction of the technology with editors. What’s this mean for an editor day-to-day?

Blum: I’ll start with the development process, and how closely our editors and developers worked together in building the back-end system. There are journalistic insights baked into every piece of the data processing engine—it’s the editors who devised how this data should be handled as well as the categories and the tagging that should be applied to it.

And then at the NLG level, these are templates that were created by editors to produce the kind of output that would be useful to readers. They account for over a dozen different fact patterns. It’s not a simple plug-and-play NLG engine, there is really this contribution of journalists and editors throughout the development of Legal Radar. Now that it’s up and running, we have editorial review of every item that’s created. We have staffing around the clock where an editor is looking over each and every item.

We thought that was necessary for two reasons—one is that the data set we are working with can be messy. We knew we needed something on the back end to protect against an error in the data producing an error in the content.

The other component is the ability of a human to enrich the content that we are putting out. These are very short, very fast-paced summaries but if something catches an editor’s interest, they will take an extra step—they will open a case, they will open a lawsuit and add a few key facts. We think it’s incredibly valuable to have the human judgment at the end of that process to resolve any questions or enrich what we are producing using the automated system.

Connectiv: A lot of publishers are taking a look at AI and trying to understand what they can do. As someone who’s successfully built an AI tool, what takeaways ca you share about working with AI and building and AI-driven product?

Blum: I have two main takeaways from this experience: first is to focus on the end user and not the tech. It’s easy to get enraptured by cool tech but the best practice is focus first on what you want to deliver and then focus on how the tool gets you to that result. In my role, learning about new tech and seeing how other companies are applying it is eye opening and can spark that creative process but it’s essential to stay user-focused.

The second thing is to build truly cross-functional teams. Creating Legal Radar required journalists, programmers, product designers and business strategists to all be around the table in a way that was really new for our organization. We tend to have content creators in one area and developers in another. For Legal Radar, content creation and technology are so intertwined that we had to break down the walls and get editors and programmers talking together to solve problems. Not only has that made our product better, it’s made our company better.

Connectiv: What was the biggest strategic takeaway from this experience?

Blum: Staying open minded. When we first started, we had a different data set in mind that we thought we’d be using to produce automated coverage. We learned early on that data set wasn’t workable for us, we had to pivot to something else.

One other thing that I’ll mention, we are working with Automated Insights and it’s a great product, but we found we had to build a lot of solutions at the front end before the data is fed into Automated Insights and at the back end before the content goes into the Legal Radar newsfeed. That’s not something we necessarily anticipated at the outset—how much thought and creativity we’d have to apply both to the data feed going into Automated Insights and how we would handle the content on the back-end.

Connectiv: As the head of newsroom innovation, what are you excited about with content and media? And conversely, what do you think is overrated?

Blum: I’m interested and excited in the combination of human and machine intelligence. I love watching how other news organizations are using technology, using algorithmic journalism, using AI and combining it with the expertise of their journalists to come up with solutions that are incredibly rich. That’s kind of the secret sauce in my view.

In terms of what I think is overhyped, I hate answering that because I’m sure I’ll be back talking about this a year from now, but I will say that smart speakers and developing news products for Alexa. I don’t get that one yet. I’m not convinced we’ll be receiving our information from smart speakers in the near future.

Connectiv: You’ve talked about journalists and AI working together. What’s your reaction to the idea of AI replacing editors and writers?

Blum: That’s the natural fear that people in our industry have as we begin learning about automated journalism. The more I’ve learned about it, the less that fear seems grounded. What technology is capable of is so different from what humans are capable of that it’s really through combining the two that we will see the most exciting advances. Technology is great at processing reams of data very fast, but in the business I’m in, which involves asking questions, exploring trends, talking to insiders, there’s no potential at this point that a machine will take over those functions.

When you combine the speed and data processing capabilities of the technology and turn that over to a human being to do the investigation and talk to real people, that’s where magic happens. I think journalist jobs will change–my own changed dramatically–and journalists will be forced to become more tech-savvy and be more open to using data processing in their work, but the core job of a journalist isn’t going away and cannot be replaced by a computer or an algorithm.