A New Way to Build a Query List

I've blogged about querying a lot. It's one of those topics fellow writers always want to read about, and if I'm being completely honest, I have more experience than most:) A while back, I covered who to query. Today, I want to talk about who to query when.

Coming up with a list of names is the (relatively) easy part. You can research their track records, their client lists, and their agencies and decide which agents you think you might be interested in working with. But how do you decide which order to query them in? I've heard of people making A lists, B lists, and C lists based on some instinctive measure of how good an agent is, but that's kind of subjective, and no one can decide whether you should query your A-listers before your C-listers, or your C-listers before your B-listers, or some mysterious combination of the three. I'd rather focus on responsiveness, which is a quality I value and can calculate:

overall average response time

response rate

Yes, I said calculate, so settle in and buckle up for your math lesson for the day. I call this happy formula the responsiveness quotient (RQ) because that's what it is--and because calling it that makes it sound cool and scientific. The best place to find the numbers you need to calculate it is on QueryTracker. You can capture the overall average response time in several ways, but the most straightforward one is simply to look at the "Query Response Times" report. This report gives you the agent's average response times broken into two categories, positive and negative, so to calculate the overall average response time, you have to average these averages:

(average positive response time * positive responses) + (average negative response time * negative responses)

positive responses + negative responses

That calculation looks scary, but it's really not. Let's look at an example. Suppose Agent Awesome has an average positive response time of 2 days, an average negative response time of 33 days, 36 positive responses, and 185 negative responses. Then her overall average response time would be

(2 * 36) + (33 * 185)

36 + 185

or 27.95 days.

Now for the response rate. Thankfully, QueryTracker calculates that figure for us. You can find it directly under an agent's contact information on his or her profile page. (For instance, if you look up Kristin Nelson, you'll see her response rate is 89.9%, which is .899 in decimal terms.) So if Agent Awesome's response rate is 77.9%, her RQ would be 27.95/.779, or 35.88.

So what does this number mean? The best way to think of it is in terms of days. It's essentially a weighted average response time, or an expected response time: since Agent Awesome generally responds 27.95 days after she receives a query 77.9% of the time, you should expect to hear back from her in approximately 35.88 days. If she responded to every query, her response rate would be 100%, and her RQ would be the same as her overall average response time. On the flip side, if she never responded to queries, her response rate would be 0%, and her RQ would be infinitely large (which is the mathematical way of saying you shouldn't expect to hear back).

If you're still with me, way to go! But if you started skimming a few paragraphs ago, I don't blame you this is where you'll want to start reading again. Once you've calculated the RQs for all the agents on your list (which really won't take too long once you get the hang of it, especially if you're using a spreadsheet), you can use these rankings to help you decide which order to query them in.

Now, I'm not suggesting that you start with the agent with the lowest RQ and just march straight down your list. Responsiveness may be an important factor, but it's not the only factor, and established agents are often slower to respond to queries because they're selling manuscripts and taking care of their existing clients. That said, I do think it's not a bad idea to query agents with lower RQs, especially at first. If they respond quickly to queries, they'll almost certainly respond quickly to clients, and when you're shopping a new manuscript, you need to figure out how effective your query and first pages are.

Class dismissed!