4 Ways NLP can Transform Enterprise Performance

Natural language processing is empowering organizations and enterprises to reshape how they approach data-based tasks. 

NLP helps these enterprises to improve decision making, automate interactions and to enhance business performance. 

In this guide we will delve into 4 of the ways natural language processing is helping to transform performance for enterprises: 

• Round the clock automated support
• Quantitative business intelligence (BI)
• Compliance
• Recruitment and Talent Management

1. Round the clock automated support

Customer service teams help to provide a direct connection between enterprises and their customers. Good customer service can boost customer retention, build an enterprises’ reputation and increase revenue.

However, even the best customer service teams have limitations, whether in working hours or gaps in their knowledge. 

That’s where NLP-powered virtual assistants and chatbots come into play. These smart chatbots can be trained on extensive customer service data, analysing everything from contact form logs to training manuals, to become fully-versed in all aspects of an enterprises’ customer service support. 

From there, these custom trained virtual assistants can provide instant, tailored responses to customer queries. Not only does this provide round the clock customer support, but it also significantly reduces the workload for the human agents, freeing up their time to focus on more specialist or productive tasks. 

Advanced AI chatbots are trained to proactively spot opportunities to hand off the conversation to a human. Sometimes, this will be a message explicitly asking to talk to a human. 

Other times, the AI will need to notice when the inquiry is beyond their capabilities, and seamlessly introduce a human into the conversation to take over. By using this hybrid approach, it combines the instantaneous benefits of AI customer service with the specialist expertise and nuance that only a human agent can provide.

2. Quantitative business intelligence (BI)

Emails, reviews, call transcripts and social media posts all hold valuable insights that an enterprise would benefit from, but manually reviewing these conversational logs would not represent an effective use of time and resources for larger organizations. 

Instead, NLP can be deployed to analyse the unstructured data at scale, identifying trends, sentiment and spotting any emerging issues. Not only do NLP tools provide actionable insights, “for artificial intelligence tools - and business intelligence platforms in particular - to be useful, business employees must be able to directly ask questions of the data” - Forbes. 

Using NLP for business intelligence enables enterprises to perform complex queries without needing the technical expertise or knowledge associated with conventional data analysis approaches involving tools such as spreadsheets or databases. 

By doing so, enterprises can become more agile in their business intelligence, widening the data analysis participation to all sectors of the business, not just those skilled in data.

3. Compliance

Regardless of the industry, regulatory compliance involves vast volumes of documentation and data. NLP can help to streamline the compliance process by extracting key obligations and terms, flagging potential risks and comparing contractual clauses against regulatory requirements. 

For example, tenants and landlords can use NLP to analyse tenancy agreements. By doing so, the NLP tools will recognize and extract the key clauses that require attention, such as those pertinent to rent payments, property maintenance and termination conditions. 

These custom NLP solutions for property can also provide layman’s term translations of complex legal jargon, helping tenants to understand the clauses within their contracts. 

Text summarization is one of the most common usages for NLP. It involves the condensing of lengthy texts into short, concise summaries that preserve key information, meaning and context. 

There are two primary approaches to NLP text summarization: extraction-based and abstraction-based. 

Extraction-based summarization models will identify a subset of words that best represent the most important points to pull from long text, combining them to make a summary. 

These results derive from the original material but may not be grammatically correct. Within extraction-based text summarization tools are subsets to leverage word frequencies, leverage similarity and to leverage embeddings and clustering.

Abstraction-based models use advanced deep learning models to paraphrase and shorten text. These abstractive models will generate new phrases and sentences that represent the most important information within a passage of text, and unlike the extraction-based approach, the summaries should be grammatically correct.

Extraction models are still widely popular as they are easier and less resource intensive to develop compared to the abstraction-based approach. 

Within the field of compliance, using either an abstraction-based approach or an extraction method will help enterprises to identify key areas that require special attention.

4. Recruitment and Talent Management

Hiring teams have historically had to scan through swathes of CVs and personal statements. 

In the UK, employers will typically receive 25 applications per vacancy, although some will receive many more than this. During the 2020 pandemic, this average rose to over 500 applications per low-skilled vacancies. 

Recruiters would therefore resort to quickly browsing the text, rather than reading the information in-depth. As such, important details or context could be overlooked and the right candidate ignored. 

With the help of NLP, enterprises can screen CVs and cover letters to identify top contenders, eliminate irrelevant applications, spot fraudulent candidates and even to analyse the language of job descriptions to improve diversity and inclusion. 

NLP tools like NetGeist HR, built to screen CVs will automate the process of finding relevant keywords, skills and experiences. The benefits that this delivers organisations and enterprises include:

• Increasing the efficiency of reviewing large volumes of resumes, especially in the early rounds of the recruitment process
• The use of NLP algorithms can help to improve accuracy and reduce the risk of human error in overlooking strong applications or approving weak candidates
• Manual screening always carries the risk of inherent biases from the HR team coming into play. NLP solutions help to eliminate biases, ensuring a fairer and more equitable evaluation of candidates; as long as the training data was free from the biases in the first place
• Using NLP for text summarization of resumes can provide valuable insights into candidate trends and preferences, such as any noticeable shifts in which skills candidates are reporting 

How NLP can be used for CV screening

1. Firstly, collect the data. The resumes should be stored in a structured format
2. Text preprocessing will clean and standardize the data to remove any noise or inconsistencies
3. Feature extraction will highlight key features from the text, such as keywords, skills and experience
4. NLP techniques such as tokenization and stemming will analyze the extracted features
5. The features analysed by NLP will be compared against a predefined recruitment criteria, helping to identify the most suitable candidates for a role

Boosting performance with a custom NLP solution

To truly benefit from the power of NLP, enterprises require a custom solution that is tailored to their challenges, data and requirements. Building your own solution isn’t without its challenges, namely:

• The accuracy of NLP solutions depend upon the quality and relevancy of the training data. For many firms, collecting the volume of relevant data can be difficult, with inconsistent or unstructured data hindering the process
• Contextual understanding is often the hardest part of training NLP tools, especially for industry specific jargon. Poorly trained models will struggle to understand the nuanced context, leading to inaccurate interpretations and results
• When training NLP models, it is vital to address and mitigate potential biases within the training data to ensure accurate responses

Fortunately, you do not have to attempt to train your own NLP models. NetGeist specialise in creating NLP tools that tackle textual challenges through the automation, processing and summarizing of information. 

No project is too big – our goal is to develop customized NLP solutions that would fit the concept of your company. From virtual assistance to information gathering or financial advice, receive insightful input that would boost the efficiency of your workflow. 

Let us simplify your textual tasks with a unique solution, tailored specifically to your requests. Contact us to get started with your custom NLP solution.