The Quest for Holy Grail Part 2: User Intent
Last year I started a trilogy of articles to share the path we’ve taken, and the discoveries we’ve made, in the quest to find search happiness, starting by looking at the search journey. Time has run a little away with me but I now want to pick this up again and explore further the second fundamental step; user intent.
This not a post about the three classical “SEO search intent scenarios”:
In this article, we’re going to decrypt and demystify on-site user intent
Is this possible?
Seneca said that: all human beings aspire to be happy, but each one defines happiness in a different way.
This was demonstrated within my first post where I explained how search happiness depends on the user intent.
In fact, if we could understand a user’s intent then we’d be closer to understanding their level of happiness. By creating a scale we’re able to define whereabouts the user sits in the customer journey and whether they’re more in the buying mode or the discovery mode.
The Buying mode is when the user wants to Get something (known), therefore they have a High Intent
The Discovery mode, on the other hand, is when the user wants Inspirationto discover (unknown) new things, therefore he has a Low Intent
So how can we detect which mode the user is in?
Well, there are some signals that can help us to define it:
1. Query type
2. User behaviour
1. Query type:
Having explored millions of eCommerce intentions, we’ve discovered that there is a clear correlation between the intent and the query type. In the below image, you can see the different types of queries that define where the user is based on the query entry.
To complete this analysis, it’s also necessary to add into the equation how the user is interacting with the site search.
2. User behaviour
It’s not only the query entered but how the user interacts with all the elements of the search journey that helps to determine their position on the scale of buying intent.
In my first post we saw that a good search experience doesn’t necessarily lead to a purchase. This is because there are other factors at play; the visit could be an initial one of discovery, the customer may want to try the physical item on in store or the user may be researching on a journey into work say with plans to pick up again later. Likewise, sales don’t always stem from a good online experience. The purchase may take place due to necessity despite an incredibly bad experience.
Which led us to the conclusion that a defining feature of search happiness is that of user intent. This requires tailoring the online experience to relate to the right phase of the customer journey. Whether that’s one of inspiration and discovery with low purchase intent or if the intent to buy is high due to a necessity to get something.
There are several ways to identify the user intent and in this post I’ve tried to outline the variables that we can use to create this matrix. These include the type of query and the user behaviour, which both help us to position where a user sits on the customer journey. By understanding the user intent, retailers cannot only understand and therefore help their shoppers more but they have the conditions to create a framework for delivering search happiness.