Ten reasons why web analytics can fail

Wednesday 31st, July 2013

Have you ever wondered why your web analytics solution doesn’t seem to deliver as expected? Read on to find out what could cause the lag in meeting expectations.

Competitive web analytics solutions are everywhere today. But despite so many options, marketers sometimes find that these solutions aren’t living up to their expectations. Here are some reasons why this could happen:

1. Inaccurate identification of metrics that need to be tracked:

When what you seek from web analytics tools is not met, you may need to analyze the problem from its roots. Starting with an analysis of whether the identified metrics are apt for the organization is a good idea. More often than not businesses end up tracking metrics that are not relevant or don’t contribute data to the kind of insights they are seeking.

2. Inaccurate alignment of metrics:

Metrics need to be aligned to Key Performance Indicators (KPIs) which in turn need to be aligned to business goals. Misalignment can mean inadequate insights.

3. Lack of adequate investment of time and resources:

Web analytics isn’t something that you’d want your intern to try out for kicks. If your organization is serious about gaining information from web data, there needs to be judicious investment of dedicated resources and time.

4. No integration with external data

Web analytics data can provide a very one-sided view of your customers’ behavior. It can tell you how your customer interacts with your brand online. But what about their experiences with your brand offline? When online and offline data are combined, it provides a holistic understanding of what your audience is seeking.

5. Narrow view that lacks big picture understanding

Looking at data from one particular metric generally provides a very narrow understanding of a given situation. Sometimes looking at other data can give you much deeper insights than otherwise.

6. Inaccurate interpretation of data:

While data can be fine-tuned to the highest accuracy possible, interpretation of the data is prone to human bias. While this might seem like a necessary evil, cognizance of the fact helps analysts stay on guard.

7. Technical hiccups:

Technical errors are common reasons of failure of web analytics tools. They can include incorrect implementation of tracking codes, improper tagging setup, site architecture problems and so on.

8. Unclear data conclusions:

The conclusions drawn from data gathered can be misleading if not deduced appropriately.

9. Data-centric information to inappropriate audience:

When presenting insights and conclusions to internal or external stakeholders, it is important to consider the audience. If the audience consists of people who can’t comprehend data like analysts, it must be tweaked in order to be intelligible and of value.

10. Being report-oriented rather than being insight-oriented:

Web analytics is not about generating reams of reports. It is meant to provide clues to what the customers’ psyche is like so that marketers can reach them in relevant ways.

Web analytics solutions when implemented properly can provide valuable information that your business requires to get a boost. The solution won’t work like magic; it will require monitoring and readjusting but a little effort can go a long way for your organization’s well-being.





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