Customers’ winks, their ticks, their choice of words make up a map of their desires and doubts. Natural Language Processing algorithm helps parse through the language and read the customer
In 2016, automaker Kia added extra star power in Super Bowl by employing IBM’s Watson to identify “social media influencers” who can buoy its message before and during the 60-second spot. Watson’s Natural Language Processing (NLP) algorithm parsed through the language used on social media to determine which influencers exhibit the personality traits desired by Kia, such as “openness to change,” “artistic interest” and “achievement-striving.” Dissecting the psyches of social media personalities for the sake of sedan sellers may not seem like the most natural use of Watson, but it did successfully. By activating influencers to execute its strategy, Kia reported a 30 per cent increase in brand engagement with its brand.
On the most basic level, we all use NLP every day. You might have even used them today if you’ve consulted a spell check app, Google translate, Siri, Cortana, Echo or Google Voice. All these apps use NLP so that you can interact with them.
NLP is aimed at training the computer to perceive and generate human language directly, without transforming it into computer algorithms. When you give any NLP system some text, it uses what it has been told to look for to decipher what it is looking at. If the creators taught it parts of speech, then it will find nouns, verbs. If the creators taught it to look for people’s names then it will identify word pairs and match them against lists trainers gave them. The system then processes the things it found and provides results.
BERT (Bidirectional Encoder Representations from Transformers), an NLP model that Google rolled out in 2019, has the ability to consider the full context of a word based on the words that come before or after. Put simply, BERT helps search engines better understand intent, most notably for longer searches that contain multiple prepositions.
For example, Google called out the query “2019 brazil traveler to USA need a visa” in a post. The word “to” is crucial here. Before BERT, Google would have returned results about US citizens traveling to Brazil. Post-BERT, Google can recognise that nuance and return a more relevant and helpful result.
BERT can handle the “common sense” test from the Allen Institute. It can also handle a reading comprehension test where it answers questions about encyclopedia articles. In another test, it can judge the sentiment of a movie review. Is the review positive or negative?
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Thanks to NLP, which is data driven, increasingly, large brands are creating successful marketing campaigns using market research into customer sentiment, the buyer’s journey, social segments, competitive analysis and content strategy. Digging into unstructured data like customer reviews, social media posts, articles and chatbot logs, NLP is enabling automation, consistency and deep analysis, letting organisations use a much wider range of data in building brand awareness.
Google uses NLP to identify the context or intent of search queries. What this means for digital marketing teams is they can also use NLP in their campaigns to make sure that their message reaches their target audience — people or organisations who can benefit from the content, product, or service.
Numbers say it all:
Eighty one per cent of marketers are either planning to or are using AI/NLP in audience targeting or segmentation, according to econsultancy.
Sixty Two per cent of B2B service companies use AI to personalise content for their content, according to CMO survey.
NLP market size will grow to 26.4 billion by 2024 due to increased smart device usage, and NLP-based applications to enhance customer service, according to Markets and Markets.
Twenty Eight per cent of top performing companies use AI/NLP as a marketing tool, according to Aumcore.
Marketers are investing significant funds into the applied data science and machine learning technologies to power their business, and the most attractive and rapidly evolving solutions in this field is NLP. According to experts, some of the most revolutionary uses of NLP centre in and around its applications in marketing.
Social segmentation
Segmentation helps organisations to accurately understand its audience and tailor its digital marketing campaigns to reach and persuade them. NLP helps identify patterns and trends, mining which keywords are trending, learning what people say about the brand on social media. Also, help to break down the major categories and topic clusters that a brand’s customer base is focused on and, finally, distil the most important information and key points from large collections of tweets or emails.
Social prospecting
Instead of manually scouring the web, marketers use NLP for lead identification. It can sift through data such as social media to monitor brand mentions, extract and filter data by keyword, and understand context and semantics. It can also accurately identify metadata relationships such as cause and effect, enabling marketers to optimise and better react to propensity signals.
Determine customer sentiment
A crucial metric for brand awareness is customer sentiment, which is how customers, experts, influencers and media speak about a brand at scale. Each piece of information is assigned a value, typically a number indicating that a sentiment is positive, negative or neutral. With such data in hand, marketers can make more informed decisions in developing strategies and forecasting demand for goods and services, improve branding, marketing messages, and product positioning.
Improve performance of chatbots
NLP helps improve chatbots usability, and thereby their customer experience. Chatbots are useful in lead qualification, as they can help identify if someone is ready to buy and use that insight to pass leads over to sales for immediate action. It can also be combined with marketing psychology and targeting to actually increase conversions and sales. Companies like Sephora, Marriott and Coca-Cola are starting to see returns.
The eCommerce players have begun using chatbots in a number of ways that are quickly adding dollars to their bottom line. In 2018, Asos increased orders by 300 per cent using Messenger Chatbots and got a 250 per cent return on spend while reaching 3.5 times more people.
BERT has emerged as improving chatbot performance. With a pre-trained corpus of 3.3 billion words, BERT has demonstrated an accuracy score of 93.2 per cent. Platforms like Drift and Intercom are typical, offering automated response platforms that can also gather information about your visitors.
Also Read: How NLP Powers Conversational AI Through Intent Analysis
Competitive Analysis and Creating Content
By scanning the internet for articles about the sector and using the information to feed, an NLP module detects semantic relationships between companies, helping businesses to significantly simplify and automate the process of scanning the competitive landscape. While also helping to create quality content to drive traffic to a brand’s site and engage visitors, NLP can audit a site’s content assets and conduct competitor research as these things can help ensure that brands have all the bases covered.
Big data market revenues are projected to increase to $103 billion in 2027, and since one of the keys to marketing seems to be the analysis and application of big data insight, NLP, as it evolves, will continue being one of the main AI technologies for marketers, with applications ranging from trend identification and summarisation, content and ad generation. It’s important for marketers to learn how to get the most out of it, to keep up with the current AI trends.
