Artificial Intelligence in Retail Industry 2020
Machine Learning in Retail
Retail me not or did I say artificial intelligence in retail stores? Retail is merging with AI and Machine Learning by using an artificial intelligence technology (AI) of IBM's Watson analytics... the supercomputer's cognitive computing capabilities to more acutely predict and serve customer wishes.
The idea of using AI capabilities are wide-reaching, as is evidenced by the Amazon Echo and Google Home
It's no wonder ai in retail is a great fit. The ai technology can understand and interpret customer preferences to make more accurate product suggestions, manage inventory based on predictive modeling and even identify ideal store locations.
The retail industry or retail sector or reatil business, is currently valued at approximately $126 billion, 5 years ago (in 2015), is expected to reach $3 trillion by 2027.
New retail in well-timed intelligence? Many new retail business have adopted some form of AI in the past year. But some are
in jeopardy of being too late?
Online retail stores with retail websites are better equipped to get up to speed; they cut their teeth on clickstreams and piles of customer interaction data. Retail me not, what about brick-and-mortar retail services?
Retail websites or online retail stores do collect data, often via retail news loyalty programs, but putting those insights to action can be a complex endeavor with so many store-related distractions at hand.
And in many cases, their data hierarchy and retention capabilities need significant work in their retail industry analysis.
Right now in retail news, enter a host of new AI applications for retail systems, which are now more targeted to suit specific retail stores needs and therefore more approachable. In this retail comic context, perhaps late is better than never.
The key to faster adoption, according to retail management is the ability to see tangible results. Brands are expanding the amount
of data they have outside their core businesses.
All retailers, retail stores, retail shops or retail services have data within their business; the question is how can they best get actionable insights from it, as widely expected or predicted that AI in retail will be a major retail trends in 2020.
AI and machine learning aren't a retail comic or a fad; they're a new technological innovation that means huge sets of data can be leveraged into decisions that could formerly only be made by people.
Many of those decisions are now being made by a technology with a human name: IBM Watson.
There's room for everyone in artificial intelligence in retail space which is why we're going to present to you 6
retail business which want to use big data IBM Watson artificial intelligence technology.
Infact, this year 2020, so many retail stores are testing and using ibm watson predictive analytics, and not all are online retail business exclusive. They are:
- 1-800-Flowers.Com or 1 800 flowers coupon - Using IBM Watson supercomputer's cognitive computing capabilities,
1 800 flowers owner of digital florist and gift company uses IBM Watson to create GWYN or Mr GWYN - a virtual gift concierge.
Mr GWYN... intuitively guides customers through their shopping experience to help them select the perfect gift. Mr GWYN can interpret questions such as "I am looking for a gift for my boyfriend" and then ask related questions about the occasion and sentiment to make reliable suggestions.
- Macy's store or Macy's department store - Before macy's online, a traditional brick-and-mortar retailer, Macy's has invested
big in online and omni-channel merchandising.
Includes the Macy's On Call app, which combines IBM's Watson cognitive computing with location-based software to answer shoppers' in-store questions, such as where a specific clothing brand is located. Macy's On Call app program was tested in 10 retail stores through the fall of 2016.
- Under Armour or new under armour commercial - Under armour business (maker of high-tech activity apparel) is now using
IBM's Watson to create an app (or under armour application online) that helps customers track their health and fitness
activities, including sleep and nutrition.
As a result, it provides the users with coaching based on their data, as well as the results of other people who have similar health/fitness profiles. Also, it pulls from nutritional databases, physiological and behavioral data.
- Staples or staples more account business - Staples business account online has implemented IBM's Watson technology to bring to life its Easy Button. Easy button infused with the IBM watson technology, can now take Staples orders by voice, text, email, messaging app or mobile app.
- Sears or sears appliances - The Sears com service making a wise move. Sears store trading since 1893, a
127 year-old department store chain is using IBM's Watson to boost one of its tried-and-true categories - tires or tires on sale at sears.
The AI enabled apps, called Digital Tire Journey, which helps the shopper with questions and matches the most appropriate tires with driver preferences.
The digital tire journey app identifies the shopper as a Comfort Warrior, Value Seeker, Off-Roader, High Performer, Safety Seeker or Winter Warrior and presents several purchase options (buy online, schedule an appointment or reach a call-center employee).
- The North Face or the north face coats for women - The North Face style (the outdoor-gear chain) is using the IBM's Watson-powered
digital shopping tool that presents online coat-shoppers with a series of questions, such as "Where and when will you be using this
The answers to the north face outfit are used to generate relevant coat suggestions. Shoppers who use the tool are more likely to buy than those who do not. The north face retailer is exploring different ways to use the AI technology.
However, it's the savviest; shrewd and knowledgeable; having common sense and good judgement and most forward-thinking brands that are looking more deeply at machine learning, analytics and AI in retail across all aspects of their retail businesses.
Embracing/accepting willingly and enthusiastically self-learning algorithms gives retailers the ability to sell more products with less discounting, understand competitors' pricing, have correct product assortments and minimize gaps, spot key trends early and capitalize on them with maximum efficiency.
True true, retailers are adopting AI in retail industry but it not all happening online. Nor is it all relying on the power of IBM's Watson analytic.
2019 Robots or robots in 2020 or industrial robots are making its noise. The robots being tested at Lowe's, called LowBots, come to mind for research robots.
They can process natural language to respond to customer questions, and can even tell the difference between people and objects.
AI Online Shopping and Forecasting of Trends in Fashion Retail Industry
Apparel manufaturers and retailers in fashion industry face a range of key decisions, including selection of plant locations,
production planning and scheduling, marker planning, cut order planning, apparel assembly line balancing, reatil sales forecasting and marketing.
Traditionally, such decisions depended on the experience and judgement of key staff.
However, as the market has shifted to short production runs to meet rapidly changing customer demands, and costs have been squeezed in favour of just-in-time production methods, these decisions have become more complex.
At the same time, production has become more automated and integrated, allowing greater control of supply chain.
How'd you do? Are you calm, normal or tense? Why?
The answer is simple: You see, you are about to learn how fashion retailers need to forecast sales accurately as well as expand their customer base and increase profit from existing customers.
- Pick a trip (variable) at random,
- Pick a new driver-vehicle pair,
- If assigning the pair to the trip does not create any conflict, the assignment becomes effective, otherwise, a new driver-vehicle pair is assigned to all the trips in conflict if a consistent assignment is possible. As a last resort, these conflicting variable are unassigned.
Here we present a knowledge-based system that is able to create automatically the forecasting of trends in fashion represented by colours to use in a new collection.
In order to reach this goal, at first we have to analyzed the creative process followed by a stylist, identifying a set of words/concepts and a picture as the most significant information used by him/her to make the forecast.
A possible second step we could use data mining techniques for eliciting, from a dataset of past colour proposals, the knowledge necessary to create a new proposal.
Lastly, we could developed a prototype of a knowledge-based system that effectively uses the elicited knowledge to support the colour proposal creation.
However, there are a lot of retail ventures out there where the money was made one way (in a disciplined, hungry way) and is being depleted by a new, less disciplined business.
For example, if you go to Tribeca in NYC or tribeca NYC or tribeca ny you'll see a lot of cute retails stores occupying some incredibly expensive real estate. These retail stores make no commercial sense and are run by the wives of Wall Street guy who need something to do with themselves.
Anyone trying to emulate those businesses will blow themselves up because they're not real businesses. They're hobbies.
The stylist that has supplied the starting dataset has found the elicited knowledge and the performance results very interesting.
In particular in her opinion it is valuable the ability of the Bayesian network to make explicit the words/concepts and picture colours most influencing each colour choice.
Sales forecasting is the foundation for various phrases operation planning. It is a significant task in supply chain management under current dynamic market demands and thus greatly affects fashion retailers in various ways.
Without accurate and reliable sales forecasts, operations can only respond retriactively, which causes poor production planning, lost orders, inadequate customer services, and poorly utilized resources.
Recent research has shown that effective sales forecasting enables imrpovement in supply chain performance.
Because of ever-increasing global competition, sales forecasting plays an increasingly prominent role in supply chain management when profitability and long-term viability rely on effective and efficient sales forcasts.
With regard to the fast-expanding Chinese market, the approximately 6%-to-10% growth rate each year leads to a great increase in disposable income, which attracts more and more fashion reatiling companies to enter this potential market, and thus the fashion retail industry becomes a blooming business.
The uS fashion retaile or UK fashion retailes or in general fashion retail business is characterized by short product life cycles, volatile customer demands and tremendous product varieties. Most fashion items are of strong seasonality.
Uncertain customer demands in a frequently changing market environment and numerous explanatory variables that influence fashion sales cause an increase in irregularity or randomicity of sales data.
Such distinct characteristics increase the complexity of sales forcasting in the fashion retail industry. For most fashion products, market demand is uncertain until the selling season has started.
When the actual demand deviates from the forecast, fashion retails may not have time to respond to changes. Stock outages may occur for certain styles or sizes of fashion products and thus affect the profitability for fashion retailers.
In fact, most fashion retailers (not just french fashio retailers) still rely on forecasting professionals assessment and experience for production planning and stocking decisions before the launch of their products.
And, when these professionals (i.e. fashion buyers) leave, their replacements may fail to develop reliable sales forecasts without their predecessors know-how.
Currently, top uk fashion retailers or fashion online retail businesses usually make sourcing budgets on an annual and/or seasonal basis by forecasting the total sales amount of each fashion item.
The fashion buyers determine which items need to be purchased or produced in each fashion item category, which consists of multiple items with common attributes.
In a business, categories are usually unchanged, while items in each category frequently change in different selling seasons.
Cross Selling and Upselling
Today in 2020, by cross selling and upselling we mean the competition in fashion retail online business is keener
than ever as we saw in the embracing/accepting willingly and enthusiastically self-learning algorithms to gives retailers the ability to sell
Fashion online retailers realize the importance of retaining existing customers and gaining new ones. However, customers are likely to switch from one fashion brand to another in response to attractive nad competitive offers.
The competition for customers, particularly in mature markets, turns retailers into a revolving door of acquiring and losing customers.
Fashion online retailers currently adopt different strategies to retain customers, including better service quality, better business decisions, and identifying customers purchasing behaviour through analysing transaction data.
Various types of customer relationship management system using data mining techniques have been developed to cater for the needs of these retailers, and allegedly enable retailers to make decisions concerning replenishment, inventorycontrol, and marketing and promotion strategies.
Through the use of data mining techniques, customers purchasing behaviour can be analysed and new knowledge about retail business discovered.
However, adoption is still limited, since the systems are not tailor-made for the fashion industry withits characteristic short cycles of fashion trends, volatile customer demands, and tremendous product and style varieties.
Fashion retailers online are well aware that, in addition to expanding their customer base, they need to increase profitability obtained from their existing customers.
It is suggested that cross selling and upselling strategies can increase the number of transactions per customer and consequently lead to growth of profits and customer loyalty.
By definition, Cross selling refers to sales of additional items to a customer in relation to items that he has already purchased. While Upselling is a process through which a customer is persuaded (usually by a salesman) to purchase an upgrade of his target item.
To actualize these strategies, online retailers inevitably make use of historical transaction data to identify customers preferences and rely on sales staff for successful execution.
However, historical data can become outdated and poorly reflect customers tastes. Quality of salesmanship also varies widely between salesman.
What is Dark Patterns?
What is a dark pattern? - By definition, Dark patterns are techniques that are quite perverting across the internet and they're
based on the behavioural psychology principles. Users can be perhaps tricks sometimes to taking an action that they wouldn't normal do.
Dark Patterns is what online retailers used or the online tricks to make you spend money.
Believe or not, online retailers use a range of sly methods to get you to spend more and to part with our data. In the retail industry they call some of these tricks built into websites "Dark Patterns".
Who invented dark patterns? - A British retail web designer Harry Brignull is credited with coining the phrase. Finds out more about what these tricks are.
Dark patterns are all around the web online, and lots, and lots of different apps. A common one is an advertisement in disguise as content or navigation just to get you clicks on them.
Another common one is a false continuity, which is when a free trial comes to an end and your credit cards starts to get charge without warning.
In UK, laws have been change to curb the online dark patterns - you have to tick boxes to accept web online offers now. In the past, you have to tick box to opt out them.
A new study by the leading web designers, says the online retailers are getting around the restrictions by inventing a new more sophisticated types of dark patterns to persuade us to spend.
A global leading user experience, digital design and online designer agency called Sigma did the study. These dark patterns can be seen across large websites, mainly across retails.
They are on the rise and it is very difficult to police this things. No one governing body are so to speak.
|Artificial Intelligence Retail Journal||"Artificial Intelligence in Retail Description 2020"|
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|MICAI 2005: Advances in Artificial Intelligence||This three-volume proceedings contains revised selected papers from the Second International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011, held in Taiyuan, China, in September 2011. The total of 265 high-quality papers presented were carefully reviewed and selected from 1073 submissions. The topics of Part I covered are: applications of artificial intelligence; applications of computational intelligence; automated problem solving; biomedical inforamtics and computation; brain models/cognitive science; data mining and knowledge discovering; distributed AI and agents; evolutionary programming; expert and decision support systems; fuzzy computation; fuzzy logic and soft computing; and genetic algorithms.|
|New Frontiers in Artificial Intelligence||This book constitutes the thoroughly refereed joint post-proceedings of three international workshops organized by the Japanese Society for Artificial Intelligence, held in Tokyo, Japan in June 2006 during the 20th Annual Conference JSAI 2006. The volume starts with eight award winning papers of the JSAI 2006 main conference that are presented along with the 21 revised full workshop papers, carefully reviewed and selected for inclusion in the volume.|
|Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)||Practitioners in apparel manufacturing and retailing enterprises in the fashion industry, ranging from senior to front line management, constantly face complex and critical decisions. There has been growing interest in the use of artificial intelligence (AI) techniques to enhance this process, and a number of AI techniques have already been successfully applied to apparel production and retailing. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail provides detailed coverage of these techniques, outlining how they are used to assist decision makers in tackling key supply chain problems. Key decision points in the apparel supply chain and the fundamentals of artificial intelligence techniques are the focus of the opening chapters, before the book proceeds to discuss the use of neural networks, genetic algorithms, fuzzy set theory and extreme learning machines for intelligent sales forecasting and intelligent product cross-selling systems. Helps the reader gain an understanding of the key decision points in the apparel supply chain Discusses the fundamentals of artificial intelligence techniques for apparel management techniques Considers the use of neural networks in selecting the location of apparel manufacturing plants|
|PRICAI 2012: Trends in Artificial Intelligence||This volume constitutes the refereed proceedings of the 12th Pacific Rim Conference on Artificial Intelligence, PRICAI 2012, held in Kuching, Malaysia, in September 2012. The 60 revised full papers presented together with 2 invited papers, 22 short papers, and 11 poster papers in this volume were carefully reviewed and selected from 240 submissions. The topics roughly include AI foundations, applications of AI, cognition and intelligent interactions, computer-aided education, constraint and search, creativity support, decision theory, evolutionary computation, game playing, information retrieval and extraction, knowledge mining and acquisition, knowledge representation and logic, linked open data and semantic web, machine learning and data mining, multimedia and AI, natural language processing, robotics, social intelligence, vision and perception, web and text mining, web and knowledge-based system.|
|Progress in Artificial Intelligence||This book constitutes the refereed proceedings of the 19th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2006, held in Annecy, France, June 2006. The book presents 134 revised full papers together with 3 invited contributions, organized in topical sections on multi-agent systems, decision-support, genetic algorithms, data-mining and knowledge discovery, fuzzy logic, knowledge engineering, machine learning, speech recognition, systems for real life applications, and more.|
|Real Estate Site Selection||The purpose of this MBA Project is to investigate and provide a comprehensive overview of the current real estate site selection industry while showing applications of how artificial intelligence can improve the selection process. The goal is to identify and document both the specific industry practices primarily utilized and the principal uses of artificial intelligent algorithms for site selection and sales forecasting. The results of this project can be applied to military retail facilities (exchanges and commissaries). The current business model for military retail facilities may not be optimized based upon current trends market data. Optimizing the location and allocation of goods and services through artificial intelligent algorithms can provide previously unrealized cost savings to the Department of Defense.|
|Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing||The 9th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, held in Phuket Thailand on August 6 - 8, 2008 is aimed at bringing together researchers and scientist, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. This publication captures 20 of the conference's most promising papers, and we impatiently await the important contributions that we know these authors will bring to the field.|
|Sorts and Types in Artificial Intelligence||This book reflects substantial research done in AI on sorts and types. It is of great importance for researchers interested in natural language understanding and knowledge representation.|
|Strategic Retail Management||This book is devoted to the dynamic development of retailing. The focus is on various strategy concepts adopted by retailing companies and their implementation in practice. This is not a traditional textbook or collection of case studies; it aims to demonstrate the complex and manifold questions of retail management in the form of twenty lessons, where each lesson provides a thematic overview of key issues and illustrates them via a comprehensive case study. The examples are all internationally known retail companies, to facilitate an understanding of what is involved in strategic retail management and illustrate best practices. In the third edition, all chapters were revised and updated. Two new chapters were added to treat topics like corporate social responsibility as well as marketing communication. All case studies were replaced by new ones to reflect the most recent developments. Well-known retail companies from different countries, like Tesco, Zalando, Hugo Boss, Carrefour, Amazon, Otto Group, are now used to illustrate particular aspects of retail management.|