LinkedIn recently updated its interface for monitoring who has viewed your profile. The most useful change has been to include a segmentation of views by the top four sources on each job-related characteristic of users. Hovering over the "other" segment of each pie chart reveals the next four following the top four, as well as the number of visitors who concealed their information for that metric. "Unknowns" are not counted in the top eight but are pre-pended to the top of the list that shows ranks five through eight under "other."
Those who want further percentages may divide the categorical subtotals by the dimensional totals to calculate relative frequencies for all segments shown. However, I give the top two most-frequent occurrences by industry and general occupation.
The plurality of visitors to my LinkedIn profile hide their industry of employment or leave it blank (17.5 percent). The plurality of visitors who make known their employing industry known are within the nebulous field of education management (19.9 percent), which includes such widely differing roles as chancellor, financial aid specialist, school board president, and freelance tutor.
The plurality of visitors to my LinkedIn profile hide their job title or leave it blank (18.4 percent). The plurality of visitors who make known their job title are members of one or more nonprofit boards (6.6 percent). As you might infer from the plurality and near-plurality having such small percentages, I attract profile views from many different industries and job roles.
And while I dislike using the term multiple times within a paragraph, there really is no technically accurate equivalent for the term "plurality" -- "majority" and "most" speak in comparison to the entire sample, when in fact a coalition of categories is necessary to achieve a share greater than half the entire sample size.
That phenomenon is why third-party candidates can swing an election for or against one of several more popular candidates: the unpopular candidate may leach away less dedicated fans of a candidate who would otherwise lead in the polls if not for this electoral distraction by the oddball candidate. For a job seeker -- whether you're marginally attached to the workforce or firmly established as a world beater -- knowing your profile visitor's livelihoods in the aggregate reveals the types of occupations where people are most interested in you.
The dispersion of views among sectors is not necessarily a benefit: Those who are seeking to advance within an industry should have the greatest concentration of profile views from that industry, or else you're some person who has professional identity issues. And even if you have plenty of online observers, viewership information alone does not disclose the proportions of gawkers to admirers.
Impression quantity and impression quality are components of impression management: LinkedIn profile views give you a ballpark figure of your reach but nothing in terms of how many leave your profile with a favorable, unfavorable, or undecided impression. Perhaps adding a thumbs-up / thumbs-down mechanism to LinkedIn profiles would facilitate a quality-of-impression metric -- but as with any popularity contest, that feature would be prone to gaming. Then again, it would reflect the reality of office politics and would therefore have some convergent validity with the work world.
I'd say that before LinkedIn bothers with that, it should streamline its skill keywords to more systemically parse into clearly defined, exclusive XML tree branches such as that different capitalizations and punctuations of a skill would all parse into the same skill tag; they currently do not.
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