The confluence of SEO and CRO

Optimising for the click (SEO) and what happens after the click (CRO / UX)

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Angus Carburns

Angus Carburns
Insights Analyst

Date:
26 October 2017

Office:
Glasgow

Insights Analyst, Angus Carbarns, addressed the audience at the recent ManyMinds conference. He explored the rising role of machine learning and the confluence of SEO and CRO in creating truly effective user-first search experiences. Here, he recaps his key points, examining the current landscape, looking to the future, and providing useful recommendations for marketers.

With the rise of the user and user experience metrics playing an increasingly key role in Search Engine Optimisation (SEO), I believe that Conversion Rate Optimisation (CRO), which is chiefly the practice of optimising a user’s on-site journey and experience, is now of paramount importance to creating content that adequately meets users’ needs and is ranked highly by search engines.

SEO interest in CRO focuses

I believe that CRO has an important role to play in helping create future-proof SEO strategies that engage and help users. Recent research from sources such as SEMRush and WordStream has helped cement the importance of user experience metrics as key signals impacting rankings.

As discussed at length by Will Critchlow at SearchLove Boston 2017, links and traditional ‘authority’ signals aren’t always a strong indicator of a piece of content's ability to rank, nor its quality in this new machine learning era. I tested this theory for myself – and found my own blog – which has very few authoritative links – often outranks some industry big hitters such as Search Engine Land, and just below the likes of SEMrush, albeit for niche terms. This just wouldn’t have happened a few years ago.

The search experience is becoming increasingly fluid and personalised, with a number of factors influencing search engine rankings. We know that machine learning is helping Google improve semantic understanding and with RankBrain being applied to all queries. And that it’s being used to understand increasingly complex human-brand interactions, and even make predictions around user behaviour. I thoroughly recommend checking out Google and SOASTA’s case study on building a predictive machine learning algorithm for proof of this. So what does it all mean for us marketers? In this new machine-learning age, optimising for the algorithm is like trying to shoot a moving target – something that might well be fruitless.  

 In 2016 Edmond Lau, former Google Search team engineer, hit the nail on the head:

 “It’s hard to explain and ascertain why a particular search result ranks more highly than another result for a given query. It's difficult to directly tweak a machine learning-based system to boost the importance of certain signals over others.”

How do you optimise for something that’s, at least in part, unknowable?

Obviously, there are technical elements at play, but let’s focus on the user-oriented factors. Firstly, we know that ‘usefulness’ is important according to the quality-rater guidelines. Marketers should focus on making the purpose and benefits of content clear and identifiable by search engines. Secondly, searcher satisfaction appears to be important to Google. Research suggests that engagement with content demonstrates to Google that a user is satisfied, and finds the content useful. SEMrush Ranking Factors Study 2017 shows Time On Site, Pages Per Session and Bounce Rate, as three of the top four ranking factors for Google. This reveals a clear crossover between SEO and CRO: that on-site user behaviour is having a direct impact on how content is being ranked in the SERPs.

In a post-PageRank world, we’re optimising not only for the click, but for the post-click experience.

CRO specialists come in handy here. Or at least a bit of CRO-led thinking does. Likely involved in improving a site’s performance in a number of critical ways, CRO practitioners are, irrespective of techniques or practices, committed to finding out why visitors are behaving in a certain way, not completing a certain action, and fixing the problems causing this block. Clearly a focus and outcomes that would improve SEO rankings…

An industry ill-prepared for change

In line with the rise of user experience metrics playing a role in SEO, SEO practitioners are becoming increasingly interested in optimising around the user. However, I don’t believe our industry has quite mastered the creation of shared frameworks, or had the necessary dialogue, to collaborate fully and optimise for the click (SEO) and what happens after the click and the quality of that on-site experience (CRO/UX). Good search UX means meeting technical and user-oriented requirements. It’s a complex ecosystem of inputs to generate positive outcomes. How might this work in practice?

A collaborative SEO / CRO framework

Taking this into account, I’ve looked at how the adoption of ‘CRO-thinking’ – the mindset and rigour of optimising around the user –  helps to provide the foundations for this new era of SEO in which creating ‘engaging’ experiences is a must. I’ve created the following collaborative SEO / CRO framework which I believe helps, at the very least, to encourage dialogue and collaboration. It’s something we’ve been working on at Dog, and continue to refine in practice.

 

1. Goal definition

In essence, we need to define a specific problem and the issues at play, and create a basic engagement framework for working out whether what we propose to do works or not. It’s pretty much that simple. We can attribute points to 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, we can better understand how content helps or hinders them. And this gives us 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 that we can 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.

We’ve moved way beyond keyword research. Using the data we have available across multiple channels, we can draw insights around specific issues our customers have, brand sentiment, customers’ attitude to competitors, and even personality attributes which may impact their behaviour. We can even use machine learning to process unstructured data at scale, helping us figure out critical behavioural and semantic patterns. And once we’ve collected all of this data, we put this intelligence into context to draw meaningful and actionable insights. Once you have this figured out, it’s time to move on to the next section of the framework.

 

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. Google Analytics segments can be used make hypotheses around behavioural traits and specific audience groups based on real data rather than assumptions. Furthermore, it 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

It’s time to test those hypotheses. 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, of course, we rinse and repeat. Ever improving and never resting on our laurels. Our industry moves far too fast for that.

To sum up, the rise of machine learning in Search 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. Success is achieved when SEO and CRO teams collaborate, applying the mindset and rigour of a CRO approach as part of a shared framework. The sweet spot comes when they converge completely, user expectations are met, brand ambitions are achieved, and the search engines recognise this satisfaction by ranking highly.

You can view my deck below, and please do get in touch if you have any questions for me.

 

 

About ManyMinds Meets conference

The one day conference was organised by Kirsty Hulse of ManyMinds, a specialist Search and content agency based in London and took place in central London on Friday 20th. In the last few years, Kirsty has spoken about SEO and content at conferences around the world such as Moz Con and SearchLove. After being quizzed by attendees about how she got her break in speaking, she decided to set up a conference to give first-time speakers the opportunity to ‘give it a go’. Pitches were welcomed from digital marketers across the country, and 10 speakers took to the stage to discuss everything from brand strategy, technical SEO and online PR to analytics and CRO.