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How Machine Learning Can Enhance Community Engagement

Posted by Kenny S. on Jul 4, 2017 8:00:20 AM

2 minute read

One of the most important aspects of a community manager’s job is to curate and deliver valuable content for their members. While that may not seem like a tall order for a small community, what happens when you have over 2,000 members?

Community managers all over are asking the same question: “How can I use AI to scale engagement across my community?” Let’s take a look at how one community is making it happen.

NVIDIA and User-Generated Content

NVIDIA is making important strides in machine learning to process user-generated content (UGC). When NVIDIA hears from someone on one of its social media channels, it's machine learning algorithm immediately kicks in.

First, natural language processing analyzes the sentiment of the text. Then image processing determines the exact visual content of the post.

If the content is valuable and worth sharing, NVIDIA’s AI automatically responds and asks the user for permission to retain their content for future use.

NVIDIA’s Logan Rosenstein captures this process in three words: identify, classify, and notify. It’s a winning formula that brings success.

Using AI to Find and Share Relevant Content

Not only is machine learning a powerful tool in catching and promoting high-quality UGC, but even better, it can scour the internet for relevant content to share with your community.

Here are five ways machine learning can step in for you:

1. Segment - Break up your community into multiple segments by industry or interest. This way, you can tailor specific types of content to relevant subsets of your community.

2. Monitor Multiple Channels - Formulate target keyword phrases for each of your community segments. Monitor multiple social and web channels for keyword usage.

3. Identify Incoming Data - When you get a hit on any of your keywords, AI kicks in to analyze the content—judging each piece for validity, authority, and relevance.

4. Classify According to Segment - The newly discovered content pieces are sorted based on their relevance to each community segment. They're also tagged for future access.

5. Notify Community Members - When high-quality, relevant information is retrieved by the system, AI turns around and pushes it to the appropriate community members through any number of channels (social, direct message, email, forum posting).

Tweaking Engagement

This entire process is one large feedback loop. When a piece of content is shared, machine learning analyzes your community’s engagement with the content. As people interact, the AI learns which types of pieces garner the most attention and generate the most substantive conversation.

None of this is entirely new. Our Facebook and Apple News feeds are two everyday examples of AI's ability to monitor engagement and tailor content accordingly. With a few tweaks, you can apply this technology to your own community to keep your members consistently engaged.

It takes way too much time and energy to consistently monitor the web for new content that will appeal directly to your community’s interests. With machine learning, you’ll soon be able to offload this massive energy drain and focus more on what matters most: building relationships.

How do you curate enough content to keep your community engaged? Have you tried using AI?

Topics: Community, Marketing, News

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