Personalization, dominant in 2017, is focused on creating an experience that simultaneously feels familiar and new. Finding the right balance of utilizing data effectively and in a way that seems natural to consumers is key in the evolution of digital experiences.

In the 2017 digital transformation landscape, it seems as if the same element is popping up at the center of every new experience: personalization. Creating a fresh and tailored touchpoint that still feels comfortable and familiar to the user is the holy grail for nearly every industry. This effort involves a delicate balancing act: Marketers know they can’t come across as being too informed about a new consumer, or they may be perceived as overly attentive and Big Brother-like. Displaying some knowledge of the user’s personal attributes, however, is necessary to deliver an experience that feels empathetic and relatable. With in-person marketplace interactions, vendors can adjust their communication tactics depending on their perception of the other person in the conversation and, over the years, sales techniques have developed these perceptions into a fine art. But with the science of marketing in its current state undergoing a disruptive leap, as natural language processing (NLP) eliminates the need for laborious statistical segmentation and extends personalization to an unprecedented level, companies find themselves rethinking the more basic aspects of this new interaction.

The Technology of Natural Language Processing

NLP is a set of artificial intelligence processes that can crunch human conversational language, yielding a precise understanding of what is being communicated. This hasn’t been an easy matter to accomplish, as the way human beings actually use language is ambiguous and imprecise. Without going deeply into the intricate science behind NLP, we’ll briefly review the basic challenge it addresses. The art of decoding what a person actually means when they say or write a statement requires the NLP platform to engage with five aspects of language:

  • Phonology: the sounds that make up spoken language.
  • Morphology: the study of how words are formed, and the way that they relate to one another as language is naturally used.
  • Syntax: parsing grammar at the sentence level and making sense of word order. Webopedia gives the following example: “Baby swallows fly.” The meaning of each word in that sentence depends on whether “baby” is a noun or an adjective.
  • Semantics: the overall meaning of a statement, once its grammar has been sorted out.
  • Pragmatics: the intent of the statement, or the reason behind its speaking. What is the speaker trying to accomplish?

While NLP and computational linguistics (the science it’s based on) are still actively evolving, their progress to date is sufficient to make the technology a valuable component in every marketers’ digital strategy.

Extending Personalization

Online personalization as it’s typically used today can be clumsy, and as likely to be annoying as helpful. If a user looks at a pair of running shoes on Amazon for five minutes, they’ll be besieged with ads for those same shoes in the midst of every site they visit for hours or days afterward. That’s not a positive experience — and so, by definition, it’s not true personalization. The irritation that such tactics cause to make the market ripe for disruption. While NLP will continue to be developed in labs around the world, a set of sufficiently mature language processing technologies is already available for marketing use. These applications can be leveraged to the benefit of any company seeking to capitalize on the promise of personalized marketing by delivering a truly empathetic “marketed to” experience. The true innovators and leaders in the digital transformation space are now able to reach deeper insights than a simple web history was able to provide. Their goal is to move the needle in the direction of delivering what an individual customer truly wants, even before the person has interacted with a specific company.

Sentiment Analysis is the Beginning

NLP capabilities are being applied across numerous digital functions, doing everything from “data-fying” input text to guiding decision trees or automated chat interfaces. Digital assistants like Echo and Google Home are rapidly gaining popularity. For marketers in 2017, the relevant NLP applications begin with reading emotion (“sentiment analysis”). The ability to keep a finger on the pulse of consumers’ reactions to a product on social media is immensely valuable, since, in the words of Apiumhub, “It has the potential to turn all of Twitter or Facebook into one giant focus group.” Countless SaaS offerings accomplish this by scraping the text away from social or publishing platforms and providing actionable insights based on interpreting those statements. However, a better understanding of consumer sentiment, while essential, is now only the beginning of the marketer’s NLP toolkit.

In fact, countless components of a potential consumer journey can be mined with the same datafied approach, creating a thoroughly personalized marketing touch point and driving sales on the basis of an overall sense of each consumer’s style and preferences.

Stylistic Customization

As previously stated, the simple Amazon tracking that occurs when someone views a product page is a one-size-fits-all operation: The ad engine isn’t quite sure whether the consumer bought the item or not, and so it surfaces an ad for that item repeatedly in the middle of almost every subsequent site they visit. All too often, this yields nothing but annoyance and a sense of being stalked. If marketing content could make meaningful use of expressed language from the user’s entire web journey, including search terms, social media posts and reviews written customers would receive information in their preferred medium. Marketers with insight into a customer’s preferred content type could use that knowledge to shape the form in which advertising appears; the same information can be delivered via infographics, bullet point lists, long-form narratives, slideshows, videos and so on, depending on an individual customer’s tastes.

Furthermore, just as a sales representative adjusts their face-to-face conversational style to suit the person they’re talking to, NLP allows marketers to deliver messages in a vocabulary and emotional style appropriate to each individual. A vast data pool gleaned from users’ social media posts, reviews written, sites visited, and other web activity can be mined for information on communication style and decision-making process. This personalization would encompass content, form, and tone:

  • Content: If a customer tends to shop on the basis of cost, they would receive marketing content with a different emphasis from someone who seeks brand glamor or the security of warranty protection.
  • Form: Users’ preferences for receiving information would shape the form and channel in which marketing is delivered to them.
  • Tone: Reading level, typical vocabulary, and the use of analytical vs emotional language would all be factored into a company’s communications with new users. Automation would serve to match the natural empathetic adjustments that a human being makes as they engage in face-to-face conversation.

Natural Conversation is the Goal

NLP technology in its current form has already allowed marketers to better connect with their target audiences in a more natural way. For Kohler, the challenge was pivoting their brand around a few key events they were sponsoring, using the language of the event itself in real time. Using Brandwatch, the company was able to better understand the scope of conversation around the Bonnaroo Music and Arts Festival and The PGA Championship, (two very different events). By understanding the communication style of attendees at these events, Kohler was able to realize a greater ROI from their position as a sponsor.

Knowing the natural language thought process of someone in the market for a sports car could also be invaluable for a car manufacturer. Contextualizing the relative priorities that people have regarding a car’s features and functions allows retailers to emphasize those elements that are most important to customers. By removing the rote survey process that traditional enterprises are forced to undertake, NLP could empower consumers to give more freeform feedback and reactions for truer analysis. That analysis would, in turn, result in better, more targeted marketing to the cohort of consumers who are actually in the market for that specific make and model of car.

If an ecommerce retail company is struggling to communicate the intrinsic nature and component of its offerings, natural language offers a quite natural fit. Creating a customer-centered search addresses the issue of not being able to adequately anticipate the user’s true needs. For example, when the user searches for a “baggy sweater” with the intention of finding a sweater that fits loosely, and instead the search returns sweaters made of a bag-like material, or sweaters with tote bags depicted on them — this is not product marketing on par with a premium ecommerce shopping experience. However, if NLP technology can adequately match the context of that search with the correct outcome, it can apply correlating tags to tailor specific uses of conversational English to generate better-targeted results to the user, which they will encounter in every interaction. NLP leverages the text-based interaction that is the heart of digital communication: instead of assessing feelings, NLP is identifying and anticipating needs through understanding the context and expression given in the search term.

Words Distinguish the Individual

The disruptive promise of NLP is centered on the fact that marketers are no longer forced to speculate on a consumer’s preferences, based on general group demographics. Instead, less and less guesswork is required, as NLP analytics narrow personalization down to finely delineated verbal gradients. The truth of the matter is that every spoken word can be datafied, even the language that human beings use to express emotions. When verbal expressions can be transformed into aggregable data, there’s no limit to how granular marketers can make personalization work. NLP and its deepening insights into the essence of communication are clearly key to the future of digital marketing.