It's often interesting how businesses can be lulled into a false sense of security by looking at superficial "topline" numbers without investigating what's really going on at a more granular level. This often seems to be the case in online channels involving registrations or subscriptions.
A story from a business I worked at makes my point. I had recently arrived at the business and was starting to get my head around how it worked. One key metric the business looked at was the number of people who had registered to use the service.
At the time, this metric was growing very quickly. We had more than a million registered users, the graph was going up and to the right, and all was well with the world. Or so it seemed.
I wanted to see what parts of the service people were using the most, so I asked for a data extract showing which service type each registered user had actually used. When I got the data file from the database guy, I thought he had made a mistake. Out of the million-plus registered users we had, the data file he gave me only had details on about 20 percent of the users.
So I told the database guy that there seemed to be a problem with the data and sat down with him to check it out. Sure enough, when we looked at the data more closely it turned out that a massive chunk of the registered users never actually used the services they had signed up for. If they had, then they generally had only used one service once and a very significant proportion of all the activity on the site was due to a relatively small proportion of registered users.
This was an "aha" moment. We were tracking the wrong metric. Instead of focusing on the number of "registered" users, we needed to track the number of "active" users.
It was certainly a case of be careful what you measure, because what you measure is what you'll get. Because the business was focused on measuring registrations, the drive was to generate as many registered users as possible, irrespective of the quality of those registrations and whether they were likely to actually do anything valuable on the site.
Because of that experience, I'm skeptical about reports or claims about the numbers of subscribers, the number of customers or the number of registered users. The reality is likely to be the same pattern of behavior as I found when I started to look in more detail at that business.
Economist Vilfredo Pareto was definitely right. It's important to take an in depth look at data and understand in detail what the activity levels look like.
For example, consider a site that relies on user-generated content. Of all the people who have signed up to upload content, how many have actually done so? How many have done it more than once? When was the last time that they did it? How many people have done it in the last 30 days, 60 days, or 90 days?
These metrics are far more revealing about the health of the business that the superficial top line numbers that are often reported on.
Neil is off today. This column was originally published on Sept. 1, 2009 on ClickZ.
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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.
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