Consumer demands continue to escalate and businesses are still struggling to keep up. Soon the level of knowledge needed to remain competitive will be impossible for humans to process. But sentiment analysis algorithms, a feature of increasingly popular A.I. software, are set to change everything. For the first time, it may be possible to exceed every single customer’s expectations.
What is AI?
Simply put, AI (Artificial Intelligence) is the theory and development of computer systems that are capable of making decisions that only a human could have made in the past. For businesses, this presents huge opportunities for automation. Once trained, AI can free up hours of human time and resource currently spent on minimally cognitive tasks.
What Makes AI Unique?
Technical product features are typically designed to work for as many businesses as possible. There is little to no opportunity for total customization unless companies build out tech in-house, which can be expensive. Developing a single app still costs between $25,000 to $1 million depending on complexity.
Artificial intelligence works in the opposite way. It is a blank canvas that businesses must train for very specific functions. But after that training AI systems can outperform entire teams of humans, with greater accuracy. In some cases, AI can iterate so quickly that it invents innovative solutions by itself that humans have never even considered.
This is mostly due to the vast amounts of data that the specialised technology can analyse and consider in seconds. The sources that A.I. can use to make these conclusions are also incredibly diverse, allowing it to conduct sentiment analysis from unstructured data.
“Sentiment analysis algorithms could mean an end to irrelevant email, advertising and messaging for consumers.”
Structured vs. Unstructured Data
Most of the data that organisations collect and analyse is known as structured. It can be easily organised and searched through, as with POS (point of sale) data, but only because it must be stored within strict parameters like rigid terminology or checkboxes.
Unstructured data is almost everything else. It can include insights from social media posts, customer service interactions, and web blogs. Generally, this data is not easy to store and search. It is critical however to determining the real motivations for actions that lead to conversions, which consumers expect businesses to understand.
What Are Sentiment Analysis Algorithms?
Sentiment analysis is the process of deducing emotional intent via natural language processing. As an AI is trained, it develops algorithms based on the information it is given.
Advantages of AI Sentiment Analysis
In this way, AI can understand the emotional motivations that result in purchase intent. With enough access to data, it can not only understand what caused customers to convert but accurately predict what they will need next.
Data: Too Much of a Good Thing
In 2017 90% of the world’s data was created in 24 months according to figures from cloud computing company Domo. It is unsurprising that humans are unable to decipher this vast amount of information, and scalable AI provides the only viable solution.
Marketers’ minimal analysis of convenient, incomplete data will no longer be necessary. AI already has the power to give marketers all the context they need to reach out effectively. Sentiment analysis algorithms could, therefore, mean an end to irrelevant email, advertising and messaging for consumers.
IBM released a trailer explaining, promoting their world-famous AI Watson:
Give the People What They Want
For years, the practice of creating generalised personas has helped marketers identify target demographics of consumers. Since 2012 industry influencers have claimed that this no longer works. Customers are apathetic to generalised messaging and companies that stick to these methods are in danger of going extinct.
In this way, the move to AI may be an inevitable evolution, driven by the market. But not everyone is so convinced that sentiment analysis algorithms are the perfect answer to a complex human problem.
Dangers of AI Sentiment Analysis Algorithms
Most of the criticisms levelled at sentiment analysis algorithms and AI in general are ethical in nature. Privacy legislation and the danger of unjust biases are two of the most common arguments for marketers to approach AI with caution.
Exchanging Privacy for Accuracy
New privacy legislation like the GDPR in the European Union limits access and some of AIs autonomous decision-making capabilities. AI is only as powerful as the amount of data it can consume, so the accuracy of the technology depends on the erosion of personal privacy. As a result, it is highly likely that China will develop more powerful AI than other nations.
That being said, the wealth of information that exists on social media alone should be enough for marketers to leverage startling accuracy on demand. Compared to the blindness of POS data, even a restricted AI system could dramatically improve customer relations if nothing else. Provided it is handled carefully and educated against its development of biased models.
“The accuracy of the technology depends on the erosion of personal privacy.”
AI: Bias Beware
In 2016 Microsoft’s now infamous AI bot Tay demonstrated one of the other major disadvantages of sentiment analysis algorithms. Within 24 hours, Twitter users had fed the bot a number of racist and misogynistic tweets, leading it to generate a number of offensive messages of its own accord. This incident proves that leaving AI to its own devices can be extremely dangerous.
Bias checks are so important that they are an integral part of commercial A.I. use. IBM includes a number of videos on their Watson website to explain how users can assess the levels of bias within their systems, based on the data it has consumed. It is also expected that users will need to regularly check the reasoning behind AI’s decisions, which is also shown in the product demo.
IBM is currently preparing to release AI OpenScale, which claims that it can automatically review and eliminate bias. Whether it is possible for AI to self-police is unknown and an area of concern for many researchers around the world. But it is highly likely that the technology will be adopted by businesses desperate to meet the demands of modern consumers.
AI: Up for Adoption
The consequences of self-regulating AI and the effect of new privacy legislation are hard to predict and will depend heavily on the speed of adoption. But sentiment analysis algorithms promise to achieve the impossible for marketers, which may be far too tempting a prospect to ignore.
The arrival of new technology has caused a wave of digital disruption in recent years. Businesses that have struggled to keep up have either disappeared or lost significant market share. Companies are acutely aware of this, so it is unlikely that the potential of sentiment analysis algorithms and AI, in general, will be underestimated.
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