Adding Context to Your Web Traffic Data
It is common for web analysts to report basic web traffic in various reports, but it is very important to add context around the data and analysis. Based on my discussion with many specialists through conferences, I still feel that there are gaps between what analysts are providing vs. what managements are taking away from it.
While more companies are starting to on board web analytics, we have to make sure to focus on providing insights beyond the out of the box reports. I mentioned in my previous posting that it is very common for business to ask basic question, but it is always in everyone’s best interest to understand how it is impacting the bottom line.
To give you a practical example, web analytics gurus are talking about segmentation all the time, and I think that is a very good one. You can segment data in many ways. Google Analytics provide us with a free to set up Advanced Segmentation around the data we want to quickly model. Omniture has props and events tags to separate data into different bucket to see the difference. Webtrends allow us to create custom reports and nest different data into different buckets. Use the shit out of it to add context to your reporting and analysis.
Start with the basics, and evolve your end users to ask challenging and deeper questions. Get that to a point where I can’t answer that without X, Y, and Z. That’ll give you the leverage to go to the next step into your web analytics journey. In Avinash’s book, he has a nice diagram on Web Analytics 2.0 with different circle getting into an insight. The X, Y, and Z are basically that; qualitative data, competitive data, etc.
To give you some basics examples you can start with:
- Don’t just report campaign traffic. Do you see any correlation between other lift in traffic trend? Did campaign period life more traffic coming from Organic Search / Advocacy / Comments / engagements / Mentions on Twitter / etc.
- People who DO vs. Don’t DO X, what do they do, what is the difference at the outcome level.
- Change in data, what’s causing the change? Easy to speculate, but very difficult to validate it, because you have to cut, slice, and dice many different data to gain confidence in your findings. All of these data will not necessarily be plotted in your report, so need to be creative in articulating it. Web Analytics tools need to be set up to do that good, too.
- Cost vs. Outcome. Always be conscious about asking this to your self. I believe part of the reason why many middle managements don’t get web analytics is because they don’t see it tie to costs/investments/spend.
- Stupid questions like “give me average time on page”, could potentially be…
“Give me avg. time on page after we did X; and how much efforts (costs) did it take to impact the bottom line while we see avg. time on page change and what does that mean?”. These kinds of questions make me excited.
Even if the business is not asking this, add context to your data so that business can ask in such form in future. I see it work for most of the people.
Enjoy analyzing data. Web Analytics is fun!!
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