YesPlz AI re-imagines product discovery to be intuitive and delightfully accurate, allowing shoppers to seamlessly discover their favorite styles of clothing, shoes, and jewelry. Let our fashion AI serve as your stylist and shopping assistant. Explore the possibilities at https://yesplz.ai and elevate your e-commerce venture to the next level.

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What is traditional eCommerce search? eCommerce search is an essential part of the shopping experience, particularly when it comes to text search. Users type in their search queries and expect to receive relevant search results. In a traditional eCommerce site search experience (without any AI technology), a user’s keyword search is matched against a manually configured glossary of keywords. Unfortunately, due to ever-changing fashion vocabulary and the complexity of search queries, traditional eCommerce search still falls short in reaching its full potential, leading to poor matching results and frustrated shoppers. The Current State of Search Technology in 2023 In 2023, the fashion eCommerce industry has evolved to use algorithms like NLP (Natural Language Processing) to further interpret shoppers’ search language, attempting to define intent and meaning behind search queries more effectively. But, the hypothetical value is often not the same as the reality, because of: 1-high prices 2-large data sets needed to train the algorithm 3- the sheer complexity of search queries The two types of algorithm-powered text search models are: Contextual: : Matches user search intent using context clues relevant to the individual user. Search results are specific to a user. In theory Difficult to provide relevant search results, and expensive to run Reality: Semantic: Uses NLP to identify meaning and logic behind search. For example, a “work dress” search is synonymous with “wear-to-work” or “office dress” In theory: Depends on quality of product tags, which may be mismatched.
Too slow to capture up-to-date, modern fashion keywords.
Requires manually updating glossaries. Reality: In 2023, the majority of search technology still requires manual configuration, is expensive, and is still prone to errors. And, it’s not that these search models are bad–but there’s still untapped potential in making text search even better, more optimized, and more relevant. But, how? By using fashion tagging at its core, we’ve created text search that combines different types of AI, unlocking a new, revolutionary approach to traditional text search. How YesPlz Is Solving Common Text Problems in eCommerce Search The most common problems in “traditional” eCommerce search are: irrelevant search results; poor understanding of search intent; and 3) low-quality (or missing) product tags. At YesPlz, we’ve built a comprehensive text search system to solve these problems. Our formula to resolve the problems includes: Laser-accurate AI image tagging, to better create matching, accurate search results, without the need for product descriptions Machine learning that analyzes past user inputs and interactions to predict best search intentions Fresh, updated keywords to keep search keywords relevant and modern Thanks to machine learning, we can customize search results for each of our clients’ specific needs. Here’s the YesPlz antidote to inaccurate, one-dimensional eCommerce text search: Outdated Solution YesPlz AI Solution aka updated keywords with most popular trends and suggestions Fresh, popular keywords Lacks up-to-date, popular keywords. Product discovery isn’t quick or effective. Automatically update with latest keywords based on past user input Autocomplete suggestions No keywords to nudge or guide shoppers. Mismatched search results. Suggested autocomplete keywords, based on popularity or similarity. Instant search results with product preview Search results only show after typing in complete search keywords. High error rates for typos, and unmatched keywords. Updated search results displayed with each keystroke. Additional Filter Paired with Text Search Hundreds of low-accurate matching search results, leading to higher bounce rate. Fully integrated filter alongside search results. Shoppers filter down further, increasing possibility for add-to-cart. Enhanced image tagging Search results limited to matching text descriptions.Huge missed potential for matching results. Key product attributes extracted from image, not product descriptions. Increases percentage of higher matching results. Brand recognition No brand recognition from catalog. Missed opportunity to help shopper discover favorite products from brands. Brand recognition from search, leading to search results that match brands. YesPlz Enhanced eCommerce Site Search Solutions vs. Top Competitors YesPlz’s eCommerce site search solutions are designed to stand out to both retailers and shoppers, from its underlying technology to its customizable features. But, what exactly is the difference between YesPlz eCommerce search and top competitors? Here’s a quick overview of all the features your eCommerce could be unlocking: The impact of added features for text search: Accurate product tags when using enhanced image tagging, building the foundation for personalization Higher conversion rates– Customers who land on an autocomplete page suggestion are than those who don’t. six times more likely to convert Increased average cart size and volume with additional integrated product filter Lower bounce rate with intuitive UX design Fresh updated keywords keep eCommerce relevant and on top of the latest trends More relevant, precise search results when powered by both visual and text AI The power of the above features, when combined, means outstanding text search that always provides relevant, updated results. Before/After: Creating Enhanced eCommerce Search Solutions for W Concept W Concept’s story: A trendsetting online retailer for luxury fashion, W Concept has captivated the attention of shoppers worldwide with a vast selection of clothing, shoes, handbags, and beauty products. But, W Concept had a problem. Its current text search technology didn’t have instant search, lacked autocomplete to make search intuitive and accurate, and had low matching search results. Here’s the before/after snapshot of how YesPlz transformed W Concept’s text search: 1. No automated product tagging, leading to inaccurate search results Before: Product tagging for entire catalog, completely automated by fashion-trained AI, that isn’t reliant on vendor information, increasing the accuracy of matching search results After: W Concept’s robust product catalog also required robust product tags to set the foundation for enhanced text search. No product tags? No problem. With thousands of vendors, W Concept was missing product information, and had some inaccurate information from third-party vendors. YesPlz easily generated these product tags using visual AI. 2. Plain text match searching Before: Rich search suggestions based on both keyword and similar search After: By training the data to understand what other styles resemble a search term, we made W Concept’s search more accurate and robust. For example, if a shopper searched for “tank top,” behind the scenes, search results are based on data that understands the number of similar items to a tank top (including products that don’t have the term “tank top” in their description). A sleeveless shirt, ruched tank top, or square neck tank top still fall under the search query–but wouldn’t be picked up by traditional search unless the terminology was an exact match. In addition, autocomplete offers the best of both visual and text based search, with updated keywords on the most popular, modern trends. No product preview images, not sparking the visual nature of shopping Before: Product previews combined with instant search After: All it takes is a single keyword to show matching results with a product preview, resulting in a much more intuitive and better quality discovery experience. Shoppers needed to click on a search term to filter further Before: Integrated side filter helps shoppers quickly filter down as they search After: W Concept shoppers needed to click on a search term, load a new page, and then were able to filter based on their preferred product attributes. This not only wasted time, but wasn’t providing the most optimized product filtering experience. With an added integrated side filter, shoppers can filter based on their text search (for example, a white tank top text search would lead to further filter options related to brand, silhouette, fit)--with white tank top as a preselected first filter. YesPlz filters are designed to be visual, and UX-friendly, unlike text-heavy filters. By creating a custom, enhanced eCommerce search that solved all of the above problems, YesPlz transformed W Concept’s text search–resulting in always relevant, effective eCommerce site search that converts. Can your eCommerce text search do all of this? When powered by visual and text AI, YesPlz text search is revolutionizing the way shoppers search. If your text search provider isn't doing enough, contact us for a free demo. is next-gen, all-in-one product discovered, powered by artifical intelligence. YesPlz AI Also published . here

Traditional vs. Enhanced eCommerce Search: Unlocking New Text Features

A shopper lands on your website and navigates to the search bar. She wants a red skirt, mid-length, with a fun, playful print. When she types in her search query for “red midi skirt,” the shopper receives dozens of relevant search results. The building blocks of these search results (and even product recommendations) is image tagging (also known as fashion tagging). Behind the scenes, a human tagged each of the product attributes by hand, a painful task when considering the hundreds or thousands of products uploaded on an eCommerce website per day. Unfortunately, manual image tagging can take hours to complete and is prone to errors. For example, one human tagger might label the red skirt as “mid length skirt” and another human tagger might consider the skirt to be a “midi-skirt,” leading to inaccurate product data. But, is there an easier way to tag that saves time, energy, and is more accurate? The answer: building an artificial intelligence that understands fashion product attributes to correctly tag information. In this blog post, we’ll walk you through: Image tagging 101 What is good image tagging? How YesPlz image tagging works The impact of good fashion tagging Image Tagging 101 Image tagging is the process of labeling product attributes on an image. A product attribute can include all different elements of a product, from a design style to color and material type. A single clothing category can have between 20 to 60 different attributes, which can take significant time to tag the attributes of a single product image. If we look at the dress below, it would have about 9 different attributes including dress category, neck design, material, and mood or occasion. Traditionally, a member of the merchandising/eCommerce team is responsible for tagging. Designers and third-party vendors provide tagging information, and the in-house team is responsible for cleaning up and organizing the tags. But, when information comes in from third parties, it can be inconsistent. Depending on the vendor, they may have a different word for "lace"--and when you take into account the sheer volume of products that need to be tagged, this inconsistency creates a mess for fashion eCommerce. Alternatively, the in-house merchandising experts may make tagging mistakes (we're all human, after all). Because of the complications of fashion tagging, some fashion eCommerce brands may skip it altogether to save time. While it may be tempting to skip, without your eCommerce brand can't: accurate image tagging, provide a superior search experience to shoppers categorize products create product descriptions improve search results offer more filtering options generate recommendations utilize powerful personalization tools Image tagging contains crucial information that enriches a shopper’s online experience. If your eCommerce is looking to implement the next great personalization tool, image tags provide the building blocks to do so. Image tagging creates the foundation for a superior shopping experience When asked to describe a superior online shopping experience, shoppers will consistently answer that search and recommendations are key differentiators in the crowded fashion eCommerce space. Without fashion tagging, shoppers need to rely on very limited information to find what they’re looking for–and they have difficulty finding search results that meet their needs. Bad image tagging creates a bad online shopping experience. While shoppers may not understand the mechanics of why their experience is bad, they will interpret a poor online shopping experience as a cue to not return. Image tagging doesn’t need to be a “necessary evil” for retailers–but can be an enjoyable experience. For retailers, fashion tagging is a necessary but tedious task. When manually tagging, a single batch of products requires days of work–including tagging and double-checking fashion terminology. And we know that retailers are tired of doing work–and receiving little in return. Unfortunately, it’s not an option to skip image tagging–without it, retailer product catalogs would be disorganized. Without fashion tagging, you can’t have powerful and accurate site search, fashion-forward recommendations, or well-organized data to personalize user experiences. But, poor automated tagging tools can still cost retailers hours of time, correcting mistakes and re-sorting taxonomies. What makes a good image tagging solution? There are hundreds of image tagging tools on the market, but not all of them are created equal. Even if they’re powered by artificial intelligence, it’s important to remember that artificial intelligence is only as good as the input data that trained the algorithm. And, when artificial intelligence makes mistakes, fashion experts with a deep understanding of fashion should be the ones to review those mistakes. Other considerations when choosing a fashion tagging tool are the quality of the tags (are they accurate?) and speed (slow tagging means more work for retailers). Lastly, we recommend that you review the types of tags you’d like to see (specific styles, details, moods, or occasions) and make sure that the image tagging tool can quickly and accurately tag for your criteria. Image Tagging In Action: Searching For A Shirtdress A shopper wants to buy a shirtdress, defined as a women's dress with a collar and buttons. She types in her search query and receives two sets of results: Result #1: Search Results Without Image Tagging The retailer relied on text tagging to create product tags, leading to mixed search results that include t-shirt dresses and men’s shirts–unrelated to the shopper’s original search query. These search results highlight the importance of image tagging–a good image tagging tool would be able to identify the product type and product attributes. Result #2: Search Results With Image Tagging Next, the shopper searches for a shirtdress with YesPlz’s Style Filter. She receives the following search results, which are clear and accurate: The difference between the two search results? Image tagging. In the second set of search results, YesPlz AI image tagging has correctly identified and tagged the product attributes, leading to accurate, robust search results. Are you curious to know more about how works? Let’s go behind the scenes and explore the YesPlz process and why we’re different from other solutions. YesPlz image tagging How Our Image Tagging Process Works Here is the YesPlz process for ensuring accurate and fast image tagging: Step 1: Before building our tool, we defined key fashion attributes that matter to shoppers through user interviews. We started with user interviews and sat down with shoppers to define what key fashion attributes they care about the most. Our interviews took hours of probing each individual to find out more about their most important attributes. And, as a result, we’re happy to say that our image tagging tool reflects the real needs of shoppers. Because of the preparation we’ve put into our image tagging tool, retailers can be assured that our fashion tagging tool tags product attributes that shoppers are searching for, such as fit, style, and occasion. We have a clear understanding of the criteria for image tagging, leading to more accurate tagging from the start. Step 2: We train the AI using image and text–then fashion experts correct the AI. Through a combination of computer vision and Natural Language Process (NLP) technology, YesPlz AI is able to do deep image tagging which is accurate, fast, and seamless. AI Training: In addition, our technology can identify product attributes even in all sorts of image types whether the clothing image is on or off a model, even if the image has a noisy background or is user generated content. Step 3: Fashion expert intervention: AI will inevitably make incorrect inferences–but who is catching those mistakes? At YesPlz, our fashion data annotators are from Parsons School of Design and Fashion Institute of Technology. They understand the details of fashion, and are up-to-date on the latest fashion trends and terminology. We have fashion data annotators in-house that speak the language of fashion, leading to highly accurate tagging. The result of AI training with computer vision and NLP, combined with our data annotators from fashion backgrounds is fast, accurate image tagging for retailers. Step 4: We provide data enrichment and image tagging output for retailers. The last step in the YesPlz image tagging process is output which helps retailers make better decisions through data. We provide retailers with data enrichment using an API that allows retailers to easily receive image tagging information in an easy-to-use format that fits their preferences. In addition, our data output is available in English, Korean, and Arabic. The impact of good fashion tagging Good, quality fashion tagging is necessary to build out dynamic search and recommendations and hyper-personalization. While image tagging is very much “behind the scenes,” your customers are affected by poor image tagging when they receive mismatched search results or off-target recommendations. Image tagging, when accurate and quick, can provide better quality data for your fashion eCommerce, which you can then use for personalization. But, the common objections we’ve seen to image tagging are: it’s inaccurate it takes too long YesPlz AI is solving these problems for retailers by providing fast, accurate tagging. Our AI is trained to recognize product attributes in all types of pictures, even low-quality images, and is then reinforced with the input of fashion experts. Are you interested in keeping up-to-date with the latest retail and eCommerce trends? Follow YesPlz AI on LinkedIn for updates. This story was published by next-gen fashion search. YesPlz AI,

YESPLZ AI

yesplz.ai

1-10 employees

Since 2018

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YESPLZ AI

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YESPLZ AI

yesplz.ai

1-10 employees

Since 2018

COMPANY RANKING

YESPLZ AI

EVERGREEN INDEX #12021

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