A recent Bloomberg Businessweek article titled “The Future of AI Depends on a Huge Workforce of Human Teachers” discussed what to expect in the future of artificial intelligence.

It’s clear from this article that the tech industry and professional investors are betting big on AI. But what the Bloomberg article shows is they are specifically putting their money (more than $50 million in 2017 alone) towards data labeling startups who collectively employ over 1 million people worldwide to train AI software. The bet they’re placing is on a technique known as supervised machine learning.

Supervised machine learning is a subsection of artificial intelligence and one of two popular ways which computers learn:

• In supervised machine learning, a computer model is fed raw inputs that are already labeled. This method is the equivalent of technology learning like a student working with a teacher. The teacher (or human) provides examples to the student (a computer) of what is right and wrong, correcting the student’s work along the way. In this method, the student learns the general rules of a subject and applies these lessons to predict the right answer to a new question in the future.
• In unsupervised machine learning, a computer is fed raw inputs without any labels to analyze. The result is that the computer must find patterns in that data on its own without any human assistance. This method is the equivalent of trying to learn a foreign language by reading millions of pages of untranslated text without the assistance of an instructor, verb conjugations, or vocabulary dictionary.

If you look at the best applications of AI, they need humans to provide feedback on what is right and wrong. For example, Tesla Autopilot, a driver assist feature offered by Tesla Motors, uses supervised machine learning to train its self-driving technology. In this case, Tesla Autopilot is taught how to drive by the human owners who are operating their cars every day. As Tesla owners drive their cars, sensors in their vehicles collect hundreds of millions data points on driving, from GPS coordinates to actual videos captured from the car’s front and rear cameras. The vehicles then wirelessly send this data back to Tesla’s Autopilot data model, creating a massive library of knowledge around how to drive. The human feedback loop is essential here because if there is an error, the damage could be catastrophic.

By creating this library with the help of human drivers, Autopilot can use visual techniques to break down the videos and understand why drivers reacted the way they did. So, when a ball or child crosses the self-driving car’s path, Autopilot recognizes the pattern and reacts accordingly—stop!

Utilizing AI in Media Intelligence

Many automated media monitoring solutions say they use artificial intelligence and machine learning. But if there is no dedicated human analyst anywhere in the process to check or label the input data (i.e. your organization’s media), it means one of two things:

1. The results you’re getting from your company’s media intelligence solution are inaccurate because it uses unsupervised machine learning. The solution using AI is teaching itself and you probably shouldn’t be counting on it to make informed decisions without labeled data.
2. You, the customer, are expected to be like the coders discussed in the Bloomberg article. This means taking the time out of your own schedule to train the algorithms on what is right and wrong or hiring someone to do so.

Some people say a supervised machine learning solution is expensive, but supervised machine learning boosts media intelligence accuracy and helps communications pros make better decisions. To get accurate data, you need to either hire an analyst to train the algorithm so that it learns the right way, or spend time doing it yourself. Otherwise, you may end up paying in a different way with inaccurate results.

How AI Can Affect Media Intelligence in the Future

AI and machine learning are going to have an enormous impact on public relations and media intelligence. AI will give you more than just analytics, it will give you answers to what is happening in your media coverage and why – all on demand. Not only that, but it will forecast what topics could be a problem in the future, where those problems will occur, and how long the problems may last. These predictive analytics will make you proactive rather than just reactive.

As AI gets “smarter”, it will make you better and more prepared. But AI will only become as smart as the human teachers who train it along the way. It’s a virtuous upward cycle of humans and technology making each other better. Humans can train the machines, and pick up the last pieces of the most complex analyses that AI can’t like idiomatic phrasing and sarcasm. If done right, the benefits are truly worth the investment.

PublicRelay continues to invest in AI. In fact, supervised machine learning is embedded into our solution both in terms of making the analysis better and faster as well as in the outputs to the client.

Contact PublicRelay to learn more about how accurate data can help you with media intelligence.

Unfortunately, most of our clients have lived through the following scenario. The Head of Communications is delivering a presentation to the CEO (or the Board), showing graphs with various data points like spikes in positive coverage and then someone in the room challenges them. “What was that uptick from again?” “Really? Can you dig in deeper on that?” “That just seems off, based on what I’ve seen in the media.” And they go back to their team to get more detail.

Then it happens – they uncover that not only was that spike based on irrelevant media hits, but they’ve been inaccurately analyzing the content in their data set. Now what?

• Go ream out the staff you put in charge of not only finding, but correctly using, a media intelligence tool
• Own this problem and make sure it never happens again

We all know that (b) is the “right” choice, but how are you supposed to accomplish that, with so much on your plate? There is no way you are going to sit through vendor meetings if they are just demos of “tools” that you aren’t going to use yourself. But YOU own the outcome and YOU are going to have to go into future executive meetings and tell your stories with confidence.  That means this process must entail more than just hiring a “tool” provider to solve the issues; it must get you the answers that make a difference in executive-level conversations.

Many of our clients have been in this situation and never want to go through it again. And when we dive into how the problem materialized, we uncover the same underlying issue – no one on the PR team was hired to be a data scientist –  they were hired to be communications pros, helping to execute strategy in their area of expertise.

While this may seem overly dramatic, it’s an unfortunate truth. The tools most communications teams use simply collect and generally categorize content and tone from keywords found in the text. If anything is irrelevant or incorrect at this point in the process, the analysis is useless and can send your team in a completely wrong direction.

So, your teams spend valuable hours constantly training the system. Trying over and over again to get the perfect mix of keywords and Boolean strings. And what is perfection? Never missing a single article or post AND not cleaning out their relevant mentions? Is this really what you are paying your team to do? (And by the way, if your agency is managing this on your behalf the same frustration is happening on their end and you’re paying the bill.)

How important is demonstrating to your Board and CEO that you’re making strategic decisions? Is it as crucial (or even more crucial) than choosing an agency of record? Would you leave your agency decisions entirely up to your staff? If not, then why would you entrust them to run your analytics strategy and hire business partners on their own without your guidance?

Now is the time to take the reins on your measurement and analysis. Focus less on finding “tools” to track your programs and more on ways to deliver the answers your business leaders expect. This way, you can confidently make data-driven decisions that tie to the company goals. Never again will you worry about the perceptions of the C-Suite –YOU can come equipped with key insights when they start asking hard questions – and the hard questions will come.  Fortunately, hard questions are easy to answer when you have the right approach and reliable data to back it up.

9 Questions You Need to Ask Your Media Intelligence Solution Provider >>

Media Intelligence is crucial to high-performing communications teams – but how accurate is this reporting?

The reality is that many PR/communications teams today remain in the Dark Ages when it comes to performance measurement and, unfortunately, most CCOs don’t realize they have bad data until it’s too late.

In this blog, we’ll explore what it really means to have accurate data and, more importantly, how the accuracy of the data you start with affects objectives you track and the outcomes you present to your board of directors.

Common Roadblocks with Media Monitoring  

Typical media monitoring tools are, at best, only 80% accurate. Most vendors have an out-of-the-box approach to data collection that centers only on keyword tracking, and that yields many problems.

Analyzing Irrelevant Information

Most media analysis tools provide counts of keyword mentions and overall tone which is not insightful.  Brands need to accurately report on media mentions as there is strong potential that a significant number of those mentions are stock quotes or auto-published press releases on spam sites.

Additionally, mentions of product or company names may appear in counts, even when they have no meaning to the business. For example, impression counts might include the use of “sprint” as a verb even though the PR team is looking to track references to Sprint, the telecommunications company. Or they might include a news article that refers to an employee who once worked for the company. Automated tools don’t catch these things, and this can really skew your metrics.

Missing Out On Valuable Information

To filter out irrelevant articles or mentions, Boolean or smart technologies are used to narrow down keyword results, but this also yields inaccuracies because what if you accidentally filter something out that is important?

Poor Sentiment Identification

Although technology has come a long way, media monitoring systems still have a difficult time understanding human emotions like sarcasm. What if your brand is mentioned in a publication such as The Onion? Most tools could never analyze the real tone of that article. They also can’t catch if your company is wrongly named – think Equifax and Experian in the recent data breach crisis. Because there are only three major credit bureaus, Experian is often mentioned in articles about Equifax that are negatively toned.

Misinterpreting sarcasm and catching false mentions are just some of the many downfalls of automated solutions. Overall, automated sentiment analysis is widely considered to only be 60% to 80% correct.  For example, a pharmaceutical company that produces cancer treatments found that their automated media intelligence solution marked all mentions of the word cancer in articles as “negative.” Adding the element of professional human analysis to your media monitoring can help avoid potential pitfalls like these.

Lack of Context

Automated monitoring systems give every mention the same weight. Yet, a reference in the Wall Street Journal or by influencers in the company’s niche that can amplify content they care about, can mean more than a remark in a local media outlet.

Locating Non-Keyword Concepts

Automated tools can’t analyze concepts in your media intelligence reports that don’t show up in a keyword search. High-level concepts like “brand quality,” “thought leadership” or “workplace experience” are critical for building strategic plans and measuring goals. Yet these terms are rarely stated explicitly in written text and can only be recognized by a person who has read the article.

Emphasis on Outputs Not Outcomes

Automated technologies that many PR teams use to monitor media coverage track KPIs—such as reach and impressions—that quantify outputs, not outcomes. To be taken seriously by leadership, PR/communications teams must track metrics that are accurate and meaningful to business performance.

What Can Two-Pronged Media Intelligence Do for CCOs?

A two-pronged approach to data collection and analysis helps CCOs answer high-level questions focused on custom strategic outcomes. Confidence in what you are presenting to your C-Suite or board of directors is critical – you must trust that the data that your team is sourcing is A) accurate B) being analyzed in a meaningful way that you can tie to your objectives.

By combining technology with human analysis, organizations can benefit from accurate data and actionable analytics surrounding outcome-based metrics. Only then, can you move from simply monitoring output to strategically accomplishing objectives such as the following, accurately and confidently:

• Increase thought leadership among your key audiences and regions
• Identify opportunities for media coverage that your competitors do not already dominate
• Determine key subtopics that are impacting your reputation
• Confirm that your messaging is accurately reaching your target audience as intended

Contact PublicRelay to find out how our media intelligence accuracy and human analysis sets us apart and can help you make data-driven decisions.

Recently, a Business Insider article caught my attention when it sought to quantify the brand boosts that Intel, Under Armour and Merck experienced when their CEOs resigned from the White House Manufacturing Advisory Council. To measure the impact of the walkouts, the piece relied on mentions and tonality data provided by a well-known social analytics tool.

The quote that stuck out was about Merck and their very high percentage of negative tone. The provider clarified by stating “the only reason it is negative is because people are criticizing Trump for singling out Merck and Frazier and not the other CEOs, and the algorithm can’t decipher that context.”

Here’s the problem: algorithms can mine data well and even analyze its tone using keywords, but only a human can interpret the results in the context of current events. But what happens when you ask bigger questions? Such as what was the negativity in the social data referring to? This is where our approach gives the insights that machines can’t.

Because the overall tone of a post is not an accurate measure for effect on reputation and brand, our analysis focuses on getting to the “so what?” answers. And these are the answers we uncovered when the context of the Merck posts were analyzed for our test:

• What was the overall sentiment toward Merck? Mostly positive – less than 30% of the posts were negative about the brand – a sharp contrast to the statistic pulled by an algorithm in the Business Insider.
• Were the posts about any of the brand drivers that the business cares about? (see chart) Yes – and the tone of each subtopic was analyzed for more clarity.
• Did traditional media have any impact on social? Traditional media sharing activity concentrated largely on positive coverage. A Los Angeles Times editorial praising Ken Frazier for his courage was among the most shared articles, generating over a quarter of a million Facebook shares.This is exactly the type of insight communicators can use to bring the right perspective to their executive teams.

Reporting on counts and tonality in social media can be a starting point. But to deliver true value to your organization, you need to uncover context. Pairing human analysis with technology gets you the story behind the story. Communicators need specific, timely, and trustworthy conclusions to track their company’s brand and reputation when a major (or any) event occurs.

We are in a new world of public relations management where missing out on a story or tweet from an influencer can potentially cause your brand a disaster.  As communicators, it is essential to perform ongoing media monitoring and social listening for mentions of your brand and key topics that you’re tracking. This can be especially daunting when your brand has a name that is cited thousands of times a day in social and traditional media. Picture how difficult it is to sift through all that information.

On average, we’ve found that less than 20% of all social media data analyzed for clients is relevant to their brand and key messages. A lot of the irrelevant data is generated because brands have common names.

Take Chipotle, for instance, whose brand name is also a common dictionary word for a type of sauce or pepper. How can Chipotle’s communications team monitor thousands of tweets and news articles that use the word ‘chipotle’ and isolate when they are referring only to the brand? Merely using keywords to track online mentions is not enough.

Most media monitoring solutions encourage users to use Boolean logic to pare down the volume of data collected, but applying too many filters puts brands at risk of missing valuable information. For instance: what if a tweet discusses a bad batch of chipotle peppers used at a Chipotle location? If you are filtering out “pepper”, you would have likely omitted this result.

Tide is another example of a brand that deals with common name issues. How can Tide’s communications team focus on articles about the Tide brand and not changes in sea levels or Roll Tide Bama or any number of uses? A typical media monitoring solution would suggest that you solve this problem by filtering out articles that contain keywords like ‘tide’ and ‘ocean’ together. But, that could still cause you to miss essential coverage such as an article claiming that Tide detergent is causing water pollution.

The bottom line is that simply automating keyword tracking and pairing it with Boolean logic is not a strong enough media monitoring solution. You need to pair automated media monitoring with high-quality human analysis to ensure that you are working with data that you can trust.

Pushing an organization’s key messages is a priority for communications professionals. Yet effectively promoting that messaging can be daunting, especially in a noisy industry with many different players and trending stories. Strategizing a messaging campaign requires quickly identifying the influencers and media channels surrounding an important topic.

A Fortune 500 telecommunications company wanted to better understand the media landscape for a hot-button industry topic, data privacy. The company particularly cared about how influencers framed that topic in relation to top telecom companies. They needed specific, tailored data for their industry, but they were uncertain where to start.

Don’t Miss: “How Third-Party Influencers Can Shape Your Media Strategy“

Working with their media analytics team, they quickly pulled together a year’s worth of data about authors and publications writing about the topic. The team had already been cataloging these concepts and their connections to the company and its top competitors. This accurate data enabled them to easily visualize the dominant players in these critical conversations.

The data provided other valuable insights for the company. They had details about the authors who wrote positively or negatively towards each telecom companies’ stance on privacy. They also could see which authors’ articles tended to go viral on different social media channels.

Equipped with this analysis, the company efficiently and effectively created a messaging plan around data privacy. They cultivated relationships with top authors who write positively about data privacy and were able to reach wider audiences. The company was also able to predict the influencers that would most likely cover their privacy practices unfavorably and respond to that negative messaging in traditional and social channels. With access to rich, curated data, the company continues to stay informed and remains a part of the data privacy dialogue.

If you’ve ever done PR work for a telecommunications company, you know that special events matter. In an industry where customers are always shopping for the next best deal, the media buzz around a new product launch, data plan, or ad campaign can make or break the company’s bottom line.

With all this fast-breaking news, understanding an event’s success in near-real time can be difficult. Here are 5 questions our top telecom clients ask during a special event:

• What is the general sentiment towards the event? Distinguishing the positive and negative coverage by authors and outlets can help you strategize in real time. You should be able to quickly locate and share the favorable and unfavorable reporting from your organization’s point-of-view.
• What does our media audience look like? Identifying the reach of publications writing about you and the stories going viral on social media can ensure you stay informed about top influencers. Use advanced analytics to marry the impact of traditional and social media on your event.
• Are our key messages pulling through? Drilling down to the messaging of your event news can help you measure the effect on your overall brand. Having a dedicated human analyst ensures that even the tiniest details about your media coverage are recognized correctly – including concepts and topics that do not appear in text.
• Is our CEO or other key executives mentioned? Knowing how the news cycle relates the event to its top decision-makers can help you deliver valuable media feedback to your leadership. Discover how an event’s coverage can affect your CEO’s reputation or whether executive mentions can influence a story’s pick-up.
• How does this event stack up to previous events? Use past performance analysis to be proactive in your event strategy and set more informed goals. Then quickly debrief on your current efforts – from message pull-through to social amplification and author tonality to share of voice.

Getting to the big answers behind these event questions takes more than traditional media intelligence solutions can deliver on their own. When the best technology is paired with human analysis you get the most accurate and timely answers that drive smarter business decisions

Read Next: “6 Steps to Measure PR At Your Next Event“

Company crises can be deal-makers (or breakers) in the career of a PR or Communications professional. C-suite execs want proof that a crisis strategy is working, both during and after the event. How can teams prove that their handling of an event is sufficient, and that it results in a return to business as usual as quick as possible?

When a major safety hazard threatened a pioneering tech company, a PR exec encountered a media firestorm. New to the company and the role, the communicator pushed an unexplored strategy to manage the story. In doing so, he needed to prove that the change in direction was worthwhile.

His strategy for doing so? A focus on strong analytical data. However, prior to his arrival, the Communications team had never provided measurement data to the Executive team.

With a lack of historic media analytics, he couldn’t see if his leadership brought the company back to business as usual faster than in previous crises. He turned to a new measurement method: technology plus human media analysis. Utilizing skilled analysts who could analyze months of data within the context of the current situation allowed him to compare the current crisis messaging to that of past incidents.

Analysts worked round the clock to track coverage across media platforms, isolating the impact in real time and providing visuals that could help the C-suite truly understand this event by comparing it to the past.

As the public debated the company’s safety and future, the robust data gathered by the intelligence team made one thing clear: the communicator’s strategy had worked. The positivity of coverage increased dramatically compared to a prior, identical incident. Social media of the coverage was triple the volume of the past event. And the negative coverage decreased significantly.

The verdict? The communicator’s strategy had worked. His crisis strategy was better, and he had reliable data to prove it. To the C-level execs at this numbers-driven tech giant, the data was convincing.  Not only did reliable media intelligence enable him to establish a measurement standard, it also earned him a seat at the table at a crucial moment and moving forward.

By now most everyone has seen the LifeLock commercials poking fun at monitoring your credit versus doing something about the fraud that’s found. That shift in terminology and mindset is happening across all industries, moving from “reporting on” things to giving you “actionable intelligence”.

The Public Relations and Communications space is no different, moving from using solutions for media monitoring to media analysis and media intelligence. Now that we’re calling it intelligence, can I do anything different with the output I’ll get or is it more of the same?

Media solutions are technically easy to replace so if you are not happy with the one you have, you simply choose another vendor and hope the experience is better. But what if the issue isn’t that your solution doesn’t have great charts or an intuitive interface, but that the output must be cleaned up just to get the story that you are comfortable taking to your CEO or Board? Can you draw market conclusions or make decisions on what you are receiving directly from your vendor?

While this list is not exhaustive, I think these nine questions will help you uncover the quality and accuracy of the intelligence you can expect to receive from the solutions you are evaluating.

9 Questions to As Your Media Analysis Partner

1. Do you extract sentiment from a social posting? Can you handle sarcasm? How?  Can you distinguish between acronyms with multiple meanings (SMH = “Shaking my Head” and “So Much Hate” or more obscure abbreviations)?
2. Can you exclude trivial mentions of products (like “Let’s carpool and meet in the lot next to the Exxon station”)? How?
3. How do you extract out concepts like Social Responsibility and Technical Innovation if the words are not used in any manner in the post? If you cannot, how would we work around it?
4. Historically, what percent of search result postings that you provide are relevant to the brand and product team? (versus those that are peripheral postings that triggered a keyword but were not really relevant and/or actionable for our efforts?)  How do you know?
5. How do you determine who is influential? Is it keyword hits plus attributes like reach, likes, and retweets?  Can you take into account whether the tone and substance of their postings align specifically with our brand’s values and philosophy?
6. Can you show the actual results for our business live in your system right now?
7. Can you tell me the authors and outlets that are covering three of my seven competitors and five of the topics (not keywords) I am interested in but have not yet written about my company?
8. After the account is set up in your system, who maintains evolving keywords, brands and hashtags?
9. With imagery analysis, how do you capture the overall feeling that an image is conveying (cool, exciting, pensive, tranquil, apprehensive)?

These are questions any communicator with a complex brand or intense competition should be asking.  The answers will help tell you how sophisticated and truly insightful your potential provider is.  And if nothing else, this list might provide you with some new ideas and inspiration for additional metrics that you are not looking at today – but should be.

As you dissect the landscape of media monitoring and analytics solutions available to your team, it’s important to understand how best-in-class Communications professionals are tying measurement to business outcomes.

In our last blog post, we shared the results of an MIT study testing how untrained machines fare when it comes to measuring a brand’s key messages, identifying appropriate sentiment, and determining customer experience. The findings were less than stellar at 9% correct for key messages, 20% on sentiment and 33% on customer experience.

The focus in that post was on human vs. machine, but what happens when you pair the two together?

A hybrid approach to media analysis, one where a highly-trained human analyst utilizes top technology to track and monitor your media coverage and then correlates that data back to the stated business goals, produces exponentially better results.

The outcomes outlined below are achieved with a partnership of top technology and a level of human analysis that understands the context and nuances of media coverage specific to each unique company.

• Identify a new competitor– The C-Suite at a manufacturing corporation was specifically concerned about their coverage in high-stature industry publications, especially as it pertained to competitors. Although they were initially monitoring for a predetermined list of competing companies, a dedicated Analyst noticed that a young and untracked company was emerging, taking over a share of attention from important outlets. Proactively tracking that young company allowed the C-Suite to immediately pull powerful SOV reports and keep an eye on the new business, keeping them in the know on what was most impacting their business before it was too late.
• Stay in the know in a volatile market – A major telecommunications provider frequently needs timely media insights and reporting regarding product announcements, competitive announcements, or for ad-hoc internal presentations. They do not have the time to rework “directionally correct” data to get to the coverage analysis they need. Utilizing a dedicated media analyst with access to state-of-the-art reporting allows the team to be responsive and deliver their CEO the analytics they trust to make important decisions in the moment.

Ultimately, the power of pairing human with technology when it comes to media analysis is that it gets to the story behind the story, giving you the specific, timely, and trustworthy conclusions you need to be a brand hero with your C-suite.