Researchers, of course, are not only the end users of scholarly content and publisher platforms. They’re also editors-in-chief, editorial board members, peer reviewers and, at least as importantly, authors of journal articles and/or books. The fact that researchers typically receive no compensation for most of these important contributions means that publishers should feel a particular obligation to understand their needs and motivations via analytics, not only because it’s the right thing to do but also because those publishers that treat these important customers better will almost certainly enjoy a larger share of the best content than they would otherwise.
Analytics are critical not only to editorial decision-making, but also to decisions informing overall organizational strategy and policies, marketing strategy and tactics, and technology development.
What do we know about researcher motivations, and what does that tell us about the types of decisions a publisher may need to make and, therefore, the analytical approaches that can inform that decision-making? Most researchers care very much about making a significant contribution to their field of study. In doing so, they want to be recognized by their community, not only for their research but also for their efforts as editors, peer reviewers and other roles that contribute to the research ecosystem. Finally, most researchers have the basic need to advance professionally, whether that means getting a promotion, achieving tenure, or receiving some other kind of compensation for their work.
Related Reading: Using Data Analytics to Drive Strategy in the Academic STM Librarian Space
Analytics are critical not only to editorial decision-making, but also to decisions informing overall organizational strategy and policies, marketing strategy and tactics, and technology development. As always, before determining the questions you want to answer via analytics, you should first articulate the decisions, both strategic and tactical, that your publishing organization needs to make. These might include:
- Does a given journal need a new editor-in-chief?
- What high-growth markets should you prioritize, and what actions can you take to expand your author base there?
- Should you start a new gold Open Access journal?
- Whom should you approach to sit on the editorial board of a new journal?
- Should you adjust your OA APC’s?
- What actions can you take to increase your share of the best submissions that might otherwise go to a competing journal?
If you focus on the decision around actions to take to increase your share of the best submissions, some questions you might answer with analytics are:
- Who are the most “valuable” authors in the given field? (Value here would be determined by your organization’s strategy and priorities. It could mean driving usage or driving citations, and it could even mean driving social media attention and altmetrics.)
- How should you segment your potential author base?
- What are the specific priorities and pain points of the key author segments?
- What marketing approach, in terms of both medium and message, has the greatest impact with the key author segments?
- Would improvements to your submission and editorial management systems increase the volume of quality submissions?
- If you were to take x% of rejected manuscripts from your top-tier journal and entice the authors to publish them in a lower-tier Gold OA journal, how might that affect submissions, OA revenue, and Impact Factor? Do authors the field have a demonstrated openness to this?
The potential data sources for such analytical questions are numerous. You might need any combination of:
- Internal, proprietary data, such as usage data, submission data or OA sales data
- Third-party datasets, such as the Web of Science, Scopus, Altmetric.com, etc., or data provided by an intermediary partner or vendor
- Data produced by primary market research that your organization or a vendor undertakes
As mentioned in our post on academic librarians, many analyses can be handled in-house using a basic tool like Excel. The bigger challenge might be getting your hands on all of the data you need and ensuring that data is clean. The increasingly widespread adoption of ORCID certainly helps increase the likelihood that researcher data from one source can be matched with researcher data from another source, but expect to invest significant time in data cleansing of one sort or another.
Finally, it’s particularly important to consider whether your organization needs a KPI, or at least some operational metrics, related to researchers. Researchers and the content they produce are the lifeblood of any scholarly publisher. On any given day, there are probably more decisions being made by people in editorial roles than in any other part of the organization. Might embedding analytics into the day-to-day processes of editorial colleagues help to drive the kinds of actions that will support the successful achievement of your strategy?
About the Series
Analytics–everybody wants some, everybody agrees they’re incredibly important, but many STM publishing organizations just aren’t sure how to use them to positive effect. Looking backwards to see what happened in the past can be really interesting (or really boring!), but what does that tell us about what we should do right now, or what we should plan to do in a year? This is the first in a series of blog posts to attempt to provide STM publishers with some guidance on how best to use analytics–not simply to report on what happened, but to guide decision-making and drive impactful actions to achieve commercial and strategic advantage. To do that, we’ll put the focus exactly where it always should be: on our customers. Specifically, we’ll explore how to use analytics to better meet the needs of librarians, researchers, and corporate customers.