Machine Learning and the Confluence of SEO and CRO

• Updated on: 2017-12-31
• Read original article here

Building on this qualitative insight, it seems entirely possible Google could be making moves to develop this from an algorithmic point of view, as discussed by Bill Slawski in reference to a patent around the 'long click' filed by Google .
As discussed in the SEMrush Ranking Factors Study 2017 , Time On Site, Pages Per Session and Bounce Rate were all found to be positively correlated with high ranking content, suggesting that these user engagment signals bare relevance for SEO. True, these metrics are flawed from a standalone perspective, but their correlation with rankings provides an interesting insight into how the algorithm may currently be intepreting user engagement signals.
In a post-PageRank world, we’re optimising not only for the click, but for the post-click experience.
CRO practitioners and UX analysts will be involved in improving a site’s performance in a number of critical ways. This could involve anything from detail user journey mapping, user testing and qualitative analysis to A/B testing.
Irrespective of the techniques used, CRO practitioners are committed to finding out why visitors are behaving in a certain way, not completing a certain action, and fixing the problems causing this block. So, if as SEO's we're increasingly concerning ourselves with optimising for intent and user satisfaction – surely there needs to be a stronger dialogue between the SEO and CRO community; a way to arrive at some similar definitions and conclusions perhaps.
An industry ill-prepared for change?
Call me cynical, but I don’t believe that the SEO and CRO communities have really established a set of shared understandings or definions so far. And my concern is that without that dialogue and collaboration, neither will ever really be able to fully optimise for the click and what happens after the click as the singular experience that is actually is.
It comes down to a fundamental truth about this ‘post-Pagerank’ ecosystem – that good user experience and good SEO ought to share the same goals and talk the same language. That all sounds a bit utopian, and possibly far from reality at the moment. Which got me thinking, how might this work in practice? I don't claim to have the answer - but rather I wanted to share a way of thinking that I thought teams could adopt and share to help work towards shared Goals, underpinned by a pretty simple process.
We need a collaborative framework for SEO & CRO
I’ve thought a lot about how the adoption of ‘CRO-thinking’ – the mindset and rigour of optimising around the user and all that encompasses –  helps to provide the foundations for this new era of SEO in which optimising for task completion or engagement is critical. In thinking about this, I’ve borrowed some ideas from CRO to try to build a bit of a broad framework which I believe helps, at the very least, to encourage dialogue and collaboration.
This sytem, can be broadly be defined as following this cyclical process:
 1. Goal definition
Where CRO excels as a practice is often in its simplicity. Teams orient around a singular goal, and optimise and measure around that specific goal until a benchmark is met, then they can either move on or reiterate to improve on that benchmark again.
In essence, as SEO's, we need to try to think in similar terms when optimising for user intent. What exactly is it we're trying to do? Do we have a metric in mind that we need to focus on? Do we even have a framework of way of measuring whether we're creating more engaging experiences or not? 
Assign value to micro-interactions to better understand contents' usefulness
Google Analytics is an incredibly powerful tool, but one that's woefully under-utilised in my opinion. Instead of customising our configurations to focus on what matters, a huge number of marketers still focus on largely flawed and useless vanity metrics like Bounce Rate and Time on Page to evaluate whether a page is engaging or not.
There is so much more we can and should be doing to customise our Analytics configuration to better inform us about what's really important. Instead of focusing on vanity metrics, consider the customer engagement points that matter to you as a site owner or brand, and where these cross over with the user's needs.
Using Google Analytics Goal Value, we can then begin to attribute values to specific micro-interactions and user behaviours that we believe are important to the brand and to the user. By recognising that a user will not always convert on a macro-goal, we can use these micro-interactions better understand how content helps or hinders them in completing what they need to do. I mocked up the following example based on a hypothetical understanding of what a beauty brand might consider important user/brand interactions:
Micro conversions, such as a user navigating to a service page from a blog post, or watching 100% of our brand video help give us a little more of a holistic view of what’s working and what isn’t, which we can then optimise and improve. It’s a simple-to-implement and scalable approach to defining meaningful Goals that we can use to gain valuable insights from, even when massive data sets are at play.
 2. Collection & Analysis
With access to such granular intelligence, and user expectations at an all-time high, we’re dealing with millions of audiences of one now: individuals and real people rather than simple demographics. We need to fully understand who our audiences are, their needs and their emotional drivers as much as we can, to create better experiences on an individual level. And have those experiences ranked highly in SERPs, of course. The thing is, traditional keyword research may not be enough. According to IBM (2017):
88% of consumer conversations are now in private messaging apps.
Using data we have available across multiple channels, from Social, to email and even chatbots, we have the ability to draw insights around specific issues our customers have that go way beyond the remit of keyword research. Developing a more holistic approach to keyword research that incorporates the wider sphere of communication around a brand, it's possible to develop insights around brand sentiment, customers’ attitude to competitors, and even personality attributes which may impact their behaviour.
While we're probably not quite there as an industry yet, there are interesting moves being made using machine learning to speed up the analysis of large datasets. By using tools like Watson's natural language API , we can build a more detailed and rich view of the user that goes way beyond traditional keyword research and gets us closer to the audience's needs as a whole.
 3. Hypothesis & Ideation
As Avinash Kaushik once rightfully said, “ all data in aggregate is essentially crap ”. It’s essential that we take all this analysis and create hypotheses which can then be applied to form ideas around making improvements to the user experience. We can use Google Analytics segments to inform hypotheses around behavioural traits and specific audience groups based on real data rather than assumptions.
For example, let's say we want to better optimise for a specific target audience, Google Analytics can provide us with a window into specific behavioural traits of that audience:
Furthermore, this process can be applied in line with your engagement scale to identify key groups of users, their pain points and weaknesses in your content or their journey.
 4. Testing & Iteration
Split-testing at scale for SEO is a commonplace tactic as part of an integrated SEO strategy. However, we can go further nowadays by drawing on what we might previously have thought to be more CRO-focused practices. There are options we can apply at a landing page level to test the hypotheses we formed. Platforms such as Google Optimize ( I’ve written about getting started here ) enable us to test specific iterations across key segments, measuring the performance of variations in terms of user experience and engagement.
Having tested, we can deploy the winning variation, or perhaps dig deeper and try again. We have at our disposal, platforms such as Sitecore, that enable us to serve personalised experiences to specific audiences permanently, creating those laser-targeted relevant experiences we’re all looking for as marketers (and consumers!) At this point, we rinse and repeat.
Combining SEO and CRO for a better overall Search experience
Machine learning is shaping a more fluid, intelligent, user-first search engine. This is having a huge impact on SEO, with user experience metrics being used by search engines to rank content. In my opinion, we're at the beginning of a new phrase of SEO in which success will be achieved SEO and CRO teams collaborate, applying the mindset and rigour of each other's disciplines as part of a shared framework. The 'sweet spot' will come when these practices converge and user expectations are met.
This blog post was adapted from a talk I gave at ManyMinds' Give it a Go conference in October 2017. The slides are below: