If you’re currently relying on social media engagement metrics to measure the success of your latest campaign or product launch, it’s time to step up your game.
Artificial Intelligence for social media analytics is more accessible and more advanced than ever before. Natural language processing (NLP) is a rapidly advancing field of AI, making it easier than ever to get deep insights into your social media audience.
In this blog, we’ll discuss what AI social media analytics is, and we will explore some of the benefits that AI-driven social media analytics can offer businesses as well as the challenges and how to overcome them.
What is AI social media analytics?
AI social media analytics is the process of using AI algorithms to analyse social media data. This can be done for a variety of purposes, such as understanding audience sentiment, or building an accurate customer profile.
Forms of AI social media analysis:
- Sentiment analysis: AI can be used to understand the sentiment of social media posts, comments, and reviews. This is useful for understanding how your audience feels about your brand, product, or service.
- Customer profiling: AI can be used to build accurate customer profiles. This is useful for segmenting your audience and tailoring your marketing messages.
- Mention tracking: AI can be used to track mentions of your brand, product, or service. This is useful for understanding how often your brand is being talked about, and in what context.
- Engagement tracking: AI can be used to spot which type of social media content is getting the most engagement and predict what content will be most successful in the future.
- Competitive analysis: AI can also be used to track and compare your social media performance against that of your competitors.
AI vs. Traditional social media analytics
Without the help of AI, most people stick to analysing numerical data that’s easy to gather and process. Namely, they limit themselves to analysing simple engagement metrics such as the number of likes and followers you’ve gained. These are often termed “vanity metrics” because they don’t give you any insights into why people are engaging with your content. AI social media analytics can help you go beyond vanity metrics, and gain a deeper understanding of what is driving engagement on your social channels.
Traditionally, if you want a more thorough analysis of what’s being said about your brand online, you have to do it manually. This involves many man-hours of combing through vast amounts of unstructured data looking for patterns. Not only is manual analysis time and resource-intensive, but it’s also susceptible to human error and bias. AI social media analysis meets many of these challenges.
Benefits of using AI for Social Media Analytics
Process large amounts of data
AI can help you process and analyse large amounts of data more efficiently. This is especially useful for social media data, which is often in the form of unstructured, long-form written text. With AI social media analytics you can process thousands of comments and reviews within a matter of minutes.
Identify patterns and correlations
AI can help you identify patterns and correlations that would be difficult to spot manually. For example, you can spot correlations between topics and customer satisfaction levels.
AI can help you improve the accuracy of your social media analytics. This is because AI algorithms are not biased by human factors, such as personal beliefs or preconceptions.
Make better decisions
Leave vague insights and guesswork behind. AI social media analytics provide insights for data-based decision-making for your marketing and product development teams.
Challenges of using AI for Social Media Analytics
However, AI social media analytics is not without its challenges. Below are three issues to be aware of when choosing a tool for your business:
Quality of AI Algorithm
Not all AI algorithms are created equally. If the AI uses poor quality training data, or if the AI algorithm is not well-designed, then that will be reflected in the quality of the insights.
At Symanto, our state-of-the-art NLP models are capable of understanding how word meanings change in different contexts. For example, in the Pharmaceutical industry, the word “kill” is extremely negative, but in the gaming industry, the word can be neutral or positive. Our agile team of AI researchers and data scientists is constantly improving our AI algorithms to make sure our clients have the most advanced AI solutions.
When using AI social media analytics, you will be dealing with large amounts of customer data. If you’re using a third-party tool you need to be assured that your data is in safe hands.
Symanto’s systems are protected with the most advanced digital security available. We are GDPR compliant and use encryption while handling clients’ data to safeguard against any unauthorised access or use.
Over-reliance on AI
AI is a powerful tool, but it should not be used in isolation. AI social media analytics can help you gain valuable insights, but you should always supplement AI insights with human expertise.
Symanto is designed to help facilitate researchers and analysts. We present the results of our analysis in the form of intuitive data visualisations so that your researchers and in-house analysts can easily understand and draw insights from the data.
The cost of AI social media analytics can be a challenge for businesses on a tight budget. However, the benefits of AI social media analytics often outweigh the costs, in terms of both time and resources saved.
In conclusion, AI social media analytics is a powerful tool that can help you gain valuable insights into your social media data. However, you should be aware of the challenges involved in using AI social media analytics, and bear that in mind when choosing the right solution for your business.