AI for fighting the plague of disengagement

Narjes Boufaden, Founder and CEO, Keatext

At the beginning of the new millennium, the Web 2.0 promised a more participatory world where people would have conversations with companies, brands and all sorts of organizations. Social media would allow users to directly impact businesses by sending comments to decision-makers like managers and CEOs. Almost two decades later, the Web is flooded with reviews, tweets and Facebook posts, and businesses are overwhelmed by massive volumes of customer and employee feedback. The anticipated meaningful participation has become a stormy sea of background noise.

The result of this broken promise of participation is not just disenchantment with a technology that failed to deliver, but disengagement from businesses and organizations that didn’t take action despite the warnings. The cost has been untold dollars and opportunities lost. For example, consider the plague of employee disengagement. Employees who feel actively disconnected from their employer cost the US economy almost $550 billion per year in lost productivity. Another far-reaching example is millennials who lack any sense of brand loyalty. A recent study conducted by the management consulting firm Gallup concluded that only one in four millennials is fully engaged with a brand, product or company.

To resolve the disengagement that afflicts their bottom line, people-centric businesses have started to grasp the importance of listening attentively to their customers in the hope of understanding exactly what’s being said. The last decade has seen the popularization and development of sentiment analysis tools and text analytics techniques that allow businesses to identify relevant issues conveyed in comments and feedback.

Early iterations of these technologies permitted the monitoring of preselected keywords, with the goal of associating feedback related to the keywords with positive or negative emotions. For example, a store manager could preselect the keyword fitting rooms, and then identify and monitor customers’ positive and negative feedback related to that word. In this way, the business would keep its finger on the pulse of customer feeling about its fitting rooms, as well as other issues.

But what if we could do more than detect people’s emotions based on their feedback? Disengagement isn’t a sum total of spur-of-the-moment emotions, rather, it’s a mindset that develops in a rich, broad linguistic context. To take advantage of what this context can offer, technologies are required that allow a better understanding of human language. Fortunately, these technologies are at the heart of the rapid evolution of research in artificial intelligence (AI). New algorithms such as deep learning allow natural language understanding (NLU) to go beyond keyword-based analysis to involve context.

This second wave of text analytics technologies doesn’t rely on preselected keywords, which by their nature can’t be all-inclusive and limit what businesses can understand. Instead, it comprehensively – and accurately – captures all the meanings in customer feedback, including people’s questions and recommendations for improvement. By going further than monitoring positive and negative sentiments, AI solutions, through truly identifying what people are saying, attack the early symptoms of disengagement and provide the possibility of a cure.

To return to the example of the store manager: while a keyword-based solution could alert the manager to potential issues with the company’s fitting rooms, the actual issue – that customers are unhappy about the level of cleanliness – could easily go undetected. The second wave of AI-powered text analytics will precisely inform the manager about the problem with cleanliness, who will then take corrective measures to forestall customer discontent that could lead to a loss of trust in the store. Discontent and crumbling trust is how disengagement begins.

Disengagement affects the lives of all of us in numerous ways. An obvious example is the catastrophic consequences of political disengagement in democracies around the globe. But we’re approaching the next decade with technologies that will make real conversations possible and keep alive the promise of a more participatory world. With the tools to develop a clear picture of what people are saying, those in power will be able to listen, learn and respond appropriately. It’s now in the hands of decision-makers to effectively fight the plague of disengagement.

Keatext

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