October 8, 2021

Using Natural Language Processing for Marketing

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What is Natural Language Processing?

Natural language processing (NLP), also known as language analytics, is an emerging artificial intelligence (AI) technology that harnesses business insights from unstructured data, such as customers’ written or verbal responses to your product or service.

Related: What is Natural Language Processing?

How can NLP be used for Marketing?

As marketing strategies become increasingly customer-oriented, businesses are turning to language analytics to extract insights about customer motivations, intentions, buying journey, etc. from large qualitative data sets.  

Applications of NLP for Marketing

NLP can assist your marketing strategy in a variety of ways, but we’ll focus here on NLP’s application for branding, content, customer success, and lead generation. 

Branding

Tapping into qualitative data can help your company better understand how current and potential clients perceive your company. The manner in which your company’s name appears in conversations can inform how on-point your brand positioning truly is. IBM Watson Natural Language Understanding tool is one that can help your company assess the effectiveness of its branding strategy. This tool helps you find out what current and prospective clients say about your brand by analyzing large data sets from a variety of sources like webpages, social media outlets, etc. to identify keywords, sentiments, and more. 

Related: Top BI Tools for Data & Predictive Analytics 

Content strategy

Your brand positioning goes hand in hand with SEM strategy which, in turn, depends in large part on your company’s ability to consistently produce relevant, high-quality content. Jarvis AI is one tool out there that can help your content team generate relevant content more quickly. It can auto-suggest the next sentence based on an initial input sentence. Jarvis AI can also produce long-form content, according to a set topic. 

AI is indeed getting better at producing content that meets Google’s increasing demands for high-quality content, but a content-generating tool cannot and should not replace your content team. Another key part of boosting content quality and thus domain authority is your content’s ability to emotionally connect with readers. A tool like Jarvis AI can certainly help your content team churn out good material more quickly, but content strategy can’t forego that human touch. At the end of the day, content written for humans needs to be built and curated by humans to ensure that content really resonates.

Customer experience/VoC

The “voice of the customer” (VoC) is integral to a customer-centric marketing approach. NLP tools can extract insights from your customers’ experiences with your product or service. IBM’s Watson Assistant tool, for instance, uses language analytics to process and respond to customer feedback in voice or chat format. 

The information that you acquire through NLP tools for customer support helps your company identify and rectify recurring issues. Supporting customers on their buying journey and post-sale is only part of the equation, however. That’s where a tool like Relative Insight or SAS Visual Text Analytics come into play to help you learn from the data collected in these interactions. While Relative Insight requires that you upload the unstructured data into its platform, the SAS tools sync seamlessly to data input from apps on mobile devices. Tools such as these analyze unstructured data for patterns that inform your decisions and get you the information you need quickly.  

Targeted lead generation

Natural language processing tools can aid your marketing strategy, especially if you engage in a targeted approach like account-based marketing (ABM). With NLP tools, your marketing team can keep its finger on the pulse of conversations regarding potential customers’ needs, problems, and maybe even about your company. The information that you glean from NLP can inform whom and how you target in your marketing, based on their level of brand awareness, intent to buy, etc. For example, Terminus now harnesses the power of language analytics in its B2B marketing tools, so that its customers can find the right clients and pitch tailored content to them. 

Why you should adopt NLP for your marketing strategy

Focusing on quantitative data like click-through-rates, conversions, etc. is important. However, by concentrating on these KPIs only, you’re missing out on the valuable insights that qualitative data can provide to your marketing and sales teams. Balance out your approach by accounting for the rich data contained in spoken and written communication. NLP tools like those mentioned here can help you tap into the knowledge gold mine of unstructured data sets to better understand current and potential clients.