Twitter Pitching: Hard Numbers from #PitMad by Dan Koboldt

Twitter Pitching: Hard Numbers from #PitMad by Dan Koboldt

Last week, hundreds of aspiring authors took on a new challenge: pitching their completed manuscripts in 140 characters or less. The event was called #PitMad; the premise is fairly simple: throughout the day, authors tweet a brief pitch for their manuscripts under a common hashtag. Agents and editors monitor the feed, and favorite the pitches that’d they’d like to see as a query or submission.

Tweets can’t exceed 140 characters. The hashtag, readership level (YA, NA, etc.) and genre codes take up 10-12 characters or more. So really, we’re talking about summarizing an entire novel’s character(s), conflict, and stakes in about 130 characters. That’s hard to do, especially in a way that’s coherent and intriguing.

The PitMad Dataset

Because it’s on Twitter (and drew wide participation), the contest also provided a good opportunity for data mining. As a working scientist I couldn’t resist the temptation to learn about my fellow authors en masse by analyzing the feed. Here you’ll find my analysis of 6,500 tweets from the Twitter feed.

For the sake of a clean dataset, I required:

That the user had at least 10 followers

That the tweet contained a tag for readership level (YA, MG, NA, PB, or adult) and/or genre (UF, SF,  etc.).

That the #PitMad tag was located near the start or end of the tweet.

These steps helped remove the spam, the extensive non-pitch discussion about PitMad, comments from agents/editors, etc. It also removed a fair number of tweets (~1,500) that might very well have been pitches, but lacked age/genre tags, so keep that in mind. In the end I had 4,302 tweeted pitches from 709 unique Twitter users.

Age Level: YA Predominates

First, a breakdown by readership level of the work being pitched. Technically, this pitch contest was open to all age levels and genres. Because Brenda Drake runs this contest, however, we should probably expect a Young Adult (YA) bias. A quick look at the pie chart confirms this: 55% of pitches were for YA novels. The other categories were roughly equal: adult (15%), new adult (11%), middle grade (10%), and picture book (9%).

The slight excess of adult relative to other non-YA categories is probably an artifact, because while parsing the twitter feeds, I automatically assigned certain genre tags to adult (e.g. women’s fiction) if no other age tag was provided. Not shown in the pie chart were comic book (CB) works that represented <1% of pitches.

Pitches by Genre: Fantasy & Sci-Fi Rule

Breaking down the pitches by genre is probably a more useful exercise for aspiring authors: it gives an idea of the competition. Overall, urban fantasy was the most prevalent genre, followed by science fiction and then fantasy. Together these three genres accounted for over 60% of pitches. Romance (9%) and paranormal (6%) were the most common after that, followed by LGBT, thriller, historical, and women’s fiction.

Literary fiction and mysteries were the least-prevalent of the genres that I parsed out, which demonstrates the participant bias as well as anything. These are both genres with huge numbers of aspiring authors… just not in the #PitMad contest. I also broke down genres for the most popular reading level (YA) in a separate pie chart, and here the distribution was more broad, with urban fantasy and sci-fi leading the pack (both at 27%).

Tweet Rates, Spamming, and the Top Tweeters

As a participant in this contest, I can tell you that it’s hard to resist the urge to pitch too often. Suddenly you see the dream agent on there, and you want to make sure he/she sees your pitch! At the same time, you don’t want to come across as desperate. The unofficial rule of thumb was 1-2 times per hour, and if you look at the distribution of pitch frequencies, nearly all participants kept to that. There were no “bad apples” who were pitching every five minutes, at least from my analysis. T

That said, when #PitMad began trending on Twitter in the U.S., the hashtag quickly attracted its share of spammers. They were a nuisance, nothing more. If anything, they’re simply more evidence of just how popular the event was.

Agent and Editor Picks

I’d love to tell you that dozens of agents and editors were visibly hitting “favorite” on well-written pitches left and right. But let’s remember that these are busy people, most of whom are currently swamped with New Year’s resolution submissions and holiday catch-up. In fairness, there were thousands of tweets to read. If I looked away from the feed for a minute, it seemed like another 75 or 100 tweets had shown up.

That said, there were agents who generously gave time to participate. I pseudo-randomly chose five agents from different agencies. WIth just a *little* bit of Twitter stalking the day after the contest, I could determine just how many pitches some of them had favorited:

Laura Bradford, Bradford Literary: 1

Pam van Hylckama, Foreword Literary: 13

Lindsay Ribar, Greenburger Associates: 3

Carly Watters, P.S. Literary Agency: 7

Eddie Schneider, JABberwocky Literary: 22

Together, these agents picked 46 pitches as their favorites. Even assuming that there were no overlaps, with my estimate of 709 bona-fide author participants, the odds of success with these agents were around 6.5%.

That might seem depressing at first blush, but remember that getting a favorite (or not) is not necessarily a reflection of the quality of your work. There were lots of good pitches that didn’t get a favorite, at least that I saw. Literary agents must be selective, even in free-for-all contests such at these. They might not represent your genre, they might have another client with a similar book; you just never know.

Attracting the interest of an agent was not the only good outcome possible from this contest and others like it. Participants who gained some new Twitter followers, made friends with other writers, improved pitching skills, or just had fun should consider it a win.

About the Author

Dan Koboldt (@DanKoboldt) works in genetics, writes science fiction, and tries not to mix up the two.