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Machine Learning for Social Media Personalisation - Why Your Content Marketing Strategy Needs It

Updated: May 26, 2020

Most content marketers understand machine learning and the basics of how it works: self-learning algorithms. That’s the easy part. The hard part is knowing how to use it effectively in social media campaigns and to gather insights about customers from social data. Also, why do you need it?

Machine Learning for Social Media Personalisation - Why Your Content Marketing Strategy Needs It
Machine Learning for Social Media Personalisation - Why Your Content Marketing Strategy Needs It

One good reason is that by 2025 it is predicted the total number of internet users worldwide interacting on social media is expected to be a third of the Earth’s population: 2.95 billion people. That’s a lot of tweets and Facebook posts.

How Machine Learning is Used to Analyse Social Data

The simplest way to think of the technical aspects of machine learning is to think ‘algorithm’ (a formula that solves a problem using a sequence of actions) plus ‘tools’ (computer program that supports other programs or applications, such as a database) plus ‘processes’ (instance of a computer program being executed). That’s it.

Computer programmers use these three things to create a machine learning program that accesses a set of data, learns from the data, learns more when fed more data and so on. The data derived from the machine learning program is then analysed for insights.

One significant advantage to machine learning programs - in this age of Big Data overwhelm - is that the information used to feed the program can be structured data (highly organised with clearly defined data types) or unstructured data (unorganised with undefined data types). Any marketer understands how difficult it can be to gather intelligence from large sets of unstructured data using rules-based automation created by humans.

In walks machine learning. A machine learning program can analyse the body of unstructured data, find patterns or common elements, then sort and categorise the information. This intelligence can be used to profile a company’s customers activity on social media accounts, identify buying patterns, product preferences or similarities between different demographics.

Online filtering systems can help public relations manage a brand’s image online. “AI systems are able to monitor millions of user comments across a range of platforms and note emerging crisis situations before they spiral out of control.”

User generated content in social media campaigns is another marketing tactic. Two platforms to help source user generated content for you are:

  • ShortStack - create user generated content using giveaways, quizzes, promotions and social contests.

  • ReadyPulse Experticity - vetted experts brands can partner with.

Two other ways machine learning programs can be used to gather insights using social media are: predictive analysis and pattern recognition.

1) Predictive Analysis

Predicting the future - or how your customers will act at a later date - is powerful. Statistical or mathematical models can be used to integrate and analyse large amounts of historical information with current data to find patterns - or you can use machine learning.

Predictive analytics powered by machine learning gives you the ability to forecast what type of content will work best with what method of delivery (mobile, website, Facebook, Twitter), prevent churn, predict lead scoring, determine product fit, identify upselling opportunities, and determine a customer’s lifetime value. 

Most importantly, once you have a program in place, data can be continually inputted and the results will become more accurate as time goes on. A few predictive analytics vendors that provide this service for content marketers are:

  • 6sense - intelligence engine to find active buying cycles for products and services.

  • EverString - self-service AI platform for B2B marketing and sales.

  • BrightTarget - customer profiling & retention, product recommendation and predictive lead scoring.

  • Mintigo - predictive marketing platform to profile each account and contact in database.

  • IBM Watson - create, train and deploy machine learning models.

Cloud service providers now have machine learning offerings (or assistance creating a program using their platform) such as IBM Watson (above) in the IBM cloud and Microsoft AWS. 

2) Image Recognition

Visual data - not the written word - is now king in the online world. Videos, pictures, infographics, charts, screenshots, collages, comics... what about T.V. shows? It is proven visual imagery increases retention of information. The human brain remembers 10% of what we hear, 65% of what we see.

Visual content marketing is key to increasing brand engagement on social media. Users want images, not text: 80% would rather watch live video from a brand than read a blog, and 82% prefer live video from a brand to social posts.

Image recognition software can help convert overflowing streams of unstructured data into actionable insights. An example is Facebook’s DeepFace AI that uses images to target ad campaigns.

A multitude of insights are possible with facial recognition programs, even sexual preference (91% success rate). It is predicted this technology will soon enable marketers to monitor eye movement, posture and facial expressions to predict future behaviour and influence what someone will purchase. 

Machine Learning to Help Marketers Manage Social Media

Personalisation is all about delivering the right message at the right time - by a seemingly real human. When it comes to multiple platforms and millions of users, the ‘seemingly real human’ behind the scenes is an automated application or program; sometimes a machine learning program.

Statistics show one of the most effective ways a business can boost engagement with their brand is to post relevant, interesting content on social media platforms. Those who have jumped on the social media bandwagon tend to use it a lot, and have multiple accounts.

According to research by GlobalMediaIndex, the average internet user worldwide has 5.4 social media accounts.

For a company with a target audience of 20,000 customers, that’s 100,000 of messages that need to be crafted and delivered - every day. Not only does the content of the message need detailed analysis, the actual delivery of the message itself needs automation of some form.

Though in reality the message has to appear as if it is coming from a real human - in order to meet the goal of personalisation.

A software vendor to help manage multiple company social media accounts and personalise messages is:

  • Oktopost - manage content, measure business value and amplify your social media marketing efforts.

Above are a few ways machine learning can be used in social media marketing, as well as some convincing arguments back by statistics as to why you will want to start using it today. 


TRAC Marketing is a UK-based company that provides marketing automation consulting and solutions for small businesses. Speak to our marketing automation consultants today to learn more about putting marketing automation to work in your company.

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