Advanced analytics

The omnichannel retail environment has changed dramatically over the past year, with consumers quickly embracing online ordering, both for pickup and delivery.

The trend continued in 2021, with first-quarter e-commerce sales up 39.1% from last year’s levels and accounting for around 13.6% of total retail sales, according to the US Department of Commerce data.

The increase in omnichannel volume, combined with continued pressures in the labor market and challenges in the macroeconomic environment, is forcing retailers to rethink their supply chain operations and highlights the need to invest in both in deep learning and automation. Retailers are seeing faster inventory turns and inventory misalignments due to changes in customer habits, while poor inventory visibility leads to delays and higher costs, resulting in lost sales and an impact on margins. Retailers are now focusing on aligning their supply chain with the aspirations of their omnichannel customers.

Major retailers have taken advantage of advanced data analytics to react quickly to market changes and reduce their logistics costs. Increasingly, retailers are turning to artificial intelligence (AI), machine learning (ML) and deep learning (DL) solutions to improve the efficiency of their supply chains and automate processes. supply chain decisions.

For example, a recent report from McKinsey & Co.¹ found that the successful implementation of AI-powered supply chain management has enabled early adopters to improve logistics costs by 15%, stock levels 35% and service levels 65%, compared to slower competitors.

“Retail supply chain operations seek to use AI-powered models to more accurately forecast demand, improve throughput at distribution centers and optimize last mile delivery,” said Azita Martin, Managing Director of AI in Retail, Consumer Staples, and Restaurants at NVIDIA.

“AI is becoming an essential element in creating the operational agility necessary to respond to market opportunities and challenges,” she said.

Supply chain solutions leveraging AI, DL and ML help retailers, consumer staples manufacturers and restaurants to optimize demand forecasting, reduce omnichannel fulfillment costs and to streamline warehouse, shipping and other logistics operations, promoting both efficiency and customer satisfaction.

Demand forecast, which has often relied on batch processing that can take weeks using incomplete data, is one of the key areas where AI is driving a return on investment in retail, CPG and CPG operations. catering.

It allows retailers to adjust sales forecasts faster based on trends, consumer behavior, and other variables to ensure inventory levels are optimized by store location and products are in stock to boost the sales.

“With faster models, retailers are able to make frequent decisions aligned with their sourcing and on-site delivery policies,” said Nick Underwood, CTO Retail, Dell Technologies.

Amid increasing demands for efficiency in the supply chain, retailers can use hyper-tagged real-time demand data and the ML powered by graphics processing units (GPUs), to improve sales forecasting with instant learning, he said.

“It can transform the performance of customers’ supply chain, from purchasing to warehouse transfers, production planning and shipping,” said Underwood.

Likewise, demand forecasting also plays an important role in the fast food industry by helping to regulate production levels to avoid wastage and thus improve margins. Operators in this segment use advanced analytics to predict customer flow and more accurately define production levels, as well as optimize ingredient inventory.

Adapting to omnichannel retail

In retail, the sharp increase in consumer demand for curbside pickup and online shopping, in-store pickup (BOPIS) services, as well as rising expectations for day-to-day fulfillment Likewise, have put pressure on retailer distribution models and increased interest in automation.

Target, for example, reported that sales through its drive-up service, in which it processes same-day orders by bringing them directly to consumers’ cars, increased 600% last year.

Retailers are responding to omnichannel demands by exploring automated solutions in the form of micro-distribution centers using automated order picking, warehouses with robotic pickers and autonomous vehicles, and even autonomous vehicles that deliver directly to customers’ homes. AI supports vision-based systems for these solutions with advanced product recognition and navigation technologies that learn on the job and can even be trained in the lab using digital twinning.

To meet the demands of picking and shipping and packaging operations that require rapid visual inspections of items moving on conveyor belts, companies are also using computer vision-based visual inspection for quality control. and precision.

Retailers are also looking to minimize shipping costs by using advanced “sorting” technologies that minimize carrier handling costs. The automation of the sorting process to deliver products to people’s homes, as well as the optimization of delivery routes for carriers, helps speed up delivery times, increase successful deliveries and reduce spending on last mile. For example, businesses are using AI to estimate delivery times and monitor the impact of potential disruptions, allowing retailers to react instantly and select alternate delivery routes.

With the challenges of today’s work environment, the need for increased supply chain efficiency, and the growing demand for omnichannel shopping, there has never been a better time for retailers to make their supply chains ‘smarter’ and more agile with technologies that leverage AI.

Visit this Web page to learn more about NVIDIA’s AI solutions for retail.

Learn more about Dell Technologies Retail Solutions.

PLANO, Texas, August 3, 2021 / PRNewswire / – CROSSMARK, a leading sales and marketing services agency, announced the nationwide launch of Accelerator, a leading data analytics platform for the sales and marketing activities of GIC. The custom platform, developed with industry partners IBM, Microsoft and other leading technology companies, combines one of the largest CPG datasets with best-in-class analytics power to deliver intuitive visualization of critical analytical tasks for smarter, faster selling. growth.

We live in a world that is constantly creating more data. CPG operators have access to detailed information on virtually all transactions in supermarkets, pharmacies and mass merchants. However, the sheer volume of this data makes it difficult to sift through and find the important information. Accelerator tackles this problem head-on with a design that weaves together different sources of information to uncover distinctive ideas, then recommends the best course of action. The platform is designed to evolve over time by leveraging machine learning (AI) capabilities and ingesting new data sources. “Customers deserve the power of artificial intelligence and an advanced data platform to make sense of a world of big data. And CROSSMARK delivers exactly that with Accelerator, ”says CROSSMARK CEO, Chris Moye.

Developing the accelerator has been an organizational priority involving hundreds of associates organized into cross-functional teams totaling over 100,000 hours of work over a 20-month period. With the active participation of Insights and Sales, Accelerator is unique in the industry for having been designed from the ground up for the operation of a sales agency. Said Nawshi williams, Vice President of Insights, “Accelerator will be our associates ‘tool of choice to quickly analyze issues, uncover opportunities and develop the best possible plan to exceed our clients’ expectations. This platform makes such a difference that we have decided to make it available to all of our head office customers, and not just the privileged few. “

It is important to note that CROSSMARK has invested in its staff to develop new analytical and presentation skills. All Sales and Insights Associates have completed a rigorous training program using the CROSSMARK method. Combining instructions on sales, information, category management and e-commerce, the CROSSMARK method creates a set of standards so that clients and customers benefit from a consistent and professional approach that delivers better business results. President of the CROSSMARK sales agency Jami mcdermid said: “Our value proposition has always been people, tools and training. The combination of the accelerator and the CROSSMARK method enables our employees to sell smarter and accelerate the growth of those we serve. “

To learn more about the CROSSMARK accelerator, please visit, https://www.crossmark.com/accelerator.

ABOUT CROSSMARK, INC.

CROSSMARK is a leading sales and marketing agency specializing in the growth of retail brands in nearly every category of the consumer goods industry. For over 100 years, CROSSMARK has accelerated the world’s most powerful businesses, driving sales and managing brand success. Our core services include head office sales, retail services and marketing services. This includes e-commerce solutions, omnichannel expertise, insights and analytics, and checkout, as well as in-store and out-of-store consumer engagement.

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DUBLIN, July 29, 2021 / PRNewswire / – The Advanced Analytics Market Size, Share & Trend Analysis Report By Type (Big Data Analytics, Business Analytics), By Deployment (On-Premises, Cloud), By Company Size, By end use, by region and segment forecast, 2021-2028 “ report was added to ResearchAndMarkets.com offer.

The global advanced analytics market size is expected to reach $ 74.99 billion by 2028, registering a CAGR of 25.7% over the forecast period. Growing use of analytics by incumbents in Retail, IT & Telecom and BFSI, among others, for various functions, such as behavior analysis, recognition of buyer patterns , forecasting demand and optimizing business promotion, is expected to drive the market growth. The outbreak of the COVID-19 pandemic has particularly triggered the adoption of advanced analytics, as incumbents in various end-use industries resort to remote working and subsequently opt for advanced analytics to ensure operational stability. and optimization of business processes by leveraging data-driven decisions. manufacturing. On the other hand, the growth of the IT industry in emerging economies, such as Brazil, Argentina, The Philippines and Egypt, fueled the demand for workflow optimization, which is subsequently pro
ected to stimulate demand for advanced analytics.

Aggressive investments by incumbents from various industries and capital-intensive industrial sectors, such as manufacturing, automotive and pharmaceuticals, in adopting advanced and digitization technologies for process optimization are expected to drive growth of the market during the forecast period. The use of big data analytics in process optimization allows industrial manufacturers to identify variables that affect production quality in real time, rapidly improve the production process and strengthen the supply chain. supply. At the same time, as the demand for predictive solutions increases, market players are aggressively launching cutting-edge analytics software solutions that can be delivered in the cloud. For example, Angoss Software Corporation, SAS Institute, Inc., and International Business Machines Corp., among others, offer their respective advanced analytics software solutions primarily in the cloud.

Advanced analysis emerges as an essential tool for predicting and forecasting exchange patterns, electricity consumption patterns and peak-hour traffic conditions. Advanced analytics can potentially help organizations improve strategic decision making, run operations smoothly, and subsequently increase profitability. Along with incumbents in various industries and verticals, such as military and defense, banking, healthcare, and professional services, government agencies are also aggressively investing in big data analytics. Massive adoption of analytics for processing and protecting large volumes of stored data using cloud services, such as Google Drive and Microsoft OneDrive, is expected to drive market growth.

The outbreak of the COVID-19 pandemic has prompted organizations to reconfigure and reorganize their business operations. In particular, progressive analysis enables companies to reorganize their analytical models and data management processes according to changing business environments. At this point, the measures implemented to contain the spread of the coronavirus, such as closures, restrictions on travel and homework, and remote work policies, coupled with changing end-user behavior, are expected to play a vital role in driving the market growth during the forecast period.

Highlights of the Advanced Analytics Market Report

• The big data analytics segment accounted for the largest market share of over 30% in 2020. The growing popularity of social media and the growing number of online virtual offices have drastically driven the growth of the segment.
• The cloud segment is expected to grow over the forecast period as companies can store business data, including procurement data, customer data, and transactional data, in the cloud and adopt advanced analytics based on the cloud to support remote working and enable real-time decision making.
• Large enterprise segment is expected to experience significant growth during the forecast period as large enterprises focus on improving productivity and efficiency by adopting various advanced analytics solutions, such as analytics business and customer analytics, to efficiently manage large volumes of customer and asset data.
• The BFSI segment is expected to register a CAGR of 25.1% from 2021 to 2028. The growing need to optimize processes, identify fraudulent transactions and facilitate risk management in banks and financial institutions is expected to drive growth of the market.
• The Asia Pacific regional market is expected to reach $ 13.33 billion by 2028. As the growing e-commerce industry in countries such as India, China and Singapore, has increased the demand for predictive analytics and demand forecasting, the increasing incidences of fraud in the BFSI industry have also triggered the demand for advanced analytics in the region

Main topics covered:

Chapter 1 Methodology and Scope

Chapter 2 Executive Summary

Chapter 3 Advanced Analysis Industry Outlook
3.1 Market Segmentation and Scope
3.2 Market size and growth outlook
3.3 Advanced Analysis Market – Value Chain Analysis
3.3.1 Supplier landscape
3.4 Advanced Analysis Market – Market Dynamics
3.4.1 Market Driver Analysis
3.4.1.1 Increase in the amount of data generated worldwide
3.4.1.2 Growing popularity of online shopping and growing penetration of social networks
3.4.1.3 Growing need to meet industry-specific challenges
3.4.2 Analysis of market challenges
3.4.2.1 Lack of data integration and connectivity
3.4.3 Analysis of market opportunities
3.4.3.1 Growing demand from the BFSI sector
3.5 Mapping of penetration and growth prospects
3.6 Advanced Analysis Market – Porter’s Five Forces Analysis
3.7 Advanced Analytics Market Company Market Share Analysis, 2020
3.8 Advanced Analysis Market – PESTEL Analysis
3.10 Advanced Analysis Market – COVID19 Impact Analysis

Chapter 4 Outlook Advanced Scan Type
4.1 Advanced Analysis Market Share by Type, 2020
4.2 Big data analysis
4.2.1 Advanced Big Data Analytics Market, 2016 – 2028
4.3 Business analysis
4.3.1 Advanced Business Analysis Market, 2016-2028
4.4 Customer analysis
4.4.1 Advanced Customer Analytics Market, 2016 – 2028
4.5 Risk analysis
4.5.1 Advanced Risk Analysis Market, 2016 – 2028
4.6 Statistical analysis
4.6.1 Advanced Statistical Analysis Market, 2016-2028
4.7 Others
4.7.1 Other Advanced Analytics Market, 2016 – 2028

Chapter 5 Deploying Outlook Advanced Scans
5.1 Advanced Analytics Market Share by Deployment, 2020
5.2 On site
5.2.1 Advanced On-Premises Analytics Market, 2016 – 2028
5.3 Cloud
5.3.1 Advanced Cloud Based Analytics Market, 2016-2028

Chapter 6 Advanced Analyzes Company Size Outlook
6.1 Advanced Analysis Market Share by Company Size, 2020
6.2 Large companies
6.2.1 Advanced Analytics Market in Large Enterprise, 2016 – 2028
6.3 Small and medium-sized enterprises (SMEs)
6.3.1 Advanced Analytics Market in SMEs, 2016 – 2028

Chapter 7 Advanced Analysis of End-Use Outlook
7.1 Advanced Analysis Market Share by End Use, 2020
7.2 BFSI
7.2.1 Advanced Analytics Market in BFSI, 2016 – 2028
7.3 Government
7.3.1 Advanced Analytics in Government Market, 2016 – 2028
7.4 Health care
7.4.1 Healthcare Advanced Analytics Market, 2016 – 2028
7.5 IT & Telecom
7.5.1 Advanced Analytics in Information and Telecommunications Technology Market, 2016 – 2028
7.6 Military & Defense
7.6.1 Military and Defense Advanced Analytics Market, 2016 – 2028
7.7 Others
7.7.1 Advanced Analytics in Other End Uses Market, 2016 – 2028

Chapter 8 Regional Perspectives for Advanced Analysis

Chapter 9 Competitive Landscape
9.1 Altair Engineering, Inc.
9.1.1 Company overview
9.1.2 Financial performance
9.1.3 Product Benchmarking
9.1.4 Strategic initiatives
9.2 Fair Isaac Corporation
9.2.1 Company overview
9.2.2 Financial performance
9.2.3 Product Benchmarking
9.2.4 Strategic initiatives
9.3 International Business Machines Corp.
9.3.1 Company overview
9.3.2 Financial performance
9.3.3 Product Benchmarking
9.3.4 Strategic initiatives
9.4 KNIME
9.4.1 Company overview
9.4.2 Financial performance
9.4.3 Product Benchmarking
9.4.4 Strategic initiatives
9.5 Microsoft Corporation
9.5.1 Company overview
9.5.2 Financial performance
9.5.3 Product Benchmarking
9.5.4 Strategic initiatives
9.6 Oracle Corporation
9.6.1 Company overview
9.6.2 Financial performance
9.6.3 Product Benchmarking
9.6.4 Strategic initiatives
9.7 RapidMiner, Inc.
9.7.1 Company overview
9.7.2 Financial performance
9.7.3 Product Benchmarking
9.7.4 Strategic initiatives
9.8 SAP SE
9.8.1 Company overview
9.8.2 Financial performance
9.8.3 Product Benchmarking
9.8.4 Strategic initiatives
9.9 SAS Institut Inc.
9.9.1 Company overview
9.9.2 Financial performance
9.9.3 Product Benchmarking
9.9.4 Strategic initiatives
9.10 Trianz
9.10.1 Company overview
9.10.2 Financial performance
9.10.3 Product Benchmarking
9.10.4 Strategic initiatives

For more information on this report, visit https://www.researchandmarkets.com/r/5zkkf3

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As part of continuing to strengthen its position in the world of golf, the LPGA Tour made the timely announcement of a partnership with KPMG LLP, the US audit, tax and advisory firm.

It comes the same week that the 2021 PGA Women’s KPMG Championship at the Atlanta Athletic Club takes place. The event is scheduled to end today.

The new development is seen as an initiative that “will further elevate women’s sport by introducing KPMG Performance Insights, a technology platform that will provide data insights and advanced performance analytics for the LPGA Tour”.

The solution was launched this week at the Atlanta Athletic Club and implemented across all LPGA Tour events, the Tour said.

The LPGA has “undertaken an intensive initiative over the past five years to restructure” its database of historical scores and statistics.

“The introduction of full-field shooting-level data and advanced analytics through KPMG Performance Insights, such as shots won, proximity averages, and performance indexing, is the next step towards the long-term progression of KPMG Performance Insights. data on the LPGA Tour and provides critical information for players to diagnose and improve their performance.

LPGA Sales Manager Kelly Hyne said, “Stats and scores are key to telling truly dynamic stories of our LPGA Tour players. This new partnership will provide us with the next level of advanced data to improve storytelling for our players and key stakeholders.

“We are proud to have a partner like KPMG who shows their commitment to our players and to women’s football as a whole.

KPMG US President and CEO Paul Knopp said, “We’re bringing this transformative business solution to the LPGA Tour, bringing our company’s industry-leading data and analytics capabilities to a game-changer. “

ATLANTA – Advanced analytics is officially coming to professional women’s golf.

In partnership with accounting firm KPMG, the LPGA Tour announced the deployment of a performance analysis and analysis platform at this week’s PGA Women’s KPMG Championship. The platform will “provide data insights and advanced performance analytics” for all LPGA events, including proximity averages, performance indexing and hits won.

“There is a disparity between what men value in terms of data and analytics and what women have,” said Paul Knopp, CEO of KPMG. “We thought it wasn’t fair. We wanted to see a fairer world when it comes to golf. “

With this injustice in mind, Knopp and the entire KPMG team decided it was time for a change. They started out as the title sponsor of the PGA Women’s Championship in 2015 – almost doubling the size of the purse at that time – and are now turning to data and analytics.

KPMG took the principles it used to deliver data analytics-based insights for Fortune 500 companies and applied them to this new platform. The result is that top female golfers finally get an advanced insight into their games.

“We aspired, as part of our own reflection on diversity and inclusion, to see professional women in golf elevated to similar platforms and levels of success to those men enjoy,” said Knopp. “We really wanted to do something for women in golf, while also thinking about women in general in the world. “

The LPGA plans to tout the new functionality across all of its media platforms, including broadcast integration and presence on its digital and social platforms.

This platform will not only allow viewers and media players to preview information typically seen only on the PGA Tour, but it will also transform the way LPGA players view their games.

“It’s hard to put into words what this program will mean to me and to all the players,” said Stacy Lewis, 13-time LPGA Tour winner. “The information, data and statistics will help me improve my performance and the way I approach every stroke, set and tournament.”

Publisher of Golf.com

Zephyr Melton is associate editor for GOLF.com where he spends his days blogging, producing and editing. Prior to joining the GOLF.com team, he attended the University of Texas, then stopped with Team USA, the Green Bay Packers and the PGA Tour. He assists on all instructional matters and is the staff’s self-proclaimed “expert” on development tours.

TYSONS, Virginia, June 16, 2021 / PRNewswire / – Case point, a leader in cloud-based legal technology solutions, today announced that its AI and advanced analytics technology, called CaseAssist, has been significantly enhanced to give users more information and control over the scanning process with enhanced visualization capabilities and configuration templates. The enhancements to CaseAssist come at a pivotal time for analysis in the legal profession. A recent LexisNexis to study finds that 70% of law firms now use analytics for both legal practice and legal matters, 98% report legal analytics has improved their firm’s performance, and 92% plan to increase their use of analytics over the next 12 months.

Predictions generated by Casepoint’s CaseAssist technology in eDiscovery, surveys and other document intensive review projects eliminate the need for users to review documents that are almost certainly irrelevant, saving thousands of dollars in review time. With CaseAssist Active Learning (CAL), users can choose to train one or more models without any sample set requirement and CaseAssist will ensure that relevant documents are prioritized for review. Whether your use case involves sowing an algorithm with known relevant examples or from a complete blank slate, CaseAssist will quickly separate the wheat from the chaff.

Casepoint’s Dynamic Batch Review workflow integrates seamlessly with CaseAssist to easily transition from prioritization to linear review. by Casepoint patented AI technology provides users with faster relevance predictions with extremely high levels of accuracy and quickly identifies connections between people, documents, dates, and terms. Casepoint speeds up the preview up to 83%. For example, a complainant company used advanced analytics to collect data in a case involving a group of commercial airlines.

“At Casepoint, we don’t think of AI as ‘nice to have’ – rather it’s an essential part of our technology,” said Vishal Rajpara, co-founder and CTO at Casepoint. “Increasingly, law firms and legal departments recognize that the use of analytics can increase competitiveness, efficiency and knowledge in a wide range of activities, including case strategy, early case assessment, litigation or settlement decisions, and more. Human-centered design approach being developed with the goal of making advanced eDiscovery solutions accessible to users of all technological abilities. It’s part of our commitment to continuous innovation so that we can respond quickly to the business needs and rapidly evolving use cases of our customers. “

by Casepoint advanced analysis provide powerful data visualizations, communication analysis, charts, concept search, topic grouping, email discussion thread, and near-similarity / nearly fooled detection. In addition to guiding the user through the often complex workflow of advanced analysis, Casepoint’s CaseAssist Active Learning uses active learning based on user input to continuously predict and categorize unrevised documents. CaseAssist Active Learning supports full verification, accuracy, recall, and F measurement reports. With CaseAssist’s multi-classifier classification feature, users can train the system to identify multiple tags in the same training set . CaseAssist is commonly used in intelligence mining, early data analysis, survey analysis, exam prioritization, and coding validation. CaseAssist also includes chat-guided workflows with natural language interaction and allows users to quickly find key documents by answering simple questions.

CaseAssist is constantly learning from the decisions and behavior of its users, translating that insight into actionable insights and recommendations. Casepoint’s patented AI technology helps litigators improve how they perform critical tasks such as:

• Better understand the data involved in legal matters and use that information to drive a smarter eDiscovery process from the start.
• Improve the document review management process and help legal teams make the most of review resources.
• Enable stakeholders to make quick strategic decisions on legal matters – save money and achieve better business results.

“CaseAssist has been widely adopted in our firm, and there are good reasons for it: It’s fast, it’s easy, and lawyers know it’s there, so they don’t hesitate to ask or to use it themselves, ”said Dana wesley sarti, Head of Litigation Support at Robbins Russell. “While timeline analysis and sender / recipient analysis are probably the most popular features for our teams, we also use Casepoint analyzes to do things like spot gaps by date or file type in the file. content loaded, determine data volumes by individual custodian, identify odd file types, and identify sender and receiver domains to bulk identify documents that are unlikely to be relevant for initial or early review. Because CaseAssist is so easy to learn, more of our lawyers are using it themselves. Additionally, the use cases are virtually limitless, which is why adoption continues to increase across the enterprise. “

For more information visit Casepoint AI and advanced analytics

About Casepoint
Trusted by leading companies, government agencies, law firms and legal service providers, Casepoint empowers organizations to maximize efficiency and reduce risk through its innovative legal discovery platform. Casepoint’s robust, cloud-based, highly scalable and secure legal discovery platform was designed to easily handle increasingly complex litigation, investigation and compliance needs, with cloud collections, data processing, advanced analytics, artificial intelligence, review and production, all in an easy-to-use web interface. The Casepoint platform represents the next generation of legal technology that will enable your organization to increase its productivity, efficiency and business results. For more information, visit www.casepoint.com.

Media contact
Carolyn Depko
RP plate4orm
[email protected]

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“Times are unpredictable, but with SAS and Microsoft we have two key partners who we believe can help us adapt and thrive in any circumstance,” said Aaron LeValley, Senior Vice President of Commercial Operations and Strategy at AEG Sports. “We might not know what’s going to happen, but we do know that SAS and Microsoft have our goals in mind and will allow us to meet any challenges we may face.”

“Customers like AEG benefit from the strategic partnership between SAS and Microsoft because they can seamlessly move workloads to the cloud and discover new efficiencies and real-time responses,” said Rodrigo Rocha, Managing Director of Cloud Solutions and ISV Alliances at Microsoft. “Today more than ever, digital transformation is essential as organizations learn to adapt to new customer behaviors. SAS Viya on Azure helps make this transformation easier and faster, delivering immediately actionable and much-needed insights.

Analyze fan sentiment
Using analytics to understand fan sentiment has been crucial. Using SAS Viya, the AEG Sports analytics team created models to gauge fan perspectives on things like returning to the games in person. By continuously analyzing fan sentiment during the pandemic, AEG Sports was able to personalize messages based on interest and feelings about personal safety, and identify fans who needed targeted retention efforts. By using these tactics, AEG Sports kept the fans happy and increased the ROI of the marketing campaigns.

“Much of our communication with fans during the COVID-19 pandemic has been quite informal, often through surveys, which we use to gather and analyze their attitudes and feelings,” said John D’Onofrio. , business analyst at AEG Sports. “I’m going to take all of the survey data in SAS Viya and use text topics to analyze sentiment and identify key themes to see how fans are feeling and what the overall attitudes are. As a result, a lot of our fan engagement has less of a sales tone and more of a conversational tone. “

“I’m a big believer in customer focus – or in this case, fan focus – and it’s exciting to see AEG Sports use analytics to build stronger relationships with their fans,” said Jennifer chase, Senior Vice President and Head of Marketing at SAS. “Sports teams have a unique relationship with their ‘customers’, and AEG Sports’ passion for their fans has been evident during the pandemic as they use analytics to truly understand fan reaction to disruption and determine how best to deal with it. supporting them through it all. A fan experience with heart and humanity will inevitably lead to stronger bonds with fans, especially when fans recognize that their team understands their needs – in good times and in bad times. “

Use analytics to make optimal decisions
The company also uses the integrated capabilities of SAS Viya for contingency planning. The ability to quickly ingest and model data from multiple sources – including ticketing, partnerships, marketing, weather reports, and health experts – is helping AEG Sports reduce uncertainty related to the pandemic.

“SAS Viya helps us analyze different scenarios and choose the right option,” said Mike marsocci, Senior Director of Data and Analytics at AEG Sports. “Now, instead of being stuck in endless scenarios, we can tweak settings as they happen and quickly determine the best course for our teams to take.”

When fans can safely return to the arenas, AEG Sports will need a flexible way to price tickets based on changing factors such as ticket demand, venue capacity, public health restrictions and other variables. Machine learning – a subset of AI – will help AEG Sports determine the ideal price for tickets. “The range of machine learning enhanced models and visualizations offered by SAS Viya are extremely useful in helping us analyze millions of records to identify these optimal prices,” added Marsocci.

Data visualization is the key to rapid decision making
AI technology is at the heart of AEG Sports’ drive to continually increase the efficiency of its modeling efforts. The outputs from these models often become data visualizations, which AEG Sports uses to chart its daily operations.

“Data visualization is essential to our business,” D’Onofrio said. “Most people are visual learners, so being able to show a visual representation of the data has been extremely beneficial in gaining buy-in. Today, almost every department in our business uses data visualizations to make decisions and operate more efficiently. “

The ability to create these visual reports is especially useful during the hectic hockey season.

“We need to transform data for games quickly, so it’s critical to have daily reports with visualizations that sales managers can see and understand immediately, especially when there’s a new game every two days.” , explained D’Onofrio. “SAS Viya makes it easier for users to understand the results of data analysis in order to make reliable decisions. “

For more information on SAS Sports Analytics, please visit sas.com/en_us/industry/sports.html

Today’s announcement is in conjunction with SAS Global Forum 2021, the first global conference on AI and analytics. Due to the ongoing pandemic, this year’s conference is once again virtual and free.

About SAS
SAS is the leader in analytics. Through innovative software and services, SAS empowers and inspires customers around the world to turn data into intelligence. SAS gives you THE POWER TO KNOW^®.

About AEG
Based at Los Angeles, California, AEG is the world leader in sports and live entertainment. The company operates in the following business segments: Facilities, which, through its affiliation with ASM Global, owns, operates or consults over 300 world-leading arenas, stadiums, convention centers and performance venues; Music through AEG Presents, which is dedicated to all aspects of contemporary live music performances, including the production and promotion of global and regional concert tours, music and special events and world renowned festivals such as the Coachella Valley Music and Arts Festival; Real Estate, which develops world-class venues, as well as major sports and entertainment districts like the STAPLES Center and LA LIVE, Mercedes-Benz Platz in Berlin and O2 in London; Sports, as the world’s largest operator of high-performance sporting events and sports franchises, including the LA Kings, LA Galaxy and Eisbären Berlin; and Global Partnerships, which oversees global sales and sponsorship servicing, including naming rights, premium seats, and other strategic partnerships. Through its global venue network, portfolio of powerful sports and music brands, and integrated entertainment districts, AEG entertains more than 160 million guests annually. You can find more information about AEG at www.aegworldwide.com.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the United States and other countries. ® indicates United States registration. Other brand and product names are trademarks of their respective companies. Copyright © 2021 SAS Institute Inc. All rights reserved.

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Many analysts and members of the media see an NFL schedule and immediately start making predictions based on the statistics from the previous season – usually the opponents’ winning percentage. Naively following this trend would tell you the Falcons have one of the easiest schedules in the league as their 2021 opponents finished with an overall winning percentage of 45.4% – the fourth easiest schedule by this metric. .

I never understood how to assess a team in terms of wins and losses from one season to the next. The NFL is a much more complex system than that, and while there is no perfect strength of timing measures, the use of future win totals provided by Vegas and other advanced measures such as the opponents’ offensive and defensive efficiency are much better indicators of future results. . Vegas win totals more accurately describe what teams might look like going forward rather than seasons past.

Based on those win totals, the Falcons have the 12th easiest schedule, according to SharpFootballStats. Atlanta has nine clashes next year against teams expected to win nine or more games – better than a record 0.500. By itself, there are eight games where the clashes are against teams expected to win eight games or less – below a record 0.500. Calendar strength jumps eight slots when using an opponent’s future win totals from the previous year’s record.

There are other ways to analyze how difficult or easy a team’s schedule will be, such as the opponent’s effectiveness. Based on the effectiveness of the opposing Falcons team, Atlanta has the 20th easiest schedule in the league – much different from the fourth easiest based on last year’s winning percentages. You can further break down opponents into offensive and defensive efficiency. Based on the opponent’s offensive efficiency, the Falcons defense has the 11th easiest schedule. This means the Falcons will play more below-par than above-average offenses. By changing sides of the ball, the Falcons’ offense will have the easiest 24th schedule depending on the opponent’s defensive effectiveness.

Warren Sharp is an analytical football pioneer and has looked at the other limits on each team’s schedule. Although Falcons fans are made worse by the lack of home games – only seven games at Mercedes-Benz Stadium – Atlanta doesn’t have much downsides in terms of the team’s days off. Actually, there is a slight downside, but the timing is not as bad as it looks. When ranked by cumulative net rest, the Falcons placed 20th with a -3 net rest advantage.

resting edges for the 2021 NFL season

sorted by cumulative net rest

each block indicates play with rest imbalance

positive means a quiescent front which wk

negative means inconvenience of rest pic.twitter.com/5IxROXtpVL

– Warren Sharp (@SharpFootball) May 14, 2021

Sharp also used readiness variables in combination with the rest variables to illustrate how much rest teams score versus opponents on a weekly basis. The Falcons sit 12th in the pre-break standings.

I don’t think people realize how important the calendar release is

it’s not WHO you play, we knew that

the NFL does not control that

the NFL controls WHEN you play, how much rest you get and whether you have an advantage

it’s crazy how unbalanced

?? https://t.co/agtoYL560c pic.twitter.com/YyACYIaTbl

– Warren Sharp (@SharpFootball) May 14, 2021

Basically, I’m saying all of this because there are thousands of ways to analyze a team’s schedule, but the moving parts of an NFL team are incomprehensible to ordinary people like you and me. There are more factors than the winning percentage from the previous year. But no one can know how the teams are really going to behave; these are all projections.

The NFL rosters have more turnover than any other in professional sports – coaches, players and front office members included. The change of scheme, the change of personnel, the regression of players all play a role. The only thing that can be agreed with confidence is the time and place of the match. An example of what I mean by this is that the Falcons face the Bills at Highmark Stadium at the end of the season, which obviously has the potential to suffer from inclement weather. That can be certain, but not how well the bills will fare at that point. Sure, we’d expect Buffalo to be a Super Bowl contender, but that’s if all goes well, which it rarely does.

I keep fuming, but I’m almost done. No one knows if the schedule will be tough or not until we are done with the season. The early bye is unfortunate, and the home game in London is unlucky, but other than that it’s all speculative.

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Whether your organization is already running like a well-oiled machine, whether it still has a few tough spots, or is considering a new energy exchange and risk management system (ETRM) or upgrade, establishing a strong capability for advanced analytics and data visualization will immediately increase the value you get from your systems and underlying data.

FOLLOWING: Key Practices for ETRM Conversion, Failover, and Commissioning

From trading and risk control to finance and accounting, putting data to work for your organization empowers your team to understand the wealth of information deeply embedded in your organization’s systems and make better, faster decisions. in complete confidence. The advantage, in terms of value, is immense.

But building an effective analytics platform isn’t easy. To do this, the organization, from the leadership to the front lines responsible for data entry, will need to embrace the value that can be created to put the prerequisite steps in place and build a solid base of high quality data.

(Example of visual representation – Source: Opportune LLP)

Data analytics and visualization are buzzwords in today’s information age, but what are the real world applications in ETRM? Here are some areas of a downstream business that an advanced analysis initiative will have a significant impact:

• Trade – Everyone knows that there is not a single screen or dashboard that can contain everything a trader wants to see. In today’s digital age, there is more data stream to process, not less. For a trader, useful analyzes must meet at least two main objectives: 1) ease of access and 2) responsiveness. Traders need to be able to quickly access new data feeds as they monitor the markets and determine how one data feed will (or not) impact another. And, when they identify the data they want to analyze, the responsiveness of the solution to deliver the analyzes is critical. Of electricity markets which move minute by minute to crude markets moving at a slightly slower pace, if a trader has to wait for a response then someone else in the market can seize the opportunity.
• Risk control – Real-time visibility of positions in corporate portfolios gives risk offices the ability to make quick decisions to protect against rapidly changing market conditions. Accurate forecasting of long-term positions, coupled with up-to-date solvency, enables the risk office, finance teams and traders to make sound contractual decisions that meet compliance standards and maximize future profitability.
• Planning – Planners want to go beyond summary reports and create dashboards to forecast inventory issues, integrating inventory with planned movements, lift trends, and price data to highlight points of potential pinching.
• Accounting – For transactions with a high number of transactions (trolley sales), viewing the entire data flow from bill of lading (BOL) to cash application (school bus report – make sure all children go up and are all delivered to the correct bus stop) allows accounting to work proactively and based on exceptions.
• Finance and management – The combination of operational and accounting data via corporate performance management tools allows management to understand which operational levers have a historical impact on results and, more importantly, in the future. This turns the process of budgeting, planning and forecasting into an ongoing process that adapts to changes in the business and the market.

FOLLOWING: The cybersecurity risk posed by your old ETRM system

Sounds good, but the business needs to invest in three pillars – a strong database, accessibility of information, and self-service capabilities – to leverage the data produced by your organization. Let’s take a look at each of these cornerstones in more detail.

1) Data foundation

Begins with the secular acronym GI = GO (garbage in = garbage out). It almost sounds silly to say today, but there are still many organizations struggling with data quality in ETRM and ERP systems. There isn’t a silver bullet that fixes all data quality issues, but there is a common thread for organizations that lead data quality:leadership and responsibility. From trading to the mid-office to the back-office, it is up to the management of each group to stress the importance of data quality and to hold their group (s) accountable.

• Strong master data management and governance: Master data management must be centralized to standardize data between the different systems used by the business, such as customer data, locations and products. This makes reporting across multiple systems easier and faster by avoiding the repetitive and tedious exercises of data mapping and translation to make data consistent across different data sources. This is reinforced by data governance to effectively maintain data consistency between groups.
• Data capture: Simple, user-friendly tools should be used by the business to capture data with validation checks to ensure data is captured in the correct formats. This can be done through user engagement web platforms such as Microsoft Dynamics 365 Business Apps, Salesforce, Mendix, OutSystems, etc., which offer modern user experiences to enter or download data when and where a user wants ( for example, native mobility). These solutions can scale quickly with changing business needs through low-code development.
• Data storage strategy: The democratization of analysis will quickly overwhelm most transactional query systems. A data storage strategy is therefore essential to deliver the necessary data without disrupting day-to-day transactions. This includes determining what data should be available, as well as when and how to replicate it, and how to streamline the process for new data requests.
• Understand what “real time” means for the business: No one wants to wait for the data to be available, even if maybe not in the analysis system. Each business scenario will have different definitions of what “real-time” data means, and business and IT need to be able to articulate the value of the cost of on-time performance.

FOLLOWING: Software quality assurance and implementation project of your ETRM

2) Accessibility

Modern analysis tools like Tableau Software and Microsoft’s Power BI surface data integrated deep into disparate systems provides greater access to information for business users than ever before. There is power in information. Empower the organization not only to understand the health of the business and how individual business areas contribute to the business as a whole, but also to predict future performance and identify the levers that boost performance.

To do this, the company will need:

• A corporate business intelligence tool – It is a single platform that serves as a hub for all reporting and analysis needs across the enterprise. Many companies have multiple reporting solutions managed by different groups. This inevitably leads to different and competing versions of the truth, which creates conflict and long reconciliation across conflicting perspectives. Avoid this by establishing a common, centralized analytics platform.
• A minimalist approach to design – Dashboards and reports need to be clear, simple, and intuitive to reduce friction for users to understand what’s in front of them. Testing new relationships with users and listening to feedback will help identify sticking points when consuming information.
• Mobility – Whichever analysis tool is selected, it should provide mobile functionality to enable users to access information from their mobile devices while on the go. It usually starts off as a ‘nice to have’ feature until users see the possibilities, and then becomes a ‘must have’.

3) Self-service analysis

An organization should develop an analytical approach that allows users to access reports, dashboards, and information in general, without having to rely on someone else in the organization to run a report or extract data. . It must also establish a path to quickly adapt the analyzes developed by users to the business in order to capitalize on value.

• Improve the company’s skills – Leadership should equip the organization with access to training and learning materials and encourage a culture of continuous learning so that employees develop new business analysis skills, whether it is to obtain data (e.g. SQL) or analyze data with modern tools (Tableau software, Power BI, SAS) / languages ​​(R). Equipping business units with people who know how to work with data and extract insight from it will open the doors to unrealized value creation. Major analytics platforms often provide high quality training materials or self-service platforms, so access to learning materials shouldn’t be a barrier.
• Citizen development – Leadership should also give the company the ability to create reports / dashboards with a framework that controls the quality of the reporting artifacts provided to the company, but allows for speed from idea to implementation . Balance is the key here. Too many approval thresholds can hamper the process of creating new analytics tools and discourage future ambitions. In addition, a lack of control often leads to less precision and more reconciliation efforts.

“From negotiation and risk control to finance and accounting, putting data to work for your organization enables your team to understand the wealth of information deeply embedded in your organization’s systems and to make better and better decisions. faster with confidence. “

Conclusion

Turning your organization’s business data into business insights takes work, but with the right leadership, governance, and support capabilities, you can provide your organization with the analysis and visualization to pull all the cylinders off.

Two years ago, before the last NCAA tournament started, we introduced a system that could help narrow the range, using advanced stats from recent seasons.

It worked – barely.

Virginia, one of six teams that passed all of our statistical tests in 2019, did indeed win the national championship. All it took was one last-second shot to force overtime in the Elite Eight, three last-second free throws in the semi-finals and another late 3-point shot that forced OT into the semifinals. the title match.

Never a doubt.

We could give up while we’re ahead, but where’s the fun in that? Instead, it’s time to bring the same system back for another attempt to narrow the field.

Review: This method uses six stats in which every champion since 2008 has placed in the top half of Division I. All of these stats are available at barttorvik.com – and we’ll use that information to narrow down this year’s contenders.

Stat n ° 1: General classification

T-Rank is the primary measure of overall team strength on barttorvik.com. Every champion since 2008 has been in the top 23 of this ranking, based on the team’s performance before the NCAAs.

This gives us the following initial list of title contenders: Gonzaga, Houston, Baylor, Illinois, Michigan, Iowa, Ohio State, Alabama, Florida State, San Diego State, Connecticut, Wisconsin, Loyola Chicago, Villanova, Virginia, Tennessee, Texas , Texas Tech, Southern California, West Virginia, Colorado, Creighton and Arkansas.

Stat # 2: Adjusted Offensive Effectiveness

A stat based on points per 100 possessions, adjusted for the opponent’s strength. The last 12 national champions have all placed in the top 55.

That means two teams are eliminated from this year’s roster: Loyola Chicago (ranked 65th in that statistic) and Tennessee (56th).

Stat # 3: Adjusted Defensive Effectiveness

Every national champion in our sample was in the top 40, so that’s the cutoff point. This eliminates several teams, including a No.1 seed.

Baylor (47th), Iowa (61st), Ohio State (78th), Villanova (72nd), West Virginia (60th) and Creighton (42nd) all fall out of consideration.

Statistic # 4:% defensive eFG

The effective percentage of field goals (eFG%) is a measure of the percentage of field goals in which the 3 points are given additional weight. Every national champion since 2008 has entered the tournament in the top 102 in defensive eFG%.

This statistic does not change anything this year. Every team that does not meet this standard has already been removed due to stat # 2.

Stat n ° 5:% TO

Spin percentage measures how often a team returns the ball, while taking into account the number of possessions. You don’t have to be great in this category to win it all, but each of the last 12 champions has placed at least in the top 125.

So we say, nervously, in some cases, goodbye to Illinois (129th), Alabama (157th), Florida State (231st) and Texas (239th).

USC (124th) barely remains in place.

Stat # 6: Defensive Free Throw Rate

A measure of how often a team puts their opponents on the line. Every champion since 2008 has been in the top 121.

This eliminates Houston (333rd), San Diego State (198th), Connecticut (292nd) and Texas Tech (284th).

It’s also worth pointing out that Baylor (175th) and Illinois (152nd) would not have reached this standard if they hadn’t already been dropped. These two No.1 seeds certainly have a lot going for them, but there are areas where they could be vulnerable.

No one needs to know that this tournament can produce weird results, and in this tough season the advanced stats may be a little less reliable than usual – but the seven teams that have passed this process without failing any of the tests are: Gonzaga, Michigan, Wisconsin, Virginia, USC, Colorado and Arkansas.

Michigan could be limited by the injury to Isaiah Livers, and Virginia has been facing COVID-19 issues. It may really be Gonzaga’s year.

Especially if fortune smiles on the Bulldogs like it finally did for those Virginia Cavaliers a few years ago.

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Follow Noah Trister on www.Twitter.com/noahtrister

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More AP College Basketball: https://apnews.com/Collegebasketball and https://twitter.com/AP_Top25