This post is something akin to a rant. It is about the web analytics process and the ability to make it more functionally efficient. Its something we can all relate to:

It crossed my mind recently that there is so many facets of the analytics programs at maturity that there must be some means to internally optimize the process. I’m not talking about software or the many hundreds of small start-up companies or analysts throwing their hats in the ring, or any feature which they can add to our decision machines. I’m talking about grassroots analysis to grow business success through decision empowerment and process efficiency.

Quite simply, the steps to getting a data process start with the willingness to accept certain truths about the processes:

Knowing the objective of a website, and how that goal ties into the business model is essential in understanding where to plant an water web analytics. The connection between these can be elusive. Often, this is an aspect which comes to late in implementation or is overshadowed by a perceived need to begin producing reports. Unfortunately, this is good for the ego of the champion for analytics, but a detriment to properly installing functional analysis at the core of the human resource ecosystem.

Fueling a websites design and development machine requires making powerful assertions which can win support alone, or banish dissent with pure statistical science. Coming up with ideas is easy. Coming up with great ideas can be challenging. Coming up with great ideas and getting administration, colleagues, and the nay-sayer to go along with it can be impossible. There is a reason for this. It has to do with appeals. Appeals are the way in which information is communicated to reach the part of the receivers brain which helps them build a concept for themselves and the group. Everyone has a way they WANT to hear or see your information presented and how it affects their world. In that respect, you can either be a communications genius, or, build the case for math…which appeals to everyone. (I will write an entire post on appeals and how to use them some other time. This is a fascinating area which I think has very high relevance to analytics) Math is the key to making the process sprout. It is the foundation, the proof, and the method of delivery.

If making math work for the business routine of web analytics is the goal, we should break down the subroutine. This seems to be the main point of struggle for recent practice adopters and parties interested in participating in analytics. Each component is arguably a necessary step to producing useful reports which detail insights and suggest action, based on math. Here is HOW, piece by piece, you create action from analytics.

Just looking at reports day to day, any analyst should start to recognize patterns. The human mind is amazing with regard to this. We see lines and columns with comparable trends and past performance which either fit or do not fit. Here, you begin to ask questions. Remember this part, because this is the first major function of any analytical process. You find things that intrigue you, and seek to explain them by using the data.

With continued observation and investigation, you might begin to see correlations. Some might be as simple as every weekday visits decrease consistently by 60% between 12-2pm EST. You might be in the business of selling medical supplies. Where you might already know it is a lunch related subsiding, why does this happen? Why is it that consistent? What do these people DO during that TIME which prevents them from visiting and buying between 12-2pm?

If you’ve ever worked with a pharmacist or in a doctor’s office, or a hospital, you’ve likely experienced a fairly standard phenomena of their lunch habits. For 2 hours everyday, administrative work in the medical world is reduced to a skeleton crew. This impacts the amount of work which can be done outside of necessities, which, sadly, does not include purchasing catheters or bidets from BioRelief.com. What this does, though, is provides a certain correlation for, or explanation to the dip.

High value correlations should be based on the number of times when the desired action occurs, the consistent recurrence of that action, and how many different places this same action can be observed. These collected actions are not, in themselves, the goal, but rather, checkpoints which may or may not positively indicate some motive to achieve, or explain diversion from, the goal. The correlations give us an idea of how to cater our information to and find more avenues to improving the experience for the user.

Correlations lead to understanding and understanding should lead to hypothetical assertions. The place for making bold statements is in this stage. Take the pieces of correlated information yielded from curiosities and chasing rabbits and start to use them in building profiles or saying things like, to use the example above: “Shutting down our Search Marketing campaigns between 12-2 pm daily will save a total of $95 in fruitless clicks per day, which, when calculated is an estimated $25,000 in savings per year.”

Assertions should be elaborated on; then tested for validation and range. The world of online testing does not exist to give us something to talk about. The discussion is, instead, the result of seeing value in testing. A/B testing, using a control group versus variables, can be useful in all aspects of campaign or site optimization. Multivariate testing is an extremely efficient component for providing statistical validation of elements, as well as uncovering additional unintended correlations for further discussion. (Element design, additional perspectives, and other assertions from the design and development owners should help provide resources and add value to multivariate testing). Usability testing can place a single session under such scrutiny as to not only validate an assertion, but provide an immediate solution. (and, by the way, another great way to get people involved in the process).

Test results need to be scrutinized by analysts AND potential dissenters prior to reporting. Have colleagues and co-workers pick apart the outcomes. Get more input. This has the amazing effect of not only involving common adversaries in the project, but giving them an sense of where you are coming from. It helps them understand you. In doing to, you break down uncertainty and increase communication. (also a subject which I discussed at this venue). The business will begin to build around research based decisions.

By the end of this round of web analysis, you should have answers to report to the group, participation from people whom have been involved in the process. and the data and statistics to back up your position on any arguments you encounter. This will win favor, promote action, place value or at least validity behind statements, and, hopefully, spawn more questions, deeper analysis and better understanding.

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