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Introduction

 

You can learn Japanese, speak Spanish, or beautifully write Chinese characters, but the native language of a computer is still a machine code, also known as machine language. Any device communicates in ones and zeros to describe the logical actions. The majority of people are unable to comprehend the sophisticated language of machines, so we need to find some way to communicate. That’s where NLP comes in.

What is Natural Language Processing?

 

Let’s go straight to the NLP definition – Natural Language Processing is the part of Artificial Intelligence that enables computers to recognize, process, translate, and use human language. Machine Learning and Deep Learning are AI subfields that help NLP do its task.

 

The concept of NLP isn’t an entirely fresh idea. Programmers have found different ways to communicate with computers since they were invented. But in recent years the availability of powerful computing, advanced algorithms, technologies like big data, and most importantly growing demand, led to the quick advancement of NLP.

 

Nowadays, Natural Language Processing is the heart of apps like OK Google, Alexa, Siri, and Cortana. Microsoft Word and Grammarly use NLP to check grammar, while Google Translate uses it to recognize multiple languages. There are also Interactive Voice Response (IVR) applications apps used in call centers to respond to customers request – we can call it an example of NLP for CRM.

 

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The Mechanics of Natural Language Processing

 

As we mentioned above, Machine Learning is among the branches of AI that are essential for NLP. In fact, the most important one. To make Machine Learning algorithms work, such as Text Classification and Clusterization based on topics, we need to convert text into the set of numbers. This variety of ones and zeros, also known as a vector, have to represent the text in the most accurate way. To accomplish this task, we have to perform pre-processing stage of working with text using Natural Language Processing Python Libraries like Spacy, Gensim, NLTK, etc.

 

Pre-processing includes the following:

 

  • Tokenization – this simply means to divide the text into the smaller elements, for example, one word.
  • Erasing punctuation.
  • Lemmatization – determining the canonical form of the word based on its intended meaning.

 

In the cases, where its necessary the following could be added to this stage:

 

  • Part of Speech tagging – meaning that token’s part of speech is identified.
  • Erasing stop words.
  • Highlighting N-grams – words that create unique meaning in combination, for example, New York.
  • Named Entity Recognition – recognizing specific people, places or products in text.

 

At the Processing stage text is being vectorized with one of the methods – Word2Vec, Doc2Vec or TF-IDF, depending on the task. To give you an example of Doc2Vec vectorization: imagine a vector of the word “Queen”, minus the vector of the word “woman”, and adding the vector of the word “man” to “King”, as a result.

 

Now we can use processed vectors to automatically classify text. Depending on our goals we can put it in categories. For the news sites, NLP could define content and keywords. We can also include sentiment analysis to detect the mood of the text. You can imagine finding a lot of reviews for the movie or some product, and then quickly analyze the mood in it, whether it’s good or bad.

 

processing words

 

Natural Language Processing Applications for Businesses

 

Chatbots

While chatbots go back to the ’60s, combined with NLP and voice recognition, they elevated to the entirely new level of popularity. Gartner predicts that they will take over 85% of customer interactions by 2020. It’s no wonder because now chatbots can collect personal data in from conversations, offering a personalized experience, and recognizing emotions. Talking about their usage in sales: chatbots can find new prospects, start a dialogue, schedule meeting and more. They already prove their business value and potential. Asos claims to receive 300% order increase thanks to FB Messenger Chatbot with 250% ROI, reaching 4 times more target users. Sephora accomplished 11% increase in makeover appointments implementing FB Messenger Chatbot.

 

Creditworthiness assessment

Even if you never took any credits, you still use the Internet and smartphone for activities that leave a digital footprint. This creates an opportunity for NLP uses in Banking. NLP algorithms can process your locations, browsing habits, social media history to get information about your habits, friends and your relationships with them. Based on a massive amount of user’s online behaviour, NLP Machine Learning software can predict your further activity and what to expect from you. Of course, your personal information is provided with your consent and is protected from the third parties.

 

LenddoEFL presented Lenddo application that is based entirely on Natural Language Processing techniques and text mining. It helps in creditworthiness assessment. They had over four years of their own online lending activity, working with billions of data points. In 2015, they shared their practical experience with the API for the banks, lending companies, and credit card companies all over the world, to help them reduce risks and improve customer service.

 

Opinion Mining

 

NLP can offer a brilliant solution for the retail industry, known as sentiment analysis. It suggests how exactly customers feel about your products and your company. Just like in the example with news and movie reviews above, sentiment analysis can collect negative, neutral or positive comments about your brand on the internet. Sentifi, a company from Switzerland, is doing just that – leveraging NLP to find your brand advocates and haters online. NLP algorithms can distinct happy, sad, angry or annoyed mood. With these tools, marketers are introduced to an entirely new world of possibilities to build their strategies.

based on nlp machine learning solutions

 

Our NLP-based tool for CRM Project

 

Here at SPD-Group, we had experience in creating our own CRM project using NLP Machine Learning technology. It is called “Hots” and “Colds” – Automatic Customer Tagging Tool which divides customers to “hot” and “cold” categories.

 

Our client, the CEO of CRM Platform, had an idea of making his platform startup richer in functionality since 2018, when ML success stories caught his attention. The client’s CRM technology uses text emails and voice messages to personally reach out on your behalf.

 

The challenges for our client were to increase lead retention rate, find a technology partner with a powerful Data Science team and return the development expenses within the first year after release. On our part, we managed to deploy a quality tool within the given time, provided reliable support on every development stage and created a viable MVP model to see first results right from the start.

 

NLP was used here for a derivation of the main keywords and expressions from the text, and also evaluating its sentiment. This allowed us to integrate the tool which optimizes the work of sales managers in their interactions with customers into the CRM Platform. The customers are divided into:

 

“Hot” – customers that are looking forward to work with the company

“Cold” – customers with little to no interest in the company

 

Text Clusterization and Classification with Sentiment Analysis are the NLP technologies we leveraged to accomplish this goal.

 

Our client gave us a massive amount of data in a form of customer-manager text dialogues, and they were unlabelled (meaning there was no distinction between positive and negative ones). We used unsupervised methods of Machine Learning – topic extraction with LDA/LSI and coherence model of genism. LDA/LSI method can adjust the number of topics which must be extracted from a set of sentences. There are plenty of effective NLP methods for meaning and topic extraction, which allow processing data with minimal important data points and topic losses. N-Gram method allows to detect word collocations, most relevant as to those showing negative or positive sentiment, and categorize conversations accordingly. In the process of development, we were choosing between different binary classifiers to define two sentimentally opposite groups with the highest accuracy rate and ended up with 87% after checking the model on validation data.

 

As a result, our CRM-embedded Customer Tagging Tool helped our client company salesmen turn 20% more leads into clients within the first half a year after release, with automatically marking their intentions basing on conversations. It provided 80% more successfully established relationships, and increased Lead retention rate by 40% in the first half of a year.

 

 

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Conclusion

 

Natural Language Processing, in combination, with other Machine Learning data processing methods, has yet to reach its peak, and have a bright future for businesses in many industries. Have you ever considered improving your business with this innovation? If you do, or if you just want to discuss the possibilities, feel free to contact us at SPD-Group. We have Artificial Intelligence & Machine Learning expertise to help your business stay ahead of the competition!