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<h3><strong>What are Query suggestions?</strong></h3><p>'Query suggestions' is a feature of a search engine that allows it to suggest search phrases to the user as they type in the search box. It works as an enhancement of the autocomplete functionality and allows faster discovery of customer intentions as they type.</p><h3><strong>Benefits from query suggestions</strong></h3><p>The main benefit of this feature is improving customer experience through easier product search. It is especially useful if you have many of them on your website. Query suggestions can also increase the effectiveness of searching on your blog or any other website which is based on text content.</p><h3><strong>Requirements</strong></h3><p>First of all, if you want to start using queries you have to get content from all pages and subpages on which you would like to use this feature. You should gather all content from those places and pay special attention to every element of the content which can be used later in your search.&nbsp; &nbsp;</p><p>Based on collected content, we&nbsp;are able to&nbsp;prepare catalogs in&nbsp;Synerise. You&nbsp;are able to&nbsp;basically add any information needed, the one obligatory piece of data is Item ID. Of course, your catalogs may look different depending on where the search is used and which elements you want to add.&nbsp;</p><h3><strong>How does it work?</strong></h3><p>If you want to create a new query suggestion - the most important part will be to prepare a ranking of attributes collected in the catalogs. Query suggestions match the search phrase as best as possible with the set of suggestions we create in a few ways. In&nbsp;Synerise&nbsp;you can search based on search statistics, attributes and defined phrases&nbsp;and also&nbsp;use a blacklist.&nbsp;</p><figure class="image"><img src="/uploads/Querry1_da243f9402.png" alt="Query suggestions settings in synerise"></figure><h4><strong>Suggestions based on search statistics</strong></h4><p>In this setting the phrases suggested to the user are based on the popularity of search phrases. So, the index of the suggestions in this case is built out of popular search phrases. The suggestions will be scored on the similarity to the phrase searched for and returned upon request. The index can be set up to contain only popular searches above some threshold of popularity, for example the number of searches. Also, the time interval for&nbsp;which the statistics shall be&nbsp;taken into account&nbsp;can be set.&nbsp;So,&nbsp;if your search phrase is similar to something that is popular, you can add it in a hint.&nbsp;</p><h4><strong>Suggestions based on attributes</strong></h4><p>It is possible to set up the suggestion index to be built on an attribute from the Items Catalog. It is also possible to add multiple attributes from multiple Items Catalogs. The suggestions will be returned based on the similarity of these attribute values to the search phrase being entered by the customer.&nbsp;</p><h4><strong>Suggestions based on defined phrases</strong></h4><p>Another possibility is to define a list of phrases manually and assign them a score, also manually. This can co-exist with the previous two settings.&nbsp;</p><h4><strong>Black list</strong></h4><p>You can also create a blacklist. It can be created containing specific queries or be based on expressions. The blacklist based on expressions will blacklist any suggestion containing the defined phrase. These phrases will never be returned as a query suggestion.&nbsp;</p><h3><strong>Practical use case</strong></h3><h4><strong>Basic search based on existed categories&nbsp;</strong></h4><p>In case of e-commerce companies, which have a lot of products and categories on their websites, the best case is to implement query suggestions based on attributes. This can help customers to more easily find products and categories. To do this, we&nbsp;have to&nbsp;start from gathering all data about products which made it possible to create a catalog with all characteristics important for us. Let’s assume that it will be brand and category.&nbsp;</p><figure class="image"><img src="/uploads/Querry_2_5868fc96d7.png" alt="Adding attributes in query suggestions"></figure><p>In the attribute section you&nbsp;have to&nbsp;just choose the proper attributes. Based on this example a suggestion index will be created based on all “category” and “brand” values from a given item catalogue.&nbsp;</p><p>If a user starts writing a phrase like “jeans”, he can get a list of all subpages with “jeans” in its name&nbsp;like;&nbsp;straight jeans, skinny jeans, loose jeans etc. Also, if he starts writing the name of a category with “new”, he will get brands with “new” which exist on this page like e.g. new look, new balance etc.&nbsp;</p><p>If we mix two or more categories like in this case, you can start typing “skirt” and can get hints like “skirt&nbsp;mohito” or “skirt new look”.&nbsp;</p><h4><strong>Advanced search based on popular searches</strong></h4><p>You do not have to base your query suggestions only on existing categories and product names. You can&nbsp;take into account&nbsp;popular search phrases.&nbsp;</p><figure class="image"><img src="/uploads/Querry_3_91cab604f5.png" alt="Stats engine suggestions which are based on the minimum number of queries and phrases"></figure><p>Based on this example, query suggestion indexes will include only those phrases searched by the user which have been searched at least 10 times within the last 10 days, have at least 3 results and at least 3 characters. Other phrases will not be included / returned while querying a suggestion index.&nbsp;</p><p>In this way you&nbsp;are able to&nbsp;easily start to use autocomplete with the most popular phrases that are searched by your customers.&nbsp;</p><p>We can also add manual suggestions and define lists of suggestions returned as enhancement of autocomplete option. And of course, you can add phrases which are not connected with your offer but are often typed by customers (or products which are no longer in your offer) and add them to a blacklist.&nbsp;</p><h3><strong>Summary</strong></h3><p>Query suggestions can be extremely helpful in case of ecommerce, blogs or in any other sites where you constantly add new content. It is&nbsp;really important&nbsp;to be user friendly and do your best to help your customers find what they are looking for. If not, it’s very likely that they will leave the site unsatisfied.&nbsp;</p><p>To learn more about query suggestions read our&nbsp;<a href="https://help.synerise.com/docs/ai-search/query-suggestions/">post in documentation &gt;&gt;&gt;&nbsp;</a></p><p>You will find technical requirements and more useful information. Watch also our video tutorial to learn more about query suggestions!</p><figure class="media"><div data-oembed-url="https://player.vimeo.com/video/460895613"><div style="position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;"><iframe src="https://player.vimeo.com/video/460895613" style="position: absolute; width: 100%; height: 100%; top: 0; left: 0;" frameborder="0" webkitallowfullscreen="" mozallowfullscreen="" allowfullscreen=""></iframe></div></div></figure>

AI search with query suggestions presenting search of word "play" and results like playstation

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'Query suggestions' is a feature of a search engine that allows it to suggest search phrases to the user as they type in the search box. Discover how it works, its benefits and requirements.

Query Suggestions - New Way of Searching | Synerise

Query Suggestions - Synerise New Way of Searching

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<p>Synerise cooperates with Orange Polska, the market leader in mobile telecommunications, internet and data transmission, by supporting their digital transformation and providing customers with the best experience of using modern technologies. Synerise solutions help Orange acquire and retain satisfied customers, driving an increase in the share of digital channels in overall sales.</p>

<p>Building better customer experience and growth in e-commerce</p>

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<p>A high degree of flexibility, the possibility of implementing multiple omnichannel scenarios and fast time-to-market give the chain an advantage in digital channels.</p>

<p>increase in the average basket value when containing recommended products</p>

<p><strong>LEROY MERLIN implements digital innovation with Synerise</strong></p>

Retail case study

<h3><strong>Before you start</strong>&nbsp;</h3><figure class="image"><img src="/uploads/image_7e2283c8ea.png" alt="Blue squares, charts and client icon "></figure><p>The process should start even before the actual integration goes ahead. Take a closer look at your website and the AI solution you will be using, in order to know how to better plan the implementation.&nbsp;</p><h4><strong>1.&nbsp;&nbsp; Analyze products on the website&nbsp;</strong></h4><p>It’s a good practice to analyze product frames on the website. This will help you speed up the whole integration by preparing an appropriate product catalog (the source of e-commerce product stock). Pay attention to information about products that you already have on the website — maybe apart from the title and price there are some special labels like “eco-friendly”; stars with product ratings; special prices for club members, etc. After you define this, you will know what information should be included in the product catalog and so the frame with recommendations will visually fit the website layout.&nbsp;</p><h4><strong>2. &nbsp;Verify the website structure&nbsp;&nbsp;</strong></h4><p>You can also examine the website in terms of places to put recommendations. Areas like the home, product or category pages are obvious but perhaps there is a better space to add recommendations such as in the drop-down menu or on a landing page which describes one of the brands you sell? <strong>Each website should be analyzed individually to find the places where recommendations can be added to enhance the customer experience.</strong> It will also help you to plan the implementation because you will know how many campaigns will need to be prepared.</p><h4><strong>3. Obtain basic knowledge about the </strong><a href="https://help.synerise.com/ai-7-days/"><strong>AI solution&nbsp;</strong></a></h4><p>You will learn about any platform once you start using it, but it’s important to know earlier about the range of services it offers. You need to go deeper by getting to know the requirements of each AI product offered. That will indicate what you will need to prepare on your side, making it easier to better plan the integration. As a result, you will get an idea of what you should focus on to implement campaigns that you are planning, and what should be provided in the short and long term.&nbsp;</p><h3><strong>Integration</strong></h3><figure class="image"><img src="/uploads/image_9063727d97.png" alt="Product attributes like name, price, color, link, image, category, season"></figure><p>Since you already have a concept in mind of what you want to achieve, all you have to do is take action. Start by carefully planning the steps of the integration and providing events and a product catalog.&nbsp;&nbsp;</p><h4><strong>4. Add custom attributes in the product catalog&nbsp;</strong></h4><p>The <a href="https://help.synerise.com/developers/product-feed/">product catalog</a> is the source of information about your products, which should contain basic data like their titles, images, links and prices. But it’s recommended practice to add more attributes, which will provide more details about each product. Thanks to more detail, you will be able to filter your recommendations in a more advanced way. Those attributes can be universal, like ratings, or strictly connected to a specific industry, like seasons for fashion, or energy consumption in the home appliance industry.&nbsp;&nbsp;</p><h4><strong>5. Include unavailable products in the product catalog&nbsp;</strong></h4><p>If your website includes products which are currently unavailable or which can be bought offline only, add them to the product catalog anyway but with the information that they are out of stock. Then such products will not appear in the recommendation’s frame, but you will be able to show recommendations on a product page. In other words, you can recommend similar products which are available, and which may interest your customers and convince them to make a transaction.&nbsp;</p><h4><strong>6. Provide events as soon as possible&nbsp;</strong></h4><p>AI models need data to learn, so don’t wait with <a href="https://help.synerise.com/developers/web/event-tracking/">event integration</a>. Even if you have to enhance the product catalog (see previous point), provide events for better recommendations. The more of them you add, the more accurate the models will be. Another thing which may be helpful is the import of historical transactional events - it will also allow you to get more appropriate results faster.</p><h3><strong>Campaign implementation&nbsp;</strong></h3><figure class="image"><img src="/uploads/image_7c292b6ce4.png" alt="Zero page results showing recommendation on pages where customers don’t view products"></figure><p>Preparation is a crucial part of integration but now it’s time to finally put into practice the data you’ve provided and create campaigns. Let's look at how to do just that.&nbsp;</p><h4><strong>7. Give recommendations at every step of the customer journey&nbsp;</strong></h4><p>To enhance the customer experience, <a href="https://synerise.com/blog/perfect-customer-journey-based-on-ai-recommendations">add recommendations at every step of the customer journey</a>.&nbsp;</p><ul><li>Put personalized recommendations on the home page where customers will be displayed products best suited for them, instead of the same set of products for everybody.</li><li>When customers go to the categories page, show them personalized recommendations tailored to the current category, placed above or below the list of products.</li><li>On the product page, add similar or visually similar recommendations near the main information about the product.</li><li>When a customer adds a product to the cart, add cross-sell recommendations in the pre-cart pop-up to increase the cart value.</li><li>Finally, when customers get to the shopping cart page, add cart recommendations to interest them in additional products.</li></ul><h4><strong>8. Enrich pages where customers don’t view products&nbsp;</strong>&nbsp;</h4><p>You can also help customers by implementing personalized recommendations on pages where they usually do not see any products, like 404 pages, zero search results, empty carts, pages with campaign rules. This gives you more opportunity to attract customer attention with offers specifically tailored to them.</p><h4><strong>9. Go beyond the website&nbsp;</strong></h4><p>So far, we have covered recommendations for most places on the website, but you should go beyond this and use other touchpoints to promote recommended products. Use emails to send personalized offers when the user ends the session, offer complementary products after they buy something, or show similar products based on the last seen item. Send web push messages that link to a landing page with products chosen for the customer. Transfer personalized products to a remarketing platform so that your customers get better suited advertisements.</p><h3><strong>Effectiveness</strong>&nbsp;</h3><figure class="image"><img src="/uploads/image_7efd25046a.png" alt="Violet KPI icon from squares and triangles"></figure><p>Once your recommendation campaigns are up and running, you have to know how they perform. Being able to measure progress towards your goals is key. You should know how and what you want to measure, even in the implementation phase, to be sure you have everything needed beforehand.&nbsp;&nbsp;</p><h4><strong>10. Set your KPI&nbsp;&nbsp;</strong></h4><p>Decide what you want to check. Revenue? Conversion rate? Average order value? CTR? Be specific in your choice of indicators, limit the number of <a href="https://synerise.com/blog/how-to-choose-and-track-right-KPIs-in-marketing">KPIs</a> you choose to focus on and make sure that you collect all the data needed to count them. Set your goal and regularly <a href="https://help.synerise.com/docs/analytics/">check the results</a> so that you will be able to detect any anomaly.&nbsp;</p><h4><strong>11. Use a control group</strong></h4><p>How do you know if your KPIs are good? You should be able to compare them, but compare to what? To a different time period? A lot of conditions can influence results. To isolate one factor (recommendations) and measure it, you should create a control group – randomly selected users who will not get any recommendations. Thanks to doing this, you will be able to compare two groups - people who see a recommendation and those who don’t. Based on that, you can check the effectiveness of AI campaigns.&nbsp;&nbsp;</p><h4><strong>12. Prepare A/B test&nbsp;</strong></h4><p>If you’re not sure which recommendation will be better on the product page - similar or visual recommendations - or you’re wondering if you should put the product frame higher or lower on the category page, you don’t have to guess. Just prepare an <a href="https://synerise.com/blog/ab-tests-your-way-to-read-customers-minds">A/B test</a>. After a selected time period, you can check the results of each campaign, including its CTR, revenue, number of transactions and products bought, conversion rate and more. Knowing which campaigns perform better helps you to make the best business decisions.&nbsp;</p><h3><strong>Cooperate</strong>&nbsp;</h3><p>The tips above will give you a head start but integration is a process, during which you will learn what can be changed, what else you need and what works best for your customers. With your business knowledge and the experience of the integration team who will support you, you will be able to truly elevate the experience of your customers. Remember, that cooperation is the key to success.&nbsp;</p><p>&nbsp;</p>

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Here’s what you should pay attention to in any AI implementation journey, in order to uncover the most value for your business. Check the best practices!

How to boost AI product recommendations in e-commerce - best practices! | Synerise

How to boost AI product recommendations in e-commerce - best practices!

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