What you should know when tracking unique visitors or new vs. returning visitors. First in a two-part series on metrics to watch.
In courses and workshops that I run on Web analytics these days, "data governance" is a topic that frequently comes up. As so much data explodes from Web analytics systems, voice of the customer programs, social media listening tracking, and so on, we need to know where data comes from and how it's created. By understanding how our data is created, we are better prepared to interpret it. So, it's worth going back to "analytical basics" and reminding ourselves how some of these metrics are created and what that means in terms of the way we use them.
The number of unique visitors is a fundamental metric of Web analytics. These days, it's still used to measure the overall level of traffic to a site and is particularly important for those sites that are dependent on advertising revenues as a major source of income. The actual definition of a unique visitor is: "The number of inferred individual people (filtered for spiders and robots), within a designated reporting timeframe, with activity consisting of one or more visits to a site. Each individual is counted only once in the unique visitor measure for the reporting period."
These days, the vast majority of Web analytics systems use "cookies" to identify "people." Most use first-party cookies. However, cookies are not the same as people, so this metric must always be treated with caution and never taken at face value. Counting cookies is not the same as counting people. Why? Tracking cookies can be blocked, cookies can be deleted, and people may use more than one device or multiple browsers to access a website. In turn, the unique visitor count can be underestimated or overestimated. Generally, the most common reasons above will cause the unique visitor count to be inflated.
In one project we did recently for a client, we were able to look at the number of cookies we could associate with a unique account number. We found that about 10 percent of accounts had more than one cookie attached to them, but 10 percent accounted for about 30 percent of all the cookies. So, for unique visitors, we are wary of using the absolute values. But as an old boss used to say to me: "A trend is a friend." Assuming that there isn't a massive shift in the number of people deleting their cookies or using multiple devices or browsers, then it's probably safe to assume that any change in the numbers over time is reflecting a genuine trend.
The other key thing about unique visitors is that it is a "non-addable" metric. The count of unique visitors relates to a specific period of time, such as a day, week, or month. Still, people take the unique visitors for the seven days of a week, add them up, and call that the weekly unique visitor count. This is wrong. If I visit on Monday, then I get included in the unique visitor count for Monday; if I visit again on Thursday, then I get included again in the count for Thursday. If I then add together all the unique visitor counts for each day or the week, I will be counted twice instead of once. So, adding up the daily numbers will tend to inflate the weekly numbers. Some Web analytics systems are better than others at producing correct unique visitor counts for different time periods, so it's worth finding out how yours works.
New vs. Returning Visitors
As a direct result of the cookie issues described above, the new vs. repeat visitor metrics need to be treated with caution as well. A visitor to a website is identified as being new if the device does not already have one of the Web analytic system's tracking cookies. If the device does not have a cookie (assuming that the device does not block cookies), then it is given one. The fact that the device has a cookie means that visit gets treated as a return visit.
The problem comes when a consumer visits the website a second time and either her device doesn't accept cookies, she has deleted the cookie, or she's using a new device or browser. The reality may be that she has been to the website before, but as far as the Web analytics system is concerned, she doesn't have a cookie on her device and so it will treat her as a new visitor. As a result, the proportion of visitors who are considered to be new is generally an overestimate. But as I said before, "a trend is a friend," and since there has been a change over time, it is probably reflecting some real underlying behavior.
This may seem like basic stuff, but with more and more business people being exposed to Web analytics data in reports and dashboards, it's worth reminding ourselves where this data comes from. Next time, I'm going to take a look at the use and abuse of ratios and averages. Till then...
Revolutionize your digital marketing campaigns at ClickZ Live San Francisco (August 10-12)!
Educating marketers for over 15 years, our action-packed, educationally-focused agenda offers 9 tracks to cover every aspect of digital marketing. Join over 500 digital marketers and expert speakers from leading brands. Register today!
Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
US Consumer Device Preference Report
Traditionally desktops have shown to convert better than mobile devices however, 2015 might be a tipping point for mobile conversions! Download this report to find why mobile users are more important then ever.
E-Commerce Customer Lifecycle
Have you ever wondered what factors influence online spending or why shoppers abandon their cart? This data-rich infogram offers actionable insight into creating a more seamless online shopping experience across the multiple devices consumers are using.