In the last couple of years there has been a rapid rise of companies partnering with social media ”influencers”. These influencers can be celebrities, models, known personalities in various industries or a simple person with a huge following.
What this means is that businesses tap into the followers of these celebrities, vloggers or bloggers by sending them free products for them to review, posting ads on their sites or by doing paid partnerships with them where there is clear product placement.
Businesses have discovered that investing more in social media can provide a wider and faster reach for them.
Alongside investment in social media is the aggressive investment of many companies in data analytics.
Businesses want to crunch numbers faster, generate actionable insights better, and act quicker.
Machine Learning´s Use
The current thinking is that the business that can understand its client´s preferences, predict its buyer´s behavior, and can personalize its customer´s experience gets to win the game.
Machine learning today is used by businesses in their attempt to comb through incomprehensible amount of social media data in order to identify emerging opportunities and useful trends.
It is also used to forecast the earnings of the company faster than any of their analyst could ever predict and to look into the concerns of decision makers as found in thousands of corporate documents.
Machine learning comes up with theories for companies to test and points of interests that it tells its humans to look deeper into because there might be an opportunity waiting to be discovered.
The goal continues to be to move further and further away from having to deal with spreadsheets and instead gain quick insights through machine learning´s ability to crunch data promptly.
Machine learning today is used across several departments in an organization from marketing to finance to sales.
The Future of Machine Learning
While machine learning is certainly powerful in its data handling, it is not without restrictions.
Today, machine learning still lacks imagination and an ability to take into consideration worldly or political events that has taken place or are about to take place in its predictions.
It lacks human intuition and it lacks the sophisitication to aptly put together pieces of the past, the present, and the future without needing a massive amount of data arranged chronologically.
The ability of machine learning to form a holistic opinion that encompasses economic trends or unique crisis is not quite there yet. It is continuously developing to this day but we aren´t there just yet.
Sales departments in organizations have the ambition for machine learning to be able to forecast metrics more accurately and quickly in order to maintain a significant lead against its competitors.
Marketing departments continue to aspire to use machine learning to help them make sense out of customer actions and consequently predict their customer´s next move at every step of their journey.
Finance departments have the ambition that machine learning could help them spot investing opportunities beyond what they currently can find through their spreadsheet wrangling.
Today, the future of machine learning is very much dependent on the monetary value it can rake in for the companies investing in them. As more and more businesses shell out funding for machine learning, it will be interesting to see how far it can go and how much value it can add.
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