NLP INSIDE THE ORGANIZATION: GOALS AND USE CASES

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Natural Language Processing (NLP) is a cool tech buzzword these days – it is especially exciting as it deals with language, a natural and fascinating capacity of our brains. In our work, we have the pleasure of meeting many entrepreneurs who want to “do something with NLP”, and organisations who want to count NLP in their digital stack. But when it comes to business, using NLP for the sake of NLP is not a good idea – it is a tool and should be implemented with a specific use case in mind. It can be used to increase productivity or enhance existing knowledge. And, when married with specific expertise and intuition from the actual business domain, it can activate your creative self and trigger disruptive ideas and new ways of doing things. The following chart shows how online discussions of NLP are distributed between various business functions:
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In the following, we explain three overarching goals for implementing NLP inside a business and share our experience about priorities and common pitfalls for each category.

Streamlining existing tasks

You can save costs with NLP by increasing the productivity of your work with existing text data. NLP can be used to streamline routines that involve texts. This scenario is not only easy to understand, but also easy to measure 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 prominent and familiar example is customer service. Tasks in customer support are often focussed 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. On the receiving side, NLP can be applied to analyse and classify 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 allow to evaluate large quantities of customer feedback, thus giving the business the opportunity to react and win on customer centricity.

There are many other examples for NLP automation, such as the use of machine translation software and intelligent document analysis. The nice thing about most of these applications is that training data is already available in the organization – your task is to set up a stable, sustainable supply of clean data and a feedback loop via your employees. The actual NLP implementation can be based on standard algorithms, such as sentiment analysis and entity recognition, which should be enriched with business-specific knowledge. This “customization” is often lexical – for example, to tailor an entity extraction algorithm to specific business needs, it will be useful to add the relevant products and their features to the entity recognizer. Finally, in production mode, human verification will still be needed for those cases where the NLP algorithms are not confident about their output.

Supporting your decisions with better information

This second area allows to enhance existing data analytics use cases with the power of unstructured 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 analytical techniques still focus on statistical and quantitative analysis of structured data – i.e., the “other” 20%. By adding unstructured data sources to the equation, a business can improve the quality and granularity of the generated results. Ultimately, NLP generates a unique information advantage and paves the way to better decisions.

An example where this scenario applies is the screening of target companies for M&A transactions. The “traditional” target screening process is highly structured and quantitative. It focusses 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. Many of the less tangible, but central aspects of a business – for example, its intellectual property, public image and the quality of the team – don’t surface in the result and have to be manually investigated on the basis of additional data sources. NLP allows to leverage a large body of text data that contains information about a company – social media, business news, patents etc. – to efficiently extract this information for a large number of companies.

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 require a more involved layer of business logic. While NLP is used to structure large quantities of data, additional knowledge of the business and its context has to be applied to make sense of this data. The M&A screening examples first requires a definition of the goals of an M&A transaction and, from there, the relevant parameters: if the goal is to expand B2C sales into a different geography, the perception of the target company by consumers is crucial. On the other hand, the acquisition of a complementary technology will direct the focus on the intellectual assets of the target.

Conquering the greenfields

So far, we have seen relatively defensive approaches to 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 justify whole new businesses. This journey requires the right equipment – not only solid domain knowledge, but also market expertise and the gift of finding 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.

Further examples for disruptive use cases can be found in various industries, such as drug discovery in healthcare, automatic broadcasting in media and the never-ending stream of innovation in advertising and marketing. 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 base.

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. They should also be willing 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 use cases down the road, but also uncover new strategic potentials for benefiting from NLP in their organization.

Author Janna Lipenkova
Data of publication: January 8, 2020
Image rights: anacode
Original Post: https://anacode.de/nlp-goals-and-use-cases/



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