Innovative Retailing with the Power of Natural Language Processing

Natural-Language-Processing-in-RCG

Data has been referred to as gold, oil, you name it. The advancement in data analytics has been nothing short of revolutionary. Natural Language Processing (NLP) is one of those burgeoning advancements in data analysis. NLP stands at the crossroads of computer science and linguistics and is delivering data insights in new and exciting ways.

In the retail world, AI-driven chatbots that leverage NLP are now just about everywhere and they are multiplying rapidly. But that just scratches the surface — NLP also has more sophisticated applications for retailers that apply understanding to the data you’ve cultivated.

Let’s look at how NLP works, and how it can provide new and exciting insights to your data — ultimately helping you pinpoint your customers’ intent so you can create more profitable experiences.

What is NLP and how does it work?

Natural language processing is a specialized branch of artificial intelligence that enables computers to understand and interpret human written text or speech. First, NLP organizes data in a logical format — for example, breaking down text into smaller units of language, such as sentences, sentence fragments, words, or word roots. From there, text is processed and interpreted based on pre-defined grammatical rules and machine learning models which use statistical methods to become more precise over time. It’s technical stuff, but new tools are emerging and being perfected all the time to remove the complexity of NLP and make it usable for most companies.

NLP systems can process all kinds of unstructured information including social media comments and customers reviews and turn it into actionable data that you can use to improve weaknesses and ultimately strengthen your brand.

Let’s look at some of the techniques NLP uses to process all this data.

Optical Character Recognition

Optical character recognition (OCR) technology is a business solution for automating data extraction from printed or written text on a scanned document or image file. The text is then converted into a machine-readable form to be analyzed by an NLP model. OCR systems can be used in the retail industry to scan and extract data from bills of lading, packing lists, invoices, purchase orders, contracts, technical reports, and any other human written material that has been memorialized on a shelf or share drive.

Named Entity Recognition

Named entity recognition (NER) identifies the key elements in a corpus, like names of people, places, brands, monetary values. Extracting these entities from text helps sort unstructured data and detect important information and relationships which is extremely valuable for large datasets. NER can be integrated into email and chat systems where you can extract and collate information across multiple communication channels efficiently. NER can enable you to instantly view trending topics related to a product or brand.

Text summarization

Text summarization is an NLP technique that summarizes data to generate a new shorter version that conveys the most critical information from the original text. NLP can extract and select a subset of existing words, phrases, or sentences to get the most precise and useful information from a large document and eliminate the irrelevant or less important data.

How can NLP techniques support the Retail industry?

Sentiment Analysis

Sentiment analysis is the process of analyzing text to determine the emotional meaning of a communication.  It uses natural language processing to parse a customer’s message to uncover the customer’s underlying intention. Retailers especially should be leveraging sentiment analysis. By analyzing customer sentiment toward your brand or products, you can make more informed decisions across your business operations and focus your marketing initiatives.  Trends in sentiment analysis can be used to define actions that improve Net Promoter Scores.

You can evaluate customer messages, call center interactions, online reviews, social media posts, and other types of unstructured data for sentiment. The possibilities are endless: some of the less well-known sources of unstructured data are YouTube, Yelp, the Better Business Bureau, and Angie’s List, for example.

Sentiment analysis can be very useful to analyze your competitors, spot market trends, and conduct market research. You can analyze sentiment in product review sites, social media posts, and community forums about your competition to learn about their strengths and weaknesses:

  • Where do they excel that you don’t?
  • Are they doing something you can do to gain market share?
  • How do your products compare to competitors? Should you be highlighting specific product features?

NLP-powered chat bots

Conversational AI is the technology that enables automatic conversation between computers and humans. It is the heart of chatbots and virtual assistants like Siri or Alexa. Conversational AI applications rely on NLP and intent recognition to understand user queries and generate more relevant responses.

By using AI-powered bots, you can shift tasks onto automated systems so they direct human intervention at the right time and place in your customer’s journey. A bot interaction can be a quick question and answer, or it can be a sophisticated conversation that intelligently provides access to services. A sophisticated bot can use FAQ pages, support websites, product manuals, SharePoint documents, or editorial content through an easy-to-use UI or via REST APIs.

Advanced NLP-powered chatbots can assist in a multitude of ways, from analyzing intent (as we discussed above) so you can deliver more personalized support to solve issues, showing more empathy toward the consumer, and using those opportunities to meet the promise of your brand.

In-store bots (mobile)

NLP-powered chatbots are moving in-store as well, with touchscreen and/or mobile interfaces that can provide a more interactive customer service experience, while automating some of the work employees have historically done.

For example, a sophisticated virtual assistant could be surfaced in a mobile application that a customer uses to navigate the physical store. Or it might be manifested in a touch screen in the store that can communicate with customers via voice or text, acting as a shopping assistant to suggest products, help customers locate products, and provide information about upcoming sales or promotions.

A recent study by Juniper Research found that the global number of successful retail chatbot interactions will reach 22 billion by 2023, up from an estimated 2.6 billion in 2019. Bots aren’t going away anytime soon!

Semantic-based search

A semantic-based search is an AI-based search that uses NLP techniques to determine the meaning and intent behind search, rather than just match keywords together. More recently, it’s become known as cognitive search. The cognitive search employs AI technologies to understand and organize information as well as leverage user profiles, context, and history to better determine a user’s query and/or question.

The goals of search haven’t changed — maximizing engagement and conversion of both organic and paid traffic. Cognitive search can boost findability and conversion as well as provide a more personalized experience to increase customer satisfaction and derive deeper customer insights from search queries to better empower service, sales, and marketing activities.

Azure Cognitive Services

Azure Cognitive Services has pre-built machine learning models that enable many of the NLP techniques we’ve discussed above:

  • Determining the language of a document or text (for example, Spanish, French or English)
  • Performing sentiment analysis to determine a positive or negative opinion
  • Extracting key phrases from text that might indicate its main talking points
  • Identifying and categorizing entities like people, places, organizations, or even dates, times, and quantities

All it takes is an API call to embed the ability to see, hear, speak, search, understand, and accelerate advanced decision-making into your apps.

If your company is exploring NLP and machine learning, you should also explore Azure Cognitive Services. You don’t need special machine learning or data science knowledge to use these services. Developers access cognitive services via APIs and can include these as features within their code.

Take your customer insight to the next level

By analyzing the communication, sentiment, and behavior of your customers, you’ll get a clear picture of how to enhance customer engagement. NLP can be used to improve customer satisfaction by helping you fully understand customers’ meaning and intent. When you know what keeps buyers coming back for more, you can make proactive changes to increase those actions.

If you’re evolving your enterprise for a digital future, consider NLP. At Hitachi Solutions, we’re committed to helping organizations within the Retail and CPG industries do more with their data using innovative solutions and services, including natural language processing. All of our offerings come backed by decades of proven data science expertise — we have the resources to help your organization go further, faster, and at scale.

To learn more about how Hitachi Solutions can make NLP part of your retail process, contact us