In the nineties and noughties, the web was talked about as more measurable than any other medium.
The idea was that attribution of sales would be completely sewn up before long. Last click analysis was duly mastered and dashboarded. However, there remain difficulties in identifying customers and tracking them as web usage has splintered across devices.
There are plenty of other issues, technical and cultural. Let’s take a look at the challenges in data analysis for marketers.
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1. Skills shortage
There simply aren’t enough people skilled in web analytics. Shortage in teams is still very evident. Many fairly sizeable teams still don’t have trained or dedicated analytics bods to back up decision-making with data.
26% of respondents to the Econsultancy/Lynchpin Online Measurement and Strategy Report 2013 said they had no dedicated web analysts.
Web analytics tools depend on cookies. Whilst the EU directives on privacy haven’t served to scare companies into seeking consent (many serving warning roll-ups or roll-downs), many internet users routinely delete cookies or set their browsers to refuse them.
3. Lack of vendor support
The service provided with digital analytics products can differ dramatically. Taking a cheaper package and not getting the guidance necessary, or again not employing a specialist or consultant, can hinder implementation.
Simply, the data-driven spirit hasn’t yet pervaded across all companies and departments. The battle is becoming easier fought, but there are still some in the thick of it.
5. Access to data
This comes down to culture and legacy technology. Getting access to multiple datasets and combining them so they can provide more insights is an uphill struggle in many companies, especially where regulation is tight.
6. Belief in the silver bullet
Perhaps the hype around attribution and big data has led some to see analytics as a silver bullet.
The mindset that the dashboard solves everything can be flawed, even though it’s a major attraction of any suite of tools and can impress senior management. Analytics can only point to a problem, it can’t explain the reasons for it.
via Martin Grondin
7. Lack of statistical rigour
Drawing conclusions from data needs to be done with an understanding of where the data comes from and the limitations associated with that data.
With a growing number of digital analytics end users coming from non-technical backgrounds, there’s a danger that data will be interpreted incorrectly.
8. Lack of single customer view
An arms race is underway with devices and platforms proliferating as the power of digital analytics tools increases.
Most marketers are still far away from having a single customer view and indeed for many businesses this is not a realistic aim. It’s not achievable and arguably not that valuable for many.
A lot of businesses, in ecommerce for example, have only just organised their data in one place. The next stage involves significant financial, time and resource investment to start defining audiences and personalising marketing.