Unlock Stronger Customer Relationships With Natural Language Processing

In this article, Huanjin Chen, CTO, ShareThis, explores how natural language processing (NLP) can help brands better understand what makes their audiences tick over time or identify the passions and long-term interests that drive them forward.

The notion of maximizing ROI and conversions encouraged brands to barrel down the path of “last touch.” However, this approach ultimately sacrifices their ability to build relationships that need more focus and transparency than ever before. We’ve seen that brand affinity, trust, and long-term relationships are what grow a business’s bottom line and overall reputation. 

With this in mind, a recent report from Criteo found that 73% of US consumersOpens a new window are open to considering a new brand in at least one shopping category. Additionally, 77% of people retract their brand loyalty faster than they did three years ago, and 61% have switched brands within the last yearOpens a new window . It’s clear consumers want to work with transparent entities making a positive impact, bringing long-term utility to their lives and environments. 

But how can brands better understand what makes their audiences tick over time or identify the passions and long-term interests that drive them forward? How can brands ensure they don’t lose their lifetime value propositions with their coveted audiences? Through advancements in natural language processing (NLP), brands can develop and better maintain their unique relationships with customers and stand as partners that move with them through life, rather than just at the last minute. 

A Path to Consumer Intent

While brands have implemented a number of tools and data-driven strategies to better understand desired audiences, only recently has the technology to actualize stronger digital consumer relationships been available. Specifically, NLP focuses on understanding and analyzing the digital content consumers engage with. 

Early iterations of NLP worked to comprehend sentence structure but failed at knowing the meaning associated with the words. For example, NLP could recognize a consumer talking about toilets and toilet tissue in the same sentence but wouldn’t understand they are different things. From a business perspective, that unlocked early glimpses at conversation topics based around a given subject or object. However, it lacked the ability to understand proper context and sentiment around what was discussed. When coupled with the mountains of data needed to gather any tangible insight, early NLP just wasn’t a realistic option. 

Today’s iteration of NLP can look into the actual semantic meaning of words. Put simply, computers now understand relevancy or similarities between words and topics. For example, today’s NLP doesn’t know toilet paper is something used in the bathroom, but it does understand toilet paper and toilet tissue are the same thing. With this capability, businesses can better refine their analysis and extract important contextual information once large sums of language data are aggregated. 

A simple example of this can be seen in companies utilizing NLP for customer service. They’ll use the technology to identify responses from customers and see if they’re positive or negative. To do this, the company would utilize NLP to cluster topics within their owned channels’ comments and review sections. From there, it could aggregate the sentiment for each topic and explore why responses are positive or negative. By compiling large sets of customer review data — we’re talking millions of data points — we can see what customers are interested in and thinking about over time in regards to their experiences. At that point, the company can address key issues or evolve its customer experience strategies accordingly. 

However, despite these new capabilities around semantic understanding, there’s still a major missing link when it comes to efficiently and accurately extract intent insights from today’s NLP solutions. Computers struggle with understanding the relationship between topics or objects in conversations, thus making it difficult to rely solely on them for impactful consumer insights. This is where human analysis comes into play in a critical way.

See More: AI and NLP Tools Hold the Key to Modern Health Data Analysis

The Marriage of Human Comprehension and Aggregate Insights

Humans bring knowledge structured around a conceptual and relationship hierarchy that NLP can’t provide yet. We discern the value from learning what broader interests an audience has and how those interests are correlated. For example, we can identify “sports fans,” and explore a list of potential interests that demographic might have, such as “fast food,” “movies and music,” “hiking,” and more. NLP does not understand the relationships between concepts like “audiences” or “interests.” However, NLP shines when used to find similar content aligning with each individual concept, aggregate volume, and sentiment. By combining human knowledge of business concepts and the relationships between them with NLP’s ability to understand the semantic meaning, brands can unlock new insights and better optimize their marketing efforts. 

As the industry continues to adopt NLP, it’s finding value in extracting insights from broader searches and longer-lead engagements. This is a powerful method in developing a clearer picture of what actually drives consumers. By incorporating human-led NLP, brands can successfully learn about their audiences in a more holistic and genuine way. Too often, brands optimize every action to the feedback of a purchase, and that’s a miss. Larger contextual approaches build on the idea of relationships and work to enhance connections with coveted audiences on an ongoing basis.

In most circumstances, context is hard to glean. Each person is made up of their own desires, hobbies and work, global region, the preferred medium of contact, and so many other factors. With NLP, brands can dive beneath the surface and learn what actually drives consumers towards a purchase or activity based on their own unique circumstances. By weaving in the human analysis piece, it’s possible to identify consumer actions or interests around a brand, a product, or a product category. From there, brands can offer genuine value to audiences throughout their life, not just during a purchase cycle.

For example, an automotive dealer can identify a consumer demographic built around “parents of young students,” based on their interest in K-12 education. From there, they can split the audience into two sub-audiences, one with a clear interest in outdoor activities and the other not. They can use NLP to analyze the groups and identify which consumers have a logical need for an SUV-type vehicle. From there, the dealer could leverage these insights to sell to the right audiences.

Through NLP, brands can develop a deep understanding of consumers. The technology, when coupled with keen human analysis, unlocks a new method of forming long-lasting relationships with desired audiences and enables brands to learn what drives a person’s interest, actions, and ultimate purchase. Companies not incorporating human-driven NLP analysis will fail to realize key strategies needed to keep up with their competition and better engage desired audiences.

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