How does the Public Relations Industry use Data Science?

Data is at the epicenter of this era: it is continuing to turn entire industries on their heads and has changed management directives at many giant organizations — and the disciplines of public relations (PR) and communications, as well as the organizations, businesses, and brands for which professionals work, are not immune from this revolution — in fact, they must be at the leading edge of it!
 

The discipline of public relations isn't exempted from the list of sectors that benefit from big data. While advertisers and marketers have been at the forefront of big data and its applications, the PR business has fallen behind. Many PR professionals still create media lists by hand, and most don't have the tools they need to effectively assess the results of their efforts. 
 

(Related - Applications of Data Science in Real Life )
 

Big data will be important in developing a successful PR strategy as PR professionals strive to increase campaign performance and provide higher consumer value. 

 

PR will always be a creative profession, but data-driven insights combined with creative narrative can help your communications initiatives realize their full potential.
 

Data science has evolved as an interdisciplinary work field in which tasks revolve around producing, managing, and understanding wide and increasingly vast volumes of data, as well as finding and sharing new insights from this data. 

 

Because data is the fuel supply for any artificially intelligent system, doing data science correctly is also an important component of artificial intelligence.
 

It may not be entirely apparent to everyone how data science and artificial intelligence may influence an industry concerned for the image or reputation, favorable perception, and profitability of organizations and individuals. 
 

Today's public relations professionals must transition to a position where they rely on machine learning tools, understand how their clients, as well as competitors in the private and public sectors, utilize data, and respond to and avert potential crises caused by data mishandling, data breaches, and biased artificial intelligence algorithms. 
 

On the plus side, perhaps PR experts will be able to use AI to better handle situations and avoid a possible catastrophe.

 

As a result, it's critical for public relations and communications professionals to have a firm understanding of data science and artificial intelligence ideas, language, and techniques.

 

( Also Read- Elements of Marketing Communication )

 

How can PR professionals use Data Science?

 

They don't need to be data scientists themselves, but they do need to be able to interact with data scientists, AI experts, and all of the customers and organizations that use these technologies, all while utilizing the most advanced tools to optimize their work. 

 

Experience with data science, particularly Big Data, is becoming increasingly sought after in both PR and marketing job postings. Big Data and digital understanding allow for a more thorough study of the outcomes (or lack thereof), as well as the identification and targeting of individuals most likely to be interested in the client's products and services.

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Workflow when working with Data to harvest a successful Idea in PR

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Data science's role in public relations can range from developing effective public relations strategies to evaluating and improving them. For most of the last few decades, public relations has been regarded as a difficult to quantify, if not incalculable, communications activity. 

 

Several conventions and practices have come and gone in the eyes of organizations and customers, all of which have been rejected due to their inaccuracy. 

 

Despite the lack of a precise way to measure its impact, PR efforts generate a significant amount of data, from which numerous insights can be derived. Word of mouth generated as a result of a PR campaign is now social - it can be tracked and measured indefinitely.

 

( Also Read - Marketing Management )

 

1. The Cringeworthy Task of going Viral

 

Almost every PR professional has had the harrowing experience of being given the task of creating content that "goes viral"–it's the golden standard of marketing strategy, but no one truly understands why anything goes viral. 

 

In fact, determining why some media content succeeds successfully is one of the most difficult problems. Natural language processing (NLP) has the potential to be a very useful tool in this domain. Algorithms like Naive Bayes and Random Forests can learn by evaluating prior material's language, allowing them to train models to anticipate the influence of future content.

 

( Suggested Read : What is NLP? )

 

 

2. Targeting a Journalist for a story and Media Coverage

 

A PR professional spends a lot of time deciding which journalists to contact about a story. Techniques like market basket analysis with Association Rules might help speed up this time-consuming procedure. 

 

This strategy is used in marketing to determine which products a customer is likely to buy based on previous buying activity. This strategy can be used in PR to determine which reporters are more likely to cover an issue based on their previous coverage.

 

For example, a writer who had previously covered antibiotics and constipation was 3.7 times more likely to cover probiotics. 

 

If you work for a probiotics firm, knowing this information helps you to use tools like Trendkite to find journalists who have already covered those themes, taking some of the guesses out of journalist targeting.

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Decision-Making process used by PR professionals using Data Science

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3. Choosing the Best Media Platforms

 

Furthermore, PR professionals are frequently expected to select optimal media targets without any prior outcome data. Data that describes the previous worth of an article for a brand is rarely available. 

 

Unsupervised learning techniques like clustering can group outlets by many attributes in situations like these, allowing you to begin to comprehend the type of value that a publication provides. K-means algorithms can group your target media outlets based on their similarities, allowing you to tier them and prioritize your efforts. 

 

( Related blog - Clustering algorithms )

 

Mining for hidden patterns in media coverage is perhaps the most interesting area for machine learning in PR. Examining how particular terms in articles are related to other terms is one of the more interesting topics of investigation.

 

Listen to this Podcast from Hacks and Flacks to know more about the latest advancements in data science for marketing and public relations.

 

Conclusion

 

Finally, these are only a few examples of how Data Science could be used in public relations. Media crisis prediction and news cycle analysis are two further areas where Data Science could be useful.

 

Public relations may not yet be at the leading edge of Data Science and artificial intelligence right now, but to best serve clients in a data-driven world, PR and communications professionals must become more knowledgeable about the rapidly evolving and overlapping fields of data science and AI, as well as the tools and techniques that surround them.