• April 8, 2024
  • By Jonathan Moran, head of MarTech solutions marketing, SAS

The (Natural) Language of Marketing

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Generative AI remains the hottest area in tech and business today, with companies looking to put this technology into practice to generate content-filled insights that will optimize their business processes.

One reason generative AI has captured the public’s imagination is that it makes data and AI more accessible and approachable to people with varying job roles and experience across an organization.

This includes marketers and advertisers, who seek to boost campaign effectiveness, improve hyper-personalization, and build customer trust and long-term loyalty.

Underpinning generative AI are two related technologies that help transform raw data—including unstructured data like social media posts, customer chats, and emails—into analytical insights.

Natural Language Processing (NLP)

Natural language processing (NLP), a combination of natural language understanding (NLU) and natural language generation (NLG), includes techniques that all have as their basis sentiment and text analytics. NLP gives marketers and advertisers the ability to not only process and understand strings of text and apply a sentiment score to those strings, but also to process and then generate natural language across languages and locales.

NLP requires a steady stream of quality data to analyze. Even in this age of AI, the old mantra of “garbage in, garbage out” still applies.

Deriving insight from raw “document” data—such as chat and voice logs—and from audio- and speech-based conversations informs NLP.

NLP has gained a strong foothold within brands across industries. NLP helps marketers and advertisers gain insights about consumers’ brand sentiment and perception, as well as their frustrations and joys. This helps marketers and advertisers not only adjust their strategies but also improve processes such as customer service and support.

Honing in on Natural Language Understanding (NLU)

Natural language understanding (NLU) occurs when NLP output is used for decision making, often in real time.

NLU forms the basis for what is known as conversational AI within marketing and advertising. These processes are becoming so adept that NLU techniques are often indecipherable from a human vs. machine perspective.

Robotic process automation (RPA) or decision rules can be set to allow NLU to occur in an automated, always-on fashion. For example, website chatbots can offer discounts or cross-sell and upsell products without human intervention. Similarly, support bots can issue repair tickets via service portals.

From an advertising point of view, decisions can be made on what ads to serve in stream to which audiences and at what point in the customer (or viewer) journey. Personalized ad placement encourages longer viewership on both subscription-based and advertising-based media portals and players.

Conversational AI and Marketing

Some of the most advanced and effective digital interactions rely on conversational AI, which in turn supports the broader concept of true “conversational marketing.”

Conversational marketing, like its name implies, is when a brand or company communicates with its existing and potential customers through personalized, real-time interaction, often via web, email, chat, messaging, and/or social media channels.

Conversational AI and conversational marketing are most effective if an organization or brand has strong data management (to ensure relevant, high-quality data is available) and a scalable and flexible analytics platform to transform that data into better, faster and trustworthy decisions.

With NLP, NLU, and NLG the line between human and machine is certainly blurring. Challenges arise when data-collection mechanisms, identity-resolution techniques, analysis (sentiment, text and other insight), and real-time decisioning processes are not sound. This can result in irrelevant messages, responses, actions, or interactions that lead to consumer alienation.

These challenges can be overcome through the deployment of an integrated technology stack that has data management and advanced analytics at its core.

Powered by NLP, such a technology stack can deliver conversational AI and conversational marketing, and help marketers and advertisers hyper-personalize offerings, build lasting brand loyalty, and boost campaign effectiveness.

In their campaigns, marketers and advertisers seek the right language to speak meaningfully to existing and prospective customers. With the rise of generative AI, they increasingly rely on NLP, the language of marketing that underpins this crucial technology.

NLP helps transform all kinds of data into better response rates and highly personalized messages, resulting in improved marketing efficiencies and stronger bottom-line results.

Jonathan Moran is head of MarTech solutions marketing at SAS, with a focus on customer experience and marketing technologies. Moran has more than 20 years of marketing and analytics industry experience, including roles at Earnix and the Teradata Corporation in presales, consulting, and marketing.

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