In the Analytics world, there is a wide range of tools and KPIs to choose from in order to measure your marketing data. One growing use case is Natural Language Processing (NLP), the act of getting a computer to interpret and analyze data involving human language. NLP is often used to gain a qualitative understanding of the “why” and “what” of a situation, and enables users to make more insightful decisions. In Marketing Analytics, NLP can be used to understand your audience’s intentions so that you can create smarter, more efficient marketing strategies.
Here are five examples of how NLP techniques can be used in marketing analytics:
Topic Extraction Provides Insights for Effective Content Creation
Topic extraction is the act of obtaining common themes or topics from a set of data. This can be extremely useful in order to obtain an idea of what your audience is thinking about and is a large part of what NLP works off of. Within marketing analytics, topic extraction can help you understand what your audience’s intentions or questions are, which, in turn, allows you to better serve their needs. For example, you can leverage NLP to gain an understanding of what customers are discussing on company forums in order to identify common interests and create targeted content for your audience.
Sentiment Analysis Determines If One Communication Method Receives Different Feedback Than Another
Sentiment analysis tells us whether our data has a positive or negative attitude associated with it. While there are different methods of generating sentiment analysis, common use cases include identifying the attitude expressed in a document or group of sentences in order to obtain a general notion of their mood. In marketing, this can be useful for identifying how people respond to different communication methods. For example, feedback from a chat system may return more negative sentiment than email feedback, since email feedback may imply that the user is engaged and interested in your company, whereas chatbot feedback may attract quick questions or support requests.
Audience Identification for Targeted Messaging
Improve Keyword Detection for SEO Techniques
Keyword Detection can be used to create or improve your Search Engine Optimization (SEO) techniques by generating a shortlist of popular words from your text data and matching it against your current SEO keyword shortlist. Here, you would look for the common keywords, often distinctive nouns, to help distinguish which words are most important. For example, you could analyze your customer support emails and discover that one of your products gets more questions regarding features than pricing.
From here, you can create a list of words that more accurately match the focus of the questions regarding each of your products, with one shortlist focused on features and the other on pricing. Once you understand this, you can create a (new) SEO keyword shortlist to help improve your click-through rate and ultimately gain more traffic.
Analyze Chatbot Data to Filter out Low-Priority Questions and Improve the Quality of Incoming Prospects
Using NLP techniques on Chatbot-generated data can help you group and filter out lower-priority questions. They can also help you better address incoming prospects by analyzing the common questions in your chat system and identifying whether or not they are quality prospects. If the questions are not meant towards your goals, you can create alternative solutions for your audience instead of your Chatbot system.
For example, if you realize that a lot of your incoming Chatbot questions are related to job openings for your company, you could create a hyperlink on your home page or enlarge your “Careers” button to divert some of the traffic that would have occupied your Chatbot team’s time. This could also improve the quality of incoming prospects since a higher proportion of those coming through the Chatbot would be interested in purchasing your offering (or your other goals).
Natural Language Processing is a powerful asset to have in your marketing analytics tool kit. While we’ve mentioned a few here, there are many more use cases in which NLP can be applied to help you better understand your organization’s data and what it can do for you.