The latest trend A key tool in web performance measurement is the drive to implement the use of Real User Measurement (RUM) in a web performance measurement strategy. As someone who cut their teeth on synthetic measurements using distributed robots and repeatable scripts, it took me a long time to see the light of RUM, but I am now a complete convert – I understand that the richness and completeness of RUM provides data that I was blocked from seeing with synthetic data.
[UPDATE: I work for Akamai focusing on the mPulse RUM tool.]
The key for organizations is realizing that RUM is complementary to Synthetic Measurements. The two work together when identifying and solving tricky external web performance issues that can be missed by using a single measurement perspective.
The best way to adopt RUM is to use the dimensions already in place to segment and analyze visitors in traditional web analytics tools. The time and effort used in this effort can inform RUM configuration by determining:
- Unique customer populations – registered users, loyalty program levels, etc
- Geography
- Browser and Device
- Pages and site categories visited
- Etc.
This information needs to bleed through so that it can be linked directly to the components of the infrastructure and codebase that were used when the customer made their visit. Limiting this data pool to the identification and solving of infrastructure, application, and operations issues isolates the information from a potentially huge population of hungry RUM consumers – the business side of any organization.
The Business users who fed their web analytics data into the setup of RUM need to see the benefit of their efforts. Sharing RUM with the teams that use web analytics and aligning the two strategies, companies can directly tie detailed performance data to existing customer analytics. With this combination, they can begin to truly understand the effects of A/B testing, marketing campaigns, and performance changes on business success and health. But business users need a different language to understand the data that web performance professionals consume so naturally.
I don’t know what the language is, but developing it means taking the data into business teams and seeing how it works for them. What companies will find is that the data used by one group won’t be the same as for the other, but there will be enough shared characteristics to allow the group to share a dialect of performance when speaking to each other.
This new audience presents the challenge of clearly presenting the data in a form that is easily consumed by business teams alongside existing analytics data. Providing yet another tool or interface will not drive adoption. Adoption will be driven be attaching RUM to the multi-billion dollar analytics industry so that the value of these critical metrics is easily understood by and made actionable to the business side of any organization.
So, as the proponents of RUM in web performance, the question we need to ask is not “Should we do this?”, but rather “Why aren’t we doing this already?”.
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