Keeping up with ever-demanding customers and fierce competition requires setting eyes on tiniest details. Customers were never so picky, but we cannot judge in the times of abundant choices – we have to adapt. When it comes to retail business, apart from offering a high-quality product, customers are seeking for enhanced customer experience, and that’s the aspect that makes a whole difference. Did you know that artificial intelligence (AI) in the retail business was valued at $650 million in 2017., and the predictions show the rapid growth of around 40% until 2024.?

Keep reading as we’re going to talk about the ways AI can improve the performance, productivity, and ROI of your retail business.

#1 – Effective and Precise Demand Forecasting

No being able to satisfy demanding customers can potentially lead to negative business results such as higher inventory costs or losing the customers due to not having the item on demand. AI comes in handy when we are talking about demand forecasting, as the system can effectively predict demand for certain products at a specific time.

It doesn’t only help with supply optimization and production planning, but also decreases unnecessary inventory costs, increases customer’s satisfaction, and helps businesses to plan logistics and marketing budget accordingly. There are various qualitative and quantitative methods for effective demand forecasting, but it usually demands experienced and skilled professionals that increase costs.

With the help of AI, you can quickly and cost-effectively, get a precise and detailed demand forecast for various products based on historical data and customer’s behavior.

#2 – Dynamic Pricing

Dynamic pricing is another aspect where AI can give your retail business a competitive edge. For instance, while determining the price for a particular product, you have to take into account the fixed and dynamic factors. Logistics and manufacturing costs fall under fixed costs that rarely change, so it’s easy to predict those.

On the other hand, dynamic factors are more sophisticated to predicts as these tend to be connected with socio-economic, geographical, and environmental factors. With behavioral data and an effective demand forecasting process, you can easily set your prices dynamically to increase profitability, revenue, and efficiency of inventory management.

A current Coronavirus outbreak shows an example of morally and legally questionable, dynamic pricing due to the high surge of demand caused by an epidemic virus. Similar examples can be found with services like Uber which change prices according to weather conditions.

#3 – Enhanced Customer Experience Through Intelligent Devices

Kiosk or a digital station around the stores is a practical approach for enhancing customer experience by providing accurate information that helps in conversions. As technology advances, AI becomes ‘smarter’, especially when it comes to recognizing data patterns, human behavior, and even emotions.

For instance, you can imagine AI-powered devices that serve as an information station that studies and recognizes customer behavior. That way, the tool can help in providing accurate product suggestions, recommendations, and appropriate support for each customer.

#4- Automatic Shift Scheduling

Shift scheduling is a time-consuming process that simply has to be done. Scheduling is a very complicated process, even if you run a small retail store. For instance, you have to take into account your staff availability, vacation time and sick leave, priorities, and budgets, so it’s not a surprise that creating a perfect schedule is a tough process.

With the help of AI, you can tremendously decrease the time needed for creating a fitting schedule, which gives you additional resources to steer on other critical parts of your business. Auto-scheduling is a ‘smart’ tool that automatically creates an efficient schedule for your business in a matter of a single click. Since the tool doesn’t work alone but is connected to other departments, it gives you an accurate labor forecast prediction that saves time and money.

#5 – Voice and Visual Assistants

A shopping experience in retail stores shouldn’t be complicated, but rather a friendly and easy-to-navigate process. Image recognition is still in its early stages, but it’s one of the most developing areas of AI. The visual assistant has the primary purpose of helping customers to find a product they are looking for or provide an alternative suggestion.

For instance, customers can show a photo of a specific product to AI-powered stations, and the system returns an exact location of a product in the store along with other crucial information.

Another handy way to utilize the power of AI is by implementing voice assistants around the store, such as Siri or Alexa. Voice assistants can directly help customers and increase customer satisfaction and revenue.

AI is slowly but surely becoming a new standard in the retail business

Providing an excellent customer experience is one of the fundamental aspects of being competitive. As customers are getting more demanding, AI will slowly become a crucial part of retail businesses because it can dramatically increase customer’s experience while cutting the costs and increasing the revenue.

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Deep learning which is also termed as hierarchical learning or deep structured learning uses a layered algorithmic architecture to analyze data. Its peculiarity helps organizations and researchers achieve the unachievable in a most innovative manner. Recently, researchers at the University of Eastern Finland developed a new deep learning model that can identify sleep stages as accurately as an experienced physician. This discovery paves the way for better diagnosis and treatments of sleep disorders, including obstructive sleep apnea.

To note, obstructive sleep apnea (OSA) is a nocturnal breathing disorder that causes a major burden on public health care systems and national economies. As noted by a report, it is estimated that up to one billion people worldwide suffer from obstructive sleep apnea, and the number is expected to grow due to population aging and increased prevalence of obesity. When untreated, OSA increases the risk of cardiovascular diseases and diabetes, among other severe health consequences.

Therefore, it wise to have a system that can identify sleep stages for the diagnostics of sleep disorders, including obstructive sleep apnea. Traditionally, sleep is manually classified into five stages, which are wake, rapid eye movement (REM) sleep and three stages of non-REM sleep. However, manual scoring of sleep stages is time-consuming, subjective and costly as well.

Hence to win against such challenges, researchers used polysomnographic recording data from healthy individuals and individuals with suspected OSA to develop an accurate deep learning model for automatic classification of sleep stages. In addition, they wanted to find out how the severity of OSA affects classification accuracy.

In healthy individuals, the model was able to identify sleep stages with an 83.7 percent accuracy when using a single frontal electroencephalography channel (EEG), and with an 83.9 percent accuracy when supplemented with electrooculogram (EOG). In patients with suspected OSA, the model achieved accuracies of 82.9 percent (single EEG channel) and 83.8 percent (EEG and EOG channels). The single-channel accuracies ranged from 84.5 percent for individuals without OSA to 76.5 percent for severe OSA patients. The accuracies achieved by the model are equivalent to the correspondence between experienced physicians performing manual sleep scoring. However, the model has the benefit of being systematic and always following the same protocol, and conducting the scoring in a matter of seconds.

According to the researchers, deep learning enables automatic sleep staging for suspected OSA patients with high accuracy.

The Sleep Technology and Analytics Group, STAG, at the University of Eastern Finland solves sleep diagnostics challenges by using a variety of different approaches. The methods developed by the group are based on wearable, non-intrusive sensors, better diagnostic parameters and modern computational solutions that are based on artificial intelligence. The new methods developed by the group are hoped to improve OSA severity assessment, promote individualized treatment planning and more reliable prediction of OSA-related daytime symptoms and comorbidities.

Future Applications

As noted by the American Academy of Sleep Medicine, the researchers hope the deep learning model can be used to improve the consistency of sleep staging across providers and systems while also completing the scoring in mere seconds. They also noted the potential cost savings by measuring sleep with fewer channels. Ultimately, the researchers think their methods could improve sleep apnea severity assessment, promote individualized treatment planning, and more reliably predict sleep apnea-related daytime symptoms and comorbidities. The research originated at the Sleep Technology and Analytics Group at the University of Eastern Finland, which was created to examine challenges in sleep diagnostics.

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Summarizing text is a task at which machine learning algorithms are improving, as evidenced by a recent paper published by Microsoft. That’s good news — automatic summarization systems promise to cut down on the amount of message-reading enterprise workers do, which one survey estimates amounts to 2.6 hours each day.

Not to be outdone, a Google Brain and Imperial College London team built a system — Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence, or Pegasus — that leverages Google’s Transformers architecture combined with pretraining objectives tailored for abstractive text generation. They say it achieves state-of-the-art results in 12 summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills, and that it shows “surprising” performance on low-resource summarization, surpassing previous top results on six data sets with only 1,000 examples.

As the researchers point out, text summarization aims to generate accurate and concise summaries from input documents, in contrast to executive techniques. Rather than merely copy fragments from the input, abstractive summarization might produce novel words or cover principal information such that the output remains linguistically fluent.

Transformers are a type of neural architecture introduced in a paper by researchers at Google Brain, Google’s AI research division. As do all deep neural networks, they contain functions (neurons) arranged in interconnected layers that transmit signals from input data and slowly adjust the synaptic strength (weights) of each connection — that’s how all AI models extract features and learn to make predictions. But Transformers uniquely have attention. Every output element is connected to every input element, and the weightings between them are calculated dynamically.

The team devised a training task in which whole, and putatively important, sentences within documents were masked. The AI had to fill in the gaps by drawing on web and news articles, including those contained within a new corpus (HugeNews) the researchers compiled.

In experiments, the team selected their best-performing Pegasus model — one with 568 million parameters, or variables learned from historical data — trained on either 750GB of text extracted from 350 million web pages (Common Crawl) or on HugeNews, which spans 1.5 billion articles totaling 3.8TB collected from news and news-like websites. (The researchers say that in the case of HugeNews, a whitelist of domains ranging from high-quality news publishers to lower-quality sites was used to seed a web-crawling tool.)

Pegasus achieved high linguistic quality in terms of fluency and coherence, according to the researchers, and it didn’t require countermeasures to mitigate disfluencies. Moreover, in a low-resource setting with just 100 example articles, it generated summaries at a quality comparable to a model that had been trained on a full data set ranging from 20,000 to 200,000 articles.

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Designers of machine translation tools still mostly rely on dictionaries to make a foreign language understandable. But now there is a new way: numbers. Facebook researchers say rendering words into figures and exploiting mathematical similarities between languages is a promising avenue, even if a universal communicator a la Star Trek remains a distant dream. Powerful automatic translation is a big priority for internet giants. Allowing as many people as possible worldwide to communicate is not just an altruistic goal, but also good business. Facebook, Google and Microsoft as well as Russia’s Yandex, China’s Baidu and others are constantly seeking to improve their translation tools.

Facebook has artificial intelligence experts on the job at one of its research labs in Paris. Up to 200 languages are currently used on Facebook, said Antoine Bordes, European co-director of fundamental AI research for the social network. Automatic translation is currently based on having large databases of identical texts in both languages to work from. But for many language pairs there just aren’t enough such parallel texts. That’s why researchers have been looking for another method, like the system developed by Facebook which creates a mathematical representation for words. Each word becomes a “vector” in a space of several hundred dimensions. Words that have close associations in the spoken language also find themselves close to each other in this vector space.

From Basque to Amazonian?

“For example, if you take the words ‘cat’ and ‘dog’, semantically, they are words that describe a similar thing, so they will be extremely close together physically” in the vector space, said Guillaume Lample, one of the system’s designers. “If you take words like Madrid, London, Paris, which are European capital cities, it’s the same idea.” These language maps can then be linked to one another using algorithms, at first roughly, but eventually becoming more refined, until entire phrases can be matched without too many errors.

Lample said results are already promising. For the language pair of English-Romanian, Facebook’s current machine translation system is “equal or maybe a bit worse” than the word vector system, said Lample. But for the rarer language pair of English-Urdu, where Facebook’s traditional system doesn’t have many bilingual texts to reference, the word vector system is already superior, he said.

But could the method allow translation from, say, Basque into the language of an Amazonian tribe? In theory, yes, said Lample, but in practice, a large body of written texts are needed to map the language, something lacking in Amazonian tribal languages. “If you have just tens of thousands of phrases, it won’t work. You need several hundreds of thousands,” he said.

Holy Grail

Experts at France’s CNRS national scientific centre said the approach Lample has taken for Facebook could produce useful results, even if it doesn’t result in perfect translations. Thierry Poibeau of CNRS’s Lattice laboratory, which also does research into machine translation, called the word vector approach “a conceptual revolution”. He said “translating without parallel data”, dictionaries or versions of the same documents in both languages is something of the Holy Grail” of machine translation.

“But the question is what level of performance can be expected” from the word vector method, said Poibeau. The method “can give an idea of the original text” but the capability for a good translation every time remains unproven. Francois Yvon, a researcher at CNRS’s Computer Science Laboratory for Mechanics and Engineering Sciences, said “the linking of languages is much more difficult” when they are far removed from one another. “The manner of denoting concepts in Chinese is completely different from French,” he added. However even imperfect translations can be useful, said Yvon, and could prove sufficient to track hate speech, a major priority for Facebook.

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Emerging about a decade ago from roots in statistical modeling and data analysis, data scientists are employed to help companies adopt data-centric approaches to their organizations. Since data scientists have a comprehensive understanding of data, they work well in moving organizations towards machine learning, deep learning and AI adoption because they generally have the same data-driven goals.

Data scientists help companies figure out how to extract useful information from a sea of data to help analyze and optimize their organizations based off of the findings. Data scientists focus on analyzing data, asking data-centric questions and applying mathematics and statistics in order to find relevant results.

Data scientists have backgrounds in advanced math and statistics, advanced analytics and increasingly in machine learning and AI. For companies who are looking to run an AI project, having a data scientist on the team is very beneficial to get the most out of their data, customize algorithms and weigh in on data-centric decisions.

Challenges in hiring data scientists

Since the field of data science is still fairly new, many organizations are playing catch-up with finding and hiring the right skill sets for this position. There simply aren’t enough people in the job pool who have the data science skill sets needed. Unfortunately, there are more companies looking to hire data scientists than there are data scientists to fill these positions, causing a talent crunch.

Many universities have only recently in the past few years introduced a data science program, so collegiate-level talent is still nascent.  Further, many people who are established in data science are working for very large organizations that can pay top dollar such as Google, Facebook, LinkedIn and major enterprise employers such as Capital One and American Express. There simply are not enough data scientists to go around, particularly for mid- and lower-level companies that can’t afford the high salaries.

When it comes to hiring a data scientist, there are a few key skills companies will want to look for. The first is that prospective data scientists must have backgrounds in advanced math and statistics, advanced analytics and perhaps machine learning and AI. These individuals need to extract value out of a company’s data, so if they don’t know how to understand that data in the first place, they won’t be very successful.

Another skill set that’s important — and may not be something people initially look for — is the ability to be a creative thinker. Creative problem solving is required to be a successful data scientist, and it is creativity that you want driving the thought process behind successful AI solutions. Innovation in data science can result in giving a company a competitive edge.

In addition to being creative, data scientists should have experience with various popular coding languages such as R or Python. While programming is not a core part of this role, analytical-focused programming knowledge provides tools needed to run advanced analysis on data.

Do you really need a data scientist?

Just because a company can’t find or afford a team of data scientists doesn’t mean it needs to abandon its data science goals or lose sight of advanced machine learning or AI opportunities. Depending on what a company is interested in pursuing with its AI strategy, it may or may not even need a team of data scientists.

For companies with very large datasets and complex use cases or a large-scale approach, it is likely that a company will certainly need more than one data scientist in order to carry out the project in a reasonable amount of time. However if a company is planning on pursuing many smaller efforts, it can be just as valuable to have only one or two data scientists per team to work alongside other members of a team. Depending on the need, the data scientist might be able to work closely with developers in order to reach an end goal rather than requiring everyone on the team to have that specific skill set. Data scientists can also work alongside and train members of the existing team to perform as citizen data scientists.

As the relevancy of artificial intelligence continues to grow and the talent crunch around data scientists grows with it, many companies are wondering if they can go without one. It can be difficult to find a good data scientist, and their salaries are often steep. It is possible to move towards an AI future without having a data scientist on board, but it really depends on the projects you’re looking to run.

Advanced tooling for citizen data scientists

As the popularity of AI continues to grow, a number of companies are creating tools to help reduce dependence on data scientists. One such tool is autoML, offered by a number of vendors who are creating tools and dashboards that automate parts of the data science workflow. The goal of automated machine learning tools is to automate the processes of algorithm selection, hyper parameter tuning, iterative modeling, model assessment and even elements of data preparation to speed up the overall process, and take out some of the more complicated aspects for setup that have previously needed skilled data scientists. Once an organizations’ data is run through autoML systems, it produces a machine learning model which can be used directly or analyzed by a worker. Usually, these post-autoML activities can be accomplished by employees with far less training than data scientists, or existing employees who have been trained in new skills.

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Ctc Llc increased its stake in Amazon Com Inc (AMZN) by 4068.92% based on its latest 2019Q1 regulatory filing with the SEC. Ctc Llc bought 215,897 shares as the company’s stock rose 14.09% with the market. The institutional investor held 221,203 shares of the consumer services company at the end of 2019Q1, valued at $393.91 million, up from 5,306 at the end of the previous reported quarter. Ctc Llc who had been investing in Amazon Com Inc for a number of months, seems to be bullish on the $888.18 billion market cap company. The stock increased 2.83% or $49.67 during the last trading session, reaching $1804.03. About 4.81M shares traded or 8.42% up from the average. Amazon.com, Inc. (NASDAQ:AMZN) has risen 16.83% since June 9, 2018 and is uptrending. It has outperformed by 12.40% the S&P500. Some Historical AMZN News: 24/05/2018 – The homebuilder’s new homes are Wi-Fi certified, making them perfect showrooms for Amazon’s smart home devices; 17/04/2018 – KBS Fashion Group Limited Announces Signing of Cooperative Agreement to Open Amazon and Alibaba Express Online Stores; 02/04/2018 – Nike tops Wall Street expectations; confirms deal with Amazon; 07/03/2018 – Aylaa Exclusive: Amazon Buys Ring, Maker of Smart Home Products – The New York Times; 13/03/2018 – Leclerc, fearing Amazon, to launch Paris food delivery service; 25/04/2018 – NYSE TO REMEDIATE CONFIGURATION OF AMZN, BKNG, GOOG TONIGHT; 23/04/2018 – Inside Amazon’s Possible Plan to Build a Domestic Robot (Video); 02/05/2018 – NICE Cognitive Robotic Automation Platform Expands on Amazon Lex’s Self-Service Capabilities by Transforming Chatbot Requests; 18/04/2018 – Home Depot is launching its biggest tech hiring spree ever to protect its lead over Amazon; 02/05/2018 – Verizon’s Oath is ‘doubling down’ on Amazon’s cloud

Ballentine Partners Llc decreased its stake in Automatic Data Processing In (ADP) by 30.8% based on its latest 2019Q1 regulatory filing with the SEC. Ballentine Partners Llc sold 2,997 shares as the company’s stock rose 5.92% with the market. The institutional investor held 6,732 shares of the technology company at the end of 2019Q1, valued at $1.08M, down from 9,729 at the end of the previous reported quarter. Ballentine Partners Llc who had been investing in Automatic Data Processing In for a number of months, seems to be less bullish one the $72.65B market cap company. The stock increased 1.59% or $2.62 during the last trading session, reaching $166.92. About 1.11 million shares traded. Automatic Data Processing, Inc. (NASDAQ:ADP) has risen 25.81% since June 9, 2018 and is uptrending. It has outperformed by 21.38% the S&P500. Some Historical ADP News: 22/04/2018 – DJ Automatic Data Processing Inc, Inst Holders, 1Q 2018 (ADP); 10/04/2018 – U.S. ADP March National Franchise Report (Table); 06/05/2018 – FRENCH STATE SHOULD SELL ADP STAKE, FRANCAIS DES JEUX: LE MAIRE; 02/05/2018 – Automatic Data 3Q Worldwide New Business Bookings Rose 9%; 07/03/2018 – Ingo Money Provides Real-Time Mobile Check Funding Option to ADP® Paycards; 15/05/2018 – D.E. Shaw, Sachem Head Haven’t Decided Whether to Push for Change at ADP; 17/05/2018 – ADP SAYS CANADA ADDS 30.2K JOBS IN APRIL; 14/03/2018 – ADP ADP.PA – IN FEB INTERNATIONAL TRAFFIC (EXCLUDING EUROPE) WAS UP (+3.9%); 07/03/2018 – French Government to Launch Full Privatization of ADP -BFM Business; 18/04/2018 – ADP to Release Quarterly Workforce Vitality Report With Deeper Labor Market Insights on WEDNESDAY, April 25, 2018

More notable recent Automatic Data Processing, Inc. (NASDAQ:ADP) news were published by: Finance.Yahoo.com which released: “ADP unifies Payroll and HR for growth-focused businesses in APAC and EMEA with the launch of the solution iHCM 2 – Yahoo Finance” on May 13, 2019, also Nasdaq.com with their article: “Automatic Data Processing, Inc. (ADP) Ex-Dividend Date Scheduled for March 07, 2019 – Nasdaq” published on March 06, 2019, Forbes.com published: “Strong Growth In Client Base, Improving Cost Structure Drive ADP’s Profits – Forbes” on May 14, 2019. More interesting news about Automatic Data Processing, Inc. (NASDAQ:ADP) were released by: Nasdaq.com and their article: “FOREX-Dollar recoups earlier losses as market digests weak jobs data – Nasdaq” published on June 05, 2019 as well as Nasdaq.com‘s news article titled: “CSG Systems International, Inc. (CSGS) Ex-Dividend Date Scheduled for June 03, 2019 – Nasdaq” with publication date: May 31, 2019.

Ballentine Partners Llc, which manages about $4.78B and $1.92B US Long portfolio, upped its stake in Facebook Inc (NASDAQ:FB) by 16,730 shares to 17,230 shares, valued at $2.87M in 2019Q1, according to the filing. It also increased its holding in Starbucks Corp (NASDAQ:SBUX) by 12,257 shares in the quarter, for a total of 12,857 shares, and has risen its stake in Vanguard Bd Index Fd Inc (BND).

Since January 2, 2019, it had 0 insider purchases, and 15 sales for $18.12 million activity. The insider Albinson Brock sold $566,161. The insider Perrotti Thomas J sold $176,063. On Wednesday, January 2 Dyson Deborah L sold $527,231 worth of Automatic Data Processing, Inc. (NASDAQ:ADP) or 4,082 shares. The insider Politi Douglas W sold 2,275 shares worth $295,841. $5.42 million worth of stock was sold by Rodriguez Carlos A on Thursday, February 14. Ayala John had sold 6,428 shares worth $966,713 on Wednesday, February 13.

Investors sentiment increased to 0.95 in 2019 Q1. Its up 0.13, from 0.82 in 2018Q4. It is positive, as 49 investors sold ADP shares while 424 reduced holdings. 127 funds opened positions while 321 raised stakes. 340.50 million shares or 5.38% less from 359.86 million shares in 2018Q4 were reported. Moreover, Fort LP has 0.51% invested in Automatic Data Processing, Inc. (NASDAQ:ADP). Lakeview Lc stated it has 1,991 shares or 0.2% of all its holdings. Webster Natl Bank N A accumulated 15,205 shares or 0.35% of the stock. Moreover, Research Glob Investors has 0.04% invested in Automatic Data Processing, Inc. (NASDAQ:ADP). Haverford Serv, a Pennsylvania-based fund reported 1,644 shares. Bowen Hanes & Inc reported 285,330 shares. Barclays Public Limited Liability Corporation, United Kingdom-based fund reported 1.11M shares. Ghp Investment has 28,702 shares for 0.59% of their portfolio. Wealthcare Management Limited Co owns 59 shares. 1,980 are owned by Amica Retiree Med Tru. Evergreen Management Lc has 1,312 shares. Tctc Llc has 34,370 shares for 0.3% of their portfolio. 18,000 were reported by Jefferies Grp Ltd. Country Trust Savings Bank has 0% invested in Automatic Data Processing, Inc. (NASDAQ:ADP) for 150 shares. Pinebridge Lp reported 10,268 shares.

Analysts await Automatic Data Processing, Inc. (NASDAQ:ADP) to report earnings on August, 7. They expect $1.13 EPS, up 22.83% or $0.21 from last year’s $0.92 per share. ADP’s profit will be $491.81M for 36.93 P/E if the $1.13 EPS becomes a reality. After $1.77 actual EPS reported by Automatic Data Processing, Inc. for the previous quarter, Wall Street now forecasts -36.16% negative EPS growth.

More notable recent Amazon.com, Inc. (NASDAQ:AMZN) news were published by: Benzinga.com which released: “Today’s Pickup: May Flowers For Seattle Tech Scene – Benzinga” on May 17, 2019, also Nasdaq.com with their article: “Roku Is a Ray of Light in a Dark Time – Nasdaq” published on May 15, 2019, Seekingalpha.compublished: “Amazon: Dig Deeper – Seeking Alpha” on June 06, 2019. More interesting news about Amazon.com, Inc. (NASDAQ:AMZN) were released by: Nasdaq.com and their article: “What FedEx’s New 7-Day Schedule Means for the Stock – Nasdaq” published on June 08, 2019 as well as Bizjournals.com‘s news article titled: “Site of Amazon’s Bessemer fulfillment center sold in $3.7M deal – Birmingham Business Journal” with publication date: May 20, 2019.

The post Ballentine Partners Decreased Its Automatic Data Processing In (ADP) Holding as Valuation Rose; Ctc Has Upped Its Amazon Com (AMZN) Stake by $384.30 Million; Stock Price Rose appeared first on Artificial Intelligence.