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Boosting your organisation with NLP

In my work, I have the pleasure of meeting many entrepreneurs who want to “do something with NLP”, and organizations who want to count NLP in their digital stack. Indeed, NLP is a fascinating area at the intersection between linguistics, cognitive science and the cutting-edge developments in Deep Learning. But when it comes to business, using NLP for the sake of it is not a good idea – NLP is a tool and should be implemented with a specific use case in mind. Used correctly, it can become your Swiss knife for boosting processes, decisions and innovations. In this article, I explain three overarching scenarios for implementing NLP and share my experience about priorities and common pitfalls for each category.

Streamline existing processes

NLP can be used to speed up existing tasks that involve texts. This especially applies to repetitive, monotonous routines, such as those found in customer service and standard management tasks. The upside is obvious – after a one-off setup to head-start the project, NLP allows you to reduce recurrent costs in the long run. This scenario is not only easy to understand, but also easy to argue for in terms of ROI – you simply count the cost in man-hours used for a specific task “before” and “after” the implementation of NLP.

A familiar example is customer support. Tasks are often focused around a small number of variables such as products and processes. These are perfectly known inside the business, but may not be familiar to external customers. NLP can be applied to classify and analyze standard requests such as calls and complaint emails. Responses can be automated with conversational AI, as implemented in chatbots and virtual assistants. Algorithms such as sentiment analysis evaluate large quantities of customer feedback and point us to potential improvements. Our experience shows that automated analytics can lead to savings of 30-50% on customer service expenses. The instant and granular responses enabled by NLP allow the business to win on customer centricity, the ultimate success factor in modern consumer markets.

There are many other examples for NLP automation, such as the use of machine translation software and intelligent document analysis. One great thing about these applications is that training data is already available in the organization. The challenge lies in setting up a stable, sustainable supply of clean data and a feedback loop via your team. The actual NLP implementation can be based on standard algorithms, such as sentiment analysis and entity recognition. For optimal results, these should be enriched and customized with business knowledge. Finally, in production mode, human verification will still be needed for those cases where the NLP algorithms are not confident about their results.

Enhance your decision making

In this scenario, existing analytics use cases are enhanced with the power of text data. Most of us heard that 80% of business-relevant data exists in unstructured form. However, with NLP just entering the “live” business arena, mainstream analytics still focuses on statistical and quantitative analysis of structured data – i.e., the “other” 20%. By adding unstructured data sources to the equation, a business can massively improve the quality and granularity of the generated results. Ultimately, NLP generates a unique information advantage which paves the way to better decisions.

An example for this scenario is the screening of target companies for M&A transactions. The “traditional” target screening process is highly structured and quantitative. It focuses on basic factors such as location and area of the business, formal criteria (legal form, shareholder structure) and, of course, financial indicators such as revenue and profitability. In many cases, this information is easily accessible. To get ahead of your competition, your need to dig deeper. Many of the less tangible, but central aspects of a business – for example, its intellectual property, public image and the quality of the team – have to be manually investigated on the basis of additional data sources. NLP allows to leverage a large body of text data  – social media, business news, patents etc. – to quickly systematise this knowledge without a tedious manual investigation.

NLP can enhance decision making in all areas of market intelligence, such as trend monitoring, consumer insight and competitive intelligence. In general, use cases in this category also require a more involved, customised layer of business logic to generate truly actionable insights.


Boost innovation

So far, we have seen rather conservative approaches to automating and improving what is already being done. But NLP can also trigger bold, new ways of doing things and lead to high-impact applications that might spin off into completely new businesses. This journey requires the right equipment – not only solid domain knowledge, but also market expertise and the ability to find the sweet spots at the intersection of technology and market opportunity.

As an example, NLP can be applied in the mental health area to analyze the mental and emotional state of a person. This can be used to identify endangered individuals, such as individuals suffering from severe depression and suicide risk. Traditionally, these individuals are identified and treated upon a proactive doctor visit. Naturally, the more “passive” cases are rarely recognized in time. NLP techniques such as sentiment and emotion analysis can be applied on social media to screen the mental and emotional states of users, thus pointing out individuals that are in a high-risk state for further support. As physical social isolation is getting more and more common in times of Covid-19, such remote, proactive forms of mental diagnosis will be gaining in weight.

Further examples for disruptive use cases can be found in various industries, such as drug discovery in healthcare and automatic broadcasting in media. Venturing in this space requires a high confidence and competence in one’s own industry. As everywhere else, disruptions are often pioneered by start-ups whose flexibility and innovation focus give rise to fruitful intersections between business model and technology. However, with the right amount of technological competence and a technology-driven mindset, incumbents can also strive in these areas, massively capitalizing on existing assets such as data, market expertise and customer relations.

Summing up

In the end – things don’t come for free. NLP has the potential to save costs, improve decision making and disrupt any language-intensive area. To get on this path, businesses need crystal-clear formulations of their goals and use cases and the willingness to customize out-of-the-box NLP offerings to their specific knowledge base. Those who get it right will not only reap the benefits of specific projects down the road, but also uncover new use cases and strategic potentials for NLP throughout the whole organization.
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