This is part II of the series on Analyzing and Optimizing Campaigns. I wrote in my previous post that when analyzing campaigns many web analysts just focus on the web analytics data. Some venture to include the cost and impression data of the campaign but they still don’t have a complete view.
In this post I will show you how their lack of complete view results in wrong analysis and wrong conclusions.
Below is the data I used in my last post. This is the type of data most Web Analytics tools provide and hence “Web Analysts” tend to use.
What is missing?
Where is the cost of products and profit margin data? Without that information, you don’t know if this campaign is successful or not. Right?
The sad reality is that many web analysts don’t have access to profit margin data and hence they look at what is available to them and start recommending A/B testing (see my post One Awesome Web Analytics Tip: Think Beyond Web Analytics). And their first target generally is Bounce Rate. Oh… look bounce rate is 50% it is too high, we need to reduce it. Right?
Wait...There is More...
Let’s assume that you are able to get hold of additional data. Now let’s see how the campaign looks if we add that data. Below I have added cost of Goods Sold data (keep in mind there are additional costs in real life).
It is evident now that the campaign is bleeding money. If your business goal is to increase conversions at any cost then you might be ok but if you goal is to increase conversions without losing money then this campaign sucks.
Ok, so what should we do now? If your answer is still bounce rate then you are wrong. Look at the data below, even with a bounce rate of 0% you will never make this a profitable campaign.
So next time get all the data before you jump to the conclusion that all you need to do is reduce “Bounce Rate”. Bounce Rate Optimization looks tempting to tackle but it is not always the cure.
Stay tuned, more coming soon on this subject.
Follow Me on Twitter: @anilbatra
Facebook: https://www.facebook.com/TheAnilBatra
In this post I will show you how their lack of complete view results in wrong analysis and wrong conclusions.
Below is the data I used in my last post. This is the type of data most Web Analytics tools provide and hence “Web Analysts” tend to use.
What is missing?
Where is the cost of products and profit margin data? Without that information, you don’t know if this campaign is successful or not. Right?
The sad reality is that many web analysts don’t have access to profit margin data and hence they look at what is available to them and start recommending A/B testing (see my post One Awesome Web Analytics Tip: Think Beyond Web Analytics). And their first target generally is Bounce Rate. Oh… look bounce rate is 50% it is too high, we need to reduce it. Right?
Wait...There is More...
Let’s assume that you are able to get hold of additional data. Now let’s see how the campaign looks if we add that data. Below I have added cost of Goods Sold data (keep in mind there are additional costs in real life).
It is evident now that the campaign is bleeding money. If your business goal is to increase conversions at any cost then you might be ok but if you goal is to increase conversions without losing money then this campaign sucks.
Ok, so what should we do now? If your answer is still bounce rate then you are wrong. Look at the data below, even with a bounce rate of 0% you will never make this a profitable campaign.
So next time get all the data before you jump to the conclusion that all you need to do is reduce “Bounce Rate”. Bounce Rate Optimization looks tempting to tackle but it is not always the cure.
Stay tuned, more coming soon on this subject.
Follow Me on Twitter: @anilbatra
Facebook: https://www.facebook.com/TheAnilBatra
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Thank you Anil for this example.
ReplyDeleteYou're right.
The cost of Goods Sold must be taken in account when analyzing and optimizing campaigns of a retail website. It shows that web analysts take care of the business of the company they work for.