Introduction

The rise of E-Commerce (mobile) search

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 10%* to 20%* revenue growth, many e-shops experienced a 80%* to 100%* growth 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 brought 54%* of the total online revenue, of which 57%* 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

The customer expectations for good UX is 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 preview completing their keywords and suggesting relevant products while they are typing.

Example 1: Good textual suggestions with completion arrows

For example, many e-shops like otto.de or zalando.de are completing the search when clicking on the right arrow enabling a fast progress and multi-step narrowing before reaching the search result page.

As an example here, on otto.de , typing 3 letters ('her') then clicking twice on right arrows (so staying in the preview) and then on a suggestion brings the user to only 8 products, which is easy to do on a mobile and much easier than filtering a long list of search results.

Unfortunately, it is not easy to change the search keywords in case the products do not satisfy the customer (the search bar needs to be open again, either with a full restart from scratch, or deleting 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 and can be changed, so the user doesn’t need to restart his search to modify his search criteria.

Example 2: Personalizing with prior searches

Customers often need to repeat their search to find again the products they were looking at in a prior visit.

As an example here, on ikea.de , searching for “bett” can be easily repeated when coming back on the e-shop at a later time.

However, the visitor might be interested to 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 need to be redone to find the same products again.

Boxalino Winning Interactions also developed the possibility to remember not only the prior searches, but the last filtering selection as well, with suggestions like “bett mit Farbe:rot, Grösse:200/220m”.

Example 3: Full Screen Merchandising start

On e-shops like shop-apotheke.de simply clicking on the search bar already opens a full screen mode, which maximizes the available size above the mobile keyboard, and is used to suggest top searches and best products before the user starts typing.

Unfortunately, the display in this screen-shot is not optimal (big empty space on the right) and not personalized (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 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.

A better Search with Boxalino Winning Interactions

Boxalino Winning Interactions offers an advanced search technology helping e-shops to support the 3 cases described before and to go beyond their limitations.

For example, on our client’s web-site PerfectHair.ch, the leading swiss e-shop for hair and beauty products, it all starts with the first click on the search bar.

← 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 on top of the keyboard:

• `Oft gesucht`, which presents the current search trends of the e-shop

• `Suchvorschläge`, which shows searches recently done by the customer and extend them with other searches often done by other visitors having done the same search (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 want!

Then, when the user types the first character (let’s say 'b'), a list of meaningful and non redundant suggestions appear.

In such a case, it is too early to know what type of products the customer wants, and therefore the textual suggestion (which are more helpful at this stage) play a dominant role in the limited display. The 6 suggestions occupy most of the visual space, even if products are already hinted with the first one appearing before the keyboard.

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 without leaving the search preview.

Let’s imagine the customer will first continue typing.

On a mobile keyboard, it is very easy to click on the 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 common mistake, the system corrects the spelling seamlessly and directly like if the mistake never happened.

While it seems now quite clear that the customer searches for products related to ‘baby’, there are still many possible products and therefore the textual suggestions continue to be dominant in the display.

The customer decides now to click on the right arrow of the first result ‘BaByliss' …

As 219 products are available in the brand BaByliss, it is not yet the time to focus on products, even if it is possible that the customer wants one of the top products of that brand.

As the user clicked on the right arrow before, the keyboard has now been hidden, giving the full display of the screen to the user and showing the top 4 products as well directly on the screen.

The customer has transited from typing to clicking, and it is easier for him to continue narrowing down in this way…

…

The customer continues his narrowing down and, after clicking the right arrow of `babyliss curl` (which reduces the selection to 34 products), clicks on the right arrow of `babyliss curl secret`.

…

…

The results are now down to 11 products and the system switches focus, showing products instead of textual suggestions.

And this makes sense, as the 6 products shown here represent more than 90% of the sales of the possible 11 products.

It is very likely that one of these products is the one the customer is looking for.

But such a long search like ‘babyliss curl secret’ is not always required for the system to focus on products.

For example, 3 letters might be enough, like ‘ola’ which clearly predicts the brand Olaplex, having only 17 products, of which 6 represent the vast majority of the sales.

Going from over 50’000 products down to a selection of 6 with only 3 letters and without even changing page once, this is possible with Boxalino Winning Interactions!

On the desktop, the larger display enables many elements to 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.

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 is
  

• 28% : 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