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How can Data Mining help the PR industry?

Data Mining, Top PR Firm in Mumbai, PR Firm in Mumbai, Best PR Firm in Mumbai, PR Agency in Mumbai, Public Relations Agency in Mumbai, PR Company in Mumbai, PR Company in India, Public Relations

Data and analytics assist companies to understand the market and make better decisions. They also guide companies to reduce their costs and improve productivity. As per a McKinsey Global Institute survey, the traditional style of working implemented by companies are overshadowed by data-driven companies to 23 times when it comes to attracting customers. It further adds that 71% of customers expect companies to deliver personalized communications. Those who fail to provide these experiences lose three-quarters of their customer base. The numbers show the importance of data in business decisions. Another study by PwC businesses shows that companies that utilize data and analysis in corporate decision making turn out to be better decision makers as opposed to those who do not use data at all. However, the PR industry has lagged behind when it comes to utilizing data mining and its applications, compared to advertisers and marketers. The majority of PR professionals lack the necessary tools to efficiently evaluate the outcomes of their work and still compile media lists by hand.

Data Mining:

Large databases and the development of computers have made it feasible to gather a lot of data. Consistent analysis of the data gathered can reveal relationships and aid in resolving issues. As a result, many corporations are working towards data mining to understand their clients and their expectations. 

Changing Dimensions of Data Analysis:

Times have certainly changed. Extensive market research to gain insight on new trends in social perspective is not required anymore. Tools such as Google Trends provide all data on real time basis. The most popular search phrases in real time are available with the help of Google Trends. This helps when there is a need to react to the news immediately. Even the popularity of particular search phrases can be tracked and compared over time. For instance, it doesn’t take long to locate the nation’s most popular social network over the last 12 months. Google even offers a list of related searches as well as a comparison tool for the same search in several locations. 

Changing Dimensions of PR:

Media constantly tussles with click bait, listings, and memes to attract reader attention. Major publications try to keep their commitment to publish content with valid statistical data. This shows how data plays an important role in the media and journalism. This in turn validates that the PR industry should also embrace data utilization while commencing its everyday activity. Data should inform all the content, messages, brands, metrics, and stories you present to the media. While data should inform every aspect of your PR programme—from setting and measuring goals to identifying influencers, generating leads and more—it must be at the heart of all narrative content and development.

Considering these changes and the importance of data utilization in public relations, the following are the ways data mining can be helpful to the PR industry:

Going Viral is Critical:

Almost every PR professional has had the hard experience of being tasked with creating “viral” content—it’s the gold standard of marketing strategy, but no one fully understands why something doesn’t work. Determining why certain media are successful is one of the most difficult problems. Natural Language Processing (NLP) has the potential to be a very useful tool in this area. Algorithms like Naive Bayes and Random Forest can learn by evaluating the language of past documents, allowing them to train models to predict the influence of future content.

Finding Journalist to Pitch Stories:

A PR professional spends a lot of time deciding which reporter to contact about a story. Techniques such as market basket analysis with association rules can help speed up this time-consuming process. This strategy is used in marketing to identify products that customers are likely to purchase based on previous purchases. This strategy can be used in public relations to determine which journalists are more likely to cover an issue based on their previous coverage. For example, a writer who previously mentioned antibiotics and suffered from constipation was 3.7 times more likely to mention probiotics. If you work for a probiotics company, knowing this information will help you. Using tools like Trendkite to find journalists who have covered these topics in the past helps to takes the guesswork out of picture for targeting journalists.

Media platform

Public relations professionals often have to choose optimal communication objectives without any prior outcome data. Data describing an item’s historical value to a brand is rarely available. Unsupervised learning techniques such as clustering can group stores by multiple attributes in situations like these, allowing you to begin to understand the kind of value posts provide. K-means algorithms can group your target vehicles based on their similarities, allowing you to prioritize them and prioritize your efforts. Uncovering latent patterns in media coverage is perhaps the most exciting area for machine learning in public relations. Examining how specific terms in a paper relate to other terms is one of the most interesting research topics. Data has turned the tables for every industry. Many corporations understand the usefulness of data mining for their success in the market. The multidimensional public relations industry is also going through these changes and requires adapting to new technological advancements such as data mining techniques to get faster results and stay ahead in the changing market. PR professionals face many hurdles while trying to create viral content, finding the right journalist to pitch their stories and looking for the right media platform. Data mining can help them overcome these obstacles and allow them to find the right ways to get ahead in the changing market.

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