To enhance brand awareness, an effective marketing campaign must be data-driven and leverage market research on customer sentiment, buyer journey, social segments, social search, competitive intelligence and content strategy. To obtain refined results, this research requires digging into unstructured data such as customer reviews, social media posts, articles, and chatbot logs.
Collecting this data manually is time-consuming, especially for a immense brand. Natural language processing (NLP) enables automation, consistency and deep analysis, so your organization can leverage a much broader range of data to build your brand.
NLP in IBM Watson
As we wrote in an earlier blog post, NLP enables systems to analyze immense amounts of natural language data (such as articles, documents, and social media posts) using several techniques, including named entity recognition, sentiment analysis, and word disambiguation.
At IBM Watson, we integrate NLP innovations from IBM Research with products like Watson Discovery and Watson Natural Language Understanding for a solution that understands your company’s language. Watson Discovery delivers answers and insights from data sources in real time. Watson Natural Language Understanding analyzes text to extract metadata from natural language data.
NLP can be applied to any task that depends on language analysis, but today we will focus on three specific tasks related to brand awareness.
Using NLP for social segmentation
Proper segmentation helps your organization understand your audience more precisely and deeply and tailor your digital marketing campaigns to reach and convince them. With Watson Discovery you can:
- Identify patterns and trends: Elicit opinions and identify which keywords are trending. Find out what people are saying about your brand on social media or in product reviews.
- Perform topic modeling: Get rid of the white noise in unstructured data, identify the main topics of your documents, and segment the main categories and topic groups that your customer base focuses on.
- Summarize: Extract the most crucial information and key points from immense collections of tweets or emails. Quickly generate summaries of immense data sources.
Using NLP in social search
Instead of manually searching the web, marketers can operate NLP to prospect on social media and identify potential customers. With Watson Natural Language Understanding and Watson Discovery you can:
- Extract keywords: Search data such as social media to monitor brand mentions. NLP extracts and filters data by keywords, understands context and semantics.
- Analyze relationships: Enable facet analysis in Watson Discovery to accurately identify metadata relationships, such as cause and effect, so you can optimize and better respond to propensity signals.
Using NLP to determine customer sentiment
Another key indicator of brand awareness is customer sentiment: how customers, experts, influencers and media are talking about your brand at scale.
With Watson Natural Language Understanding you can:
- Conduct unconventional sentiment analysis: Sentiment analysis finds positive and negative comments that will aid improve your branding, marketing message and product positioning. Moderately consistent branding across all channels increases company revenues by 23%.
- Create custom sentiment analysis models (beta): The modern customer feedback feature allows you to identify the context of a phrase and train Watson to understand the language and nuances of a given domain or industry. For example, the phrase “we had a lot of profits this month” is positive in an investment banking context, but negative in a retail context.
How our clients improve their brand awareness with Watson and NLP
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Using Watson NLU, Havas has developed a solution that enables you to create more personalized, relevant marketing campaigns and customer experiences. The solution helped Havas client TD Ameritrade enhance brand engagement by 23% and enhance the time visitors spent on the TD Ameritrade website.
hurry up
Thanks to IBM Watson hurry up found the right social media influencers for their 2016 Super Bowl ads. They used Watson to analyze social media data and discover which influencers used language that reflected personality traits Kia desired, such as “openness to change,” “artistic interests” and “striving for achievement.”
Based on information from Watson Natural Language Understanding, Kia promoted its sedans with influencers such as musician Wesley Stromberg and actor James Maslow, who created content in support of a Super Bowl ad featuring actor Christopher Walken.