Every marketer’s dream is to be an economic seer and prophet. Essentially, they strive to look at people’s and businesses’ wants and needs through the crystal ball of segmentation theory, use a miscellany of sophisticated tools as magic wands and, ultimately, try to outgun one another in this intricate trade. In a nutshell, up until recently, secondary indicators of an audience’s interest in whatever these marketers’ companies produce or sell were the main source of Marketing insights. Why up until recently? Because, not so long ago, a true game-changer burst on the scene, coming to their aid. This game-changer is a technique called Text Mining.
Text mining represents part of the area of knowledge known as Natural Language Processing and Understanding, that, similar to human understanding, allows deriving meaning from a larger context around an entity. Regardless of whether this entity is an individual or a business. Know what this means? Yes, you can directly get a fix on your audience’s buying intent. In addition, the technology offers some novel ways of determining the attitude of your target audience toward your brand, products, or services. Notably, the speed and breadth at which insights can be generated by NLP-powered text analytics are incomparably superior to what human-enabled processes can offer.
Let’s look at some of the ways this can be done.
Identifying B2B and B2C Buying Intent
If you are a marketer whose task is to identify individuals or businesses on the lookout for a product or commodity, with Text Mining the sky’s your limit. Let’s assume that you are after those folks who intend to buy a set of kitchens utensils and are currently poised for choice. When equipped with Text Analytics functionality, your system will be capable of finding all mentions of this product in the social network via which you promote your kitchen stuff. Next, in emulation of human understanding, the system will analyze the context in which the mention of the product in question occurs. It will do so by parsing the words and words combinations in the related phrases and comparing those with other similar contexts in order to identify buying intent. Pretty cool, isn’t it? But that’s not all yet. You can also identify those potential buyers, who are after nonstick cookware with a Teflon coating or can cook in yellow-handled pans only. In other words, you can sift through the various contexts in a multi-tiered manner by indicating product properties and related conditions. Similarly, if you are in B2B, you can sift through vast numbers of websites in order to identify your potential clients. You can search for calls for tender, partnership opportunities, and more.
Identifying Customer Sentiment By Means of Named Entity Recognition (Sentiment Analysis)
Unlike segmentation or the various rule-based approaches, a Text Mining technique called Entity Recognition allows you to split out the so called Named Entities from, virtually, any kind of textual information, be it corporate websites, forum posts, or twits. These Named Entities can represent individuals, brands, or organizations. Next, the system processes all textual information associated with a found Named Entity and explores the related context. Thus, you can find out what your audience thinks about your company or brand. This kind of analysis is, usually, referred to as Opinion Mining.
Increasing Your Email Marketing Campaigns’ Performance
When combined with some other NLP techniques, Text Mining can be used for another hugely important Marketing purpose, — predicting how efficient your email marketing campaigns can be. Your email-related textual information can be mined in order to extract a set of entities and other textual information that is meaningful for the purpose of a campaign.
Next, it is possible to build a predictive model that will take into consideration the extracted entities and other textual features, taken from your previous campaigns. For this purpose, both subject line texts and other criteria, such as for example, the email domain and season that the emails were sent, can be used. As far the subject line texts are concerned, sentence structure, thematic word groups, style, and the quality of grammar are all taken into consideration. For example, by selecting the more productive variables, you can achieve a higher Open Email Rate, thus making your email Marketing efforts more efficient.