Showing posts with label analytics data design. Show all posts
Showing posts with label analytics data design. Show all posts

Monday, September 07, 2009

Too soon a conclusion

There is something that i have observed around me that has motivated me to write about it.
Data analytics is not very natural to designers; its not a part of design training. I see people around me claiming data without understanding them. Some key points to look out for -

Number is just a "number"
Number is just a number; it doesn't convey anything without other supporting information. To give you and example - So if you say my site got 1000 Page Views the question is "What does it mean?". Nothing unless you say it got a 10% increase. So others know if things are going up or down.


Understand the Scale of the number
Secondly, understand the scale 10% of 100 is 10; 10% of 100000 is 10000. Understand this sense of scale in the number.

Number in Context
Don't make conclusions with one metric. Look around and see the other behavior. Eg. Site A is having a 10% rise in PVs but the UUs is going down by 20%. What does it mean? less users are giving you more PVs. But why are you getting less users? This can't be explained by data alone, which takes us to the next point.

Support Metric with other methods.
The data can tell you PVs are going down by "why" is a question that lies beyond data analytics.
There is a lot that data can tell you but you need to triangulate the data with other methods like doing user study (quantitative methods). There is lot that might have gone in the offline world that has influenced the performance - eg you might have run an Ad or might have done marketing. Or some event that has triggered a behavior. So keep that into account.

Where is your traffic coming in and what are the clicking or going is what primarily you might want to look at in the beginning. Next you might want to slice and dice data to look into detail - eg. you might want to segment the traffic based on age, gender, country etc.

These are some basic pointers for designers to get started on analytics. Keep in mind 'dont jump to conclusion too soon; you might land up with the wrong analysis or may only see just one aspect of the problem and may miss out other obvious stuff.'