By: Special Guest Contributor
“Not all who wander are lost.”
Good stuff for bumper stickers or if you’re trying to write a generation-defining bestseller and get Reese Witherspoon to play you on the big screen. In ecommerce, however …
A wandering customer only means one thing: a lost customer.
Having been conditioned by Google, today’s consumers have high expectations when it comes to search functionality. It’s far from surprising then that — as Nielsen Norman Group’s exhaustive E-Commerce User Experience found — “Most e-commerce customers go directly to a site’s search tool to find products.”
Given that none of the leading ecommerce platforms have strong native search, this piece focuses on what to look for in a third-party search solution along with core …
But before we get into the how, let’s address the what and why of onsite search.
What Is an Ecommerce Site Search Solution?
An ecommerce site search solution is a third-party tool used onsite to sync customer search queries — e.g., natural language — with product titles, variants, descriptions, images, videos, SKUs, and reordering codes.
Following in the footsteps of other ecommerce technologies, onsite search has become mainstream in recent years. There is now a host of options available for merchants when previously there were just one or two main providers that were cost prohibitive and required complex, resource-heavy integration.
Today, options exist for merchants of all sizes, with even enterprise-level solutions available from as little as ~$200 per month. This availability has driven a surge in popularity and usage.
At the forefront of this ‘disruption’ at the mid-level and enterprise-level ends of the market are solutions like Klevu and Algolia. Legacy providers have become less relevant, due to not being as agile (in terms of releasing new functionality) and pricing.
Algolia is a good example of a wider search technology, which provides excellent indexing capabilities and speed to all. Klevu is more ecommerce-specific and provides exceptional accuracy, primarily through the use of natural language processing.
Why Does Site Search Matter in Ecommerce?
Onsite search represents a strong opportunity for retailers going into 2018. The rise of mobile commerce demands faster and easier routes for finding things. Even on desktop, search drives higher conversion rates.
Various reports on the value of search for merchants bear this out.
1. On average, users who complete a search are 1.8x more likely to convert
2. Site search visitors can generate as much as 13.8% of overall revenue
3. In the case of detail-oriented products, while less than 10% of visitors may perform onsite searches, upwards of 40% of a site’s revenue came from them
From my own experience with sites in the enterprise space, I’ve found the average conversion rate increases to be closer to 3.5x non-search visitors, with around 5-10% of visitors using search.
In other words, retailers have an opportunity to drive more revenue through search, especially for those who haven’t spent time optimising this area. And, at the opposite end of the spectrum, there’s no better way to frustrate and disappoint customer than serving up irrelevant results or even 0 results for relatively straightforward queries.
Best Practices for an Ecommerce Site Search Solution
1. Natural Language Processing for Better Results
The demand for natural language processing (NLP) within search has increased considerably recently — allowing for accurate results even when the user doesn’t really know how to describe what they’re looking for. Having long been adopted by the likes of Google for its organic search, NLP-based search has now made the transition to ecommerce, driving real change in this often overlooked part of the online shopping experience.
NLP algorithms are based on context and relevance, rather than simply on the presence or absence of keywords in product names or descriptions. In simple terms, that means that an NLP will be able to extract meaning from the query to understand that a visitor who types in ‘red jumper’ is happy to look at sweaters and pullovers, even though the query doesn’t contain the term ‘jumper’.
The same applies to color and being able to understand variations of red. This can be really valuable for retailers, particularly with more complicated queries — an example can be seen below from a Klevu demo store.
This example shows how NLP is being used to understand more about the query - this example also benefits from more product catalog data being indexed (such as product reviews, pricing, all other available descriptive attributes etc).
Here’s another example from the same store, which is from a proof of concept implementation with an IR100 retailer.
2. Merchandising Capabilities for Products, Attributes, and Images
Often one of the main issues that leads to a third party search solution being used is the lack of merchandising options for results, with no way to change the ordering of products being returned and a lack of information being presented generally. Newer third-party tools address these areas and have built-in features to allow retailers to ‘searchandise’ their results properly and weight different items and specific attributes.
Klevu, for example, allows users to assign ‘hero SKUs’ at query level, boost products based on specific rules (based on tags and meta fields) and also assign weightings for specific queries (e.g. boost this red Nike t-shirt across all relevant queries). Another merchandising feature that I think is important is the ability to add suggested listings to error results.
Comprehensive options around filtering, the displaying of product labels and handling of things like variant information are also handled well by the top tier of providers.
Read More at: https://www.shopify.com/enterprise/ecommerce-site-search-solution
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