3 Ways to Apply Data Science to Your Social Media

3 Ways to Apply Data Science to Your Social Media

Let’s see how data science can address this. Next, you can classify the sentiment expressed by users in different conversations around your brand, or your competition. Are they fans of your other products? Cluster analysis enables you to group users in specific communities: Single girls, aged 19-25, still in college, interested in affordable hair care products and beauty hacks Married women, aged 25-35, with an average income of £35K and above, interested in ‘best’ hair care products, supplements and salon treatments What’s more intriguing though, is that an advanced K-Means clustering algorithm can help you establish even closer proximity between such user groups. Your customer profile will then look as follows: “20-25 year old Instagram fans, who access the platform three to six times per day, posting about #haircolour #hairstyles #Loreal. To create a massive marketing push, the company used NetBase Audience 3D platform to identify 3.5 million people expressing positive thoughts about them on social media over the last three years. Adopt data-driven influencer marketing Finding the right match to promote your brand can be tough in the era when anyone with an Instagram profile can claim to be an influencer. First, the company uses data science to identify the brand’s audience, profile and personality based on their social media presence. You can employ data science and cluster analysis at a smaller scale to identify opinion leaders and influencers within your targeted communities and approach them with partnership deals. Your influencer marketing campaigns should not be based on vanity metrics alone; they can be backed up with solid data.

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Every day Instagram users publish almost 50,000 photos. Twitter users tweet 473,400 updates and 4.3 million users head to YouTube to watch a video. Some of those numbers and actions will matter for your brand. Others will have no impact at all.

Identifying important social media cues, trends and signals now requires extensive analytics capabilities. So how do you stop the incoming data flood and transform it into a steady stream of distilled insights? By applying science to the most pressing problems at hand.

1. Use cluster analysis to improve micro-targeting

Social media networks allow you to reach everyone and anyone, wherever they are in the world. That’s a good thing if you want to have a lot of followers. But as a brand you are not necessarily interested in getting likes from Sydney-based Katie or Vincent from France. They may be great people, but hardly relevant prospects for a local travel agency in Leeds.

Social networks present us with a new challenge – how do I find my ‘tribe’ of customers and connect with them?

These two questions are extremely important if you are staging a new product launch and plan to test the waters with different marketing collateral. You need to understand how your target demographics will engage with your offers. Let’s see how data science can address this.

First of all, you can deploy algorithms to help you identify the most commonly discussed topics on social media in your niche. You can match the popularity of certain topics (e.g. crafts, food or beauty) to a specific platform (Pinterest, Facebook, Instagram). This way you find where your target audience hangs out. Next, you can classify the sentiment expressed by users in different conversations around your brand, or your competition. Are they fans of your other products? Did they sign up with your competitor? What do they care about in general when it comes to ___?

At this point, you should have enough data to understand who you should target. But that’s not all – you can now multiply the number of likely customers by applying cluster analysis. Cluster analysis enables you to group users in specific communities:

  • Single girls, aged 19-25, still in college, interested in affordable hair care products and beauty hacks
  • Married women, aged 25-35, with an average income of £35K and above, interested in ‘best’ hair care products, supplements and salon treatments

What’s more intriguing though, is that an advanced K-Means clustering algorithm can help you establish even closer proximity between such user groups. You can estimate how frequently they access certain websites e.g. YouTube, Instagram or a…

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