This article is a review of a model proposed by me and Avinash Kaushik in a two-part paper we wrote for the Search Engine Marketing Journal on 2009 and 2010. If you wish to read the original papers, you can download them on pdf: Part I and Part II.
Below I propose a Web Analytics Process framework that shows the steps and the flow that should be present when implementing Web Analytics in organizations.
The objective of Web Analytics is to first and foremost improve the experience of online customers, it is not a technology to produce reports; it is a virtuous cycle for website optimization. The process proposed above will enable companies to create a powerful data-driven culture, to measure customer interaction with the website, to segment visitors and understand how each group behave, to analyze campaign Return On Investment (ROI), and to optimize the website in order to increase profitability.
Following, I discuss each step in detail.
This is the first step on any website optimization initiative, you must understand your goals in order to improve your website. The answer to the following question is critical in defining a website’s goals: why does your website exist? An anecdote that represents the importance of objectives can be found on Alice's Adventures in Wonderland, when she meets the Cheshire cat in a crossroad:
"Would you tell me, please, which way I ought to go from here?"
"That dependes a good deal on where you want to get to" said the Cat.
"I don't much care where-" said Alice.
"Then it doesn't matter which way you go," said the Cat.
"-so long as I get somewhere," Alice added as an explanation.
"Oh, you're sure to do that," said the Cat, "if you only walk long enough."
Each website will have its own unique objectives. For some, the objective will be to increase pages viewed in order to sell more advertising (media sites); for others, the objective will be to decrease pages viewed because they want their visitors to find answers (support sites). For some, the objective will be to buy as fast as possible (increase revenues); for others, the objective will be to sell only if the product fits the needs of the customer (decrease products returns). But, as Jim Sterne said in his Social Media Metrics book:
"Your focus should always be on either increasing revenue, lowering costs, or improving customer satisfaction. Doing all three would be just fine."
As we can see in the Web Analytics Process proposed above, the objectives are absolutely necessary in order to start the process, only after they are defined we can proceed to build the Key Performance Indicators. It is also very important to constantly revisit the goals in the light of website analyses and optimization to fine tune them.
In order to measure goal achievement, the marketer should create Key Performance Indicators (KPIs) to understand whether the website is going up or down. KPIs must be like a good work of art: it wakes you up. Sometimes it makes you happy and sometimes it makes you sad; but it should never leave you untouched, because if that is the case you are not using the right KPIs.
And good works of art are rare, you have just a few truly touching works of art per museum; and not every work of art touches the same people. The same applies to KPIs, there are just a few truly good KPIs per company, and each person (or hierarchy level) will be interested in a different set of KPIs, the one that relate to their day-to-day activities: upper-management is touched by the overall achievement of the website’s goals; mid-management is touched by campaign and site optimization results; and analysts are touched by every single metric on the world!
Good KPIs should contain four attributes:
Following the definition of the website objectives and the metrics that will be used to measure them, we will be in a much better condition to collect the data that will be needed.
When a company starts to collect website data (or reviews its data collection), two questions should be asked:
Following I provide a few ideas that can help on the conversion of data into insights, an essential step when optimizing any website.
Important to note that data analysis can lead to three different outcomes (as seen in the Web Analytics Process chart above):
In the spirit of the African proverb above, it is very unwise to change a website completely without first trying with the tip of your fingers. When we test, we lower the risk of a loss in revenue due to a poor new design and we bring science to the decision making process in the organization. But the most interesting outcome about experimenting is not the final result; it is the learning experience about the customer, a chance to understand what they like and dislike, which ultimately will lead to more or less conversions.
The web analyst must try endlessly and learn to be wrong quickly, learn to test everything and understand that the customer should choose, not the designer or the website manager. Experimenting and testing empowers an idea democracy, meaning that ideas can be created by anyone in the organization, and the customers (the market) will choose the best one; the winner is scientifically clear.
I have described the Website Testing Process before, and the advantages of A/B and Multivariate Testing and I also discussed the choice between testing and analysis, but here are a few tips when it comes to website testing:
Brazilians have a popular saying that can be translated as "to die on the beach". It is used for situations where you are almost getting what you want (the sea) and then you lose it. A Web Analyst that overcomes all previous steps successfully and then gets stuck on the implementation of insights is dying on the beach. No implementation is a synonym of no Web Analytics. Below are some tips that can help you overcome implementation bottlenecks:
The big question is: how can a website manager convince surfers to buy a product or read an article? And the answer is: look at the data and understand what is happening in the website, listen to customers’ voices and optimize the website to better serve them; after all they are the reason for the website’s existence. Customers should tell us what to do, not consultants, friends or feelings; data and online surveys are the place to look for customers’ needs.
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