In an earlier post I referred to downside of large numbers of applications in the iTunes store. I am not surprised that the number of applications has hit 10,000. Yes, you read that correctly. I like the idea of the free market deciding whether an app is worthy of inclusion, but I don’t think Apple is there just yet. But this discussion is more about the idea around the rating system in general.
Apple has recently updated their ratings for apps in an attempt to thwart manipulation. But like ratings across the web they do not degrade over time. Think back to you days in high school when your best friend suggested an “awesome band” and you rush out and buy “International Pop Overthrow” by Material Issue and it made your summer bearable. Where is that CD now? Still in heavy rotation on your iPod? The point is that overtime the rating that at one point was really high has settled down. So what do we make of ratings?
Gimme some time
Perhaps if the rating was good for period of time the usefulness of the rating may be more beneficial to newer users. One of the problems with ratings is that it is dependent on users actually contributing to the feedback. Theoretically this should mean that over time the average will adjust up or down as user tastes change. But we know that Diffusion Theory defines different groups of adopters of a technology that display different patterns of behavior. So if you apply this theory to the ratings system then over time fewer and fewer people are contributing to the ratings.
Take this a step further. If the ‘innovator’ and ‘early adopter’ are the most likely to contribute to the ratings system but represent a small sampling of the total user base. Add to this they make their feedback on an ealier version of the application. How are issues in the earlier version of the app managed? Currently this isn’t possible.
Where to we go from here?
Naturally there isn’t an easy answer here otherwise we would already have a solution. There needs to be some context around the use of the ratings systems we have in place. What works for one product or service may not work for another. No matter what we need to think smarter about how we can leverage the use of a ratings mechanism to be relevant to new customers, especially those that fall in that last half of the Diffusion Theory S-curve.