Because you don’t know better than your customers what they want to do:
The boundaries between “desktop tasks” and “mobile tasks” are fluid, driven as much by the device’s convenience as they are by the ease of the task. Have you ever tried to quickly look up a bit of information from your tablet, simply because you’re too lazy to walk over to your computer? Typed in a lengthy email on your BlackBerry while sitting at your desk, temporarily forgetting your keyboard exists? Discovered that the process to book a ticket from your mobile was easier than using the desktop (looking at you, Amtrak!) because all the extra clutter was stripped away?
Response rates were highest for survey invitations sent out on Monday, and lowest for invitations sent on Friday. On average, surveys sent out on Mondays received 10% more responses than average, and surveys sent out on Fridays received 13% fewer responses than average.
But don’t assume these results hold true for your list, and the full post has lots of caveats. Test!
“A whale is a player that is willing to invest a significant amount of money in your game,” said Jared Psigoda, CEO of the browser game publisher Reality Squared Games, at Game Developers Conference Europe in August. “For most publishers out there … a handful of players make up a significant percentage of revenue, specifically once you get into the mid-hard-core, free-to-play type model.”
“The top 10 percent of players can account for as much as 50 percent of all in-app purchase revenue,” says Andy Yang, CEO of the mobile monetization research firm PlayHaven.
I wonder what the full distribution looks like. How much revenue does the top one percent drive? Or the bottom 50 percent?
Having all your data in one place gives you a huge advantage: it allows you to create predictive models about customer behavior. This insight helped Barack Obama’s reelection campaign prevail on Tuesday:
So over the first 18 months, the campaign started over, creating a single massive system that could merge the information collected from pollsters, fundraisers, field workers and consumer databases as well as social-media and mobile contacts with the main Democratic voter files in the swing states.
The new megafile didn’t just tell the campaign how to find voters and get their attention; it also allowed the number crunchers to run tests predicting which types of people would be persuaded by certain kinds of appeals. Call lists in field offices, for instance, didn’t just list names and numbers; they also ranked names in order of their persuadability, with the campaign’s most important priorities first. About 75% of the determining factors were basics like age, sex, race, neighborhood and voting record. Consumer data about voters helped round out the picture. “We could [predict] people who were going to give online. We could model people who were going to give through mail. We could model volunteers,” said one of the senior advisers about the predictive profiles built by the data. “In the end, modeling became something way bigger for us in ’12 than in ’08 because it made our time more efficient.”
