{"id":19956,"date":"2019-11-04T08:56:54","date_gmt":"2019-11-04T07:56:54","guid":{"rendered":"https:\/\/www.intellias.com\/?p=19956"},"modified":"2024-07-10T16:08:11","modified_gmt":"2024-07-10T14:08:11","slug":"forecasting-demand-and-sales-in-fashion-retail-bet-on-data-not-fortune","status":"publish","type":"blog","link":"https:\/\/intellias.com\/forecasting-demand-and-sales-in-fashion-retail-bet-on-data-not-fortune\/","title":{"rendered":"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune"},"content":{"rendered":"<p>Leave all the guessing to your competitors. Sales and <b>demand forecasting for fashion retailers<\/b> is a matter of collecting data and building prediction models based on it.<\/p>\n<p>Online business and eCommerce have become one of the\u00a0<a href=\"https:\/\/intellias.com\/retail\/\"><span style=\"font-weight: 400;\">covid-19 retail trends<\/span><\/a>.\u00a0Store owners, product managers, and fashion merchants often turn to the latest machine learning techniques and learn how to apply\u00a0<a href=\"https:\/\/intellias.com\/artificial-intelligence-in-ecommerce-retail-hidden-potential\/\"><span style=\"font-weight: 400;\">artificial intelligence in retail industry<\/span><\/a> to predict sales, optimize operations, and increase revenue. But machine learning requires the right data. The good news is that today, there\u2019s more than enough data available. Social media platforms, eCommerce platforms, and trackable supply chains all empower fashion brands to grasp the latest fashion trends and embrace people\u2019s desires to find the next business opportunity.<\/p>\n<p><b>In this article, you\u2019ll learn:<\/b><\/p>\n<ul>\n<li>What is sales prediction in fashion retail?<\/li>\n<li>Why and when do fashion retailers need to forecast sales?<\/li>\n<li>How to forecast sales to launch a new product<\/li>\n<li>How to use data from social media to analyze fashion trends<\/li>\n<li>The ugliest mistakes in retail demand forecasting<\/li>\n<li>How Amazon predicts sales<\/li>\n<\/ul>\n<h2>What is sales forecasting in fashion retail?<\/h2>\n<p>Fashion is one of the most volatile industries; it\u2019s difficult to predict. Trends in colors, prints, cuts, patterns, and materials change faster than you can even think of them, making retail forecasting a challenge for established brands and newcomers alike. Not every retailer risks scaling their business \u2014 not even all that have bulk production and a large customer base. It takes a lot of money and resources to regroup to catch an evolving fast-fashion trend or prepare to meet seasonal demands. Retailers suffer huge losses from unsold inventory and liquidation costs.<\/p>\n<p><b>Characteristics of the fashion retail industry<\/b><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20583\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-91.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"450\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-91.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-91-300x175.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-91-768x450.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<p>The challenge of <b>sales forecasting for fashion retailing<\/b> has been taken up by data analysts and machine learning experts, who have come up with ways to predict demand for items based on shopper data, retailer data, supplier data, and market data.<\/p>\n<p>Tech-savvy retailers use big data to follow and predict trends, prepare for customer demand, segment customers, optimize pricing and promotions based on customer preferences, and monitor real-time analytics to track business outcomes.<\/p>\n<p><b>Big data in fashion retail<\/b><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20586\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-11.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"450\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-11.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-11-300x175.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-11-768x450.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><br \/>\n<em>Source: <a href=\"https:\/\/sqream.com\/dead-or-alive-and-kicking-big-data-helps-retail-revive\/\" target=\"_blank\" rel=\"noopener noreferrer\">Sqream \u2013 Big Data Helps Retail Revive<\/a><\/em><\/p>\n<section class=\"testimonial-wrap testimonial-wrap--simple\">\n\t\t\t<blockquote class=\"testimonial-block\">\n\t\t\t\t<div class=\"testimonial-block__content\">\n\t\t\t\t<svg class=\"quote-icon\" width=\"75\" height=\"65\" viewBox=\"0 0 75 65\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path opacity=\".3\" fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M16 29.7h10c2.4 0 4.4.8 6.1 2.5a8.5 8.5 0 0 1 2.5 6.2v17.4c0 2.5-.8 4.5-2.5 6.2a8.2 8.2 0 0 1-6 2.6H8.7c-2.4 0-4.4-.9-6-2.6a8.5 8.5 0 0 1-2.6-6.2v-32A23 23 0 0 1 7 7.4 23.1 23.1 0 0 1 23.2.6H26a2.8 2.8 0 0 1 2.9 3v5.7c0 .8-.3 1.5-.9 2-.5.6-1.2 1-2 1h-2.8a11 11 0 0 0-8.2 3.3c-2.2 2.3-3.3 5-3.3 8.2v1.5c0 1.2.4 2.2 1.2 3 .9 1 1.9 1.4 3 1.4Zm49.9 0c2.4 0 4.4.8 6 2.5a8.5 8.5 0 0 1 2.6 6.2v17.4c0 2.5-.9 4.5-2.5 6.2a8.2 8.2 0 0 1-6.1 2.6H48.7c-2.4 0-4.5-.9-6.1-2.6a8.5 8.5 0 0 1-2.5-6.2v-32a23 23 0 0 1 6.7-16.4A23.1 23.1 0 0 1 63 .6h2.9c.7 0 1.4.3 2 .8.6.6.8 1.3.8 2v5.9c0 .8-.2 1.5-.8 2-.6.6-1.3 1-2 1H63a11 11 0 0 0-8.1 3.3c-2.2 2.3-3.4 5-3.4 8.2v1.5c0 1.2.4 2.2 1.3 3 .8 1 1.8 1.4 3 1.4h10Z\" fill=\"url(#a)\"\/><defs><linearGradient id=\"a\" x1=\".2\" y1=\"32.6\" x2=\"74.5\" y2=\"32.6\" gradientUnits=\"userSpaceOnUse\"><stop stop-color=\"#45D6AD\"\/><stop offset=\".5\" stop-color=\"#5DB6E1\"\/><stop offset=\"1\" stop-color=\"#BC84CA\"\/><\/linearGradient><\/defs><\/svg>\n\t\t\t\t\t<p>Organizations with above-average performance using customer data and analytics outpace competitors by 2\u20133x on sales, margins and profit.<\/p>\n\t\t\t\t<\/div>\n\t\t\t\t<small class=\"testimonial-block__footer\">\n\t\t\t\t\n\t\t\t\t\t<span class=\"testimonial-block__author\"><span class=\"author\">Harvard Business Review<\/span> <\/span>\n\t\t\t\t<\/small>\n\t\t\t<\/blockquote>\n\t\t<\/section>\n<p>Forecasting fashion trends is big business. Companies such as <a href=\"https:\/\/www.wgsn.com\/en\/\" target=\"_blank\" rel=\"noopener noreferrer\">WGSN<\/a> have trained experts who go through thousands of images, look for patterns, discover color combinations, follow fashion shows, and sort all the data to forecast trends. Collecting a large volume of data gives advantages, but still, it can be overwhelming to choose the exact data you need to make a forecast.<\/p>\n<p><b>Challenges of fashion retail data: How to identify relevant data?<\/b><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20588\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy-21.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"450\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy-21.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy-21-300x175.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy-21-768x450.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<p>Before starting any <b>demand forecasting in the fashion industry<\/b>, keep in mind the seven basic statements of fashion:<\/p>\n<ol>\n<li>Fashion is a social and psychological response. People reveal their inner and social states by wearing particular clothes.<\/li>\n<li>Fashion is similar to pop culture. It can be extremely frivolous and transient.<\/li>\n<li>Fashion is the spirit of the time. It reflects historical events and can repeat itself.<\/li>\n<li>Fashion is an international phenomenon. Designers borrow foreign styles and adopt them while adding their novelties.<\/li>\n<li>Fashion carries meaning. Designers transfer different meanings through forms and patterns, while customers love to find these hidden meanings.<\/li>\n<li>Fashion is a big economic accelerator. It drives revenue from people\u2019s desire to associate themselves with new looks.<\/li>\n<li>Fashion differs by gender. Apparel for men and women isn\u2019t the same.<\/li>\n<\/ol>\n<p>With this background knowledge, retailers can start demand forecasting in the fashion industry by taking the following steps:<\/p>\n<ul>\n<li>Research basic facts about past trends and assumptions about future trends.<\/li>\n<li>Define the reasons for changes in past trends.<\/li>\n<li>Measure the difference between past predictions and actual consumer behavior.<\/li>\n<li>Determine possible factors that will affect future trends.<\/li>\n<li>Apply predictive models and algorithms, considering the accuracy and reliability of previous forecasts.<\/li>\n<li>Track forecasts continually to reveal causes of apparent deviations from expectations.<\/li>\n<li>Adjust forecasts where needed and repeat the process with changes based on further research.<\/li>\n<\/ul>\n<p>Fashion retailers use four main types of models to forecast upcoming demand for their products, each more complex than the previous.<\/p>\n<p><b>Models to forecast sales and demand in fashion retail<\/b><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20590\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-21.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"450\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-21.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-21-300x175.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-21-768x450.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<p>Most mid-sized retail companies are applying the first two methods, while the established brands that are brave enough are implementing the third. Still, very few are actually working on the most complex fourth model, which brings true value to its adherents.<br \/>\n<div class=\"learn-more-block\">\n\t\t\t<div class=\"learn-more-block__content\">\n\t\t\t\t<span style=\"font-weight: 400\">Contact Intellias retail experts to get technology and business advice on implementing AI and machine learning to meet your unique business needs.<\/span>\n\t\t\t<\/div>\n            <a href=\"https:\/\/intellias.com\/contact\/\" class=\"btn-filled\">Get in touch<\/a>\n\t\t<\/div><\/p>\n<h2>Sales forecasting to launch a new product with the power of AI and machine learning<\/h2>\n<p><b>Artificial intelligence in fashion retail<\/b> is gaining wild popularity. One of its use cases is demand forecasting. Above, we learned how to choose the data we need to forecast fashion trends and learned about types of models to forecast sales. Now let\u2019s talk about how to apply this data to one of these models.<\/p>\n<p>Let\u2019s start with something relatively simple \u2014 forecasting in-store sales of a new product during the first week of its market life based on data from a past product launch. To do this, we can use several common algorithms for time series forecasting.<br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20592\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-31.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"597\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-31.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-31-300x233.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-31-768x595.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><br \/>\n<em>Source: <a href=\"https:\/\/www.datascience.com\/blog\/time-series-forecasting-machine-learning-differences\" target=\"_blank\" rel=\"noopener noreferrer\">Oracle+Datascience.com<\/a><\/em><\/p>\n<p>Let\u2019s start by building a neural network. First, we\u2019ll define variables to train retail demand forecasting algorithms. For the input data, we\u2019ll choose sales by months and weekdays and add sales on holidays and during promo campaigns, with previous separated sales on each weekday.<br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20594\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-41.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"789\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-41.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-41-293x300.jpg 293w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-41-768x787.jpg 768w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-41-60x60.jpg 60w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<p>With all the variables we need for training our neural network, we can build a predictive model for sales of our new product in the first seven days.<br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20596\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-51.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"500\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-51.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-51-300x195.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-51-768x500.jpg 768w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-51-370x240.jpg 370w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-51-140x90.jpg 140w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-51-310x201.jpg 310w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<p>Before applying the model to predict upcoming sales, we need to test its predictive power on a separate set of data that wasn\u2019t used for training. Let\u2019s test the model against last year\u2019s sales.<br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20598\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-61.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"500\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-61.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-61-300x195.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-61-768x500.jpg 768w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-61-370x240.jpg 370w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-61-140x90.jpg 140w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-61-310x201.jpg 310w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<p>We can see that our model\u2019s prediction built on the past year\u2019s data correlates closely with actual sales this year, which means the model should effectively predict our sales during the week of the product launch.<\/p>\n<p>Once we\u2019ve tested our model, we can proceed with creating the forecast for the first week of sales.<br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20600\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-71.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"500\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-71.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-71-300x195.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-71-768x500.jpg 768w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-71-370x240.jpg 370w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-71-140x90.jpg 140w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-71-310x201.jpg 310w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<p>In our model, the first day of the week is Sunday. We see that more sales will be made on this day than on any subsequent day. The number of sales will remain stable during the week, but average weekday sales will be slightly lower compared to sales at the product launch on the weekend.<\/p>\n<div><a href=\"https:\/\/intellias.com\/customer-churn-controlling-using-machine-learning\/\" class=\"cta-wrap \">\n                    <div class=\"left-col\">\n                        <p class=\"cta-title\">Learn how machine learning can reinforce customer churn analysis in retail.<\/p>\n                        <div class=\"cta-block__content\">\n                            <div class=\"description\"><\/div>\n                        <\/div>\n                    <\/div>\n                     <span class=\"btn-filled\">Read more<\/span>\n\t\t        <\/a><\/div>\n<h2>How to use data from social media to analyze fashion trends<\/h2>\n<p><b>Fashion retailers<\/b> may access a wide range of customer data from numerous sources, one of them being social media. Facebook, Instagram, Pinterest, and Twitter are oversaturated with data on customer behavior. People put all kinds of data right in the hands of retailers, as social media has become the preferred communication channel between brands and customers. Analyzing which images, colors, and styles get high engagement is one of the retail forecasting methods. Retailers can combine this data with insights from hashtags, geolocations, and followed influencers.<\/p>\n<p>In addition, customer data from platforms like Alibaba and Amazon, analyzed by a suite of powerful tech tools, can open up other powerful possibilities for fashion brands. On top of empowering <a href=\"https:\/\/intellias.com\/retail\/\"><span style=\"font-weight: 400;\">eCommerce fraud prevention software<\/span><\/a>, AI algorithms can predict upcoming trends that will resonate with each customer group. Simultaneously, AI helps turn large and diverse data sets into rich insights for demand forecasting in supply chains, design and manufacturing, sales, marketing, and customer service across local stores and large warehouses.<\/p>\n<div><a href=\"https:\/\/intellias.com\/unified-commerce-linchpin-of-retail-longevity\/\" class=\"cta-wrap \">\n                    <div class=\"left-col\">\n                        <p class=\"cta-title\">Learn about the ways technology helps retailers overcome unified commerce challenges<\/p>\n                        <div class=\"cta-block__content\">\n                            <div class=\"description\"><\/div>\n                        <\/div>\n                    <\/div>\n                     <span class=\"btn-filled\">Read more<\/span>\n\t\t        <\/a><\/div>\n<h2>The ugliest mistakes in retail demand forecasting<\/h2>\n<p>Applied correctly, AI and machine learning techniques can help fashion brands optimize business operations and increase revenue while reducing costs. However, even a small mistake in estimates can ruin an entire business strategy for years.<\/p>\n<p>Let\u2019s explore two of the ugliest mistakes retailers commit when <b>predicting fashion trends<\/b>.<\/p>\n<h3>Mistake #1: Forecasting true sales instead of store-level demand<\/h3>\n<p>The fashion industry requires retailers to run to the market, not make a catwalk. In their rush, companies try to speed up forecasting or simplify <b>demand forecasting models<\/b>. So they start building predictive models using a top-down method, choosing sales data for a product or a category across the entire chain. Using this method instead of an individual method for each store leads to an under-prediction bias due to the elimination of out-of-stock possibilities. How many items would a retailer have sold in June if its store in Woodbridge hadn\u2019t run out of stock in the third week of the month? Building <b>demand forecasting for retail<\/b> against true sales doesn\u2019t account for lost sales due to out-of-stocks, leading to a cycle of underestimates in predictions.<\/p>\n<h3>Mistake #2: Evaluating all misses as equal<\/h3>\n<p>In the retail industry, the relative cost of mistakes differs in many ways. Underestimating demand for an item will increase out-of-stocks. You\u2019ll plan less inventory, which will lead to reduced profits and lost sales opportunities. On the other hand, overestimating demand will result in too much inventory and headaches about where to put it all and how to get rid of outdated items. Neither scenario is great, but their severity depends on the niche a company is working in.<\/p>\n<p>For the fashion industry, overestimating demand is a deadly sin. Compared to forecasting lower demand, which leads mostly to lost sales, excess inventory will overload the supply chain among stores due to returned inventory, leading to markdowns and sunk costs.<\/p>\n<p><b>Underestimated vs overestimated forecasts<\/b><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20602\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-81.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"500\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-81.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-81-300x195.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-81-768x500.jpg 768w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-81-370x240.jpg 370w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-81-140x90.jpg 140w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-81-310x201.jpg 310w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<div><a href=\"https:\/\/intellias.com\/retail\/\" class=\"cta-wrap \">\n                    <div class=\"left-col\">\n                        <p class=\"cta-title\">Learn how Intellias created a digital commerce platform for a luxury clothing marketplace to reinvent the customer experience and drive sales<\/p>\n                        <div class=\"cta-block__content\">\n                            <div class=\"description\"><\/div>\n                        <\/div>\n                    <\/div>\n                     <span class=\"btn-filled\">Read more<\/span>\n\t\t        <\/a><\/div>\n<h2>How Amazon uses predictive analytics to project sales<\/h2>\n<p>Among companies that have already succeeded in applying AI to demand forecasting, Amazon stands out. Amazon has filed a <a href=\"https:\/\/patents.google.com\/patent\/US8615473B2\/en\" target=\"_blank\" rel=\"noopener noreferrer\">patent for anticipatory shipping<\/a>, a <b>retail forecasting method<\/b> that uses AI to predict demand for a particular product in certain neighborhoods and cities. Eventually, Amazon plans to store products with forecasted demand in small warehouses near targeted areas before shoppers put them in their carts, then deliver them right to buyers with autonomous drones.<\/p>\n<p><b>Amazon\u2019s predictive analytics for anticipatory shipping<\/b><br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-20604\" src=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy1.jpg\" alt=\"Forecasting Demand and Sales in Fashion Retail \u2014 Bet on Data, Not Fortune\" width=\"770\" height=\"450\" srcset=\"https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy1.jpg 770w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy1-300x175.jpg 300w, https:\/\/intellias.com\/wp-content\/uploads\/2019\/11\/Fashion-retailers-image-9-copy1-768x450.jpg 768w\" sizes=\"(max-width: 770px) 100vw, 770px\" \/><\/p>\n<h2>Conclusion<\/h2>\n<p>Demand forecasting in retail includes a variety of complex analytical approaches. Different predictive models can be used depending on the business case and the company\u2019s needs. This elevates the role of data scientists, machine learning experts, and AI developers within the fashion and <a href=\"https:\/\/intellias.com\/retail-software-development-services\/\">retail industries<\/a>, as they can translate business needs into business intelligence by picking the right predictive algorithms and implementing them throughout the entire supply chain.<\/p>\n<p>Just as customers\u2019 tastes and styles differ, there\u2019s no universal predictive model that fits all retail companies. That\u2019s why each unique case requires appropriate data sets, a custom forecasting algorithm, and advice on how to implement it.<\/p>\n<hr \/>\n<p><em><a href=\"https:\/\/intellias.com\/contact\/\">Contact Intellias<\/a> retail experts to get advice on implementing AI and machine learning. As a trusted <a href=\"https:\/\/intellias.com\/custom-software-development\/\" target=\"_blank\" rel=\"noopener noreferrer\">software development services company<\/a>, Intellias will help you meet your unique business needs.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to avoid overstocks, keep up with fashion trends, and optimize production lines and supply chains using sales predictions for fashion retail <\/p>\n","protected":false},"author":15,"featured_media":20606,"template":"","class_list":["post-19956","blog","type-blog","status-publish","has-post-thumbnail","hentry","blog-category-machine-learning-ai","blog-category-retail"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.4 (Yoast SEO v23.2) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data-Driven Demand Forecasting in Fashion Retail \u2014 Bet on Data<\/title>\n<meta name=\"description\" content=\"Learn how to avoid overstocks, keep up with 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