During Activate, Lucidworks General Manager of Digital Commerce Peter Curran gave a presentation on the importance of customer goals in relation to product discovery. Curran covered the potential that brands have to deliver relevant products and recommendations to customers by way of understanding the customer’s goal – not simply turning around results that seem to match the keywords of the search itself.
If brands move from reliance on lexical search methods (keyword-focused) to semantic vector search (goals and meaning-focused), they can deliver more relevant search results to their users and customers.
The search box holds incredible promise and possibility. It can be the gateway to a user’s next vacation or a coveted pair of sneakers. The search bar can lead someone to learning a new skill or answer a burning question. All of these possibilities are united by one common thread: the user’s goal.
Unfortunately, the all-too-common reality that many users experience with the search box is betrayal. They get stuck half-way through a project, get led to the wrong product, or just find more questions instead of answers. Often, users ultimately end up needing to continuously re-query the search box until the system finally understands their goal. A disconnect between the user goal and the keywords they use becomes apparent. At that point, the user is trained to adapt to the search box when it should be the other way around.
Reassess How Goals Fit into the Traditional Funnel
We can understand the concept of ‘achieving a goal’ by looking at how it works within the ecommerce purchasing funnel. We are all likely familiar with the main elements of the funnel. Most ecommerce search boxes are wholly preoccupied with diverting users to conversion or education. While this isn’t technically wrong, brands are failing to take the shopper’s goal into account in the search – instead, they’re only considering the keywords they use. The danger is that the search box is missing the bigger picture of the shopper’s intent by failing to pull up the most relevant products and information.
Understanding Intent in Action
Let’s say a user wants to make a key lime pie. A common occurrence is the user navigates to the website of their choice that hosts recipe and ingredient content, and searches “key lime pie” in the search box. A very typical result may involve a number of products related to the keywords popping up – driving the user toward the conversion point of the funnel. This could include the ingredients, a recipe, or even buying a key lime pie outright.
Depending on the website and content it hosts, the user may also be moved back and forth between education and conversion. The problem arises when the search box can’t easily point the user in the direction they are trying to go, and it falls to the user to learn how to navigate the website and potentially skip the search box altogether.
Say a different user is looking to make a very straightforward transaction for a specific drink using a popular coffee app. It makes perfect sense for the user to navigate to the search box and begin typing in the name of the specific drink they want to purchase. What we see is the search is capable of offering paths to conversion, however it misses the goal of getting the specific drink the user is looking for. As a result, the user ends up having to once again adapt to how the app organizes content apart from the search box, such as a “Browse” option.
Semantic Vector Searches Solves for Intent
Traditionally, search boxes have relied on lexical search, which focuses exclusively on keywords. This form of search has resulted in the frequent gaps in understanding of a user’s larger search goal. This is where semantic vector search can make a real difference. Semantic search as a term has been in use since at least the early 00s, but today refers to search based on meaning – with the inherent element of focusing on goals.
Within semantic vector search is a set of emergent technologies built around deep learning. The method of organizing content in a database is called a vector space. With vector space, the ability to organize and present content is not limited to two or three dimensions, but fifty, a hundred, three hundred dimensions. Ideas, products, documents, users, segments – a vast array of content can be plotted and retrieved in one location.
Incredible things are possible with semantic vector search functionality. For example, a user can search for a product that is not contained within a brand’s catalogue, and still receive results that are relevant to the intention of the user. Going even further, the user could search in a language that the catalogue is not translated to, and still receive results that are relevant to their search in the language of the catalogue itself.
When applying the potential of semantic and vector search to our previous examples, we might start to see more intuitive search box results. Instead of having to navigate an entire website to understand how to put together a key lime pie recipe, the search box can understand the user’s goal of making one and deliver them to the exact content they would require. Instead of being trained to use the coffee app, the search box can have the ability to deduce what the user is looking for and give us the same depth of options as the “Browse” function.
What’s the moral of these stories?
- Relevancy is the most important user experience consideration in search.
- The user’s goal is not always inherently understandable with keywords alone.
- Even great websites struggle to understand where the user is in the buying journey.
- Relevancy is often subjective and contextual, but ambiguity is detectable.
Deep learning and semantic vector search offer superior query understanding in ambiguous scenarios. User interfaces need to evolve to respond to ambiguity and offer users viable paths in their search journey. When we go beyond the lexical search and deterministic browse paradigms that ecommerce has traditionally relied on, we can address digital business challenges that have long required tedious curation and data work. By focusing on the user goal through semantic vector search, we can create better outcomes that connect customers with the products and information they need.
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