This is a continued post of my thoughts on the topic of Digital Marketing Analytics, specializing web analytics.
What is Web Analytics?
Web Analytics is the collection and analysis of data from a web property, let it be a single website or a collection of (sub-) domains.
The five steps of the analytical process is very similar to digital marketing overall.
- Data collection – Collect web touch-points via Google Analytics and Google Tag Manager
- Data piping – Depends, to be explained later
- Data analysis and insights – Perform analysis to obtain insights
- Visualization – Surface insights via platform reports, Google Data Studio, and/or other visualization tools
- Optimization – Digest insights and adjust programs run by marketing to optimize for conversion or KPI
What is the purpose of Web Analytics?
It is important to understand the core purpose of a function before diving into the detailed implementation.
What are the questions or insights is the business trying to answer?
In simple terms, the purpose of web analytics is to understand:
- The customer journey prior to a conversion (which is typically an email submission in B2B) via organic channels (paid channel journeys should fall under advertising analytics)
- How web projects impact web outputs to inform investment decisions
For #1 customer journey, think about every single pageview and how they relate to a conversion.
What are the most viewed pages in last month? What are the top converting pages?
Keep in mind, however, the relationship between sessions and users.
This adds complexity to the types of questions / answers we can ask because, as you can imagine, B2B web visitors do not just come to your website one time and convert immediately. They come to your website multiple times in different length of sessions and over different time periods.
For #2 web projects, think about the impact of a new page or a re-design and how it affects the KPIs that you care about.
Is the new pricing page more engaging? Does long-form content keep visitors on the site longer?
These are the questions you can answer to evaluate the time and resources you invest in your team to justify and prioritize projects.
The Complexity of Questions and Answers
At first glance, web analytics is relatively straight forward to understand.
However, once you spend a bit of time diving into the detail, you’ll quickly realize some answers are not that straight forward.
Here are two buckets that typically add complexity:
1. Joining data sources
Most businesses optimize their website with anonymous data assuming everyone is equally important.
That may be true for B2C sites where everyone could have an equal probability to check-out.
But the reality is, B2B businesses weight accounts and buyers differently depending on how they segment customers (e.g., potential account size and individual buyers’ persona/title).
While it is possible to know the attributes of a visitor once someone submits their email on your site, this capability requires an integration between your CRM and analytics solution.
While it is possible to uncover which advertising campaigns drive better quality leads, that capability also requires an integration between advertising platforms, your CRM and analytics solution.
A lot of companies also want to connect web data with their product data, especially if the company has a trial environment for people to try the product.
2. The customer journey is complex
If your marketing team has done a good job, there are probably hundreds, if not, thousands of touch-points before a record of any kind is surface-able to your CRM.
In addition, from a web analytics point-of-view, the relationship between landing pages (from different source/medium), sessions and users can be complex depending what types of answering you are looking for.
For example, you are launching Campaign A and Campaign B at the same time, each has its own conversion asset.
You want to know: Does Campaign A perform better than Campaign B, in terms of absolute conversion volume and conversion rate?
Under normal circumstances, you would look at 1) how many conversion each campaign generated and 2) # conversion over pageview for conversion rate.
However, there are some edge cases that complicate this picture:
- A single person can actually convert in both Campaign A and Campaign B
- The person may not convert in the asset of Campaign A or B, but other evergreen CTAs such as Contact-Sales or Free Trial, despite Campaign A or B may bring the traffic to site.
- When it comes to calculating a conversion rate, the traffic (or advertising budget) may not be equal in the same time period, influencing the # pageviews denominator.
- The CTA of Campaign A and Campaign B are different and therefore have different levels of friction, for example, one is watching a video and the other is downloading a whitepaper.
While the business question is straight forward, these edge cases muddle the comparison and make it hard to do an apples-to-apples comparison.
This is especially so if the sample volume is low (i.e, hundreds not thousands of conversions), which is common in B2B settings.
To address the complexity above, you either will have to understand and accept the limitations of a basics analytics solution, or decide that you want more precision.
If you prefer the latter, it’s probably time to look for a more advanced analytics solution, which will typically involve putting multiple data sources into a data warehouse for easy ETL and aggregation.
Again, it is important to start with figuring out what questions you’re asking for the business.
Then from there, understand what your current systems are capable of answering.
Remember, it will take time for your teams to get to the same page. Be patient and start smalll tackling one piece of analysis at a time.