Strategic Category and Keyword Selection for Academic Books
Properly categorising and keyword targeting makes a massive difference in how easily your book can be found online (Chang et al., 2015; Cheng et al., 2020). Researchers need to categorize their content using complex subject taxonomies and get their content into the hands of the readers using smart metadata categorization techniques.
Understanding Amazon's Category System
Amazon crawls its books collection, then uses its pretty complex category structure to determine where your book will show up in search results, and browsing around. Academic books often cross several categories and savvy selection is required to ensure high visibility with relevant but not misleading classification.
Primary Category Selection Strategy
The first category you choose is particularly powerful in terms of competitive positioning and algorithmic weighting. Authors should also research category rank popularity, the number of competing titles, and average sales volume prior to making final decisions (Aksnes et al., 2019; Bornmann et al., 2018).
Effective category research includes:
- Analyzing successful books in your field and their category placements
- Evaluating category competition levels and ranking requirements
- Understanding seasonal fluctuations and trending topics within categories
- Considering interdisciplinary appeal and cross-category potential
Secondary Category Optimization
Secondary categories give you an additional way to “tag” posts which can help drive more traffic to your site, by making it easy for people to find all content on related blog themes. It is common for academic papers to benefit from cross-discipline classification.
Keyword Research and Selection Methodology
Good keywording is the basis for any successful book marketing campaign. Researchers must bear in mind the need to use discipline-specific as well as more general search terms (Uddin & Khan, 2016; Walter & Ribière, 2013).
Good keywording is the basis for any successful book marketing campaign. Researchers must bear in mind the need to use discipline-specific as well as more general search terms (Uddin & Khan, 2016; Walter & Ribière, 2013).
Academic-Specific Keyword Strategies
Keywords of the academy require comprehension of both scholar-speak and lay search behaviour. Your keyword list should include technical and methodological terms as well as broader areas of interest.
Long-tail Keyword Opportunities
Long-tail keywords are usually less competitive when it comes to gaining visibility and bring readers ready to bite. Academic writers can exploit particular research questions, mixed-method approaches and niche subtopics.
High-value academic keyword types:
- Methodology-specific terms combining approaches and disciplines
- Geographic or temporal qualifiers that narrow topic focus
- Problem-solution combinations that address specific research needs
Emerging terminology and trending academic concepts
Competitive Keyword Analysis
Keywords used by successful books in your niche reveal to you how you should optimize. Constant keywords gap analysis allows us to find new openings and steer clear of crowded keyword spaces.
Metadata Optimization Best Practices
In addition to categories and keywords, full metadata optimization involves maximizing the use of subtitles, series and contributor details to optimize discovery through various search routes.
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Further Reading: Book Distribution Channels
References
Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. Sage Open, 9(1), 2158244019829575.
Bornmann, L., Haunschild, R., & Hug, S. E. (2018). Visualizing the context of citations referencing papers published by Eugene Garfield: A new type of keyword co-occurrence analysis. Scientometrics, 114(2), 427-437.
Chang, Y. W., Huang, M. H., & Lin, C. W. (2015). Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses. Scientometrics, 105(3), 2071-2087.
Cheng, Q., Wang, J., Lu, W., Huang, Y., & Bu, Y. (2020). Keyword-citation-keyword network: A new perspective of discipline knowledge structure analysis. Scientometrics, 124(3), 1923-1943.
Uddin, S., & Khan, A. (2016). The impact of author-selected keywords on citation counts. Journal of Informetrics, 10(4), 1166-1177.
Walter, C., & Ribière, V. (2013). A citation and co-citation analysis of 10 years of KM theory and practices. Knowledge Management Research & Practice, 11(3), 221-229.