8 Aspects in which Brands can leverage Social Media Analytics

Posted on 23 Jun, 2017

Let’s look at few insights related to social and digital media:

22% of the world’s total population uses Facebook
Over 50 million businesses use Facebook Business Pages.
2 million business use to Facebook for advertising
88% of businesses with more than 100 employees use twitter for marketing purposes
38% organizations spend 20% more than their advertising budgets on social channels

There is no doubt that social media is an essential driver for a brand’s success. However, not many brands are using analytics and data science to gain social media benefits. Even if they are using it, are not aware of key areas of analytics. In this article, I will talk about eight aspects in which brands can leverage social media analytics for their advantage.

360 degree Brand Tracking
Brand tracking is the process of monitoring the presence of a brand - their activities, their competitor activities, consumer trends, customer behaviours etc. across entire social landscape. This cross platform and in-depth monitoring of a brand provides invaluable insights which gives them a competitive advantage as well as keeps them up-to-date with their audience needs. For example, using brand tracking, detailed data driven insights can be used by the brands for an added advantage such as: What is the right platform, right time and right method to reach out to specific customers, How good is their promotional and offers strategy as compared to multiple competitors in same space.

Target Audience/Customer/Followers Analysis
Audience are the integral part of a brand’s success. Audience analysis includes a deeper analysis of a brand’s followers, fans and customers in different verticals such as audience engagement, audience sentiment and audience influence etc. Audience analysis requires different sources of data such as social profiles, timelines, survey records, transactional records and demographics data. Audience analysis unearths insights related to segmentation, demographic, mindshare, sentiment, top questions, top needs, top queries and themes associated with customer.

Content Analysis
Content analysis of social media posts (such as tweets, posts, emails, blogs etc.) of a brand could provide myriad of recommendations and facts that will ensure higher engagement and traffic. For example, by using natural language processing and machine learning on historical, present and competitor data, one could suggest right keywords and hashtags, optimal length and post type (pic, video, link), best times to post, so that a brand would garner higher and effective response.

Influencers Identification - Global and Brand Specific Influencer analysis includes identification and ranking of a celebrities and personalities across different dimensions such as - engagement rate, overall digital presence, followers acquisition and engagement rates. Influencers are important entities that share and endorses the brands and their products, analytics helps to segregate right influencers for right brands with associated insights.

Promoted Post Detection
Using classification models and sophisticated feature engineering techniques on the data of competitor social media posts, it can be predicted that which of the tweets or posts are promoted by a brand. Promoted post detection can help in demystifying competitor’s promotional, boosting and monetary strategies. By understanding these strategies, a brand look closely into their own approaches and optimize the practices they follow.

Impression Burnout Optimization
Advertisements are the key part of brand’s marketing strategies online, However, every posted advertisement is not completely effective, people start losing interest in an Ad with time. The likelihood of people clicking an ad decreases with time - this is called impression burnout. Analytics can help to optimize the features and content of an ad so that the burnout time of an ad is higher. Data Science can predict what is the true burnout period for an Ad, so that brand can either monetize the good ads and discard the bad ads.

Engagement Prediction
ML models can be trained to predict what is the likely engagement of their posts, if a post is expected to gain higher engagements, a brand can promote it more, or vice versa - if a post is performing expected to perform too bad, they can either alter its content or completely discard it with a new post.

Cross Platform Correlation
Analytics and Data Science can be used to identify which of the cross platform variables are correlated with each other. For example, a restaurant in US saw higher positive reviews on yelp.com when they started promoting personalized offers on twitter. Another example is the increase in email opening rates of a brand when they initiated a social media sales across facebook, twitter and instagram.

With the massive boom in social media interactions, brands have to identify right patterns using analytics and data science so that they can adopt a proactive and intelligent approach towards meaningful, significant social media success.