Compound Metrics in Web Analytics

Should you use a compound metrics in your web analytics reporting? This was a topic of discussion in one of my classes at UBC Web Analytics course.

What is a Compound Metrics?

Before we get into answering the question, let’s look at what a compound metrics is.
Simply stated a compound metrics is when you take two or more simple measures and combine them together to form one metrics.

So should you use it?

Short answer is - why not? Sometimes simple metrics such, such as visits, page views, clicks etc., are not enough to explain a complex concept and that's when you need a compound metrics, e.g. engagement metrics, visit quality measure etc.

But isn't compound metrics hard to explain?

It depends on how you define it but then a lot of people still struggle with
visits, visitors, hits and pageviews. With that in mind, I agree that initially it might be a little hard to grasp the compound metrics. Over long term, compound metrics actually helps simplify the measurement of a complex concept.

Some of the common uses of the compound metrics are

1. Credit Score (Everybody has one and it affects them every day but how many actually know how it is calculated?)
2. Google Page Rank
3. Twitter Resonance (it will use several factors such as retweets, clicks on links etc.)
4. Twitter measurement. Many twitter measurement tools use compound metrics since it is not easy to explain Reach, Impact, Engagement in simple metrics.
5. Facebook's "Likeability Index". Ok, I made this one up but I am sure that's coming soon.

Example

Let's look at an example and see where such a metrics will make sense in your current web analytics reporting.

Lets take an example of a product information site. The products are sold offline via 3rd party retailers and since this company does not want to compete with its retailers it doesn't sell anything online. This site provides information about the various products that this company sells. It provide white papers with pre-purchase information and post purchase information/support. The site has some videos and some stories that are published every now and then. Well there is a sign-up form to allow users to save their product information.

The whole goal of the site is to provide information to current and future customers.

Now your big boss asks "We spent thousands of dollars to build this site, is this site working?".

You reply, well... Visits are down but repeat visits are up so seems like people like it and are coming back. However, page views/visit are down. More white papers are downloaded as compared to last month. However, video views are down and sign ups are also down. Seems like some things are up and some things are down.

Boss goes...."What does that mean? Is it working or is it not? Are customers finding information?"

How do you measure that?

As an analyst you can look at all the metrics and come to a conclusion but you have to be able to convey the end result to the VP of marketing. He needs to know if the site is successful or not.

This is where a compounded metrics comes in handy.

A simple formula for this could be

(% of visits viewing X pages or more + % of visits viewing video + % of visits downloading white papers type 1 + % of visits downloading white papers type 2)/4

we used 4 in the denominator because we are using 4 different metrics with equal weight

Now you can add other metrics that matter to the business and also assign different weight to each, so your formula could be something like:

(1*% of visits viewing X pages or more + 4*% of visits viewing video + 1*% of visits downloading white papers type 1 + 2*% of visits downloading white papers type 2)/8

1,4,1 and 2 are the weights assigned to each metrics based on their importance to the business.

Once you develop a baseline for your metrics, you can confidentially tell you boss if the sites performance is better or worse than the last month. Keep in mind that you are an analysts and you should always get under the hood to analyze each component and find opportunities for improvement.

What do you think?

Questions? Comments?

Other articles on similar topics are

• http://www.juiceanalytics.com/writing/franken-measuresor-how-construct-useful-composite-/
• http://www.usca.edu/polisci/apls301/compound.htm

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