Audience Analysis

Introduction
“Know your audience” perhaps is the oldest rule if you are going to become an excellent writer. However, for technical communicators, the rule might accurately be phrased this way:” No, your audience”. When technical communicators design the outline or process of a project, not only they must learn and understand the technology, they must also analyze the audience’s factors, such as their goals, needs and preferences. After considering all the known factors, a profile of the intended audience can be created, allowing technical communicators to write in a manner.

It is well known that the general marketing strategy of the most technical companies is transformed from providing products to providing solutions. As the results, data analysis or science increasingly become a popular job now. How to collect data from audience, then analysis data, and even visualize data are significant for every companies. As technical communicators, how to plan for document content and form by audience analysis is important. An audience analysis is a tool that allows the technical writer to gain a more complete perspective of who the audience is and what their goals, interests, and needs are. Completing an audience analysis is the first step in document preparation, and without it, you can’t effectively plan the document or start writing.

This entry introduces several types of the audience research which could help the technical communicator to obtain useful data from the audience. The next, three models of audience analysis are described, which separately are classification-driven analysis, intuition-driven analysis and feedback-analysis. The last part discusses some research in audience analysis.

Three models of Audience Analysis
In, the writer describes three models of audience analysis.

Classification-driven Analysis
The classification-driven model may seem the most familiar to you, as it is often the way we are asked to analyze audiences when writing in academic settings; classification-driven audience analysis proceeds "by brainstorming about the audience and by cataloging audience demographics (e.g., age, sex, income, educational level) or psychographics (e.g., values, lifestyle, attitudes, personality traits, work habits)".

Intuition-driven Analysis
In the intuition-driven model, communicators draw on their own experiences to construct an imagined or ideal reader, either as a "composite of human characteristics" or "people they have met before that could be like the reader" or even as an ideal reader to whom they would like to appeal.

Feedback-driven Analysis
The feedback-driven audience analysis model involves real readers, bringing potential audience members "into the design process in order to draw on their ideas to guide invention”.

Multidimensional Audience Analysis
The era of “one size fits all” approach is drawing to close today. Based on various users’ specific requirements, creating different files makes your documents more competitive. In, it illustrates several dimensions for multidimensional audience analysis such as knowledge dimension, detail dimension, cognitive ability dimension and social/cultural dimension.

Multidimensional audience analysis has at least three distinct dimensions which must be explored: knowledge level, detail level, and cognitive abilities. The figure 1 shows the general ideal of multidimensional audience analysis.



Knowledge Dimension
Knowledge Dimension is the subject knowledge the user possesses about the topic. This influences word choice and determines how much supporting information must be provided. Majority of the current work in audience analysis tries to pinpoint user knowledge level.

Detail Dimension
Detail dimension is the amount of detail the user wants about the specific situation. This can range from basic explanations to highly detailed explanations about the underlying physical process.

Cognitive Ability Dimension
Cognitive ability dimension is the ability of the reader to comprehend and understand the material. Cognitive ability includes factors such as the person’s reading ability, education level, and physical/mental limitations.

Social/Cultural Dimension
Social/cultural dimension is the social and cultural factors that affect the reader. Depending on the specific situation being analyzed, social or cultural aspects might or might not for another dimension of the audience analysis.

Social Media Sentiment Analysis
Social media is changing the opportunities for technical communicator to really understand audiences, because social media enables the users of product to communicate widely about their experience, dealing with that information as part of audience analysis can be useful for technical communicators.

Sentiment analysis, also known as opinion mining, is the analysis of the feelings (such as attitudes, emotions and opinions) behind the words using natural language processing tools. Most of the sentiment uses machine learning approached for better results.

Ad-hoc Corpus building processes
In article, the author mentioned that thorough applying ad-hoc corpus building processes to crate word lists relevant to specific organizational projects or products, technical communicators and marketers can listen to their external users and identify areas of need with greater accuracy. Unlike other methods of sentiment analysis look for a solution using artificial intelligence, this method identifies the present need of a human interaction approach for finding patterns in social media posts, and understanding the socio-technical contexts from these patterns emerge. Four steps are described as followings :


 * 1) Design and compile an AD-Hoc Corpus;
 * 2) Using ReCor to determine the size of a Corpus;
 * 3) Using Antconc to analyze the AD-Hoc Corpus;
 * 4) Select specialiaed terms and remove common words.

The ad-hoc corpus method can provide insight into what the public of an organization or even a specific product openly share in communications via social media.