4 Ways to Apply Data Science to Social Media Marketing in 2018

Opinion: Complex relationship patterns and groupings can become clearer through visualizations

It’s common for people to use data science when they actually mean data analysis or analytics
ra2studio/iStock

Harvard Business Review famously described data science as the “Sexiest Job of the 21st Century” in 2012, causing a massive explosion in opportunities in this space. Today, data science has spread its hold over the digital marketing landscape.

Particularly for social media marketing, data science promises a lot. From advanced analysis of social media activity on branded content campaigns to create insightful user personas via social media listening, to complex data patterns made easy to understand via visualizations, to overcoming the perennial problem of ad fraud in advertising ecosystems, data science has potential applications that significantly improve social media for brands.

In this article, I’ll cover four ways in which brands can leverage data science for better social media marketing results in 2018.

It’s disappointingly common for people to use data science when they actually mean data analysis or analytics, and that’s not exactly right. Data science is not even business intelligence. It’s way broader in scope, and it involves exploration of multisource data to understand unseen underlying pattern that bring out important insights and relationships, which can be expressed through visualizations.

Moving beyond word clouds with data-science-powered tools

Word clouds have been trusted tools for social media marketers to analyze social conversations and understand what’s being discussed.

Although brands could often stumble upon an important pattern, word clouds are, in reality, pretty blunt tools. Unless you have a high volume of activity, word clouds can be misrepresentative, requiring marketers to carefully guard against irrelevant words.

Thankfully, marketers have access to tools that leverage the power of data science along with natural language processing algorithms in order to contextualize word usage and deliver meaningful insights.

BuzzGraphs, for instance, show you how words are linked, and which words are most frequently used. Entity analysis also helps, associating words and small word groups with their semantic types, such as a brand, a person, a website, etc. Deep diving into BuzzGraphs and entity analysis is possible in order to gather more insight.

Data science for community groupings

Social media marketing campaign results need to be measured and improved continually. Targeting strongly connected groups, naturally, amplifies campaign effectiveness.

First, identify topic areas that receive good responses as a starting point for your community grouping campaign.

Data science has tremendous applications here. Based on the frequency of keywords observed, marketers can identify the most commonly discussed topics in social conversations. The topics can then be analyzed across social platforms to classify them.

In 2015, research journals published a lot of content about the use of machine learning in social media message classification. Today, marketers can use tools to execute the same.

Next, leverage cluster analysis to identify how people participating, for instance, in a Twitter conversation are associated with each other. Such analysis can then group people together, separating weakly connected groups.

Visualizations for greater insights

Social media explosion has been one of the reasons why the volume of global data is surging every year. Each regular social media user’s timeline is potentially the story of his or her life. Visualizations make it practical for marketers to understand these stories and generate insights that can massively improve social media marketing.

Social graph visualizations, for instance, showcase the social dynamics playing out around us. SociLab, for example, lets you visualize your LinkedIn network and evaluate its “quality.”

Complex relationship patterns and social groupings can become clearer than ever through visualizations. Data-science-powered social media tools help you by creating visualizations such as scatter plots to present correlations, pie charts to show proportions, line graphs to show trends and tables to show exact values. Hootsuite Analytics, for instance, can take your social media metrics and transform them into visualizations that make them much more insightful.

Advanced persona research backed by social media listening

Customer personas are much more effective than broad demographic descriptors. Personas are meant to humanize, although they’ve traditionally been filled with marketing jargon that eventually kills the effectiveness of targeting campaigns.

Data-science-backed tools can transform how brands conduct market research using social media data. Social media listening platforms can allow marketers access to global conversations, bringing together large data volumes, capturing customer opinions and trends and feeding the data to a brand’s specific market research campaign:

  • Begin with social media listening for researching a central topic.
  • From the general data, build maps of the most crucial consumer conversations.
  • Export the data to a spreadsheet and clean it.
  • Develop a listening dashboard to monitor discussions.
  • Study the natural language of the market and build it into your customer personas, helping copywriters create social content that converts more often.

Data is being called the fuel of the present and future. Your social media analytics need to hit overdrive, powered by data science. Trust the methods explained in this guide to get started.

 

 

 

 

 

 

http://www.adweek.com/digital/guy-sheetrit-over-the-top-seo-guest-post-4-ways-to-apply-data-science-to-social-media-marketing-in-2018/



Leave a Reply