Content production and content optimization has been a staple of SEO and website optimization strategies since the get go.
Over the years, we’ve changed tactics and acknowledged changes in both user behaviour, user expectation, and accommodated the ever increasing artificial intelligence and expectancy from search engines that our content is good.
We can take this one step further however, and use the solid foundations we build for SEO to match keywords, intents, and entities, and create better experience forecasts.
This is also an amazing cross-section in SEO where the concept of EAT meets other known variables such as entities, and user intent.
I first wrote about user search intent in January 2018, and recent observable changes and correlating beliefs highlight concepts such as EAT and new SERP features such as passage indexing are taking this increased understanding behind user intent to provide search results that fully meet a users needs and caters for a wide spectrum of common user query interpretations, but now search results pages that go some distance to satisfying the query intent and user objective.
In more recent times, Frederic Dubut of Bing has been quoted as saying:
Search Engines are shifting from keywords to intent at an accelerating pace. So if you imagine, a few years ago we were mostly based on keywords, then we are a keyword based search engine with a little bit of intent sprinkled on, and what we are looking forward to in 2020 is that search engines are going to be primarily intent based.
You can hear everything that Frederic said in the video snippet below:
Whilst search engines are advancing, alongside technology and hardware, user expectations are also increasing at a similar, if not more exponential rate.
This is why at a basic level, I present the below equation:
User Intent + SEO = Experience Forecasting
By understanding the user intent and objectives, and then optimizing for it as part of general SEO activities you can create and build better content for both search engines and users.
What Is Experience Forecasting?
Experience forecasting is the notion that a user will imagine their potential future usage of a product or service.
To cater for this, we need to first understand the intent and expectations of a user and then, through our content, impact the user journey with key pieces of information to make the imagination process easier.
In psychology, imagination generally refers to the ability to mentally represent sensations that are not physically present.
We can influence experience forecasting through various content types on a webpage, these being (and not limited to):
- Google 360/Photosphere
- Product/service description content
- Supporting content
- UGC FAQs
Each of these elements can be made to work harder than simply being added to a page, and by increasing the users ability to better forecast a positive experience, correlatively you will see an increase in conversions.
It’s also very important to realise that there are elements of experience forecasting that we can both control, and those we really can’t.
User Experience Forecasting
A good example of user experience forecasting can be in the travel sector. Imagine you’re website is listing a hotel in Spain and it comes with all the amenities that you’d expect.
Everyone who read that previous sentence imagined a different hotel and experience, despite a number of potential common elements – like a light coloured building with several floors and balconies, and a pool.
The reason we imagine different experiences is because we have all had different experiences and different exposure levels of marketing and advertising persuasion.
On a base-level, these are things we can control and cannot control:
|We Can Control||We Cannot Control|
So, there’s a couple of other concepts we need to understand in order to create our own user experiences – persuasions, and early stage user queries.
Persuasions isn’t a new concept, it’s something that the marketing and advertising industry has been doing for decades and everyone is susceptible to it.
The human mind receives countless numbers of persuasions at all times through the media and entertainment we consume, through to things we observe on our commutes.
A good example of this, and how it plays into our subconscious, is this snippet from a Derren Brown television show:
The persuasions here being laid out, with some more subtle than others.
Dave Trott once said that 89% of all advertising is not consciously remembered (BrightonSEO 2019), but what isn’t quantifiable is how much of this advertising messaging subconsciously influences our decisions and behaviours for totally unrelated products or services.
Early Stage User Queries & Query Stacking
When looking at keywords and search volumes, there is an underlying assumption that all searches within a time period are both purposeful and educated.
An early stage user query, in this instance, is when a user is attempting to establish and satisfy an objective – but doesn’t yet know the correct terminology to form the right query to find the right answer. Because of this, we have to make an assumption that a certain percentage of users performing a query may be “query stacking”, which is the notion of a user performing multiple, successive queries with the same single objective, or an evolving objective based on the original intent – to find a solution to a problem.
In these user journeys a query can compound additional words, or change altogether, but the end goal doesn’t change.
Creating User Experience Forecasts
So looking back to the variables we can influence, and acknowledging the ones we can’t – how do we go about building in the necessary building blocks to enable users to effectively forecast their experiences of the product/service.
An effective user experience forecast isn’t always positive for the website in question, unless your OKR is just the volume of MQL. However, a better user experience forecast can potentially reduce the overall number of MQL, but increase the number of SQL and improve your LTV/CAC.
In other words, you should get less junk leads and more qualified leads who have a greater life time value (and lower churn rate) as they’ve been able to more accurately establish and forecast their experience of the product/service versus their intent and objectives.
To create these building blocks, we need to combine technical with content.
Page Content Decisions
When we talk about content, we oftentimes are referring directly to the written word, but for this I’m referring to everything on the page designed to provide information, persuade, or make it look aesthetically pleasing.
This can be achieved through collating and analysing a number of existing data points, through known and established processes, such as:
- Keyword research (Mangools, Ahrefs, etc)
- User intent analysis
- NLP analysis
- Image Entity Tags
With all these data points pooled together, you can then start to plan and build content that is both good for search engines, and also for users.
Image Entity Tags
Image entity tags are a filtering system Google introduced to image search not so long ago, to aid users in refining their query without making further or subsequent ones (no query stacking).
This does however give us insight into the different entities and topics Google associates with the initial search query, and we can then use these to our advantage.
For example, if you perform an image search for [ddos attack], the filters brought back are:
- syn flood
- udp flood
A lot of these words should/would have shown up within keyword research, and to someone optimising a page for [ddos protection] they may be included as par for the course, but we can also use this information in other ways.
For example, we can include on the page:
- An summary talking about the Dyn DDoS Attack, which was one of the largest attacks recorded and most publicly felt
- Include a section on the different types of attack (Botnet, Smurf), or include them as supporting content pieces
- Include a diagram, with appropriate ALT text, of what a DDoS attack “looks like”
Reviews can aid experience forecasting when there is enough information available for a user to forecast their potential experience versus the feedback of others.
To do this we need to understand the different aspects of a review, which is ultimately a review of the emotions a customer experiences.
Reviews can be broken down into three categories:
- Reviews of the brand experience
- Reviews of the customer experience
- Reviews of the product experience
The majority of websites treat reviews in the same way – they’re either a third-party API integration loaded onto the page with some schema in the background, or copied and pasted onto the page.
However, with better review gathering and better feedback you can create a better reviews experience.
In the past few months, Google introduced an almost “cliff notes” type section to Google Reviews, pulling out common themes that users can interact with and filter by.
Taking time to go through your reviews and pulling through the same TLDR parts make it a lot easier for users to make quicker decisions.
This doesn’t need to be integrated with the review provider but could be a potent module developed on your product pages.