21 Common Web Analytics Mistakes
Web analytics offer invaluable insight and direction for any company, but it’s easy to misunderstand the information they provide. With the vast array of metrics available, businesses often find themselves overwhelmed, making critical mistakes in their tracking and analysis. This can lead to sunken costs, misaligned business goals and missed opportunities for growth.
Understanding web analytics oversights and pitfalls will help avoid the classic issue of falling into their trap.
These are the 21 most common mistakes marketers make when analysing web analytics.
21 Common Web Analytics Mistakes
1. Not measuring anything
Surprisingly, major corporations often fail to do the basics of web analytics and tracking. But without taking advantage of those useful marketing metrics, there’s nothing to back up the success or failure of a campaign.
Analytics are essential in understanding what resonates with an audience, and what doesn’t. It informs future choices and provides a solid foundation to build KPIs and business strategies.
2. Measuring the wrong things
Nearly as bad as not measuring anything is placing stock in the wrong metrics. Vanity metrics are analytics that look good but offer no real information to help increase ROI.
For example, a Facebook post with 20,000 likes sounds great but gives no measurable data or actionable insights to help strengthen a campaign.
Instead, it’s important to choose a set of metrics that inform business strategies and choices moving forward and to stick to those same metrics over time. This then accounts for variations and trends over time and ensures campaigns are adjusted to align with overall company goals.
3. Measuring too much
Overall, it’s much more beneficial to choose specific metrics to track based on business goals than it is just to measure every possible thing.
Rather than segmenting valuable data, marketers who try this will find themselves recreating information already provided in platforms like Google Analytics, without adding any additional value.
4. Analysis paralysis
It’s incredibly easy to get stuck in the weeds with all the data provided. A common pitfall for marketers is to spend too long analysing the results, without actioning changes.
5. Not relating to business goals
Understanding how to present results in relation to business goals is key. The ranking for a particular keyword may be improving, but if the senior leadership team is only interested in conversions and revenue, these other measurements become an exercise in futility.
6. Not checking data accuracy
During a recent analytics webinar, Common Ground found that 47% of attendees don’t trust the accuracy of their data.
That’s because there are multiple factors that can impact data and skew its accuracy; some that can be controlled, some that can’t.
Either way, it’s important to be aware of their impact and take steps to mitigate it. Cookie policies, for example, rely on user consent, limit data collection and can impact tracking from site to site.
There’s very little an individual business can do about this, but they can ensure the accuracy of data tracking from their side and that everything is covered. This, at least, can reduce the impact third-party tracking data has on the overall results.
7. Not assigning values to conversions
Understanding ROI relates directly to deciding on budget and manhours for various marketing campaigns.
Assigning values to different types of conversions helps understand how important they are for achieving overall business goals. For example, if an email sign-up is worth 10, then a sign-up for a product demo could be worth 100.
That’s because the product demo offers a far higher likelihood of return on investment than an email subscription. As such, the leads need to be nurtured and approached in different ways.
8. Not spending the money to do it right
No one likes seeing a big upfront cost, but by foregoing it, a business can easily spend more in the long run. For example, a real estate company that relies heavily on phone calls needs software that can track those calls.
Without solid reporting tools that provide valuable data insights, success is meaningless, as it’s that much harder to recreate. Overall, that money saved becomes a false economy due to the potential revenue lost by not having it.
9. Not segmenting data
Looking at data page by page without segmentation can hide problems.
Segmenting data into campaigns, landing pages, email sign-ups, etc. makes it easier to spot any potential issues or fall-offs in the user journey.
10. Ignoring the impact of cookie policies
Did you know that cookie policies mean a business can lose up to 40-50% of its traffic data? This can have a huge impact on the information available and affect choices made by the business as a whole.
11. Unfair/incompatible comparisons
Each platform tracks its data slightly differently, leading to misalignments. For example, there may be a difference in what Hubspot and Google Analytics class as sessions.
Look at the big picture across multiple platforms to track trends and patterns, as trying to compare the two platforms can sometimes be impossible.
12. Not connecting the CRM
Without the CRM, there’s a lot of valuable information missing from analytics data. A unified system where all pieces of software speak to each other optimises web metrics and allows a business to more easily track customers across their entire user journey.
13. Ignoring attribution
A common mistake is forgetting that most analytics data work on a last-click attribution model. This means that the credit for the conversion goes to its final interaction before purchase. Ignoring multiple touchpoints before this stage disregards the user journey and loses valuable insights; certain channels might not look like they’re driving conversions, but remove them and suddenly ROI drops dramatically.
14. Ignoring the context in analysis
If you look only at month-by-month data you might see a drop in traffic that can be attributed to external factors, like seasonal trends. For example, a B2B business will see dips around Easter due to holidays, and as Easter moves, even a year-by-year comparison can miss the context of these changes.
15. Not mapping the journey
If the user journey through a site or funnel isn’t properly mapped, it’s hard to understand the steps taken and the effectiveness of each individual touchpoint. Segmenting data can also be really beneficial here in highlighting potential problem areas.
16. Not removing existing users from analytics
Returning visitors that have already purchased, but return to a site for other reasons skew analytics and inflate traffic. A SaaS company whose app is accessed via its company site, for instance, needs to ensure it segmented out users visiting the login page. Otherwise, data will be incorrect and it could end up looking like the views-to-conversion ratio is far worse than it actually is.
17. Not understanding the site’s purpose
Not every website’s end goal is to close a sale. Think about what the site is designed to do and use that as a key factor in determining what analytics to track.
18. Not using enough data for comparisons
Only looking month vs. month doesn’t provide enough substantial data to make decisions and can be misleading. Measurements restricted to set times of the month can mean the data analysed is on different days of the week, and times of the year, affecting its interpretation. Ultimately it just doesn’t give enough data to be statistically significant.
19. Infrequent analysis
As well as sticking to a set of specific metrics in the web analytics, it’s also important to stay consistent in the frequency or date the metrics are checked. Changing the date each month, or only checking once a quarter, can impact the information received and result in unsuccessful campaign changes.
20. Quality over quantity
Everyone likes to see big numbers in their web analytics, but this isn’t always the most accurate factor in determining success. High conversions may sound great on paper, but if it’s all in the wrong market group, the overall acquisition strategy needs re-evaluating.
Focusing efforts on quality over quantity can be hugely impactful. Just like how Common Ground helped Thomas International achieve an 89% increase in engaged and relevant traffic through targeted link building and funnel optimisation.
21. Not doing anything with the findings
All the analytics in the world won’t help without developing actionable strategies based on their findings. Every data report should result in a set of clear actions to implement moving forward.
These mistakes are all easy to make, and easy to fix. It’s all about understanding what factors impact the metrics chosen for tracking, and understanding how to look at the bigger picture within web analytics. Even by fixing just a few of these, data becomes more accurate and targeted to better inform your future campaigns and marketing decisions.
For more information on web analytics, common factors to determine which metrics to track, and the metrics everyone should be tracking, check out Common Ground’s full webinar on increasing marketing effectiveness here.
