Our third Analytics class focused on the various types of website traffic data points and ways to measure them, both qualitatively and quantitatively.
I learned that you can measure a vast quantity of web data points or metrics. I think it helps to try to remember them by categories, which include: conversions (rate, by source..), traffic ‘referral’ sources, geographic location, visits, visitors (new, return, unique…), time on page, time on site, bounce rate, exit rate, engagement and purchase habits (cart abandonment, days/visits to purchase…).
It was also worthwhile learning that analytics aren’t infallible and that loopholes exist. For example, a visitor may spend five minutes on a page but if they don’t go to a new page, their visit is tracked as a ‘bounce,’ which is inaccurately perceived as negative.
I think a good point to remember from the class is also that the impact or positive/negative attribute of a metric varies according to the type of site and its purpose. For example, for a service business site, like Sofia’s, you want first time visitors who scan the site, exit on the contact page and follow through to enquire about purchasing a service. Repeat visitors who never follow through offer negligible value. In contrast, for a social network (like the like the caregivers one I’m proposing for my senior project), repeat visitors are critical for the site’s longevity; you also want them to come to the site, ‘engage’ (by posting or responding to a post) and leave, most likely without going to the contact page. Ideally, you want repeat visits from those who’ve signed on as members (i.e., converted) but even those who check the site a few times before committing are ok, particularly as the site evolves.
The we did an in class exercise to clarify our understanding and from this I learned more about methods. My takeaways from this assignment, (which I ended up presenting) were:
- When trying to determine a website’s goal, always search for ‘how’ it is making revenue (unless it’s clearly run by a not-for-profit or clearly funded by another source).
- Be very specific when citing KPIs or metrics. For example, don’t just say “measure return on investment” but clarify ‘what’ you are measuring (e.g. orders), against what (e.g. operational costs) and how frequently (e.g. weekly).
- Heuristic Evaluations, such as Site Visits and Remote Testing, do not usually have a sufficient number of participants to be statistically significant, even if you are testing a metric that can be quantified.