Previously, I looked at MIDP 2 performance and heap memory and provided tools to estimate market share based off either of these measures. I also pointed out problems with any market share estimate based on just one of these measures.
In this post, I provide market share estimates based on both heap and performance criteria.
The Definitive MIDP 2 Market Share Estimator
And here it is...
Using the graph is simple. The X axis gives the heap memory (in this plot, I only show heap to 2.5 MB as it is below this figure where the rate in change in market share is greatest). The Y axis gives the JBenchmark 2 score (a measure of handset performance). The Z axis gives the market share percentile. Percentiles contour lines are plotted for convenience with the contour labels visible to the right of of the Y axis. The small grey dots on the graph show the data points from which the surface was interpolated.
Using the plot you can:
- Determine the addressable market for an existing application;
- Provide specifications for a MIDlet based on a required market share; or,
- Marvel at just how low the specifications are for a MIDlet that is really mass market.
This graph brings into sharp contrast just how important coding for speed and memory efficiency is in the MIDP world if you really want to address a large market share. At the low end, a small increase in MIDlet heap usage causes a significant loss in market share.
The graph is also notable for exactly the opposite reason.
Suppose you agree up front with your developer to live with a target of 768 KB heap and a 100 JB2 score (i.e, you want a market share of 50%). Then, say your developer then tells you that the completed MIDlet actually requires 1MB heap. The graph shows us that this 33% memory overrun will only cost 5% market share. Then, your developer tells you that, ahem, the finished MIDlet only runs on handsets with a JB2 score of 150!
A disaster? Not really. The graph shows us that the combined effect of the 33% memory and 50% performance overrun is a mere 10% reduction in market share.
Another way of saying this is that the rate in change in market share is greatest at low specification handsets. Given what we already know about the bottom 20% of the market (performance and heap), this should not come as a surprise.
This analysis is useful, but needs extending to show similar graphs on a year by year basis. The analysis also needs to be performed for each of the major handset brands. And that is what I hope to provide next time.