BeekingWhy Analyze Search in an Online Store?
The internal store search engine is one of the most important tools for customers – it allows them to quickly find the products they are looking for. For a Shoper e-store owner, search data analysis is a treasure: it reveals customer intentions and needs, points out gaps in the offer, and opportunities to improve user experience.
The conclusion is simple: it is worth looking at what customers type into the search engine and what results from it to increase conversion and adjust the offer to their expectations.
What data should you collect?
Before comparing results, check which events your Shoper, Beeking and GA4 setup collects. Do not assume every metric is available automatically. Useful data includes:
Most Popular Phrases
List of most frequently entered queries in a selected period (e.g., last week, month). Thanks to this, you know what your customers are looking for most often.
Queries Without Results
Phrases that returned no product results. These 'empty searches' are worth monitoring especially because they indicate gaps in the offer or search engine operation.
CTR After Search
Click Through Rate in search results. Shows what percentage of searchers clicked on any of the results. Low CTR may mean that the presented results were not very relevant or attractive.
Number of Searches
How many times a given phrase was entered. This indicates the popularity of the query. Combined with CTR or sales, it gives a fuller picture of the effectiveness of a given phrase.
Use a consistent period and data source. Separate query counts from people. Do not combine counters from different tools without checking their definitions and consent coverage.
How to Analyze Shoper Search Data?
The list of terms itself is just the beginning. The key is to extract insights – what the data statistics mean for your store and what you can improve based on them.
Share of Queries Without Results
Check what percentage of all searches end with the 'no products' message. A large share of queries without results is an alarm signal. It may mean that customers are looking for products you do not offer, or typing names/terms that your search engine does not recognize.
Most Frequently Searched Phrases
Look at the top 10 or top 50 searched terms. Do they match your offer? It is worth typing the most popular phrases into the store yourself and seeing what the customer sees. This is a simple audit of search engine functioning.
Search Results CTR
Compare the share of searches with at least one click within the same query group. A low rate may reflect relevance, price or availability. A click is not a purchase.
Trends and Seasonality
Analyzing phrases over a longer period, you can catch trends. For example, increasing searches for 'winter jacket' in October signal seasonal demand.
Complementing Search Analysis with Google Analytics Data
Data from the Shoper panel is the basis, but it is worth enriching it with information from Google Analytics (GA), especially regarding user behavior after performing a search.
Internal Search Tracking
Automatic GA4 view_search_results requires a recognized search parameter in the URL. Search without a URL change may need a separate integration. Verify events and avoid duplicates.
User Paths After Search
GA allows you to analyze User Flow. You can trace what happens immediately after searching. A high percentage of exits from the page right after searching suggests a serious problem.
Conversions After Search
You can check the conversion rate of sessions where the search engine was used vs. those without searching. It often turns out that users using the search engine buy more often.
Session Length and Quality
It is worth comparing metrics like average session time or number of page views in sessions with search vs. without. If customers using the search engine spend more time on the site – it's a sign that the search engine engages them in exploring the offer.
Practical Examples: What to Improve Based on Data?
Collected data and its analysis are worth little if you do not translate them into concrete actions in the store. Here are a few practical ways to use insights:
Adding Missing Keywords
If customers often type a certain word and your search engine does not find it, add it in product descriptions or tags.
Improving Product Names and Descriptions
Make sure product names correspond to what users type. Descriptions should also contain frequently searched phrases.
Optimizing Filters and Tags
Look if customers are not trying to search by features (color, size). Configure filters after search results.
Expanding the Offer
If many people are looking for a product you don't have, consider introducing it to sales. This is a direct hint from the market.
Changing Layout and Functionality
Consider highlighting the search engine (e.g., a larger 'Search' bar). It is also worth thinking about implementing autocomplete.
Alternatives to Native Shoper Search – What Does Beeking.io Offer?
Native Shoper search gives basic possibilities but has limitations. Alternatives are intelligent search engines, such as beeking.io, created with integration with the Shoper platform in mind.
Greater Tolerance for Errors
See the documented “marwis” example in csk-med, then test typos from your own store.
Understanding Synonyms and Natural Language
AI can decipher intent and link colloquial terms with products in your store.
Better Result Relevance
Beeking.io delivers relevant products even with imprecise queries, striving to minimize 'zero results'.
Built-in Search Analytics
Review queries, results and clicks. Revenue requires verified transaction tracking and correctly joined data.
Search by Attributes
Enables searching by attributes, tags, categories, making the offer more accessible.
Faster Search with Suggestions
Intelligent autocomplete shows specific products after typing just a few characters.
Solution Comparison
Check configuration first. Shoper documents attribute and variant search, plus one-way and two-way synonyms. Compare tools using the same queries and catalog after configuring both solutions. Shoper documentation.
- An existing product cannot be found even by code
- Check product visibility, catalog data, the active module and synchronization status.
- The name works but an everyday description does not
- Check searchable fields and synonyms. Record the expected product and the actual result.
- The query names a brand outside your catalog
- Separate an assortment gap from a matching error. Evaluate alternatives and label them clearly.
- Results appear but customers do not click
- Review the relevance of the first products, price, availability and mobile presentation.
Save a query set before changes, change one element at a time and repeat the test on desktop and mobile. The csk-med examples demonstrate matching, not a guaranteed sales increase. Case study csk-med.
Summary
Analyzing search data in a Shoper store is a practice that every store owner should implement. Knowledge of what and how customers search is a direct look into their needs. Use this data to improve the store, and if standard tools are not enough – consider intelligent solutions like beeking.io.
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