In any such scenario, you should focus on looking beyond. In other words, start researching into their online behavior and what makes them tick. Try to understand how and where your ideal customers are spending most of their time. And above all, how they’re socializing on various social networks. This data would be crucial to improve your social media marketing.
I know, I know there are a lot of options here! Luckily, they all have free plans so you can experiment and find the platform that works best for your social strategy. At the end of the day you can’t go wrong moving onto a social media management tool – it will likely save you time, grow your social presence, and possibly even make you some money! 

I won’t pretend to be an expert in analytics, but I appreciate the vast number of metrics available. Luckily, there are experts out there, like the fine people analyzing tweets at Buffer, Twitter themselves, and Kissmetrics, who are kind enough to give us a beginner’s guide to Facebook insights. Personally, I tend to watch post engagement (based on audience size) and URL clicks when managing social media because our goals are to expand and engage our followers while driving them to the site.

Likealyzer takes the guess work out of your Facebook strategy. The recommendations point you in the right direction. Compare your Facebook page to competitors, partners, or brands you admire. Likealyzer will recommend similar pages to watch. Likealyzer converts raw Facebook analytics into a simple yet sophisticated report. Recommendations, not just metrics. Likealyzer is a Facebook tool designed for social media managers, agencies, and entrepreneurs.


Explore your Twitter network. Discover which people interact the most and what they’re talking about. It’s also a great way to find relevant people to follow. The visualization runs right in your browser and displays data from Twitter. Mentionmap loads user’s tweets and finds the people and hashtags they talked about the most. In this data visualization, mentions become connections and discussions between multiple users emerge as clusters.
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