SEO Conversion Analytics – Demystified

Google blocked this and Google hid that.  How many times have you heard these phrases before?  Time to look past the obstacles Google is throwing your way. Let’s nail down SEO performance without Google’s help.  How else would you know what to improve/optimize in SEO?

A Quick Recap

Before I continue, here’s a quick recap: This post is a continuation of the series on SEO Analytics. In the last three years, Google has introduced several changes, including semantic search and long tail search to improve user experience. The side effect has been that its algorithm has become very complex for SEO teams to get their heads around. The days of using top 30 keyword lists are long gone. To measure how your SEO efforts are doing, you need to up your analytics game. For more background, read the introduction to SEO analytics. SEO analytics can be divided into three distinct sections. These three sections are

  1. SEO Demand Analytics
  2. SEO Conversion Analytics
  3. SEO Link Analytics

In this post, I will explain how to measure success of SEO given the increasing complexity and opacity of Google’s algorithm.


What is SEO Conversion Analytics?

Simply put, SEO Conversion Analytics measures visitor actions as they relate to business goals on your site. For ecommerce sites, that goal might be a transaction. For informational sites, it might be a subscription or a phone call. For others, it might be about content effectiveness. Whatever that business goal, the challenges in measuring SEO performance at an actionable level are three fold:

  1. Brand vs Non-brand Traffic Behavior: Google essentially killed keyword level optimization when it started hiding searched keywords in the (not provided) bucket. Several dozen articles have been written about how to “workaround” the (not provided) issue. Let me just say this – there is no workaround that even comes close to approximating how non-brand traffic converts anymore. So in this post, I outline how we have upped our game and urge you to do the same.
  1. Semantic and Long Tail Searches: The days of nicely contained 1-to-1 keyword to page relationships are long gone. Today, a single page can easily rank for 1000 keywords. This is because Google’s algorithm has become much smarter and can determine semantic equivalents of keywords. And it also encourages visitors to engage in 3-4 word phrase searches through its auto suggest feature. So keyword level conversion analysis has gotten significantly more difficult.
  1. Instant vs Delayed Gratification: Not all SEO visitors (and for that matter any channel’s traffic) make decisions on their first visit. It may be a slow tango, taking multiple visits for visitors to make their mind up. There is the well documented concept of channel attribution to address this. However, channel attribution is good for claiming bragging rights and tends to be less actionable for channel managers. So let’s focus on the problem that all SEO traffic needs to be measured so we know the true conversion potential for pages or sets of keywords.

So let’s get into the weeds of what it takes to measure SEO performance.


Deciding What to Measure

We first need to agree on the basic metrics to use for measuring performance – visits, visitors, first time visitors, repeat visitors, transactions, page views, etc. Then there are the contexts that need to be applied to view these metrics. Let’s take a look at both.

The Metrics

The core metrics we need are sessions or users and the business goal. For simplicity’s sake, let’s say the business goal is transactions.

So the basic conversion rate is

Typically, I would rant about always using users, but in this particular case, it actually doesn’t matter. I will explain why later. So use whatever the rest of your organization uses.


The Contexts

There really is no meaning to the metrics without contexts. Also known as dimensions in Universal Analytics and Adobe Analytics, these contexts allow us to look at the data in slices that make sense. Here are the contexts that are most important for SEO measurement:

Time: As a SEO manager, I would want to know how the organic channel performed on a periodic basis. That may be daily, weekly, or perhaps monthly. Then I would want to know whether the traffic and conversion rate increased or decreased compared to a baseline. You could use the previous week or to the same week last year for a baseline.


Visitor Type: SEO managers know that Google loves content. New content means new traffic. So even if that traffic doesn’t convert, we need to understand how sticky the content is. We need to know who visited for the first time as well as those who visited more than once. At this point, we don’t care whether they actually purchased anything on the site. We are just looking at the content effectiveness of SEO.


Keyword Type: And then there is the final piece of the puzzle. We need to know whether the traffic is driven through brand or non-brand search terms. Without this, we will not understand the effectiveness of SEO. There will always be speculation that the good is being driven by the brand or that non-brand is really trivial compared to brand and so on.

We have come to terms with the fact that Google has made it impossible to work with keywords (using analytics) in any meaningful way. And all the proposed solutions out there seem to be interesting in theory but have major drawbacks. The most common approach is to use a different baseline such as Paid Search or visible keywords and use that data to divide up the (not provided) keyword data set. The problem with this approach is that we have absolutely no idea what Google is hiding in the (not provided) set. All these approaches are just speculation; there is no way to prove the theory.


Getting Over (Not Provided) keywords

So here is how we deal with this (not provided) problem: We simply move on to using a different methodology. The proxy to understanding keyword performance is not another source of keyword data. The proxy is content. Let me explain.

At the end of the day, we want to understand how non-brand traffic is performing. If you think about it outside of the home page, there are probably a handful of pages on your site that get any meaningful brand traffic. All other pages probably get disproportionate amount of non-brand traffic compared to brand traffic. Bingo. We just found our proxy. Remember, we are not looking to break the data down to the keyword level – those days are gone and semantic search plus long tail search makes that approach highly impractical.


So we will approach conversion analytics much the same way we approached demand analytics – we will look at categories of keywords instead of individual keywords. If you haven’t read the article on demand analytics, I strongly recommend you do before proceeding.

Let’s use the example of a cleaning service website. In the demand analytics step, we neatly grouped the content into types of cleaning- carpet, floor, hvac, damage, etc. Here is a screenshot of how the content was categorized into 23 categories and sub-categories.

To make the connection between demand analytics and conversion analytics, we will need to map all the content into the same hierarchy. If you used data from SEMrush or a similar tool, then the keyword-to-page mapping is already done for you. You still need to map other “brand” pages into a new category.

There is still the question of brand pages, the pages that over-index for brand keywords. To group those, create another category called brand pages. If you want to get nitpicky, and want to take credit for the non-brand traffic to the home page, then use SEMrush or SimilarWeb to get an estimate of the distribution of brand vs non-brand traffic. Under no circumstance should you use Google provided data for this.


Build the Conversion Analytics Report

Now that we have clearly defined the goals and the methodology for measurement, the next steps are mechanical but still a bit complicated. Here is the plain English narration of the report you need to create:


Here is a screenshot of how I setup this custom SEO conversion report in Universal Analytics:

And the custom organic search user segment that needed to be used:

Once this report is built, pull the data into Excel and create a Pivot Table around it. Here are screenshots to illustrate how the report can be used for actionable insights:


In a single view, you can now see how the non-brand content performed by week, how effective SEO is at bringing return traffic, and the impact of brand searches.


Because of the context that was applied when creating this report, you can now drill down to a number of levels. For instance, a really useful view would be the weekly performance by content category view.


Using this report, you can now look at non-brand performance, brand performance, week over week performance, and content effectiveness or stickiness for SEO. Now you can easily explain how non-brand SEO is performing and point out that SEO visitors come back multiple times and convert via other channels!

Once you get familiar with this setup, you can get even more granular by adding the location or market as a context. Then you will be able to see just how well users from a particular location convert on non-brand content or a certain sub-category of content. This becomes a really powerful way to analyze organic search performance.


A Last Word

As Google introduces experience enhancing features in its algorithm, the perceived functioning of the algorithm is becoming more and more complex and opaque. To make sense of organic search performance, we need to use smart ways to organize and analyze the data. Bring a gun to a gunfight – use our approach to measure brand and non-brand SEO performance. We promise you’ll never lament about (not provided) ever again!

Given the challenges in measuring SEO performance in the last few years, what have you tried in this regard? What other suggestions do you have to improve our approach? We look forward to your feedback!


Does Your SaaS Need a Marketing Boost?

Grow your SaaS with a results-driven strategic marketing partner. Fill out this form to get started.
Author Profile
Abhi Jadhav
Abhi Jadhav is the head chef at Bay Leaf Digital. His primary goal includes driving value for all clients by ensuring learnings and best practices are shared across the company. When not brainstorming on client goals, Abhi focuses on growing the agency at a sustainable pace while making it a fun, collaborative, and learning environment for all team members. In his spare time, you can find Abhi at a local Camp Gladiator workout or on an evening run.