Product Discovery

6 Steps to Better E-Commerce Site Search

The more products you carry on your site and the more varied your product mix and clientele are, the more important product discovery becomes. That’s why tech giants like Amazon and Netflix rely so heavily on personalized recommendations: They were forced to, by the sheer size of their respective catalogs. For retailers, the quality of your on-site search plays a huge role in discoverability. Some shoppers may just come to browse, but many have an idea of what they want, and simply need to find it. Improving and optimizing your on-site search helps ensure that they’ll find what they’re looking for. Those improvements and optimizations will be different for each site, but the process of identifying and implementing them follows a consistent path.

Product Discovery

6 Steps to Better E-Commerce Site Search

The more products you carry on your site and the more varied your product mix and clientele are, the more important product discovery becomes. That’s why tech giants like Amazon and Netflix rely so heavily on personalized recommendations: They were forced to, by the sheer size of their respective catalogs. For retailers, the quality of your on-site search plays a huge role in discoverability. Some shoppers may just come to browse, but many have an idea of what they want, and simply need to find it. Improving and optimizing your on-site search helps ensure that they’ll find what they’re looking for. Those improvements and optimizations will be different for each site, but the process of identifying and implementing them follows a consistent path.

1. Understand the Current State of Your On-Site Search

You can’t measure improvement until and unless you understand your starting point, so begin with a cold-eyed analysis of your site’s current search capabilities. Most e-commerce site search fails on a number of fundamentals, according to ongoing research conducted by the Baymard Institute.

The biggest takeaway is that over 60% of sites require users to guess the exact “correct” search term for a product. Most sites fail to recognize (and account for) typos, different phrasings, colloquialisms, contractions or symbols and synonyms. And all too often, on-site search fails to deliver hyper-specific results from specific, long-tail searches (even though there are a handful of products in a retailer’s catalog that would fit the criteria).

There are several ways to assess how efficiently your search works and potential inadequacies. One is to look at your on-site metrics: If you’re seeing a high level of bounce after one or two searches, that’s a red flag. Another is to create a routine that flags searches that return few or no results, which gives a clear insight into specific searches that don’t work. A third and very important option is to pay actual shoppers — not your web team or your marketing team — to search the site for specific items, noting both how successful they were and how much effort it took. Most importantly, ask them what they liked and disliked about the process (and how it could be improved).

2. Offer More Impactful Filters and Facets

Adding filters and facets to your search tool and product category taxonomy is one of the most powerful ways to enhance your product discovery capabilities. Filters allow your users to choose applicable options from a list you provide them: manufacturer, color, product category or anything else that might be meaningful — getting them closer to the products they’re looking for. 

Don’t overlook the fact that this also allows users to filter out specific characteristics. It’s more important to deliver accurate results than to get as many products in front of the customers as possible. A search that returns 10,000 hits is almost as useless as one that returns none, so this is important. 

3. Add New Categories

On a similar note, you should categorize your products as much as possible, and in as many ways as possible. For example, dresses could be categorized by style, color, size range, type of fabric, embellishments, or any other attributes meaningful to your product catalog. You should also think in terms of thematic searches, something only about half of the companies in Baymard’s research handled well. These categories are drawn conceptually rather than by the characteristics of the garment itself. Examples might include “spring outfit,” “party dress” and “work shoes.” 

These categories, and the searches and toggles mentioned previously, work together to speed and empower search. For example, the phrase “work shoes” might mean very different things to a nurse, a factory worker or a senior partner at a law firm. Adding a well-thought-out set of filters to the basic “work shoes” category allows your users to further filter search results based on their style preferences. 

4. Use Deep Tagging to Broaden Searchable Data Points

One limitation you’ll inevitably encounter in your quest to improve product discoverability is inaccurate and incomplete product data  Sure, you can simply import the primary data points from your suppliers. From that base level of searchable characteristics — size, style, color, fabric and so on — you decide how many additional tags or attributes are attached to the product. But this is often done manually, so it’s costly and prone to inconsistency and inaccuracy. The more products you have, the more quickly you’ll reach the point of diminishing returns.

Lily AI’s software breaks that limitation. Our proprietary machine-learning algorithm analyzes your product line and applies deep tagging, potentially creating dozens of new data points for your products. Better yet, because it’s automated, this isn’t a massive, time-consuming project. Your site’s search capabilities will be able to utilize all of this new data to deliver accurate search results based on long-tail, subjective and objective searches within just a few weeks.

5. Make Better Search Autofill Suggestions

At this stage of the process, with the high-level “broad strokes” fixes in place, you can begin to focus on finesse. One way to do this is through the use of improved autofill suggestions in your search tool. Your search tool may base its suggestions on site-wide search history, so a user typing in “spring dress” will see suggestions for all spring-related categories after the first word but only spring dresses after the letter “d” is typed. 

You can also get more personal based on the individual’s search history, weighting search results toward the types and style of products that a specific user has favored in the past. Deep tagging makes this easier. If you’re tracking customer behavior and how they engage with detailed product attributes and leveraging Lily AI’s ability to generate psychographic profiles for each customer, you’ll be able to deliver more meaningful and personalized search results.

6. Iterate, Iterate, Iterate

An important point to remember is that improvement and optimization of your website is not a one-and-done task to be checked off of your list, but an ongoing process to build into your workflow. 

Each step in this process informs the others: Reducing bounce gives you more user searches and interactions to observe; user feedback helps you improve your search tools; clickable filters and categories tell you about your users’ search priorities; deep tagging empowers the creation of more finely tuned filters; and so on.

As you repeat this process, your grasp of your users’ search process will improve immeasurably. You’ll know which filters they use, which search tools they find intuitive and which changes to your search made the biggest differences to your conversion rates and sales figures. It’s a virtuous cycle, and a powerful one.

The Bottom Line: Superior Product Discovery

If you read customer ratings and monitor mentions of your company online and on social media (and you should, it’s important), you’ll know when your efforts are bearing fruit. How? Because your customers will rave about your brand. They’ll share, they comment, they show excitement.

Lily AI can help. Deep tagging takes you to a deeper level than objective attributes such as style, color and size. Our machine-learning algorithms infer the emotional drivers that underlay purchase decisions. This makes way for psychographic segmentation, and it empowers you with more than the “other users also searched for” approach to personalization. It’s individually and uniquely tailored to every single shopper. Contact us today to learn how you can bring discoverability on your site to that next level.

Sources:

Baymard Institute: Deconstructing E-Commerce Search: The 8 Most Common Query Types

CX That Sings: Customer Experience Management: How to Use Metrics in Your Journey Map

Google Analytics Help: Analyze Enhanced Ecommerce Data

Weidert Group: 8 Ways to GEt the Most Out of Google Alerts for Business

Entrepreneur: How to Manage (and Monitor) Your Reputation on Social Media