Recently SOLVVE talked about getting ready to embrace Industry 4.0 and how Machine Learning (ML) and Artificial Intelligence (AI) are an integral part of the upcoming change. In the previous articles, we have already explored how these technologies can be applied in healthcare for disease predictions, in eSports for matchmaking, scoring and betting, as well as in eLearning to improve students’ performance and experience. And this time, SOLVVE would like to look at different aspects and possibilities of boosting your social platform with machine learning.
Existing ML solutions for social platforms
Already today you can spot machine learning applications here and there on different social platforms. If you are using Facebook, all your friend suggestions and advertisement selections are largely governed by ML algorithms on the platforms. Instagram policies visuals and tracks profanity in messages and comments thanks to ML tools. Snapchat with its facial masks uses real-time AI technology that tracks your facial features and overlays them with designated filters. Siri and Alexa rely on ML-powered speech recognition.
These are only a few of the most prominent examples of machine learning applications in social platforms. However, its potential offers countless opportunities for businesses of all sizes to boost their performance by adding Ml and AI tools to their business instruments on a social platform. Here are some of them.
Ways of boosting your social platform with machine learning
Social media marketing
It is not a secret or novelty anymore that users see personalized ads depending on their activities on the platform, including likes, watched videos, text posts, photos, etc. Thus, by leveraging one’s activities, ML-powered marketing tools help businesses to predict users’ behavior and be the first to offer necessary services and goods as soon as the user needs them (possibly, without even realizing it).
Broadly speaking content management implies content creation and distribution. While distribution can be automated to a large extent through cross-posting and scheduling tools, the current approach to delivering unique fresh content requires a creative team to work on texts, visuals, UI/UX design, and many other aspects of it.
However, that might not be for long. ML algorithms make it possible to tailor small texts for specific ads. By analyzing previous ad campaigns it can create prompts that use language optimized for conversions since algorithms can figure out which words to use to deliver the most appealing messages. Further development of the technology will allow for auto-generated content of all sorts that will be shared across multiple channels in a matter of minutes.
Similar to the Facebook case mentioned above any social network can use ML to generate suggestions based on a user profile, including suggestions of people to expand the network depending on the type of connections you are looking forward to building. Beside Facebook, this technology is actively used on such networking platforms as LinkedIn, Meetup, and Handshake to suggest not only people but also jobs and relevant candidates.
Chatbots are great in streamlining communication and saving numerous man-hours by engaging ML-algorithms to handle customer requests, from answering the most frequent questions with predefined scenarios to scheduling appointments to providing complex sets of information to specific queries. At the same time, chatbots can collect information about customer’s interests to track and notice changes in trends and help companies and businesses adjust their strategies.
ML is good at recognizing patterns in texts and speech. If you feed enough example posts to your algorithm it will easily categorize them, for example, by topics. Or it can detect the tone of voice in a comment and tell brands a lot about how customers are perceiving the company and its products, including phrases having double meaning or sarcasm that earlier could only be detected and decoded in its true meaning by a human reader. Overall, ML and AI are the future of monitoring and maintaining your brand reputation on a global scale along with making forecasts for its development.
Sentiment analysis features are also helpful for conversation management. By accumulating data over time, it can identify trends and important conversations happening on the platform to push forward or highlight the most trendy content as well as find new audiences to reach out to. At the same time, it helps to alert administrators if someone is misbehaving, breaking the rules, posts inappropriate materials, or engages in a hateful speech which can ruin the whole experience for your customers and ruin your company’s image.
As content becomes more visual over time, it is important to gain insight not only from textual materials but also to keep an eye on images and videos on your platform. Not so long ago the only way to guarantee that there are no inappropriate materials on a platform was to rely on reports by users and then manually removing materials after reviews by employees. However, today we have massive platforms revolving around visuals only like Pinteres, Instagram, or YouTube. Handling these tasks becomes overwhelming.
Luckily, ML can learn to recognize faces, objects, logos, movements, and other entities both in images and videos. This functionality, for example, allows Facebook to alert users when someone uploads pictures with them and suggests to either tag a person or ask to remove a picture. For businesses, this algorithm can be trained to recognize your products or logos in someone’s posts or shares.
As you can see, ML and AI can equip your business with a multitude of tools to monitor, analyze, and predict a wide spectrum of metrics and user behavior on your platforms. It equips businesses with information hidden in overwhelming amounts of information and depths of conversations happening every day. Thus, if you have any questions or ideas related to boosting your social platform with machine learning, do not hesitate to contact us. Let us make this happen!