www.antuit.ai
Open in
urlscan Pro
2606:2c40::c73c:671e
Public Scan
Submitted URL: https://www.antuit.ai/e3t/Ctc/5B*113/cvnlZ04/VXgkf_1Z2bdmW6bDB5m3ql082W2VV8NG4L2Pz7N2_Qd4y3q3npV1-WJV7Cg-MpW3587l-5pbq...
Effective URL: https://www.antuit.ai/blog/driving-size-optimization-precision?utm_campaign=Email&utm_medium=email&_hsmi=215809129&_hs...
Submission: On June 08 via api from US — Scanned from DE
Effective URL: https://www.antuit.ai/blog/driving-size-optimization-precision?utm_campaign=Email&utm_medium=email&_hsmi=215809129&_hs...
Submission: On June 08 via api from US — Scanned from DE
Form analysis 3 forms found in the DOM
/hs-search-results
<form action="/hs-search-results">
<input type="text" class="hs-search-field__input" name="term" autocomplete="off" aria-label="Search" placeholder="Search">
<input type="hidden" name="type" value="SITE_PAGE">
<input type="hidden" name="type" value="BLOG_POST">
<input type="hidden" name="type" value="LISTING_PAGE">
<button aria-label="Search"><span id="hs_cos_wrapper_module_93480244_" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_icon" style="" data-hs-cos-general-type="widget" data-hs-cos-type="icon"><svg version="1.0"
xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" aria-hidden="true">
<g id="search1_layer">
<path
d="M505 442.7L405.3 343c-4.5-4.5-10.6-7-17-7H372c27.6-35.3 44-79.7 44-128C416 93.1 322.9 0 208 0S0 93.1 0 208s93.1 208 208 208c48.3 0 92.7-16.4 128-44v16.3c0 6.4 2.5 12.5 7 17l99.7 99.7c9.4 9.4 24.6 9.4 33.9 0l28.3-28.3c9.4-9.4 9.4-24.6.1-34zM208 336c-70.7 0-128-57.2-128-128 0-70.7 57.2-128 128-128 70.7 0 128 57.2 128 128 0 70.7-57.2 128-128 128z">
</path>
</g>
</svg></span></button>
</form>POST https://forms.hsforms.com/submissions/v3/public/submit/formsnext/multipart/4153407/bdfe25aa-8716-4e0e-aaea-ed57996ec5c9
<form novalidate="" accept-charset="UTF-8" action="https://forms.hsforms.com/submissions/v3/public/submit/formsnext/multipart/4153407/bdfe25aa-8716-4e0e-aaea-ed57996ec5c9" enctype="multipart/form-data"
id="hsForm_bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" method="POST"
class="hs-form stacked hs-form-private hsForm_bdfe25aa-8716-4e0e-aaea-ed57996ec5c9 hs-form-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9 hs-form-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_b7f50d9a-946f-417f-a328-c86e1ee4d658"
data-form-id="bdfe25aa-8716-4e0e-aaea-ed57996ec5c9" data-portal-id="4153407" target="target_iframe_bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" data-reactid=".hbspt-forms-0">
<div class="hs_email hs-email hs-fieldtype-text field hs-form-field" data-reactid=".hbspt-forms-0.1:$0"><label id="label-email-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" class="" placeholder="Enter your Email"
for="email-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" data-reactid=".hbspt-forms-0.1:$0.0"><span data-reactid=".hbspt-forms-0.1:$0.0.0">Email</span><span class="hs-form-required" data-reactid=".hbspt-forms-0.1:$0.0.1">*</span></label>
<legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$0.1"></legend>
<div class="input" data-reactid=".hbspt-forms-0.1:$0.$email"><input id="email-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" class="hs-input" type="email" name="email" required="" placeholder="Email *" value="" autocomplete="email"
data-reactid=".hbspt-forms-0.1:$0.$email.0" inputmode="email"></div>
</div>
<div class="hs_blog_blog_5859321258_subscription hs-blog_blog_5859321258_subscription hs-fieldtype-radio field hs-form-field" style="display:none;" data-reactid=".hbspt-forms-0.1:$1"><label
id="label-blog_blog_5859321258_subscription-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" class="" placeholder="Enter your Notification Frequency" for="blog_blog_5859321258_subscription-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1"
data-reactid=".hbspt-forms-0.1:$1.0"><span data-reactid=".hbspt-forms-0.1:$1.0.0">Notification Frequency</span></label>
<legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$1.1"></legend>
<div class="input" data-reactid=".hbspt-forms-0.1:$1.$blog_blog_5859321258_subscription"><input name="blog_blog_5859321258_subscription" class="hs-input" type="hidden" value="daily"
data-reactid=".hbspt-forms-0.1:$1.$blog_blog_5859321258_subscription.0" placeholder="Notification Frequency"></div>
</div>
<div class="hs_lifecyclestage hs-lifecyclestage hs-fieldtype-radio field hs-form-field" style="display:none;" data-reactid=".hbspt-forms-0.1:$2"><label id="label-lifecyclestage-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" class=""
placeholder="Enter your Lifecycle Stage" for="lifecyclestage-bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" data-reactid=".hbspt-forms-0.1:$2.0"><span data-reactid=".hbspt-forms-0.1:$2.0.0">Lifecycle Stage</span></label>
<legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.1:$2.1"></legend>
<div class="input" data-reactid=".hbspt-forms-0.1:$2.$lifecyclestage"><input name="lifecyclestage" class="hs-input" type="hidden" value="subscriber" data-reactid=".hbspt-forms-0.1:$2.$lifecyclestage.0" placeholder="Lifecycle Stage"></div>
</div><noscript data-reactid=".hbspt-forms-0.2"></noscript>
<div class="hs_submit hs-submit" data-reactid=".hbspt-forms-0.5">
<div class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-0.5.0"></div>
<div class="actions" data-reactid=".hbspt-forms-0.5.1"><input type="submit" value="Subscribe" class="hs-button primary large" data-reactid=".hbspt-forms-0.5.1.0" placeholder=""></div>
</div><noscript data-reactid=".hbspt-forms-0.6"></noscript><input name="hs_context" type="hidden"
value="{"rumScriptExecuteTime":1483.0999999046326,"rumServiceResponseTime":1727.3000001907349,"rumFormRenderTime":1.9000000953674316,"rumTotalRenderTime":1729.9000000953674,"rumTotalRequestTime":242.7000002861023,"embedAtTimestamp":"1654704766211","formDefinitionUpdatedAt":"1578332357241","pageUrl":"https://www.antuit.ai/blog/driving-size-optimization-precision?utm_campaign=Email&utm_medium=email&_hsmi=215809129&_hsenc=p2ANqtz--DJvpp2TLLDJ0UY_zk6HGqSYNJWVN7XfYR0IBisv3FdbmcO0Ir7FwiY5O0ijAddhMJwLrA96Uvdg3ibsZjUwrDLhGjKQ&utm_content=215809129&utm_source=hs_email","pageTitle":"Driving Size Optimization Precision","source":"FormsNext-static-5.502","sourceName":"FormsNext","sourceVersion":"5.502","sourceVersionMajor":"5","sourceVersionMinor":"502","timestamp":1654704766215,"userAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.5005.61 Safari/537.36","originalEmbedContext":{"portalId":"4153407","formId":"bdfe25aa-8716-4e0e-aaea-ed57996ec5c9","formInstanceId":"1","pageId":"49897799787","region":"na1","pageName":"Driving Size Optimization Precision","contentType":"blog-post","formsBaseUrl":"/_hcms/forms/","inlineMessage":true,"target":"#hs_form_target_module_152845875640548_blog_subscribe_1","formData":{"cssClass":"hs-form stacked"}},"canonicalUrl":"https://www.antuit.ai/blog/driving-size-optimization-precision","pageId":"49897799787","pageName":"Driving Size Optimization Precision","formInstanceId":"1","urlParams":{"utm_campaign":"Email","utm_medium":"email","_hsmi":"215809129","_hsenc":"p2ANqtz--DJvpp2TLLDJ0UY_zk6HGqSYNJWVN7XfYR0IBisv3FdbmcO0Ir7FwiY5O0ijAddhMJwLrA96Uvdg3ibsZjUwrDLhGjKQ","utm_content":"215809129","utm_source":"hs_email"},"renderedFieldsIds":["email"],"formTarget":"#hs_form_target_module_152845875640548_blog_subscribe_1","correlationId":"ddcdcdfb-a1ff-43ad-86aa-2995df16c63e","contentType":"blog-post","hutk":"e339a8b18e1540115c6a0ce63689c1f6","captchaStatus":"NOT_APPLICABLE","isHostedOnHubspot":true}"
data-reactid=".hbspt-forms-0.7" placeholder="Email*Notification FrequencyLifecycle Stage"><iframe name="target_iframe_bdfe25aa-8716-4e0e-aaea-ed57996ec5c9_1" style="display:none;" data-reactid=".hbspt-forms-0.8"></iframe>
</form>POST https://forms.hsforms.com/submissions/v3/public/submit/formsnext/multipart/4153407/93072635-b6cd-4507-9894-eb18726334f1
<form novalidate="" accept-charset="UTF-8" action="https://forms.hsforms.com/submissions/v3/public/submit/formsnext/multipart/4153407/93072635-b6cd-4507-9894-eb18726334f1" enctype="multipart/form-data"
id="hsForm_93072635-b6cd-4507-9894-eb18726334f1_4588" method="POST"
class="hs-form stacked hs-custom-form hs-form-private hsForm_93072635-b6cd-4507-9894-eb18726334f1 hs-form-93072635-b6cd-4507-9894-eb18726334f1 hs-form-93072635-b6cd-4507-9894-eb18726334f1_147cae99-b639-4829-8bf3-3755ff02ee7d"
data-form-id="93072635-b6cd-4507-9894-eb18726334f1" data-portal-id="4153407" target="target_iframe_93072635-b6cd-4507-9894-eb18726334f1_4588" data-reactid=".hbspt-forms-1">
<div class="hs_email hs-email hs-fieldtype-text field hs-form-field" data-reactid=".hbspt-forms-1.1:$0"><label id="label-email-93072635-b6cd-4507-9894-eb18726334f1_4588" class="" placeholder="Enter your "
for="email-93072635-b6cd-4507-9894-eb18726334f1_4588" data-reactid=".hbspt-forms-1.1:$0.0"><span data-reactid=".hbspt-forms-1.1:$0.0.0"></span></label>
<legend class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-1.1:$0.1"></legend>
<div class="input" data-reactid=".hbspt-forms-1.1:$0.$email"><input id="email-93072635-b6cd-4507-9894-eb18726334f1_4588" class="hs-input" type="email" name="email" required="" placeholder="Type your email address*" value="" autocomplete="email"
data-reactid=".hbspt-forms-1.1:$0.$email.0" inputmode="email"></div>
</div><noscript data-reactid=".hbspt-forms-1.2"></noscript>
<div class="hs_submit hs-submit" data-reactid=".hbspt-forms-1.5">
<div class="hs-field-desc" style="display:none;" data-reactid=".hbspt-forms-1.5.0"></div>
<div class="actions" data-reactid=".hbspt-forms-1.5.1"><input type="submit" value="-" class="hs-button primary large" data-reactid=".hbspt-forms-1.5.1.0"></div>
</div><noscript data-reactid=".hbspt-forms-1.6"></noscript><input name="hs_context" type="hidden"
value="{"rumScriptExecuteTime":1483.0999999046326,"rumServiceResponseTime":1773.9000000953674,"rumFormRenderTime":6.900000095367432,"rumTotalRenderTime":1780.9000000953674,"rumTotalRequestTime":286.59999990463257,"lang":"en","embedAtTimestamp":"1654704766233","formDefinitionUpdatedAt":"1603199639391","pageUrl":"https://www.antuit.ai/blog/driving-size-optimization-precision?utm_campaign=Email&utm_medium=email&_hsmi=215809129&_hsenc=p2ANqtz--DJvpp2TLLDJ0UY_zk6HGqSYNJWVN7XfYR0IBisv3FdbmcO0Ir7FwiY5O0ijAddhMJwLrA96Uvdg3ibsZjUwrDLhGjKQ&utm_content=215809129&utm_source=hs_email","pageTitle":"Driving Size Optimization Precision","source":"FormsNext-static-5.502","sourceName":"FormsNext","sourceVersion":"5.502","sourceVersionMajor":"5","sourceVersionMinor":"502","timestamp":1654704766241,"userAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.5005.61 Safari/537.36","originalEmbedContext":{"portalId":"4153407","formId":"93072635-b6cd-4507-9894-eb18726334f1","formInstanceId":"4588","pageId":"49897799787","region":"na1","pageName":"Driving Size Optimization Precision","inlineMessage":true,"rawInlineMessage":"Thanks for submitting the form.","hsFormKey":"f4648d5216b145d5ee08b78b5f71da86","target":"#hs_form_target_form_480575933","contentType":"blog-post","formsBaseUrl":"/_hcms/forms/","formData":{"cssClass":"hs-form stacked hs-custom-form"}},"canonicalUrl":"https://www.antuit.ai/blog/driving-size-optimization-precision","pageId":"49897799787","pageName":"Driving Size Optimization Precision","formInstanceId":"4588","urlParams":{"utm_campaign":"Email","utm_medium":"email","_hsmi":"215809129","_hsenc":"p2ANqtz--DJvpp2TLLDJ0UY_zk6HGqSYNJWVN7XfYR0IBisv3FdbmcO0Ir7FwiY5O0ijAddhMJwLrA96Uvdg3ibsZjUwrDLhGjKQ","utm_content":"215809129","utm_source":"hs_email"},"renderedFieldsIds":["email"],"rawInlineMessage":"Thanks for submitting the form.","hsFormKey":"f4648d5216b145d5ee08b78b5f71da86","formTarget":"#hs_form_target_form_480575933","correlationId":"62ec1183-0e55-49e1-a70a-2b2ac0ffe17a","contentType":"blog-post","hutk":"e339a8b18e1540115c6a0ce63689c1f6","captchaStatus":"NOT_APPLICABLE","isHostedOnHubspot":true}"
data-reactid=".hbspt-forms-1.7"><iframe name="target_iframe_93072635-b6cd-4507-9894-eb18726334f1_4588" style="display:none;" data-reactid=".hbspt-forms-1.8"></iframe>
</form>Text Content
This website stores cookies on your computer. These cookies are used to collect information about how you interact with our website and allow us to remember you. We use this information to improve and customize your browsing experience and for analytics and metrics about our visitors. Accept * Blog * Insights & Research * In the News * Research & Ebooks * Podcasts & Webinars * Videos * Events * Company * Careers * Partners * Media & Press Releases Contact Us * Consumer Products AI * Demand Forecasting & Planning * Available to Promise * Retail AI * Assortment & Size Optimization * Forecasting, Allocation & Replenishment * Lifecycle Pricing * Customers * Key Clients * Case Studies * Our AI * Platform * Demand Modeling Studio * Solution Sheets * Who We Are * Why antuit.ai * Awards & Recognition * Blog * Insights & Research * In the News * Research & Ebooks * Podcasts & Webinars * Videos * Events * Company * Careers * Partners * Media & Press Releases * Contact Us DRIVING SIZE OPTIMIZATION PRECISION June 30, 2021 | By David Barach | Retail, Inventory Optimization During the last 12 months, fashion retailers heavily invested in tightening assortments and improving their inventory efficiency. While these investments are needed, all the benefits come undone if a customer cannot find their size. Worse, tightening assortments exacerbates the size stock-out problem, damages customer satisfaction, and creates significant financial pain unless size allocation is accurate. A 20% size misallocation drops margin dollars by 50% if markdown pricing is uniform across all sizes. What is a retailer saying when they show you incredible outfits at a great price, but you can't find your size? They're saying, "This is fabulous, just not for you." In my prior post, I dove into the reasons behind this financial loss, the challenges merchants are experiencing, and the shortcomings of traditional size optimization tools. This post will address 7 key areas on how retailers can achieve precision size optimization given all their challenges and constraints. The Foundational Forecast Essential with any size optimization is the foundational forecast of consumer demand. This forecast must understand the differences between regions, stores, online, and even the fulfillment type. State-of-the-art forecasts serve as the backbone for optimization and planning technologies such as allocation, fulfillment, assortment, and pricing decisions. This approach connects operations across different decision points to accomplish common goals. Solving Data Sparsity Challenges Data sparsity is the most crucial obstacle for precise size optimization. Even if retailers had perfect data history, data cleanliness, and data access - which they do not - there remains the issue of the one-time, seasonal nature of fashion items. There is no history. Worse, if a size range changes from S/M/L to 2,4,6,8,10 or if you decide to add new fringe sizes. How do you manage the demand transference between these? To solve these data sparsity problems, you begin by: * Building profiles at every level of a custom sizing hierarchy that includes product levels and attributes. * Analyze profiles at each hierarchy level for data sufficiency, and then assign a weighting based on the sparsity of data at each level for the profile. * If needed, a composite size profile is created by combining the multiple hierarchy profiles according to weight. This process provides detailed level profiles where selling volumes are sufficient, and where it isn't, profiles are inherited from higher levels. This sparsity-weighted aggregate methodology allows the system to provide accurate profile values for new sizes compared to what has been purchased before. If a product is ordered in a new size, the relative demand impact of that size is borrowed from higher-level profiles and applied to the new size level profile. Finally, the weighting approach provides a much better basis for sales imputation. Understanding where data sparsity can lead to flawed sales imputation assumptions will avoid over or under allocation of core and fringe sizes. Providing Coverage Minimums There are multiple methods to achieve coverage minimums, from a very complex calculation to a straightforward approach. We've found that the straightforward approach is a better solution through many retailer evaluations. By establishing a threshold value, by store, for what defines a core size and then defining the unit coverage minimum for the core sizes, you can apply coverage minimum responsive to store/product selling without the complexity of detailed rules sets. Establishing User Control Size Optimization will provide no benefit if users do not trust the system, regardless of how good it is. The most critical components to building trust are visibility and control. Therefore, applications must provide a transparent analysis of current profiles with an easy way for users to modify profile details if required. A solution offers the best of both worlds by encompassing a high degree of modeling automation and profile generation alongside powerful tools for user review and entry, if needed, of profiles. Omnichannel Omnichannel must be natively built-in with the rest of the application, not as an additional add-on or +/- logic external to all decisions. Profiles should reflect store and for online sales independently as size demand differs significantly based on channel. Hence, solutions should not aggregate or intermix channel demand so that the distinctive size profiles are preserved. Size profiling demand should also allow for ship-from-store (SFS) aspects of location inventory. This profiling enables any given location size profile to represent two demand sources: native sales and online sales fulfilled from store stock. This aspect becomes even more crucial as retailers increase their stores for online fulfillment. Integration Size profiling is an integral part of the merchant ordering process; therefore, the solution must seamlessly integrate with any order management system (OMS). High-performance optimization allows quick API-driven size order quantities available as users write orders. Any value system provides intelligence within the current OMS infrastructure to enable a retailer to enjoy the advantage of advanced analytic determination of order quantities without replacing expensive ordering systems. Taking that last step Size optimization remains an unprioritized solution in many retailers' portfolios. It’s often an afterthought, because it remains at the end of an exhausting planning process, performed right before cutting the order. But what's worse, putting in that last 5% to ensure all your prior work pays off? Or skipping the last part to get it done, but then seeing all your earlier hard work not coming to fruition? Prev Post Next Post SUBSCRIBE HERE! Email* Notification Frequency Lifecycle Stage Also of Interest: * Assortment Planning & Optimization * Trade Promotion & Marketing Optimization * Optimize Inventory for Omnichannel Subscribe to our Newsletter * Consumer Products * Demand Forecasting & Planning * Available to Promise * Retail * Assortment & Size Optimization * Forecasting, Allocation & Replenishment * Lifecycle Pricing * Customers * Key Clients * Case Studies * Our AI * Platform * Demand Modeling Studio * Solution Sheets * Who We Are * Why antuit.ai * Awards & Recognition * Insights & Research * In the News * Research & Ebooks * Podcasts & Webinars * Videos * Company * Careers * Partners * Media & Press Releases * Blog * Events Copyright 2022. All Right Reserved. * Privacy Policy * Terms & Conditions