To grow brand awareness, a successful marketing campaign must be data-driven, using market research into customer sentiment, the buyer’s journey, social segments, social prospecting, competitive analysis and content strategy. For sophisticated results, this research needs to dig into unstructured data like customer reviews, social media posts, articles and chatbot logs.

Manually collecting this data is time-consuming, especially for a large brand. Natural language processing (NLP) enables automation, consistency and deep analysis, letting your organization use a much wider range of data in building your brand.

NLP at IBM Watson

As we covered in an earlier blog post, NLP allows systems to analyze large amounts of natural language data (such as articles, documents and social media posts) using several techniques, including named-entity recognition, sentiment analysis, and word sense disambiguation.

At IBM Watson, we integrate NLP innovation from IBM Research into products such as Watson Discovery and Watson Natural Language Understanding, for a solution that understands the language of your business. Watson Discovery surfaces answers and rich insights from your data sources in real time. Watson Natural Language Understanding analyzes text to extract metadata from natural-language data.

NLP can be infused into any task that’s dependent on the analysis of language, but today we’ll focus on three specific brand awareness tasks.

Using NLP for social segmentation

Proper segmentation helps your organization more accurately and deeply understand your audience and tailor your digital marketing campaigns to reach and persuade them. You can use Watson Discovery to:

• Identify patterns and trends: Mine for opinions and identify which keywords are trending. Learn what people say about your brand on social media or in product feedback.
• Conduct topic modeling: Cut through white noise in unstructured data, identify the main topics of your documents, and break down the major categories and topic clusters that your customer base is focused on.
• Summarize: Distill the most important information and key points from large collections of tweets or emails. Rapidly generate summaries of extensive data sources.

Using NLP for social prospecting

Instead of manually scouring the web, marketers can use NLP for social prospecting and lead identification. You can use Watson Natural Language Understanding and Watson Discovery to:

• Extract keywords: Sift through data such as social media to monitor brand mentions. NLP extracts and filters data by keyword, and it understands context and semantics.
• Analyze relationships: Enable facet analysis inside Watson Discovery to accurately identify metadata relationships such as cause and effect, enabling you to optimize and better react to propensity signals.

Using NLP to determine customer sentiment

Another crucial metric for brand awareness is customer sentiment: how customers, experts, influencers and media speak about your brand at scale.

You can use Watson Natural Language Understanding to:

• Conduct out-of-the-box sentiment analysis: Sentiment analysis allows you to find positive and negative comments to help improve branding, marketing messages, and product positioning. Consistent branding across all channels on average increases business revenue by 23% (link resides outside of ibm.com).
• Create custom sentiment analysis models (beta): The new customer sentiment feature enables you to identify a phrase’s context and train Watson to understand the language and nuances of your domain or industry. For example, the phrase “we had a lot of returns this month” is positive in an investment banking context, but negative in a retail context.

To learn more about sentiment analysis, read our previous post in the NLP series.

How our clients are improving their brand awareness with Watson and NLP

Havas

Using Watson NLU, Havas developed a solution to create more personalized, relevant marketing campaigns and customer experiences. The solution helped Havas customer TD Ameritrade increase brand consideration by 23% and increase time visitors spent at the TD Ameritrade website.

Kia

With IBM Watson, Kia (link resides outside of ibm.com) found the right social media influencers for its 2016 Super Bowl commercials. They used Watson to parse social media data and detect which influencers used language that demonstrates the personality traits desired by Kia, such as “openness to change,” “artistic interest” and “achievement-striving.”

Based on insights from Watson Natural Language Understanding, Kia promoted their sedans with influencers like musician Wesley Stromberg and actor James Maslow, who made content supporting a Super Bowl ad featuring actor Christopher Walken.

To learn how you can start using IBM Watson Discovery or Natural Language Understanding to boost your brand, get started for free or speak with an IBM expert. Next in the NLP series, we’ll explore the key use case of customer care.