{"id":1220,"date":"2023-07-31T07:32:00","date_gmt":"2023-07-31T02:02:00","guid":{"rendered":"http:\/\/mag.vue.ai\/?p=1220"},"modified":"2024-06-10T11:38:08","modified_gmt":"2024-06-10T06:08:08","slug":"omnichannel-personalization-ecommerce","status":"publish","type":"post","link":"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/","title":{"rendered":"The Promise Of Omnichannel Personalization: The How And The Why For Retail ?"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_65 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<p class=\"ez-toc-title\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69fb579fb51ba\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69fb579fb51ba\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#what-is-omnichannel-personalization\" title=\"What is Omnichannel Personalization?\">What is Omnichannel Personalization?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#omnichannel-personalization-the-how-and-the-why-for-retail\" title=\"Omnichannel Personalization: The How And The Why For Retail ?\">Omnichannel Personalization: The How And The Why For Retail ?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#the-glass-ceiling\" title=\"The Glass Ceiling\">The Glass Ceiling<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#the-difficulties-in-cracking-shopper-intent\" title=\"The Difficulties In Cracking Shopper Intent\">The Difficulties In Cracking Shopper Intent<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#using-computer-vision-and-continuous-learning-systems-to-decode-the-psyche-of-online-shoppers\" title=\"Using Computer Vision And Continuous Learning Systems To Decode The Psyche Of Online Shoppers\">Using Computer Vision And Continuous Learning Systems To Decode The Psyche Of Online Shoppers<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#is-convergence-of-big-data-and-artificial-intelligence-the-next-step\" title=\"Is Convergence Of Big Data And Artificial Intelligence The Next Step?\">Is Convergence Of Big Data And Artificial Intelligence The Next Step?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#read-more-about-personalization-engine\" title=\"Read More About Personalization Engine\">Read More About Personalization Engine<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#read-more-about-personalization-platform\" title=\"Read More About Personalization Platform\">Read More About Personalization Platform<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/#read-more-about-personalized-search\" title=\"Read More About Personalized Search\">Read More About Personalized Search<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">8<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span><h2><span class=\"ez-toc-section\" id=\"what-is-omnichannel-personalization\"><\/span>What is Omnichannel Personalization?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Omnichannel Personalization<\/strong> is the procedure to create a highly personalized customer experience at every touchpoint based on real-time shopper intent, powered by Customer and Product Intelligence.<\/p>\n<p>In the heavily competitive retail industry, you have to constantly ensure you&#8217;re ahead of the curve. Today&#8217;s shoppers are extremely style-conscious and to appeal to them, brands have got to keep up.<\/p>\n<p>Did you know that over 75% of consumers are more likely to buy from a retailer that recognizes them by name, recommends options based on past purchases, or knows their purchase history? Moreover, customer segmentation is not personalization, shoppers are increasingly seeking brands and marketplaces who deliver actual omnichannel personalization. This becomes even more imperative in categories like fashion, where individual preferences can navigate shopper journeys in completely different directions.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"omnichannel-personalization-the-how-and-the-why-for-retail\"><\/span>Omnichannel Personalization: The How And The Why For Retail ?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>She gave birth to a beautiful baby boy. It had been three weeks since and she was well on her way to losing those extra pounds. Spring was also just setting in and she was excited to finally browse through cotton knee-length non-maternity dresses. Before her pregnancy, short knee-length dresses always used to be her thing- regardless of which season presented itself in New York. Not just dresses that were knee-length, but ones with sleeves that covered her elbows. Not only did she not find even a single cotton knee-length dress, but only maternity dresses were recommended to her when she logged on to her favourite, most frequented online fashion label \u2014 TheUrbanFashion*. She left the site instantly and TheUrbanFashion had lost a loyal customer.<\/p>\n<p id=\"82ed\" class=\"graf graf--p graf-after--blockquote\">Maria* is an unsatisfied customer.<\/p>\n<p>TheUrbanFashion scrambles for a quick fix and reaches out to their <a href=\"https:\/\/www.vue.ai\/products\/product-recommendations\/\">product recommendation engine<\/a> service provider \u2014Curashions *. Is this an algorithmic glitch? No, the <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization engine<\/a> has worked perfectly. Maria was shown maternity dress options based on what she historically browsed through and bought over the last nine months. Less importance was given to the shorter style of dresses she was recently looking at since there was simply not enough user data for the algorithms to pick up on. The data scientists at Curashions are unfazed and their life goes on.<\/p>\n<p id=\"41d8\" class=\"graf graf--p graf-after--blockquote\">Maria represents a large proportion of disgruntled online shoppers who expect online stores to provide omnichannel personalization that is tailored, dynamic and specific to their shopping needs real-time.<\/p>\n<p class=\"graf graf--p graf-after--blockquote\">Maria also represents the precarious conundrum that AI (artificial intelligence) based omnichannel personalization\u00a0providers such as Curashions face, which is finding the balance between data-driven algorithms and real-time contextual events that occur in a shopper\u2019s immediate past, like Maria\u2019s pregnancy, and long-term past that is not easily captured through conventional technology.<\/p>\n<p class=\"graf graf--p graf-after--blockquote\">Fluctuations in browsing and spending patterns are induced by life events and the recommendations suggested on a page, must be receptive to that. Most importantly, understanding the true intent of a given shopper, ranks extremely high up on the priority list for all stakeholders in the e-commerce ecosystem.<\/p>\n<p id=\"9a9a\" class=\"graf graf--p graf-after--p\">Predictive analytics is sexy. Everyone does it, it\u2019s trendy and it\u2019s creating a buzz. Personalization is specifically designed to enhance the customer experience by showcasing specially curated products to a shopper and also making predictions about what they might be likely to buy in the future. Several studies have found that almost 62% of brands use some form of personalization as a tool to build customer loyalty.<\/p>\n<p class=\"graf graf--p graf-after--p\">However, shoppers are beginning to have unrealistic expectations of these <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization engines<\/a>, given the Big Data fever and companies like Netflix and Amazon are frontrunners in the race to crack this riddle. Highly optimized <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization engines<\/a> not only require implicit data that is traceable by technology but explicit user feedback data as well to successfully gauge the intent, context, and background of each online shopper.<\/p>\n<p id=\"a8d6\" class=\"graf graf--p graf-after--p\">Here comes the unavoidable glass ceiling.<\/p>\n<h3 id=\"1d7c\" class=\"graf graf--h3 graf-after--p\"><span class=\"ez-toc-section\" id=\"the-glass-ceiling\"><\/span><strong>The Glass Ceiling<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p id=\"8505\" class=\"graf graf--p graf-after--h3\">Recommendation system providers rely on large scale data mining algorithms to create omnichannel personalization for users of e-commerce retail sites. At the back end, <a href=\"https:\/\/www.vue.ai\/products\/product-recommendations\/\">product recommendation engines<\/a> create a goldmine of user data for companies to continually train and refine their models with.<\/p>\n<p class=\"graf graf--p graf-after--h3\"><a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">Personalization engine<\/a> models also use a ranking system that will find the best possible ordering of a set of items for a user, real-time. Scores are given to attributes that are favourable and recommendations are served in that manner.<\/p>\n<p class=\"graf graf--p graf-after--h3\">As <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization<\/a> algorithms keep improving, so does the experience of the users that use these systems, and this cycle repeats. While clickstream user data is paramount, supporting different contexts like sudden changes in user intent, fashion trends and styles, user spending patterns and other types of lifestyle decisions, requires different kinds of data.<\/p>\n<h3 id=\"612e\" class=\"graf graf--h4 graf-after--p\"><span class=\"ez-toc-section\" id=\"the-difficulties-in-cracking-shopper-intent\"><\/span><strong>The Difficulties In Cracking Shopper Intent<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p id=\"ffa4\" class=\"graf graf--p graf-after--h4\">The complex task of deciphering the intent of a customer is riddled with several challenges. First, a large proportion of customers access e-commerce sites through the multitude of channels available \u2014 social or otherwise.<\/p>\n<p class=\"graf graf--p graf-after--h4\">Interestingly, these shoppers tend to be frequent buyers. How does the intent and mindset of an Instagram shopper differ from that of an organic shopper or a traditional brick and mortar store shopper? There is no established method for collecting this type of data.<\/p>\n<p class=\"graf graf--p graf-after--h4\"><a href=\"https:\/\/www.vue.ai\/products\/product-recommendations\/\">Product recommendation engine<\/a> providers are made to grapple with the possibilities of intent through making educated decisions based on their own internal assumptions and by weaving together all the implicit and explicit data that is available to them. Taking these limitations into account, how can we train our set of codes to identify intent?<\/p>\n<p id=\"6aed\" class=\"graf graf--p graf-after--p\">Second, a customer who does not trust an e-commerce site is unwilling to provide qualitative feedback regarding the products that are suggested, further curtailing dynamic personalization. The word \u201cdynamic\u201d here implies the quick reactive and predictive nature of these algorithms to both implicit and explicit feedback and in absence of this, the engines rely heavily on just clickstream data, thereby preventing optimization. How can e-commerce companies and <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization platform<\/a> providers get their shoppers to meaningfully engage and confidently provide feedback?<\/p>\n<p id=\"0808\" class=\"graf graf--p graf-after--p\">Third, while algorithms continually learn from the ranking systems and variables, the order of listing and the decision to recommend one product over the other is ultimately made by a human being (<a href=\"https:\/\/medium.com\/r\/?url=https%3A%2F%2Fwww.rtinsights.com%2Fthree-challenges-for-recommendation-engines%2F\">Raphael, 2016<\/a>) i.e. the data scientists and product teams at <em class=\"markup--em markup--p-em\">Cushions. <\/em>Several biases exist in the way the algorithms are trained for omnichannel personalization and this can potentially expose the lack of innovation and the incapability of breaking the stereotypes.<\/p>\n<p class=\"graf graf--p graf-after--p\">The <a href=\"https:\/\/www.vue.ai\/products\/product-recommendations\/\">product recommendation system<\/a> might favour one product over the other due to factors that may not be directly related to the shopper\u2019s preferences. How can <a href=\"https:\/\/www.vue.ai\/products\/product-recommendations\/\">product recommendation engine<\/a> providers ensure that these subconscious biases do not interfere with the quality and relevancy of the personalized output?<\/p>\n<p id=\"11c0\" class=\"graf graf--p graf-after--p\">Finally, algorithms require opportunities for rapid experimentation to iterate intelligently. While a subset of users\u2019 behaviour can be modelled, it is unrealistic to expect all users to exhibit typical behavioural patterns. These atypical data points can adversely skew the results and accuracy of these engines.<\/p>\n<p class=\"graf graf--p graf-after--p\">Hesitant shoppers and the lack of established mechanisms to collect product feedback hinders with the \u201cpersonal\u201d component of these engines. Adding to this, e-commerce companies are slow in permitting any sort of experimentation on their user base, and understandably so. How can <a href=\"https:\/\/www.vue.ai\/products\/product-recommendations\/\">product recommendation engine<\/a> providers and e-commerce companies work together to facilitate continuous algorithmic experimentation and training?<\/p>\n<p id=\"776a\" class=\"graf graf--p graf-after--p\">This is not just a data science problem. This is a systemic problem.<\/p>\n<h3 id=\"fd99\" class=\"graf graf--h3 graf-after--p\"><span class=\"ez-toc-section\" id=\"using-computer-vision-and-continuous-learning-systems-to-decode-the-psyche-of-online-shoppers\"><\/span><strong>Using Computer Vision And Continuous Learning Systems To Decode The Psyche Of Online Shoppers<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p id=\"fd99\" class=\"graf graf--h3 graf-after--p\"><span style=\"font-size: 16px;\">At the outset, a persistent effort to achieve <a href=\"https:\/\/www.vue.ai\/blog\/vuecommerce\/retail-personalization-in-2021\/\">retail personalization<\/a> is absolutely necessary. What does this mean? Algorithms quickly learn with every event click and provide customized fashion choices for each user, real-time. <\/span><\/p>\n<p class=\"graf graf--h3 graf-after--p\"><span style=\"font-size: 16px;\">These omnichannel <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization platform<\/a> have to be continually improved to factor in qualitative feedback from customers in addition to long term and short term click-stream data that already exists. The various flavors of user information that flows in, helps in creating recommendations that stay current and relevant to the specific user at any point in time.<\/span><\/p>\n<p id=\"78db\" class=\"graf graf--p graf-after--p\">Apart from the data-driven continuous learning systems, visually intelligent computer vision-based processes that can extract multidimensional attributes like color, pattern, style, length, sleeve, silhouette among several others, from the product that a customer is engaging with, must be put in place.<\/p>\n<blockquote id=\"06f1\" class=\"graf graf--pullquote graf-after--p\">\n<p>Is the product click driven by color or the pattern or the style? Has the customer historically preferred pastels over bright bold prints? Is this a highly engaged style conscious shopper or a convenience seeking discount\u00a0shopper?<\/p>\n<\/blockquote>\n<p id=\"1168\" class=\"graf graf--p graf-after--pullquote\">Finding answers to these types of questions will go a long way in understanding the psyche of shoppers. Using Computer Vision-based technology, with every click on a product, the specific attribute-wise preferences become apparent and when combined with data-driven historical behavioural patterns, the <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization engine<\/a> becomes much more powerful and accurate in interpreting intent.<\/p>\n<p id=\"9e25\" class=\"graf graf--p graf-after--p\">Detecting colour similarity as a basis for personalization is a good illustration of this. One would typically assume that when two items from the same category are purchased together, they would tend to be of contrasting colors.<\/p>\n<p class=\"graf graf--p graf-after--p\">A <strong class=\"markup--strong markup--p-strong\">study<\/strong> performed by one of our in-house Data Scientists\u00a0found that the algorithms that detect similarity in color for co-bought products of a single category, observed a contradictory pattern. As seen below, the color specifications of two shoes that were bought together show that the two items (Shoe 1 and Shoe 2) were of similar color.<\/p>\n<figure id=\"d8c9\" class=\"graf graf--figure graf-after--p\">\n<div class=\"aspectRatioPlaceholder is-locked\">\n<div class=\"aspectRatioPlaceholder-fill\">\n<figure id=\"attachment_4028\" aria-describedby=\"caption-attachment-4028\" style=\"width: 750px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4028\" src=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-1-300x55.png\" alt=\"Omnichannel personalization tool\" width=\"750\" height=\"137\" srcset=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-1-300x55.png 300w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-1.png 678w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/><figcaption id=\"caption-attachment-4028\" class=\"wp-caption-text\">Color Specifications of Shoe 1 | Source: Mad Street\u00a0Den<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<figure id=\"attachment_4030\" aria-describedby=\"caption-attachment-4030\" style=\"width: 750px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4030\" src=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-2-300x53.png\" alt=\"Omnichannel personalization solution\" width=\"750\" height=\"133\" srcset=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-2-300x53.png 300w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-2.png 676w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/><figcaption id=\"caption-attachment-4030\" class=\"wp-caption-text\">Color specification of Shoe 2 | Source: Mad Street\u00a0Den<\/figcaption><\/figure>\n<\/figure>\n<figure id=\"cb77\" class=\"graf graf--figure graf-after--figure\">\n<div class=\"aspectRatioPlaceholder is-locked\">\n<div class=\"aspectRatioPlaceholder-fill\" style=\"text-align: left;\">\u00a0<\/div>\n<div class=\"progressiveMedia js-progressiveMedia graf-image is-canvasLoaded is-imageLoaded\" data-image-id=\"0*8WYuDBsJ1qg6NXkD.\" data-width=\"676\" data-height=\"120\" data-scroll=\"native\">\u00a0<\/div>\n<\/div>\n<\/figure>\n<p id=\"aca3\" class=\"graf graf--p graf-after--figure\">This pattern was observed across some other categories as well for the particular client used for the study. Hypothesis-driven studies like these, using other visual attributes of a product, allows for inferences to be drawn with respect to a user\u2019s intent.<\/p>\n<p id=\"411c\" class=\"graf graf--p graf-after--p\">Lastly, explicit qualitative feedback from end shoppers is a critical piece of the personalization puzzle \u2014 without which intent can never really be pinned down. This can be done in a couple of effective ways. Surveys, written feedback and comment boxes are easy tools to instantly get qualitative feedback.<\/p>\n<p class=\"graf graf--p graf-after--p\">Optimizing user interface design is yet another smart way of mitigating unnecessary navigational distractions and prevents drop-offs. Repositioning the graphic elements on a page, creating \u201clike\u201d, \u201cdislike\u201d and \u201cadd to wishlist\u201d buttons on product pages can help this process immensely.<\/p>\n<p id=\"ec0b\" class=\"graf graf--p graf-after--p\">Experimentation tools such as A\/B tests, that capture the various flavors of omnichannel personalization, are proactive ways to keep a close eye on the performance and ROI of each version that is released. The process flow illustrated below shows us a bird\u2019s eye view of the elements that go into a <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">personalization engine<\/a>.<\/p>\n<figure id=\"30eb\" class=\"graf graf--figure graf-after--p\">\n<div class=\"aspectRatioPlaceholder is-locked\">\n<div class=\"progressiveMedia js-progressiveMedia graf-image is-canvasLoaded is-imageLoaded\" data-image-id=\"1*lxXzubyzS7cg-w18S7DxZw.jpeg\" data-width=\"887\" data-height=\"567\" data-action=\"zoom\" data-action-value=\"1*lxXzubyzS7cg-w18S7DxZw.jpeg\" data-scroll=\"native\">\n<figure id=\"attachment_4031\" aria-describedby=\"caption-attachment-4031\" style=\"width: 750px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4031\" title=\"How Omnichannel Personalization works?\" src=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-3-300x192.jpeg\" alt=\"how omnichannel personalization works\" width=\"750\" height=\"479\" srcset=\"https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-3-300x192.jpeg 300w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-3-768x491.jpeg 768w, https:\/\/www.vue.ai\/blog\/wp-content\/uploads\/2017\/06\/Omnichannel-personalization-3.jpeg 887w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/><figcaption id=\"caption-attachment-4031\" class=\"wp-caption-text\"><strong>How Omnichannel Personalization Works ?<\/strong><\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/figure>\n<p id=\"a0ba\" class=\"graf graf--p graf-after--figure\">Inputs derived from clickstream data and computer vision-based attribute affinities and user preferences are built into personalization models. The models are trained and evaluated continuously through experimentation techniques \u2014 like A\/B tests, where different versions of the engine can be tested out with the end-customer. The outcome of these tests is fed back into the development stage of these models, facilitating a crisp dynamic learning system.<\/p>\n<h3 id=\"f4af\" class=\"graf graf--h3 graf-after--p\"><span class=\"ez-toc-section\" id=\"is-convergence-of-big-data-and-artificial-intelligence-the-next-step\"><\/span><strong>Is Convergence Of Big Data And Artificial Intelligence The Next Step?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p id=\"c01c\" class=\"graf graf--p graf-after--h3\">Algorithms are where the money is. It is no secret that Big Data giants like Facebook and Google are privy to almost all our personal information. Interestingly, they too are yet to crack the deeper nuances of personalization and, problems like the one Maria faced, continue to exist across the board, in the e-commerce ecosystem.<\/p>\n<p class=\"graf graf--p graf-after--h3\">Needless to say, there is a never-ending search for better methods of data accumulation and rapid experimentation. The Big Data revolution has undoubtedly pushed the personalization bar high and several companies market themselves as the one-stop solution for all things personalization. Proactive engagement with the various stakeholders in the ecosystem is the key to making the next big jump in personalization. The possibilities of optimization have no bounds.<\/p>\n<p id=\"73c8\" class=\"graf graf--p graf-after--p\"><strong class=\"markup--strong markup--p-strong\"><em class=\"markup--em markup--p-em\">*Note:<\/em><\/strong><em class=\"markup--em markup--p-em\"> The names used in the article are fictitious, and purely for illustrative purposes. The incidents, however, might be inspired by reality.<\/em><\/p>\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"read-more-about-personalization-engine\"><\/span>Read More About <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">Personalization Engine<\/a><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"read-more-about-personalization-platform\"><\/span>Read More About <a href=\"https:\/\/www.vue.ai\/solutions\/ai-personalization-engine\/\">Personalization Platform<\/a><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"read-more-about-personalized-search\"><\/span>Read More About <a href=\"https:\/\/www.vue.ai\/products\/ai-powered-personalized-search\/\">Personalized Search<\/a><span class=\"ez-toc-section-end\"><\/span><\/h3>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">8<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span> What is Omnichannel Personalization? Omnichannel Personalization is the procedure to create a highly personalized customer experience at every touchpoint based on real-time shopper intent, powered by Customer and Product Intelligence. In the heavily competitive retail industry, you have to constantly ensure you&#8217;re ahead of the curve. Today&#8217;s shoppers are extremely style-conscious and to appeal to  &#8230;<\/p>\n","protected":false},"author":23,"featured_media":1222,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1162],"tags":[27,8,9,29,10],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Omnichannel Personalization : The How And The Why For Retail | Vue.ai<\/title>\n<meta name=\"description\" content=\"Omnichannel Personalization by Vue.ai will help you to stay ahead of the curve In the competitive retail industry.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.vue.ai\/blog\/ai-in-retail\/omnichannel-personalization-ecommerce\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta 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