An immersive dance
Search is a unique feature in your website. Your visitors can express themselves in their own words. They have the control and decide in which direction they go. By searching, they experience much more freedom compared to browsing with your menu. And search came a long way from being a “message in a bottle”, in the times where visitors used to type long searches by themselves and hoped for the best when pressing enter. But now, since the first character typed, visitors are guided contextually with personalized queries, products, content, and communication messages. At every keystroke, they can choose to continue leading by typing or to be guided by clicking. This delicate, open, and multistep experience is what we call the search dance.
This dance between the visitor typing and the system guiding can be highly immersive as the visitor discovers not only what matches his search, but also what he could be searching for. On the other side, thanks to all the typing and clicking behaviors, your website can continuously learn to improve its guidance and become a “better dancing partner”.
In this White Paper, we will look at the most important roles and the impact this immersive search dance can have on the success of your online business.
Win by focusing on interactions rather than features
Without solid technical features, it is not possible to provide a good search experience. So much is clear. But search features, like error corrections, synonyms, or even natural language processing, are too often presented as the central piece of the solution. We will discuss such features as well and they have their importance for sure. But they are only there to support the visual experience flow.
At the macro level, Customer Journey optimization focuses on all visits throughout all your channels over time. But here, the experience flow can be viewed as each footstep in this search dance. We focus here at the micro-level, as the experience flow is defined by each event happening not only between pages but within each page of your website (and in the search case, even between each keystroke).
The 10 winning interaction search steps
1: Invite to search
The large white search bar over the dark header background of amazon.com says it all: “I am big, I am important, you should consider me if you want to find something”. If we consider that the large majority of visitors search for only one or two words, it’s on purpose that it was made disproportionally large, so it can send the message: “Here is the most important feature of the website, use it!”
In order to figure out the right way to display your search bar, for its position, size, and visual appearance to be as effective as possible, it is important to do A/B Testing on the layout, content, and visual style (maybe even on default texts inviting visitors to search or exemplifying what they can search for).
This can be done using Boxalino Narrative Layouts with A/B Testing and your online optimization team can improve all these aspects with close to no involvement of your IT team.
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Making your search bar bigger might not immediately turn into higher conversions, as it affects much more the entrance in the conversion funnel than its conclusion, so consider optimizing more on engagement KPIs on the search bar like the click rate. |
2: Individualize right away
No need to wait until visitors already started typing before guiding them. Start immediately displaying the Autocomplete, with for example the visitor’s last searches, as soon as the search bar is selected (a very useful feature which shows that personalization can really be helpful even in its simplest and most direct form). You can also inspire with “current search trends” or even better, with searches related to the prior clicks of each individual visitor.
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The Autocomplete is the overlay that appears above the page just below the search bar when the visitor clicks on it and starts typing. |
For example, galaxus.ch displays the last searches the user made, which is already helpful and a good start.
This can be done by using Boxalino Semantic Autocomplete which can tap both into the visitor’s search history as well as on frequent searches matching his prior behavior. Personalized product and content recommendations can also be displayed using Boxalino Neural Collaborative Filtering (NCF).
3: Guide since the first keystroke
The informative value brought by search suggestions should not be underestimated and are helpful for the user from the very beginning. Especially when they are personalized to match the visitor’s prior behavior. Think about it, the visitor might be searching to find something related to what he viewed already, without being sure how to type it exactly. To point him in the right direction can be a game-changer.
This can be done using Boxalino Semantic Autocomplete which groups similar searches together to provide only individually relevant suggestions without confusing plurals, filling words, or terms inversions. Each suggestion is a concept on its own, related to what the visitor already typed.
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For an even better User Experience, consider dynamically moving the search bar to the top of the screen when the visitor selects it, you will have then more space to display the Autocomplete without any scrolling needed. |
For example, flaschenpost.ch displays helpful textual suggestions very quickly.
4: Bring focus early on
For example, shampoo.ch only displays 1 search suggestion after only 3 typed letters ‘ola’. The vast majority of prior visitors who started searching with these three letters ended-up searching for Olaplex and its 6 related products. Even if people searched for “olaplex 3” or “olaplex bond maintenance”, these refinements are not helpful, as the small amount of products can be shown directly. On the mobile screen, such efficient adaptation is a must for the user experience.
Some searches are very broad (for example when visitors are searching for a brand offering many products) but others are clear after surprisingly little input from the user.
In other words, the user needs textual suggestions when there are many possible outcomes. But let’s not forget that it is a selection of products or content the user is looking for, search suggestions should only be a way to get there when helpful. As soon as a small set of results is identified the focus should shift to the results themselves.
This can be done with Boxalino Personalized Search Suggestions which will show relevant products and content suggestions personalized for each visitor behavior.
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Consider a minimum result size for search refinements matching your default result page size. If clicking on the first textual suggestion doesn’t bring more than 1 page of results, there is no need for refinement suggestions, the user can select the main suggestion or directly one of the first results directly shown and skip the result page. |
5: Inspire with refinements
Sometimes 3 characters are precise enough, and sometimes 7 characters are far from enough. In such cases, visitors don’t need only frequent matching searches from other visitors, but also categorical drill-down to give a real overview of the product assortment.
It is then important to let the user preview the results of each suggestion quickly without needing to go back and for to the result page. Such trials and errors would quickly bring frustration to the user experience.
This can be done with Boxalino Semantic Autocomplete which includes smart refinement suggestions and can automatically expand the suggestions with Autocompletion drill-downs.
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Consider a few useful drill-downs like the category, the brand, and other key refinement attributes to directly expand the suggestions of the visitors while they are typing. |
For example, in geschenkidee.ch both category and search refinements are proposed. And if the visitor simply passes over a suggestion, the products below are directly refreshed to give him a preview.
6: Promote with merchandising
For example, fischen.ch directly shows blog results next to the product suggestions.
Your search algorithm should not only care about keyword-matching products but should instead align with your active marketing campaigns:
Show both products and contents directly in the autocomplete results (blog entries, marketing landing pages, …)
Select the first products shown to be related to an active marketing campaign
Redirect to a landing page when possible or display a search message or a marketing banner on top of the search results
This can be done with Boxalino Semantic Filters which extract the matching attributes of each word in the search and the Boxalino Omnichannel Campaigns which manages and connects to marketing media like banners and landing pages.
As a result, when visitors search exactly for an attribute defining an active campaign, they are automatically redirected to its landing page (which can be fully managed by Boxalino). And if their searches include additional terms, the banner campaign appears on top of the search results (as the search results will be then more specific than the products of the landing page, and redirection to the landing page is not appropriate anymore).
For example, in flaschenpost.ch Boxalino Automated Semantic Filters automatically extract the meaning of each search word and display a promotional marketing message on top of the search results.
7: Match meaning
The definition of the search results becomes more complex when faced with the following situations:
typing mistakes
The visitor makes one or several typing mistakes.
For example, a search on PerfectHair.ch with both the brand (“loreal” instead of “L’oréal”) and the category (“shmapoo” instead of “shampoo”) typed wrong will automatically be corrected while the user is still typing.
different ways to write
The visitor types words differently, not only in a different order or with filling words, but also parts of words (compounds).
For example, on amavita.ch, a search for “Maske für Kinder” while products are written with one compound word “Kindermaske”.
synonymous wording
The visitor types a word that might be a synonym of the word used in the product data.
For example, a visitor on coopvitality.ch searching for “mundschutz” also sees the results of “schutzmaske”
broad usage
The visitor searches for terms that are often used in the description of other products, but these other products are not what the visitor is searching for.
For example, on hauptner.ch there are only 44 “Sattelgurte”, but 50 other products have the word in their descriptions. They are removed from the results.
But in fact, all these situations have something fundamental in common: the learning of what the user actually means and the learning of what is a good match for it.
It is therefore important to correct or adapt the input of the user to match your terminology and it is also important to filter and rank the results to match what the user actually expects.
The adaptation of the user input to your terminology can be done by Boxalino Semantic Parsing which automatically corrects the user input, learns important synonyms, finds the word roots, and matches the word compounds.
The filtering and ranking to match the user expectation can be done with Boxalino Semantic Filters to define the relevant segments of your product assortments and with Boxalino Self-Learning Ranking for the precise ordering of the results in these segments.
8: Recover fast
Sometimes, there is just no matching results. In can be because of a wording problem, because you don’t sell the searched products or because only some of the searched words have any results, but not their combination.
In such cases, pretending that there are search results and showing something which is not what the user expects is likely to be counter-productive. Instead, it is better to indicate that there are no matching products and to suggest interesting alternatives as well.
On puresense.ch a search for a combination (category and brand) that doesn’t exist will show both results separated (first the results of the category and then the results of the brand) while clearly indicating to the visitor that his full search didn’t match any results.
On haarprodukte.ch a search for a brand that is not sold will not bring any results but will show a selection of personalized product suggestions to motivate the user to continue his visit regardless.
This can be done by using Boxalino Narratives Layouts to manage the layout of your search page, so that you can configure different layouts for different situations (e.g.: standard layout if there are results, sub-phrases groups layout if they are results only for parts of the search and personalized product recommendation slider in case there are no results at all).
9: Empower to explore
Showing products without requiring scrolling is quite important, just as showing the expected product first. This is absolutely clear. But what if there are several pages of results and that it is not clear what the user really wants to buy? In such cases, the filtering options (of facets) play a very important role, but only if they are visually appealing.
Here are the main things to consider:
Top facets: showing a small selection of important facets on top of the results can play a very important role
Only relevant facets: hide all facets which are not related to the search results (for example, a size facets, when only 5% of the search results have any value for size)
Pre-expanded facets: Instead of showing all your facets collapsed, and then show all values when expanded, select a few facets (3-5) and show them already expanded with the top 5-7 values (display a “show more” link below to see the other values). This will highlight the most helpful filtering options without requiring the user to click on anything.
Facet Icons: using icons for each facet facilitates the user experience and makes the facet look more helpful, easier to understand, and to remember
Different visualizations: use a slider for some of your numerical facets and a search box for facets with a large number of values, as well as many other visualizations depending on each facet’s data.
Here is an example for flaschenpost.ch with top facets and 3 expanded facets on the left. All this without the user needing to click on anything.
This can be done using Boxalino Dynamic Facets and defining different visualization parameters for different groups of facets.
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Think about injecting the most helpful filtering options directly in the results grid, just as if they were another product in the list, this will be particularly helpful on the mobile as facets are otherwise not displayed unless the user clicks on the filters and then they completely hide the list, which makes them much less easy to use than on the desktop. |
10: Personalize relevance
The products (and content) shown on top of the search result list are crucial. If they are relevant, the user might not only click on them but will be much more likely to look at more products in the list below. Therefore, to select the results not only as a “best for all” but in a personalized way is a very important feature of a good search.
But personalizing the products appearing on the search result page is a delicate process for several reasons:
Prior purchase behaviors don’t always help (e.g.: a brand the visitor likes for a certain type of products might not be what he likes when searching for a totally different type of products)
Search results should not change too quickly (the visitor might want to return to his search result page later and the behavior he had in-between should not change the listing, especially if the user was in the process of paginating)
Some personalization criteria might be much more important than others depending on the search term (e.g.: visitors searching for a need might care much less about brand preference than when searching for a function)
For these reasons, we do not advise to use general “one-size-fits-all” personalization algorithms blindly, but instead to compare the effect of different personalization logics with A/B Testing.
One Personalization logic we highly recommend to test is Customer Clustering. Find out more in our recent Case Study with PerfectHair.
There is another type of personalization that can be very impactful, Boxalino Real-time Results Rating. Instead of changing the list of results with personalization behind the scene (implicit personalization), you can let the visitor indicate the results he likes or doesn’t like (explicit personalization). This way, the visitor can simply select a thumbs-up/down on any of the returned results (you can also use an X and a pin or other visual indications of what the visitor considers a good match or a bad match). The search results are then updated with the user feed-back taken into account (e.g.: similar products to a thumbs-up go up in the list and similar products to a thumbs-down go down in the list).
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Real-time Results Rating is a new feature and is currently being implemented with customers of Boxalino. Please contact Boxalino if you want to know more about this topic and test it by yourself. |
The underlying technology
In the 10 winning interaction search steps, the following technologies are referred to. We highlight here the specific ways Boxalino Winning Interactions Platform covers these functionalities with Data Science and Artificial Intelligence.
1. Narratives Layouts with A/B Testing
Boxalino Narrative REST API is the backbone of any integration and is a full-fledged Layout API. This means that, unlike other systems, Boxalino doesn’t provide you only with personalized content to be displayed on the page, but the entire layout of the page with the final content, ready to be rendered as provided. This means less work to develop your front-end, as most of the business logic of any page is done in the configuration of Boxalino, but also full freedom for your marketers to personalized, contextualize, target, and A/B test any aspect on any page driven by Boxalino Narratives.
2. Semantic Autocomplete
Boxalino processes your products and behaviors data in the Boxalino Data Science Eco-System built in the Google Cloud Platform (GCP) with Google’s best technologies (incl. Google BigQuery). As a result, Boxalino creates an index of queries to be suggested to the visitors, built with:
Smart grouping of synonyms queries together (so that you don’t recommend two queries which are in fact just two different ways to write the same thing)
Promoted position of all your important product properties (e.g. brands will appear on top if searched, even if the most frequent typed version doesn’t match exactly the brand name)
Personalized Query Suggestions
Boxalino analysis the online behavior of each one of your visitors and can include the most important and effective attributes to be used as personalization criteria:
One-to-one personalization based on the prior visitor’s query
Behavior-based positions (the searches are smartly ranked according to their popularity, based on a defined time-moving window letting new search trends quickly go to the top).
Personalization (most important criteria like key product attributes or customer segmentation like Customer Clusters)
3. Dynamic Facets
Boxalino developed an advanced configuration model for Dynamic Facets, including:
Many visualization parameters (type of facet (enumeration, slider), order of the values, number of values shown before the “show-more”, etc.)
Contextualization: dynamic logic to only show facets that are needed for a specific search or navigation context
A/B Testing
and much more
4. Neural Collaborative Filtering (NCF)
Boxalino personalization and contextualization algorithms (up-selling, cross-selling, …) are driven by state-of-the-art algorithms build in Google Tensorflow and based on Neural Collaborative Filtering (NCF). As described here NCF is supercharging collaborative filtering with neural networks and showed by far the best results we have seen so far.
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Not convinced? We can create an A/B Test it against a base strategy in your shop so you can see the effect |
5. Personalized Search Suggestions
Boxalino Winning Interactions Platform unifies the configuration of any product or content recommendations strategy for any use-case, search (and in this case Autocomplete-Search) included.
This means that all the capacities of the platform (including AI-based personalization strategies) can also be used in the context of Search Suggestions (including the above mentioned NCF).
To learn more about these functionalities of Boxalino’s Platform, please contact us!
6. Semantic Filters
A Semantic Filter is a logic that changes the search results, by either boosting a segment of the results to appear on the top or directly removing any results not matching the segment.
Semantic Filters can be defined by your team for important cases in Boxalino’s Intelligence Admin, but the most important, and largest quantity of, semantic filters are automatically generated by Boxalino’s standard Data Science processing.
This means that Boxalino analyzes the statistics of what your visitors do after they make a search and based on their scrollings, clicks, add to baskets and purchase behaviors, the system will identify the segments of results which should be promoted or even filtered.
7. Omnichannel Campaigns
In Boxalino Intelligence admin, you can define your Marketing Campaign in one place, and have them automatically:
Display campaign banners on your start pages (like the home page) with self-learning and personalization built-in
Generate an entire Landing Page automatically for your campaign without any additional manual step
Provide all the content automatically for your newsletter (and other outbound channels) without any content edition in the templates editors of your marketing systems.
8. Semantic Parsing
Boxalino Semantic Parsing covers all the standard features of a state-of-the-art search engine, including:
Stemming (removal of the endings of the words to avoid plural and other similar words to be considered as different)
Compound words tokenization (separation of the key words parts in composite words like “Kindermaske” => “Kinder” and “Maske”
Synonym self-learning (Boxalino is abale to learn automatically synonyms based on the user behaviors and the descriptions of your products)
Error corrections (typical spelling mistakes, but also unusual ones and possibly several errors per queries, all corrected while the user is typing)
9. Self-Learning Ranking
Boxalino’s platform analyzes continuously the behavior of each search visitor to draw conclusions about which products perform and which one does not, based on:
Scrolling behaviors
Pagination
Click-Through
Add to Basket
Buy-Through
(many other KPIs can be set as goals and will be also considered)
As a result, for each of the top 5K searches, Boxalino computes a ranking of the products based on the their Click-Through and Buy-Through performances and uses them to change the ordering of the products in the search results.
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Not convinced? We can create an A/B Test it against a base strategy in your shop so you can see the effect |
10. Real-time Results Rating
Boxalino’s can integrate real-time feed-backs on the list, including thumbs up/down (or similar) ratings the user can give directly on the search result list.
The page can then simply be refreshed and better results (according to similarities with the products which received a thumbs up/down) will be returned, in a fully personalized way.
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Not convinced? We can create an A/B Test it against a base strategy in your shop so you can see the effect |