Human behaviour is complex and never linear. Users employ a variety of queries at each stage of their journey. Marketers have tried creating frameworks or adopting conventional ones to explain the user journey. Sometimes we are guilty of oversimplifying the journey for our own convenience and satisfaction.
A blog written on Moz got into search journey using the popular AIDA (Awareness, Interest, Desire, Action and Loyalty) model. And the author used the cough medication example to illustrate how users would always start their journey with a query like “how to stop coughing” and proceed to “cough medicine” “best cough medicine for dry cough” and so on.
This journey or framework makes logical sense. Marketers and non-marketers can simply understand how search by a user can progress through the different stages. But in reality, human search behaviour is never linear as our life experience, location and external triggers are different. Some users may start with a query at the 3 stages of this journey and flipflop between a few to before making a decision.
Search Journey mapping using a before and after structure
Interestingly, whilst looking at the different ways search journeys are mapped, I ran into a blog from SEO Clarity that expressed a before and after keywords to a target keyword. For example, the target keyword “bronze glass” is predicted to have such keywords like “bronze glass mirror, bronze glass vase, bronze glass table lamp” as the preceding search term and keywords such as “bronze mirror, bronze tinted glass, bronze glass panels” as some of the next terms users are more likely to search for. Whilst this paints a logical picture of how users are likely to search, it is very lexically structured that it might fail to some degree to mimic human reasoning. But it does make a case that humans are more likely to progress or evolve with how they search for information.
Introducing a commonsense knowledge graph scaffold
ConceptNet provides a graph structure with nodes representing concepts that can either be nouns, phrases, verb phrases, adjective phrases, clauses e.t.c. We can model the user’s possible search journey based on these commonsense relations. Depending on the context like the location, time of the year or state of the world, certain relations and query journeys might be more prevalent.
Looking at the above example from SEO Clarity it arranges the searches before and after to follow almost a linear and funnel approach which completely ignores the dynamism and external or extrinsic dependency of human search behaviour. It is almost arranged in line with word stems and in a taxonomy sort of format.
Generating a context-rich and dynamic search journey for the term strappy Sandals
To capture a much dynamic search journey, it is important to explore as much contextual and cognitive relations connected with the term ‘Strappy Sandals.’ We need to move from seeing this term as just a keyword to a concept.
| Relation | Concept or Query Node** |
| HasPrerequisite | “Summer vacation” / “Warm weather” |
| UsedFor | “Beachwear” / “Casual summer outfit” |
| HasProperty | “Comfortable” / “Open-toe” |
| HasA | “Ankle strap” / “Block heel” |
| MotivatedByGoal | “Look stylish at a wedding” _”Be comfortable at an event” |
| Causes | “Blisters” (if low quality) |
| SimilarTo | “Gladiator sandals” / “Wedge sandals” |
| PartOf | “Summer essentials” / “Vacation checklist” |
| HasNext | “Buy ankle cushion” / “Wear with linen dress” |
| Desires | “Affordable but trendy” / “Minimalist style” |
| CausesDesire | “Influencer wore them” / “Seen on Pinterest” |
The above table gives us an example of some of the prior searches that a user would likely make as they might be related to’ things to take for a summer vacation,’ ‘summer vacation items, “” summer essentials” e.t.c.
It presents a more cognitive approach to modelling the search journey and avoids the traditional, oversimplified and linear process that bundles every search query along the paths of awareness, consideration, conversion, retention and referral or the basic intent classification of informational,navigational, transactional etc. This is an initial foray into how search journey mapping can be informed using commonsense knowledge graph relations.