The Department of Treasury had an organization-wide goal to move towards online communications. To better engage their users and improve their operational excellence, they began surveying the constituents that came to their site.

The survey was a mix of “closed” (yes/no) questions and open-ended responses. While open-ended or freeform text answers are invaluable, they are often more challenging to analyze for trends. Now, couple this with the fact that most people who completed the questionnaires routinely didn’t answer the questions that were being asked– they answer different questions entirely – the information became very difficult to categorize for insight.

Discover how PublicRelay’s analysis allowed the Department of Treasury’s Online Services to improve usage and enhance satisfaction across the site, as well as ensure the on-going success of their push towards more modern, online communications.

Read the full case study here >>

A leading financial insurance company wanted to revamp its media measurement strategy. The new CCO tasked with implementing the improvement wanted to move their strategy beyond the manual media monitoring that the communications team had been employing. The team decided to adopt a human-assisted artificial intelligence solution that yields fully analyzed, accurate results in near real-time. The communications team can now spend less time worrying about collecting and organizing all the coverage data and instead focus on macro-level strategy. Company-wide goals like being known as a socially responsible organization are now attainable with all the new insights available. The communications team also has access to intelligence on their competitors, so the company knows how well it stacks up against the rest of the field.

Click here to learn more about the benefits provided to this financial insurance company with the help of their new human-assisted AI solution: Creating a Media Measurement Strategy Tied to Business Goals.

In today’s 24/7 news cycle, staying on top of what’s going on is a Sisyphean task for companies, non-profit organizations, and industry groups (not to mention private citizens). Issue tracking is a nonstarter for many busy professionals — especially in fast-paced industries like tech. What about tracking policy issues and concepts that aren’t summarized by a quick three-word Google search, like “online content responsibility?” The bigger the players involved and the more complex an issue, the more time-consuming and tricky it is just to keep up. The minute you’ve got a handle on the conversation, it transforms into something unrecognizable. Back to the drawing board.

Finding influencers who write about these issues can also feel like nailing Jell-O to a tree. Who’s writing about my complicated topics? Which of their articles is getting traction on social media? Who has a bone to pick with my industry? What authors write favorably about my topics and might be interested in my pitch? These questions are tough enough to answer when your topic is niche. What if your industry is “tech” and your topic is “who’s responsible for content posted on social media?” Tracking big policy areas like these (and the people with influence) takes time and resources and we know how scarce both of those are. That’s why analyzing industry/topic-related news to locate influencers is something few companies have the bandwidth to do well — despite the potential rewards.

Wading through the melee is possible, though — with a plan. Recently, PublicRelay and a leading tech industry advisory group picked five hot technology-related topics and set out to get an idea of what was being written about them and who was doing the writing:

• The effects of AI on job creation/destruction
• Who is responsible for content posted online?
• Use of the internet by terrorist/extremist groups
• Hacking/online security breaches
• Antitrust issues in the tech sector

Finding relevant articles to analyze is the first step. These topics are not simple, and cannot be summarized by a word or string of words. Human-assisted AI helps to quickly cull through the slew of media content published about the tech industry and isolate the most important coverage.

Next is the analysis of the coverage. This is where you answer questions like “who is writing negative articles about anti-trust law in the tech industry that are gaining traction on social media” or “are there new authors covering the effects of AI on job creation?”.

These answers not only help the association keep an accurate pulse on policy issues their members turn to them for but also inform their communications strategy. Insights around key media conversations that impact consumer and legislative stakeholder perceptions help them tailor an advocacy and influencer outreach strategy that gets results. These takeaways help bolster the association’s ongoing efforts to develop essential standards and ensure its members are heard on the Hill.

Read Next: “How Third-Party Influencers Can Shape Your Media Strategy“

No matter what terminology you prefer – driverless car, autonomous vehicle, autonomous driving, self-driving car or something else – the future of human and non-human driving is a hot topic. And we kicked off 2018 with two major consumer events that had the media abuzz about it. PublicRelay analyzed traditional and social media around autonomous vehicles at both events and here are some of the interesting nuggets that we uncovered.

Read Next: “6 Steps for Measuring PR at Your Next Event“

CES hands down “wins” for sheer volume of coverage at 7X the number of articles written on the subject. Autonomous drive was among the top five products discussed at CES 2018, along with gaming products, computers, and smart home technology. At the Detroit Auto Show it placed in the top four with vehicle infotainment systems and electric cars.

Social sharing of autonomous driving articles also put CES in the lead by 8X – Detroit Auto Show articles were shared 19,416 times across Facebook, Twitter, and LinkedIn and CES articles were shared 151,662 times. The topic as a percentage of traditional coverage for each show was much closer – the topic appeared in 13% of total CES event coverage, and in 11% of Detroit Auto Show coverage. The chart below outlines the top topics by percentage for both events.

The most obvious reason is that CES is first on the calendar. But more probable is that CES’ focus on technology is a perfect platform for showing exactly HOW these vehicles are coming to market. The CES audience is extremely interested in technology and innovation that they can use today or in the future. How a car can drive itself certainly fits that bill.

The self-driving topic appeared in more than half [54%] of Nvidia’s traditional coverage at CES. They released a new platform for self-driving vehicles, and announced partnerships with VW and Uber. At a press conference, Nvidia’s CEO stated, “the complexity of future cars is incredible.”

While autonomous driving was covered at the Detroit Auto Show, the show focus is on what is available today for consumers. For instance, articles about self-driving features within traditional cars, rather than fully autonomous vehicles were popular.

A hot self-driving sub-topic in Detroit was government regulations and guidelines, with many articles citing quotes from U.S. Transportation Secretary Elaine Chao and other legislators. Chao appeared in 16% of the Detroit Auto Show’s autonomous driving coverage.

Learn more about PublicRelay’s coverage of CES: Understanding the Success of CES.

The world of data and measurement in PR is consistently a mixed bag. In a perfect world, organizations would have a useful bounty of accurate data and insights. Unfortunately, many times this aspirational goal is unrealistic. The major downside to data-driven analytics right now is that people simply don’t trust the data.

According to recent (2017) studies by MIT, Cal Berkeley, and Northwestern University, technology alone, including artificial intelligence (AI), is not even close to delivering the needed insights. It’s going to take some hefty shifts to gain the right information along with the industry’s trust. According to a recent survey by PRNews, over 60% of communications professionals are asked by their CEO and executive boards for data-driven analysis and metrics. Gone are the days of measurement simply to prove one’s worth – measurement is now necessary as organizations look to tie efficacy of campaigns and broader business goals.

While it’s great that executives and board members are seeing the value that strategic data and intelligence can bring to their business, more than 75% of communicators found the data to be unreliable. This number is staggering considering that both communicators and executives all desire media analysis to drive both reactive and proactive strategies.

So what do you do when the data just isn’t up to par? When practitioners are wasting time cleaning up data to figure out what insights can be derived? Here are some things that communicators can do now to take steps toward this ultimate data utopia.

Be Your Own Advocate

55% of communicators use media analysis to drive both reactive and proactive strategies…BUT only if they can trust it. It’s essential to prove to board members and business leaders that smart communications data is in fact available and deserves a seat at the table. Mapping results to business goals is the best way to advocate for a strong measurement function.

Historically, the data executives saw wasn’t smart, helpful or insightful. In fact it was rather surface level including things like mentions, reach and impressions. Today we have copious amounts of relevant, helpful and strategic data at our fingertips that should be used to inform business strategy. Communicators don’t need to use data to prove worth and ensure job security anymore; they can instead turn it into something useful and become a strategic partner to the business.

Implement Efficiency

Nearly 40% of communicators find it difficult to understand the media data they receive and are spending way too much time hunting for insights instead of developing strategic campaigns. In fact, 69% of communicators said they’d rather spend time building strategic messaging plans and 65% said they’d prefer to put more effort into pitching or focusing on influencer outreach rather than media analysis. Basic metrics aren’t helpful, and automated tools aren’t enough to add enough valuable context to business leaders. Operating efficiently is paramount.

It’s essential to know that you don’t need to go overboard on measurement, especially when you clearly understand your business goals. In both cases of B2C and B2B – it’s the context and not the counts that are going to move the needle.

Similarly, we often see sarcasm used on social media and even in traditional media which can be easily misinterpreted when looking at tone. This can be as challenging to detect as contextualizing a positive statement about you in an otherwise negatively-toned article. Analysts expertly trained at picking up these nuances are extremely efficient and accurate, giving you back time for more strategic endeavors.

Gain More Insight

Over 60% of communicators would like media intelligence to be more “insightful.” Now that is an aspirational concept that could mean different things for every business, but in reality it goes deeper than surface level measurement.

Say goodbye to content overload. In today’s media landscape, business leaders would much rather have fewer pieces of very insightful data, than a mass amount of media coverage without much context.

Now to obtain this data can provide insight into share of voice and how companies are doing against the competitors, digging into sentiment for brand and reputation drivers, and also looking at how your organization is perceived on social media. Communicators need to illustrate how corporate social responsibility efforts are progressing along with sentiment at conferences and trade shows. At the end of the day, this is the type of aspirational data that is going to put businesses ahead of the competition.

As it stands, there needs to be a fundamental shift in the way communicators generate trustworthy media intelligence. With CEOs and boards demanding data driven analytics to help make decisions, communicators need to take the next step with their analytics solutions. The industry needs to rise to the measurement challenge so the information they are delivering is insightful, reliable and exceeding expectations.

Read Next: “How Third-Party Influencers Can Shape Your Media Strategy”

It’s easy for PR pros to feel left behind in the Big Data era. While marketers have countless tools for targeting, personalizing, tracking, and measuring how their campaigns perform, public relations initiatives can seem harder to quantify. After all, how do you measure influence or public sentiment? And how can you connect those concepts to business outcomes?

Communications teams need a way to measure the impact of influencer engagement strategies, uncover new brand influencers, and be ready with data to prove the value of their influencer strategies.

Here’s the good news: using accurate data to guide your influencer outreach strategy isn’t just possible, it’s also the key to getting an edge on your competition. Using these five tips, you’ll be able to answer:

• How is our influencer engagement strategy performing?
• Do we have the right influencer relationships?
• Are they worth the investment?
• What should we do differently?

With data to prove your influencer engagement is delivering results, you’ll have more leverage when allocating budget and resources towards your PR program — and become a strategic asset to your company.

Take Your Influencer Outreach Pitches to the Next Level

Let’s face it: your brand’s ideal influencer likely keeps a busy schedule and a packed inbox, so if your pitches aren’t laser-targeted and personalized to perfection, you’re wasting time and a potentially lucrative relationship.

To make a great first impression, you need solid data about your influencer. Many author and outlet databases provide high-level details about an author’s beat and background, but that’s not enough information to engage the influencer, let alone ensure that they’re the right fit for your brand.

You need to take these insights to the next level. Analyze how someone has written about a topic, industry, or competitor. With a more complete picture of your target influencers, you can craft a pitch that catches the right influencer’s attention and piques their interest.

Pay Attention to the Interplay of Social and Traditional Media on Influencer Coverage

Say a powerful industry influencer mentions your brand in a large trade publication — that’s terrific, but what traction did this article get on social media? The sheer volume of articles written and the reach of the publication are not necessarily indicators of high social sharing probability.

With accurate media intelligence and social listening, you can measure influencer engagement on platforms like Twitter, Facebook, and LinkedIn and determine whether they have enough reach to drive your brand’s message home. Before you even identify influencers, you can get a sense of who to look for by overlaying social sharing data on your traditional coverage data, revealing which platform each topic gets shared on most often.

A firm understanding of how social and traditional media build on one another in your particular industry points you in the right direction. Look at how frequently an influencer’s messages are shared on the platforms most important to your business, and you can efficiently and effectively create a messaging plan to engage these thought leaders.

Go Beyond Authors and Outlets — Identify Third-Party Influencers

Reporters and social media stars aren’t the only influencers you should reach out to. You should also identify third-party influencers like industry experts, regulatory groups, academic researchers, political organizations, and NGOs. These thought leaders may be less visible than the influencers you typically think of, but they hold tremendous sway over media coverage of their fields.

Using accurate analytics, you can track the impact of third-party influencers by several measures, including:

• How often they are quoted or cited in articles
• How powerful their social and traditional media channels are
• How frequently they publish

Once they’re on your side, you can access industry trends just beginning to bubble up — and be top of mind once those topics make impact.

Find Industry Whitespace, and Leverage Influencers to Make It Yours

The attention economy is a buyer’s market. To engage new audiences and boost your influence, you need to find topics your audience cares about before your competitors get hip to that conversation. A data-driven influencer engagement strategy is a powerful tool for carving out that niche.

One Fortune 500 telecommunications company used this tactic to craft messaging around a topic that was picking up steam: data privacy. They identified industry influencers shaping the narrative, as well as the sentiments underlying their coverage. The company cultivated powerful relationships with many of these influencers, who helped guide the organization’s messaging plan. These alliances gave the telecom firm confidence as it joined the data privacy conversation.

Proactively Measure Your Impact

Measurement is more than a look backwards — it’s a constant, multifaceted evaluation. Start by establishing your baseline across brand values, then prove that your program is working with robust data that you can tie to KPIs.

Constantly combine data about sentiment and tone toward your brand, your executives and spokespeople, your products and services, your industry and competitors. Determine how that tone maps to your reputation drivers, corporate values, and goals. Evaluate whether the frequency of coverage about your brand and the topics it cares about is increasing or decreasing.

Finally, ask yourself the following questions:

• Are influencers helping to grow my brand’s audience, reach, and engagement?
• Is my influencer strategy helping with message penetration among key publics?

Get Results From Brand Influencer Engagement

Become an indispensable strategic partner to your business with the media insights you need to engage new audiences, boost your reputation, and reach powerful brand influencers. PublicRelay provides the expert data analysis you need to identify influencers, cultivate relationships, and prove the value of your efforts with measurable results.

Follow PublicRelay on Twitter for more news, advice, and insights on influencer engagement, and contact us today to learn more about how PublicRelay can turbo-charge your PR strategy.

In today’s world, “You are what you tweet” – as well as what others tweet about you. Take the popular “Mean Tweets” segment on ABC’s late night show Jimmy Kimmel Live!: Celebrities sit on a stool and read hateful Twitter posts addressed to them, as the classic R.E.M. song, “Everybody Hurts,” plays in the background. By showcasing the not-so-nice things users write on social media, the segment satirically underscores the power of negative tweets.

Companies too are paying attention to what social media users are saying about them, as opposed to simply measuring metrics. While an impressive volume of shares, re-tweets or likes demonstrates online engagement, after all, they don’t illustrate how the audience perceives the brand’s story. Engagement can be both good and bad, and the latter may convince you to shift gears. But does it provide you with enough insight about which direction to take?

Read: The Role of Social Media in Public Relations

This is where substantial and valuable measurement comes in, in which a partnership between humans and technology proves critical. Why? Because social media analytics delivers insights about stakeholders’ perceptions of brands. But whether positive or negative, there are always many layers to those sentiments. Disappointment and sarcasm, for example, represent key indicators of an ongoing campaign’s health — recognizing these reactions as more than just “negative” can help a company successfully redirect the campaign. Because analytics solutions will encounter some limitations here, human analysis should play a lead role in fully understanding social media posts, and discerning between feelings, i.e. the “why” behind the “what.”

In addition, accuracy remains an elusive quality, especially when organizations solely rely upon tech solutions to interpret the public’s voice. An analytics tool will misinterpret a sarcastic tweet as praise if it cannot detect what “mocking looks like. But people can often spot sarcasm, mockery, irony, etc. the moment they see it.

With people filling in “what” and “why” gaps which tech tools cannot, brands can narrow in on the nature of user sentiment, to design, readjust and execute more targeted strategies in real-time. What’s more, humans and technology can combine to bring a higher level of intelligence to predict social media storms or viral shares. With this, organizations can pinpoint factors that trigger a viral post, and then customize and model their approaches with new-found enlightenment which contextualizes user engagement. This will bring unlimited potential for highly positive conversations about your brand.

To summarize, brands must focus on two essentials in developing a social media measurement strategy, as supported by people and technology:

Directing Campaigns as Sentiments Dictate

Let’s illustrate this “must do” with a common scenario: A news story or brand initiative hits the public radar, and clearly misses the mark. Through effective social media measurement, the company in question deconstructs the intention behind stakeholders’ posts and traces it back to the specific aspect of the news story or brand initiative that drove negativity, to modify this element. Thus, near real-time insight reroutes a campaign that was headed for a cliff, while identifying what worked and what didn’t.

Understanding the “Why” Behind the “What”

To rise above the noise and really hear what online stakeholders are saying, it’s essential to understand the “why” behind a reaction, to come up with a response which is genuine and distinct. Ultimately, this reinforces a brand-stakeholder relationship built upon trust, overcoming hurdles along the way.

At PublicRelay, we ensure that our clients take full advantage of both human and technological resources to maximize the value of the morphing social media landscape. We enable them to transform quality data into actionable insights, to confidently and accurately manage and adjust strategies based upon stakeholder sentiment. We empower them to measure shares, re-tweets or likes in business terms. If this sounds like something you’d like to discuss further, then please contact us.

Everybody is talking about how artificial intelligence (AI) is changing the world and how it is the future of just about everything. Even communications professionals are abuzz with their desire to jump on the AI bandwagon for their media analytics.

It’s true; AI can be pretty impressive. It is already recommending products to consumers, catching credit card fraud, and targeting advertising with uncanny accuracy and effectiveness. Even doctors are starting to get assistance from AI in diagnosing disease through analysis of symptoms and lab results.

So how does AI “play nice” in the communications world?  In one word – carefully.

Let’s start by digging into what AI does well…

The Good

Most experts would agree that AI can be great at handling scenarios that involve a yes/no answer, particularly when there is a robust feedback loop to tell the system when its prediction is right or wrong on an ongoing basis.

Let’s look at three examples of AI usage that illustrate this strength:

Determining Fraudulent and Legitimate Charges

When a credit card is stolen, it can create a perfect learning opportunity for AI. A bank customer reports their card stolen and the identifies which charges were legitimate and which were fraudulent. Any uncontested charges are implicitly used as confirmation of valid transactions, thereby reinforcing the characteristics of legitimate transactions. If you take that information from millions of cardholders, AI can use those data points to predict with uncanny accuracy when it sees a change in a purchasing pattern that’s likely fraudulent.

AI also uses “geofenced” data to protect your credit card account – knowing the locations (geographies) where you normally visit and spend.  In addition, AI “learns” how you (and thousands of other similar voyagers) travel, using historical patterns of purchases – hotels, restaurants, etc. to approve or flag as suspicious any out-of-town spending.  Why does that work? Because AI has perfect data from a feedback loop with thousands or millions of data points and is being taught the right answer every day.  Even if you move to a new city, AI can use a real-time feedback loop to generate new data and adjust its predictions with no human input required other than normal card use in your new hometown.

Credit card transaction validation is a very effective use of a yes/no feedback loop that drives powerful AI learning and effectiveness.

Online Advertising and Product Recommendations

When you see ads on the internet, most of the advertisers are using you to help test thousands of variants of different advertising attributes such as the type and size of ad, time of day delivery, pricing deals, and even the words shown to you. They might even target specific product ads based on what you have shopped for in the past. (Who hasn’t been chased by ads across the Internet for that new pair of shoes you browsed one time on an e-commerce site?)

How are they doing this? The advertising companies are improving their targeting by using AI reinforced with perfect information. When you click on an ad and go to an e-commerce site, you either buy, or you don’t.  YES vote.  NO vote.  AI will constantly learn which advertising attributes work and cause people to click.  With millions of interactions to learn from – all tagged with reliable, fact-based results – computers can learn very quickly what works best in just about any situation.

In a similar scenario, Amazon, the e-commerce giant, uses AI to drive product recommendations.  For example, when buying a shirt on Amazon, you see a set of product photos (slacks, belts, etc.) with the headline, “People who bought this also bought these:” What the AI technology at Amazon and similar online companies does is look for patterns in people’s purchasing behavior to suggest additional items that follow that same pattern.  Of course, if you then click and buy the recommended product, that’s one more ‘plus’ vote for that AI recommendation.

Advertising customization and targeting and Amazon online shopping are more good examples where AI is learning from actual transactions. You either clicked on the ad and bought the recommended product, or you didn’t. It’s a yes/no answer providing perfect feedback.

Spam Email Identification

Identifying when email is legitimate or spam is one of the best mass-applications of classification in the supervised machine learning field. Often called ‘Ham or Spam’, AI uses patterns of words, characters, and senders to identify likely spam. Yet the system can only improve if humans flag emails as spam – or go to their spam folder to retrieve legitimate emails and flag them as ‘Ham’ (not spam).

Early spam identification systems used the feedback of the masses to apply standardized, mass email filters to individual users.  In recent years, some spam filters have begun to allow for additional customized spam determination based on individual user preferences and feedback.  This approach becomes especially helpful as some people flag legitimate e-commerce offerings a spam – offers that they perhaps opted into months or years before but no longer want to receive.  Other users will desire to keep those very same emails coming to their normal inbox.

The human yes/no feedback loop is critical to the ongoing, evolving effectiveness of these spam-filtering AI tools.

The Bad

Here are some examples of where AI is not ready for prime time.

If the answer is not known it can’t be fed back to the computer

For example, say you’re looking to hire a new employee, and the (AI) computer says you should make an offer to a person based on the data. If I hire that person and it either works out or doesn’t, that’s one piece of data. But what about the people I didn’t hire? I will never know whether they would have worked out, and AI is not able to confirm my rejections. It is hard for AI to determine the best hire when it only gets feedback on the people I chose to hire.

This is the challenge of what are called Type I vs. Type II errors. A Type I error is a false positive: someone AI recommended who turned out to be a bad hire.  We can learn from that type of error. A Type II error, on the other hand, is a false negative: someone AI passed on who would have been good, but I’ll never know that for sure. We cannot learn from that type of error.  So when AI cannot be given information on Type II errors, it has only half the necessary learning set to advance the AI properly.

Another variation of the AI challenges in hiring is when the AI system is exposed to all-new data.  For example, if your resumes to date have all been from East Coast schools and for applicants with engineering degrees, what does your system predict when exposed to a Stanford graduate with a physics degree?  AI struggles to reach a conclusion when exposed to vastly new, deviant data points that is has not seen before.

Can AI still learn in these circumstances? Yes, to a degree, but it does not see (and cannot learn from) the missed opportunities, and it needs enough of the new data points to begin to model and predict outcomes.  The data that is collected from the hiring decision represents a fatally incomplete training set.

If the data sets are small

For example, if you are making a one-time life decision such as what house I should buy (not the price I should pay, which AI is good at, but rather what house will work for me and my family), the data set would not be large. The data might suggest I will like the house for the community and the features of the house. If I buy the house, regardless of whether it works out or doesn’t, I still only have a single piece of feedback to learn from. It is hard to learn from tiny data sets, as you need thousands if not tens of thousands of data points to run through machine learning to train it to make informed decisions.

If the answer is indeterminate compared to the yes/no answer

This is probably the biggest area where unassisted AI fails at proper classification. And it is the problem that most affects those of us seeking trustworthy media analytics.

How a person sees content frequently depends on their perspective. ‘Good’ things can in fact be ‘bad’ and vice-versa. And computers can’t be taught one-answer-fits-all approaches, which is what most AI-powered automated media intelligence solutions are doing today.  Two people can read the same story and have a very different opinion of the sentiment.  Their take may depend on their political or educational background, their role in a company, or even the message the company wants to be heard in the public – when positive discussion of a taboo topic is seen as a bad thing.

In addition, AI can’t reliably interpret many language structures and use, including even simple phrases like “I love it” since they can be serious or sarcastic.  AI also struggles with double meanings and plays on words.  And AI is unable to address the contextual and temporal nature of the text and how the words, topics, and phrases used in content change over time. For example, a comparison to Tiger Woods might be positive when comparing to his early career, less positive in his later career, and perhaps quite negative in a comparison to him as a husband.

If the subject matter is evolving

Most AI solutions being applied to media analysis today use what can be called a ‘static dictionary’ approach. They choose a defined set of topical words (or Booleans) and a defined set of semantic-linked emotional trigger words. The AI determines the topic and the sentiment by comparing the words in the content to the static dictionary. Current studies like “The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods,” (Nelson et. al., 2017) have proven the dictionary methodology does not work reliably and that its error increases over time.

The fundamental flaw in this AI method is that the static dictionary doesn’t evolve rapidly as topics and concepts shift over time and new veins of discussion are introduced. Unless there is a way to regularly provide feedback to the AI solution, it cannot learn and the margin of error grows and compounds quickly.  It is a bit like trying to talk about Facebook to someone transported from the year 2004 who only understands structured publishing – they just cannot understand what you are talking about in any meaningful manner because mass social media was not yet developed.

As these examples show, AI struggles with interpreting complex situations with either small data sets or indeterminate answers that evolve over time.  So what does this mean to me as a professional communicator?

AI applied to media analytics needs to be guided to be successful. There are three specific areas where AI needs a boost to be successful analyzing media:

• Changing Conversations: As seen in the Cal Berkeley research, for an AI system analyzing media content to remain accurate and relevant, it needs to be constantly trained as conversations and popular phraseology shift over time. You need enough consistently superior-quality analysis to feed back to the computer and train it.

• Perspective: You need to tune AI specifically to understand your perspective. A solution tuned to someone else or all companies blended together just won’t work.  This is because the phrase that one person (or company) determines is relevant and positive might be viewed differently by another person with different priorities or messaging goals.

• Context: The conversation ecosystem needs to be taken into account. Often coverage is bookended by events, public discourse, and related coverage outside the sample set of coverage.  In his article just a few weeks ago for MIT Sloan Management Review, Sam Ransbotham writes, “While the pace of business may be ever-accelerating, many business decisions still have time for a second opinion where human general knowledge of context can add value.”

It doesn’t mean you have to analyze everything to train an AI system, but you need to analyze enough data so that your computer can learn robustly from them. AI alone can’t teach itself about changing social conversations, perspective, or context.

On the bright side, humans can work with AI by defining, training, and maintaining a dynamic, accurate, and reliable human feedback loop. This means persistent training, unique for each individual company, with human attention to help AI bridge the gap between what it’s trained on, and what the customer is trying to know.  Supervised machine learning is almost universally considered to be the leading approach to solving the human content analytics AI problem for the foreseeable future.

So how will you use AI?  Smartly, I hope.

PublicRelay delivers a world-class media intelligence solution to big brands worldwide by leveraging both technology and highly-trained analysts.  It is a leader on the path to superior AI analytics through supervised machine learning. Contact PublicRelay to learn more.