{"id":636,"date":"2018-03-29T03:32:25","date_gmt":"2018-03-29T00:32:25","guid":{"rendered":"http:\/\/kusuaks7\/?p=241"},"modified":"2025-08-22T12:30:52","modified_gmt":"2025-08-22T12:30:52","slug":"five-amazing-improvement-big-data-can-bring-to-retail","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/five-amazing-improvement-big-data-can-bring-to-retail\/","title":{"rendered":"Five Amazing Improvement Big Data Can Bring to\u00a0Retail"},"content":{"rendered":"\t\t
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Ready for Big Data Training & Certification?<\/em><\/strong> Browse courses<\/a>\u00a0like\u00a0Big Data – What Every Manager Needs to Know<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Data is slowly replacing experience and traditions in the way companies do business. It has already proven its value in different verticals, including finance, healthcare, and of course, retail. This revolution started two decades ago with\u00a0Jeff Bezos\u2019s manifesto<\/a>\u00a0about personalization. He was the first one to understand that mass marketing would come to an end while having millions of versions of a single store to fit each customer\u2019s quirks, would drive sales.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Data Makes You Customer-centric<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Putting the customer at the heart of your every initiative as a retailer can only spur growth and ROI. But first, you must learn everything you can about your clients. Collecting personal data is usually frowned upon as a process, yet most clients surrender to customized offers. Of course, you should keep that fine line between trying to be helpful and being intrusive. Usually, remembering details about previous searches and orders, suggesting complementary products and making sure the shipping details are up to date are considered safe.<\/p>\n

The prevalence of multiple channels has raised a new problem for organizations. Analyzing each step of the customer journey across various platforms to elevate remarketing efforts and secure a sale is one of the best use cases for Big Data. Researchers from\u00a0Itransition<\/a>\u00a0have learned that discreetly shadowing the client across the sales funnel and creating an omnichannel experience drives retention and loyalty.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Data Decreases Churn<\/strong><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Focusing on customers\u2019 needs is a way to make sure you are not always looking to replace your existing customer base. As studies show, attracting a new customer is up to seven times more expensive than keeping an existing one. Data can reveal patterns of long-standing customers and help companies identify new leads and customers who are most likely to generate comparatively more profits in the future. By tailoring promotions to these types of customers, retailers can create stronger bonds. An effective way to achieve this objective is to use clustering algorithms that divide customers into natural groups and address the needs and particularities of each one of them.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Data Improves the Supply\u00a0Chain<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Big Data can help with management duties, not only with satisfying customers. Stock analysis is one of the best ways to avoid oversupply and ensure you always have enough best-selling items available. Through predictive analysis which considers trends, seasonality and other crucial factors, a model based on Big Data can help retailers get just the right number of products or the right amount of raw materials. Through real-time visualization of sold SKUs, there is the potential to implement real-time ordering to supplement them. By aggregating internal data with external information such as weather or public news, organizations can increase efficiency and make the system highly adaptable to real market conditions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Strategic Planning<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Not only supply chains can be powered by Big Data, but long-term plans as well. Looking at the buying patterns, retailers can create more appropriate pricing models, breaking free from the traditional end-of-season sales. Dynamic promotions and strategic sales on less desirable products could increase revenue and profit margins significantly. For example, a lot of travel websites have adaptive price models depending on the demand for specific destinations and the customer\u2019s location identified by the browser\u2019s cookies.<\/p>\n

Recommendation engines can analyze past purchases and discover patterns of complementary products to be recommended for a shopping cart. Of course, you don\u2019t have to follow\u00a0Target\u2019s footsteps<\/a>\u00a0and announce a pregnancy before the family knows it, but identifying the ingredients for a recipe and recommending a suitable bottle of wine would be appreciated.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Cost Reduction<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Companies that have already implemented Big Data analysis noticed that the impacted processes also change the way employees work. While in the initial implementation phase Big Data-related modifications can affect usual workflows and slow down business, the opposite happens after they are set in place.<\/p>\n

Through a business process re-engineering, it is possible to minimize costs, automate some flows and free up more time to focus on the core tasks instead of doing admin work, already covered by the algorithm-run data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Big Data Adoption Obstacles<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Most companies are still lingering in the\u00a0exploration phase<\/a>, looking at what the technology has to offer, drafting possible roadmaps and assessing challenges and opportunities. Less than a quarter of retail companies are in the\u00a0engage<\/em>\u00a0phase, testing pilot projects and running some initiatives, and just a negligible percentage has already deployed major efforts, similar to those of Amazon. Currently, most organizations are still struggling with deployment.<\/p>\n

The first obstacle is to define the scope of the Big Data project. What are the most critical questions the company needs to answer? What data sources should they analyze? Are these already available? Is the data clean and reliable?<\/p>\n

The second challenge is related to the know-how for analyzing such data. Most companies can\u2019t afford a dedicated data science team, or that wouldn\u2019t make any business sense. Luckily, there are enough consulting companies that offer solutions scaled to any budget and purpose. Funding it no longer seems to be a real obstacle.<\/p>\n

Thirdly, security and governance issues can\u2019t be ignored. Operating large amounts of customer data can translate into operational risks. The threats of cybercrime are on the rise, but there are currently few recognized industry standards to help combat them.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Is IoT the Next\u00a0Step?<\/strong><\/h3>\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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While at the moment companies are trying to get all the insights they can from existing data, they will be looking for new sources as the advantages offered by Big Data analytics become mainstream.<\/p>\n

A possible solution to preserve the competitive advantage here is to deploy IoT. These could include tracking sensors, in-store beacons to interact with customers\u2019 phones and to instruct shopping assistants about preferences. Although at the moment it sounds like something from science fiction, simple sensors or even loyalty apps could help retailers get more information about what we like, what price are we ready to pay and what makes us feel uncomfortable and leave the shop, either online or offline. Yes, Big Brother is here, but he means well.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Data is slowly replacing experience and traditions in the way companies do business. It has already proven its value in different verticals, including finance, healthcare, and of course, retail. The first obstacle is to define the scope of the Big Data project. What are the most critical questions the company needs to answer? What data sources should they analyze? Are these already available? Is the data clean and reliable? While in the initial implementation phase Big Data-related modifications can affect usual workflows and slow down business, the opposite happens after they are set in place.<\/p>\n","protected":false},"author":264,"featured_media":3427,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[122],"ppma_author":[1796],"class_list":["post-636","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data"],"authors":[{"term_id":1796,"user_id":264,"is_guest":0,"slug":"sophia-brooke","display_name":"Sophia Brooke","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Brooke","first_name":"Sophia","job_title":"","description":"Sophia Brooke has over 3 years experience as a technology journalist; writing on subjects such as science & innovation, big data, the Internet of Things (IoT), robotics & AI for a range of publications."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/636","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/264"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=636"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/636\/revisions"}],"predecessor-version":[{"id":38062,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/636\/revisions\/38062"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3427"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=636"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=636"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=636"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=636"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}