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In the last years, E-commerce search has been growing in importance, in part because of the growth of mobile commerce. While 2021 typically brought a 10%* to 20%* revenue growth for the average , many e-shops , many experienced a 80%* to 100%* growth of revenue done by mobile customers searching in for mobile users using the search of the e-shop.
Search became one of the key pillars of e-commerce as visitors using the search bring brought 54%* of the total online revenue, of which 57%* are was done on a mobile device.
This should not come as a surprise, as on a mobile screen, navigating through menus and pages to find products is quickly time consuming. Searching in the e-shop offers a more efficient alternative, especially when the user experience is well done.
* Based on the Boxalino Winning Interactions Benchmarks for 2021
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The customer expectations for good UX is
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growing
On many leading e-shops, the search experience has improved a lot recently, especially on the mobile, raising customer expectations to new heights.
Typing on a mobile is more difficult than on a desktop, and now customers expect a fast search preview completing the search while the customer is typing their keywords and suggesting relevant products is expectedwhile they are typing.
Example 1: Good textual suggestions with
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completion arrows
For example, many e-shops like http:// otto.de or http:// zalando.de are completing the user search using a search when clicking on the right arrow on the mobile enabling a fast completion progress and multi-step narrowing event before going to reaching the search result page.
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As an example here, on http:// otto.de , typing 3 letters ('her') then clicking twice on the right arrows (therefore so staying on in the preview without changing pages) and then clicking on a suggestion brings the user to only 8 products, which is easy to go through do on the mobile without the need to filtera mobile and much easier than filtering a long list of search results.
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Unfortunately, while this can bring quickly to a small list of results, it is not easy to change the search criteria keywords in case the suggested products were do not what satisfy the customer expected (the user need search bar needs to be open the search bar again, restart the searcheither with a full restart from scratch, or delete the last words, etcdeleting words manually).
If the user would simply open the filters (as on the screen-shot here), no option could be changed, even if the user clearly indicated a size, a brand and a product type.
This is why Boxalino Winning Interactions developed Semantic Filters which are automatically set as filters which and can be changed, so the user doesn’t need to restart his search to modify his search criteria.[comment for Michael: this actually doesn’t work, but going through the user experience, I realized this would make a lot of sense to do to show the power of the semantic filters, it could be done quite easily for Flaschenpost which would be a good example case for it, I think, to be discussed]
Example 2:
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Personalizing with prior searches
Many customers Customers often need to repeat their prior search to find again the products they were looking for beforeat in a prior visit.
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As an example here, on http:// ikea.de , searching for “bett” can be easily repeated when coming back on the e-shop at a later time.
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However, the user visitor might be interested to do not exactly the same search, but another related search to find something different, explore related searches which is unfortunately not possible in this example, as only the exact prior searches are appearing.
This is why Boxalino Winning Interactions developed Personalized Search Suggestions showing related searches that other customers who made the same search also made, to help the customers explore related cases.
Also, as per the screen-shot on the left, the user might have selected several filtering criteria to find the right products and such filtering would need to be redone to find the same products again.
This is why Boxalino Winning Interactions developed Personalized Search Suggestions showing related searches that other customers who made the same search also made, to help the customers explore related products.Boxalino Winning Interactions also developed the possibility to remember not only the prior searches, but the prior last filtering selection as well, suggesting searches with suggestions like
“bett mit Farbe:rot, Grösse:200/220m”.[comment for Michael: this actually doesn’t work (the second part) but going through the user experience, I realized this would make a lot of sense to do and while not super easy (needs Simon) would not be very hard either]
Example 3: Full Screen Merchandising start
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On many e-shops like http://shop-apotheke.de the simple fact to click simply clicking on the search bars open bar already opens a full screen mode, which maximizes the available size above the mobile keyboard on the mobile , and which is used to suggest the favorite top searches and best products directly, before the user starts typing.
Unfortunately, the display here in this screen-shot is not optimal (big empty space on the right) and not personalized (it could have for example a personalized banner campaign could appear as indicated in blue).
This is why Boxalino Winning Interactions helps you optimize and personalized the content appearing on starting this search preview. This maters, because for many e-shops this display is 1.8x more viewed than even the banners on the home page.
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Boxalino Winning Interactions is the most offers an advanced search technology to fully benefit from the values of the helping e-shops to support the 3 cases described before and to go beyond their limitations.
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For example, on our client’s web-site PerfectHair.ch, the leading swiss e-shop for hair and beauty productproducts, it all starts with the first click on the search bar.
Let’s start with the mobile← Let’s look at it on a mobile device.
The search bar expands in full screen-view and the customer is presented with 3 sections of content, each clearly visible even as on top of the mobile keyboard is open:
`Oft gesucht`, which presents the current search trends on of the e-shop
`Suchvorschläge`, which retarget shows searches recently done by the customer and completes extend them with other searches often done by other visitors doing having done the same searchessearch (like what other people bought, but for search
)and `Empfohlen für Dich` which provides personalized product suggestions based on the prior clicks and purchases of the customer.
In this first step, before even typing the first letter, many customers will already fine what they wantedwant!
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Then, when the user types the first letter character (let’s say 'b'), a list of meaningful and non redundant suggestions are directly appearingappear.
In such a case, it is too early to know what type of products the customer wants, and therefore the textual suggestion (which are the most more helpful at this stage) play a dominant role in the limited display, occupying . The 6 suggestions occupy most of the visual space with 6 suggestions, even if the suggestions of products is are already hinted on with the first one appearing before the keyboard on the bottom of the screen.
The customer can now chose between continue typing, or selecting one of the suggestions by clicking on the term (which goes to the search page), or clicking on the right arrow, which fills the word while updating and staying in without leaving the search preview or keep typing.
Let’s imagine the customer will now first continue typing.
On a mobile keyboard, it is very easy to click on the letter character next to the one we wanted, and this is what is happening to our customer typing ‘babx’ instead of ‘baby’.
As this is a clear and common mistake, the system corrects the spelling seamlessly and directly like if the mistake was never madehappened.
While it seems now quite clear that the word ‘baby’ is limiting the product possibilitiescustomer searches for products related to ‘baby’, there are still many many possible products and therefore the textual suggestions are still fully continue to be dominant in the display.
The customer decides now to click on the right arrow of the first result ‘BaByliss' …
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As 219 products are available in the brand BaByliss and therefore , it is still not yet the time to focus on showing directly productproducts, even if it is very possible that the customer is interested in wants one of the top matching products of that brand.
As the user clicked on the right arrow which updates the search previewbefore, the keyboard has also now been hidden, giving the full display of the screen to the user and showing the top 4 product products as well as the product counts directly on the screen.
The customer has now transition transited from typing to clicking, after initiating the scope (219 out of over 40’000 products) to navigate within by typing only (and wrong) 4 characters…and it is easier for him to continue narrowing down in this way…
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After The customer continues his narrowing down and, after clicking the right arrow of `babyliss curl` (which reduces the selection to 34 products), the customer clicks on the right arrow of `babyliss curl secret`.
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The results are now down to 11 products and the system switches mode focus, focusing on showing products instead of textual suggestions.
IndeedAnd this makes sense, as the 6 products shown here represent more than 90% of the sales out of the possible 11 products of the search results.
It is very, very likely that one of these products is the one the customer is looking for.
But it is not always require to have such a long search query like ‘babyliss curl secret’ is not always required for the system to switch modefocus on products.
For example, 3 letters might be enough, like ‘ola’ which clearly predict predicts the brand Olaplex, with having only 17 products, of which 6 represent also the vast majority of the sales.
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On the desktop, the larger display enables many elements to be displayed at the same moment, which the Boxalino Winning Interaction smart search fully exploits, as in this example for appear at once, which we fully exploit. For example below, with the search ‘color’, showing most common searches, brands, product lines, categories and matching products, all in one structured and clean overview.
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Underlying numbers (to be removed / changed):
Desktop Conversion Rate is typically 2x higher than Mobile Conversion Rate
But the difference is only 1.5x considering visitors using the search
54% of e-shops transactions done by visitors using the search
57% of these transactions where done on a mobile device
1.8x more click on the search bar VERSUS visits to the home page
3 average search results page views / session
VERSUS
30 average search autocomplete preview / session (10x)62% of visitors using the search click on suggestions from the search autocomplete preview
24% of visitors using the search find directly their product from the search autocomplete preview
might be a bad interpretation
On a well designed autocomplete preview, close to a quarter is buying directly from there instead of going to the search result pages, which shows how well accepted this better user experience is28% : session conversion rate of visitors clicking on suggestions from the search autocomplete preview, this is often over 7x higher than the average session conversion rate of the e-shop
25% of searches (after spelling correction) can be redirected automatically to smart brand or a category page
81% of searches rely on Winning Interactions Automated Semantic Filtering** Self-Learning
11% of searches rely on Winning Interactions Automated Weighted Synonyms*** Self-Learning
81% : Prediction Accuracy**** in Winning Interactions Automated Query Suggestion
2.5% of search queries automatically corrected for their spelling
PH - nov-dec 2021 versus nov-dec 2020
desktop revenue : +11%
mobile revenue : +43%+23% increase of mobile revenue not using the search
+83% increase of mobile revenue using the search
** The Winning Interactions Artificial Intelligence learns automatically interpret the meaning of the keywords of the search of the users to to change the search results or redirect them to the appropriate page
*** The Winning Interactions Artificial Intelligence learns automatically synonyms which are relevant for the e-shop based on an open thesaurus and the search online behaviors of the clients on the e-shop
**** calculated on the frequency of textual suggestion matching the next typed character by the user