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RapidAI Expands AI-powered Vascular Imaging Globally




March 16, 2021 — RapidAI, a leader in advanced cerebrovascular imaging, announced record-shattering 2020 milestones for the company and industry, as well as an expansion of its scope into the larger vascular market. As RapidAI momentum built around the world, in addition to new products, partnerships, and awards, 2020 saw the company expand from stroke to aneurysm and lay the groundwork for other vascular diseases, both acute and non-acute. The RapidAI footprint grew to over 60 countries, with its technology utilized in over 1,800 hospitals.

RapidAI offers widely used advanced cerebrovascular imaging products for patient care, research, and clinical trials across the globe. RapidAI clinical products help save lives. In 2020, RapidAI workflow and messaging technologies helped stroke teams save time, and RapidAI analytics and business intelligence products began helping stroke networks reduce costs and improve patient outcomes.

“2020 was such a challenging year for so many around the world, it’s gratifying that our growth and success meant many more lives saved from cerebrovascular disease across dozens of countries,” said Don Listwin, CEO of RapidAI. “Last year we expanded our global footprint, we introduced important new technologies, we helped hospitals understand their businesses better, we made our first strategic acquisition, and we expanded our scope from stroke to include other vascular illnesses, such as aneurysm and pulmonary embolism. With 2020 in the books, we join the world in looking toward a brighter 2021.”

RapidAI Growth:

  • Building on years of profitability, announced a $25 million Series B, to accelerate strategic growth initiatives around the world
  • Acquired EndoVantage and expanded scope to address aneurysm
  • Expanded to 60 countries, including Australia and Argentina
  • Worldwide adoption of the Rapid platform reached 1,800 hospitals
  • Processed more than 1,000,000 stroke imaging scans in 2020 alone, more than a 1,900% increase over 2017
  • Achieved greater than 100% year-over year increase in patients scanned
  • Became RapidAI. The company that leads AI-enhanced stroke imaging around the globe, embraced the future with a new brand and website,
  • Rapid LVO became one of the first software products to qualify for the New Technology Add-on Payment (NTAP), a significant advancement in stroke care reimbursement
  • Launched the RapidAI Technology Partner Program
  • Partnered with Penumbra, for expansion into pulmonary embolism

“RapidAI’s credibility can be assessed from the fact that their solutions are used by over 1,800 hospitals in over 60 countries, and them being profitable since their founding year – an extremely rare occurrence in the imaging AI sector,” said Parth Shah, Senior Research Analyst at Frost & Sullivan. “Not resting on their laurels, in 2020 RapidAI added features to their solutions to address the workflow for stroke interventions, thus providing an end-to-end capability from triaging to intervention. Ultimately this will result in even faster time to treatment, further enhancing patient outcomes and reducing long-term costs.”

RapidAI Innovation:

  • Rapid ASPECTS received Food and Drug Administration (FDA) clearance as the first neuroimaging analysis device in the CADx (Computer-Assisted Diagnostic software) category. Rapid ASPECTS is the only neuroimaging product shown to improve physicians’ interpretations of Non-Contrast CT (NCCT) scans using a standardized ASPECT score
  • RapidAI offered Rapid Aneurysm, a comprehensive aneurysm management platform
  • Rapid LVO received FDA clearance for detecting suspected Large Vessel Occlusions. Rapid LVO helps physicians speed up triage or transfer decision-making
  • Introduced RapidAI Insights, a valuable solution for multi-site systems and referral networks looking to standardize stroke care processes and optimize operational efficiencies that help to reduce costs across sites, while simultaneously improving patient outcomes and saving more lives
  • Launched the Rapid Web App, which offers stroke teams a convenient way to view notifications of new cases, preview Rapid results and source files, and enable workflow communications among team members via their desktop or laptop

RapidAI Awards:

  • RapidAI received the 2020 Global Company of the Year Award from Frost & Sullivan
  • RapidAI won a Minnie. named RapidAI The Best New Radiology Vendor of 2020
  • RapidAI was awarded the 2020 Global Growth, Innovation & Leadership Frost Radar Award

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How AI is Improving the Fitness Industry




Kuldeep Kundal Hacker Noon profile picture

@codersKuldeep Kundal

Kuldeep Kundal is CEO and co-founder of Coders dev, which specializes in android and iOS apps.

People are now becoming dependent on technology to overcome their wellness and fitness problems. Many new fitness apps, gadgets, and wearables are being launched in the market and creating all the buzz. A recent report from Research n Reports revealed that the global worth of fitness technology was $17.9 billion in 20119 and it is expected to grow to $62.1 billion by the year 2025. You may be unaware, but Artificial Intelligence (AI) is assimilating deeper into our lifestyles. 

The fitness industry is going through a major transformation as IoT and AI are bringing innovations in fitness product offerings. Reports and Data research firm predicted that by the year 2027, the annual revenues of the fitness app market will be $14.64 billion, with around 100.2 million active fitness users by 2024. 

The demand for AI-integrated applications for health, fitness, and nutritional sectors is increasing and most of the businesses are inclining towards fitness app development integrated with AI to leverage the benefits for their business, 

Undoubtedly Artificial Intelligence is not new but it is bringing innovations in the fitness space. Mo Iqbal, founder, and CEO of Sweatworks believes that “A.I. is being implemented by looking at your data and what you’re doing to suggest something that might be a fit for you,” User data is collected from fitness wearables that reads your daily activities such as total steps walked, sleep timing, distance traveled. Artificial Intelligence reads this data to offer improved workout strategies according to your behavior and feedback. AI and Machine Learning integrated into fitness applications use complex algorithms to find the data patterns through your daily workout sessions. 

How AI benefits health and fitness 

Through AI and ML, various tasks can be performed in less time with more accuracy, which is typically done by humans.

One major innovation that AI brought to the healthcare sector is the ability to detect early cancer.  

In the last few years, AI has become an important part of the health and fitness industry. The AI solutions used in healthcare and fitness are revolutionizing the industry by reshaping the individual habits of people. 

AI tracks the exercise patterns and health behaviors of the user to offer better approaches towards their fitness goals. ML and AI are integrated into smartwatches and smartphones to provide accurate results for making users healthier and fit. 

Improving the Fitness Industry with Artificial Intelligence

Fitness and healthcare are such elements that can be quantified through Artificial Intelligence and Machine Learning. Here are some AI trends for the fitness industry that promise to make you healthy and fit. 

1. Smarter Fitness Wearables

Ai integrated wearables work more efficiently and smarter by analyzing your health data such as heartbeats, diabetes signatures, pulse rate, calorie count, weight, etc. To help you out with better exercises the next day, fitness applications also track your daily exercises, time count of daily workouts, and food requirements. 

Apple, Google, and Android are some famous tech giants that are planning to create wearables with virtual assistants that can virtually assist the users according to the health data collection. Moreover, exercise and fitness equipment are also being integrated with Artificial Intelligence so that the users can use them more efficiently to maintain their fitness. 

2. AI Fitness Coaching 

For beginners, AI-based fitness programs may be of incredible help! Fitness industries are creating fitness applications that can offer real-time analytics and customized command through synthetic intelligence. Some organizations are working towards the development of sensors that can be installed in your exercise clothes to reveal your movements. 

As per the body movements, the sensors will ship out customized commands to flow your body. They are planning to partner up with brands to integrate those sensors into branded garments. Currently, these sensors work best with yoga, but soon it is going to revolutionize other industries also. This is how an AI fitness coach can help you a lot. 

3. AI Fitness or Training Apps 

In this fast-paced and hectic lifestyle, people usually do not get enough time to hit a gym or home workout. Hiring a personal trainer is not possible for everyone as it costs a lot. 

But now, fitness applications integrated with Artificial Intelligence are available for smartphones that allow the users to work out anytime from anywhere. You can set your goals in the app, get an affordable premium plan and start working out at your home or in the garden by watching the videos of trainers. 

What more interesting about these apps is that they have inbuilt AI personal trainers that act like human trainers to guide you with proper exercise and workout to keep your fi and healthy. These AI-integrated fitness applications offer you personalized training and fitness plans according to your fitness goals, eating habits, current fitness levels, and other data. 


Artificial Intelligence has already set its foot in almost all types of industries including fitness and health. Fitness apps integrated with AI help the user to achieve their fitness goals without going to the gym. Today various fitness applications are integrated with AI personal assistants, in the market that offers exercises according to fitness goals and eating habits of users.   

Some of these applications even help the users to track their daily fitness routine. Apart from these exercise benefits, AI also helps businesses to generate sales by making better decisions according to analyzed and collected data.

by Kuldeep Kundal @coders. Kuldeep Kundal is CEO and co-founder of Coders dev, which specializes in android and iOS apps.Coders Dev


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Why Monitoring Machine Learning Models Matters




Monitoring machine learning models is crucial for any business that has operating ML models in production. This can be even more important than some other software systems that DevOps teams are more used to monitoring for multiple reasons. Firstly, ML models may fail silently if not monitored, and thus errors can potentially go undetected for a long time. Additionally, ML models are often some of the more essential components in the larger software systems. They are responsible for making intelligent decisions, and we rely on their predictions heavily.

Despite the importance of monitoring machine learning models in production, there is not yet a standard practice or framework for doing this, and thus many models go into production without proper monitoring and testing. This is because the technology of ML models is only starting to mature, and MLOps, the intersection of DevOps and ML, is still a new field. In this post we will discuss the importance of machine learning model monitoring and potential issues that may arise with your model in the production environment.

Illustration of the full lifecycle of a machine learning model from conception to production. In practice, many models do not go through a proper monitoring stage. (source)

Can Go Wrong?

The short answer is a lot. ML models are often treated as black boxes. Many software engineers don’t understand how these models are constructed and how to evaluate their performance, and if the model endpoint is alive, they assume that it is performing as expected.

ML models are often treated as a black box, which could lead to costly undetected errors and loss of control (source)

However, even a frozen model does not live in a frozen environment, and thus we must continuously evaluate whether our model is still fit for the task. We will discuss some of the reasons your model may not perform as expected.

Drift and Concept Drift

One of the common causes for model degradation over time is data drift. Any change of the distribution of the input data over time is considered data drift. This can be caused either by a shift in the “real world” (e.g. new competitor affects the market, pandemic changes user behavior) or by more technical changes to the data pipeline (e.g. incorporating data from additional sources, introducing new categories to categorical data). Similarly, Concept drift occurs when there is a shift in the relation between the features and the target.

More specifically, during training, the model learns to fit the patterns in the training data. Usually a test set is set aside for evaluation to simulate performance on real world data. However, if there is a significant shift in the makeup of the data, we cannot expect our model to perform flawlessly, and our model may become stale.

Taxonomy of types of concept drift (source)

Integrity Problems

The data pipeline can be very complex, involving different data sources that may be owned by different bodies. ML models can be extremely intelligent when it comes to identifying patterns in the makeup of the DNA, but when it comes to understanding that two columns have been swapped, or that an input attribute changed scale, they are completely stupid. Monitoring the data that is fed to your model constantly, will help you identify any change to the data schema early on, and enable you to fix the issue before any significant damage has been done.

Between Dev and Production Environments

Deploying an ML model in production is a very different setting from the development setting. This difference can potentially manifest in the structure of the data that is fed to the model from the moment it was deployed. Without proper monitoring, you may wrongly assume your model is performing exactly as it did in the development environment.


Since ML models can be relatively “hidden” in the production environment, it may happen that your model is not even providing the basic functionality of generating predictions given an input and you may not know about it. Issues like this can be caused by high latency, misconfiguration of the production environment and the model endpoint and so on. Monitoring metrics such as number of requests processed per endpoint can assure you that your model is fully operational

Your Model

Proper implementation of machine learning monitoring will enable you to detect all these issues early on, and notify you when it’s time to retrain your model on up-to-date data, update the way a feature is calculated or fix the data pipeline. Thus, you will be able to be in control of your ML models, and answer any of the following question instantly:

– Is my model starting to degrade?

– Does the current input data have a similar distribution to the data used for training?

– Is the data pipeline and the schema of the input data intact?

– Is there any increase in biases with respect to attributes such as race or gender?

– Is my model handling the number of requests it receives as required? Do we need more resources? Would less be sufficient?

– Are there any subsets of the data where my model performs better or worse?


Monitoring and testing machine learning models is an area that’s often overlooked. The data science team may be content with achieving good results in the experimental environment, while the DevOps team does not always understand the terminology and concepts of machine learning. If you want to ensure that your ML models are not only theoretically useful but a product that gives added value to your business, continuously, it is worth investing in a proper monitoring system.

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The future of e-commerce: Trends, tips, traps to avoid




Amazon is approaching its 30th anniversary, set to mark the milestone in 2024. The World Wide Web hits 35 the same year. E-commerce, the buying and selling of goods and services over the internet, has grown up — and it has gotten big. Worldwide e-commerce sales for the retail sector alone exceeded $4 trillion in 2020, according to eMarketer. The research firm expects the figure to hit $5 trillion in 2022. Global B2B e-commerce sales, meanwhile, hovered around $6.6 trillion in 2020, according to research firm Frost & Sullivan.

As the value of e-commerce has risen, so has the complexity of online transactions. E-commerce today means more than simply processing electronic payments and enabling internet sales. It’s also more than knowing your customer, crucial as that is. E-commerce sales in 2021 depend upon the robust performance of just about every aspect of modern enterprises, from operations and supply chain to delivery services and customer loyalty programs.

Organizations must harness all the power of integrated back-office systems in tandem with intelligent customer insight systems to deliver personalized, seamless digital transactions that — in the lingo of the age — delight the customer. Personalized, seamless transactions must happen whether the customer is an individual consumer buying his or her first product, or a global business ordering for the 100th time under a multiyear procurement contract. Buyers demand as much — whether they’re ordering from their computer or their smartphone, via Alexa or through another connected machine.

“E-commerce transactions are becoming ubiquitous, and expectations are going up. People have expectations that it’s always going to be as easy as using Uber to get a ride,” said Mike Welsh, chief creative officer at Mobiquity, a digital consulting agency.

Amid rising customer expectations, however, many organizations are falling short on their e-commerce operations. A Gartner report on the COVID-19 pandemic’s impact on digital commerce predicted that “through 2020, 50% of large organizations will have failed to unify engagement channels, resulting in a disjointed and siloed customer experience that lacks context.”

The bar is high, said Lisa Woodley, vice president of customer experience at NTT Data Services. “E-commerce [covers] every stage, from acquisition to loyalty and advocacy. It’s your customers telling their friends, ‘I had a great experience; go do business with this company.’”

In this look at the future of e-commerce, we examine the evolution of buying and selling over the internet — from the early corporate websites that functioned as online brochures to today’s powerful, concierge selling sites that can be accessed through multiple channels. We offer expert analysis of the impact of COVID-19 on digital transactions, delve into the challenges enterprises face in meeting customer expectations in 2021, and provide detailed advice on overcoming those challenges.

From ‘product-centric’ to ‘solution-centric,’ e-commerce evolves

A combination of factors is driving the evolution of e-commerce. At the core is the internet.

Companies once mostly competed on the so-called four P’s of marketing: place, price, product and promotion. But the web’s search function and the internet’s reach neutralize one or more of these differentiating factors. Shopping online, a customer can easily get the same or similar product at the same or lower price with comparable shipping times and costs.

As a result, other factors are emerging as key differentiators, with personalization being the catch-all term for the new elements that drive buying habits in the digital realm.

“The concept of e-commerce is shifting from online sale transactions [and a] static webpage to a personalized and interactive experience,” said Eleftheria Kouri, a research analyst with the tech market advisory firm ABI Research.

“Customers have access to a wider range of capabilities when visiting an online store, including product virtual try-on and gaming and interactive storytelling concepts that increase engagement and educate the consumer about products [and] brands.”

Penny Gillespie, vice president at Gartner and a fellow in its customer experience/digital commerce team, said that in the e-commerce marketplace of 2021, companies must figure out how to deliver the product and the solution to a customer’s problem. To do that, they must understand the online customer’s intent.

For example, a retailer serving a customer searching for a black dress should be capable of using digital tools, as well as general and personal data, to understand that the shopper doesn’t simply need a dress but rather needs an outfit for an event. In fact, the color of the dress may in this case be irrelevant — with black dress being nearly synonymous with cocktail dress.

“Understanding intent is part of personalizing an experience,” Gillespie explained. A retailer that understands this concept can ensure the products in the search results actually match that customer’s needs, guaranteeing the sale of a dress and other relevant items (e.g., accessories) — and ensuring repeat business.

Customer intent is relevant in B2B transactions as well. Here, it could mean understanding a customer’s unique procurement process by, for example, automatically displaying any special prices specified by an existing procurement contract, facilitating any approval requirements, and anticipating needs based on past ordering histories.

“It’s a work in progress, with some sellers being much better at it than others,” Gillespie said.

In both the B2C and B2B spaces, online selling has gone from being reactive to being proactive and participatory, said Gillespie: “It’s a move from being product-centric to solution-centric.” She used the sale of an exercise bike online as an example.

“It’s not just selling an exercise bike online, but rather delivering it to the buyer’s house, setting it up and then helping them maximize its value through use,” she said. “The bike is a product; when it’s in my house and working, it is the solution.”

COVID-19 pushes companies and customers into the digital realm

The evolution of e-commerce from static webpages to interactive customer “solution” sites was enabled by sophisticated technology, but it took a global health crisis to make the future of e-commerce the new normal. The shift to online-everything in 2020 due to pandemic-induced social restrictions and quarantine orders pushed physical transactions into the digital realm.

According to findings from consulting firm McKinsey & Company, e-commerce as a percentage of overall retail sales in the U.S. grew 3.3 times more in 2020 than the average annual rate in five years before COVID-19. E-commerce sales as a share of overall retail sales grew 4.6% in 2020 vs. an average of 1.4% growth in previous years.

“Consumers are demanding more digital access than ever before,” said Nicole West, vice president of digital strategy and product at Chipotle Mexican Grill.

In November, the restaurant chain opened its first “digital-only restaurant,” the Chipotle Digital Kitchen, in Highland Falls, N.Y. The location offers pickup and delivery only, a prototype the company said will allow Chipotle to enter more urban areas that don’t support its full-size restaurant concept. The new restaurant requires customers to order in advance via, its app or through third-party delivery partner platforms.

Providing an exceptional digital experience has become a priority for the 28-year-old chain, West said. She added that Chipotle is “relentless when it comes to UX and making it fast, easy and convenient” to place digital orders.

She cited the company’s 2020 rollout of Unlimited Customization. A feature in the Chipotle app and on the company’s website, it allows customers to customize orders, just as they do when ordering in person at a restaurant. Earlier in 2020, the company launched ordering on Facebook Messenger and a Group Ordering feature, which allows multiple people to participate in the ordering process simultaneously on the Chipotle app and And it’s now testing Chipotle Carside at 29 restaurants in California, an in-app feature that lets customers have their Chipotle orders delivered to their parked cars.

Chipotle’s digitalization efforts have shown real-world business value. Digital sales for Chipotle have grown 177% year over year, West said, and they accounted for 49% of sales in the last quarter of 2020. More than 19 million people joined the company’s customer rewards program via digital sales, West added, noting that the company’s digital pickup orders are currently its most lucrative transaction type.

E-commerce trends

The appetite for digital access is unlikely to abate. The McKinsey report cited above noted that approximately three-quarters of people who used digital channels for the first time during the pandemic said they plan to continue using them when normalcy returns.

There is no denying that the COVID-19 crisis and the at-home new norm have reshaped consumer behavior and boosted e-commerce/online shopping, which is expected to continue growing after the end of the pandemic, ABI Research’s Kouri said.

The technology powering these e-commerce trends also continues to evolve rapidly, Kouri noted, citing technological advancements in smartphones — such as high-resolution cameras and displays — enhanced connectivity, mobile-friendly websites and the rise of social media shopping.

Amazon, of course, has continued to make online shopping easier with innovations such as its Add to Cart and Buy Now buttons. The Home Depot and Lowe’s are often lauded for their use of instructional videos that give customers confidence to make purchases, as well as for apps that help customers navigate their stores. And the use of various technologies to let customers see how their items will look on them or in their homes before they buy is becoming standard practice.

From the customer’s perspective, Gartner’s Gillespie noted, the benchmark for all digital transactions is “the last great experience they had.” Keeping up with that moving target will require a panoply of technology and continuous technology innovation.

E-commerce technologies

Although the internet was the enabling technology for e-commerce, it is far from the only technology needed to deliver the experience that customers expect now and moving forward. Some of these broad technology capabilities include the following:

  • Customer-facing capabilities. Sites must be easy to navigate and user-friendly as well as quick and responsive. Sites should have the features that matter most to the target audience and be able to interact with other sites — social media sites for young consumers, for example, or company procurement systems for corporate customers.
  • Data-related technologies. Organizations must be capable of collecting and using their own data as well as data from outside sources. This allows the organization to anticipate a customer’s needs even when it has little or no data on that specific customer; the company can use its other data sources to compile an understanding of what that one customer needs based on its interactions with similar customers.
  • Automation technologies such as RPA. Robotic process automation can speed and streamline processes that service the customer by minimizing errors in data collection, enabling self-service by providing access to back-end systems.
  • Customer journey orchestration engine software. This class of tools help organizations analyze real-time data of individual customers to predict future interactions with that customer, using predictive models, decision trees, matrix rules and machine learning.
  • Augmented reality. AR lets customers bring products into real lifelike situations and virtually try on or fit items before purchasing. “The introduction of digital tools, such as augmented reality, in e-commerce platforms or apps not only assists brands to differentiate from the competition but transfers static websites/2D images to interactive and personalized experiences,” Kouri said.
  • Artificial intelligence. Organizations can use AI to offer personalized online experiences. A cosmetics brand, for instance, could use AI algorithms to provide skin analysis and recommend suitable products.
  • Back-end systems. Companies need modern infrastructure and current IT architecture that can support all these other capabilities. Typically, this means moving from legacy systems to cloud computing and SaaS applications to quickly enable scale and speed when needed; leveraging microservices to increase agility and flexibility; and breaking down silos through integration and the use of APIs. “There’s actually a lot more on the back end needed to reach our goal of making the front-end experience as seamless as possible,” Woodley said.

Specific tools, such as geofencing platforms that provide location-based services to help organizations and customers pinpoint their locations, and payment systems also have an important role in an e-commerce strategy, as do the technologies and processes companies use to optimize their warehouse and supply chain management.

“Building competitive e-commerce experiences requires the synergy of numerous technologies and tools, from AR to AI and secure payment systems,” summed up Kouri.

E-commerce challenges

Bringing all these parts together to work consistently and flawlessly is, not surprisingly, a significant challenge.

“Personalization is not a one-size-fits-all approach. You really need to consider your business model, value proposition and customers before you create your strategy. Once those pieces are solidified, you can then begin to seek out the right tools and technologies needed to be successful,” said Britt Mills, senior director of customer experience at Mobiquity.

Organizations also need the data experts, technologists, marketing team, logistics workers, supply chain personnel and other executives and supporting staff who can competently contribute to that vision.

“Stores can rush to market with a new technology to enable customer safety and convenience, but they shouldn’t do so at the customer’s expense,” Mills said. Training employees to use the new technology is essential. “If your associates can’t support this new expected experience, your customers won’t be satisfied. It doesn’t matter how great the technology is.”

In addition, companies must have a strategy for dealing with emerging data use laws that put more control over personal information into the hands of individuals. And they must be able to mitigate against escalating cybersecurity risks.

These capabilities and safeguards are hard to achieve. Experts have acknowledged that the difficulty of developing and implementing know-your-customer processes — from collecting the necessary data to analyzing it to turning it into action items — has been oversimplified and glossed over in many conversations.

It’s not shocking, then, to learn that most organizations are struggling to develop the capabilities required to deliver seamless, personalized service, especially as the number of engagement and delivery channels have increased.

Research from Verint, a provider of customer engagement management products, found that 82% of the nearly 2,300 business leaders it surveyed said the challenges of managing customer engagement will increase in 2021, but only 50% said they’re well prepared to support customer engagement priorities moving forward. The vast majority of those surveyed pointed to nearly every aspect of customer engagement as challenging for their organizations, indicating the following problems:

  • understanding and acting on rapidly changing customer behaviors (94% cited);
  • managing the growth in volume of customer interactions (88%);
  • achieving a unified view of customer engagement and overcoming data silos (79%);
  • using customer feedback to improve experiences (78%); and
  • building enduring customer relationships (77%).

Customer journey mapping

How do traditional organizations become as competitive in the digital sphere as they were in the brick-and-mortar heyday? It starts with mastering customer journey mapping, according to Peter Charness, vice president of retail strategy for UST, a digital technology and transformation IT services and solutions firm.

“[Organizations] need to ensure the digital journey is well aligned to a shopper’s needs, using a high degree of personalization and creating relevant interactions and conversations,” Charness said. He laid out six areas where organizations need to benchmark their capability:

  • Interest generation. Determine how successful your organization is at getting potential customers to its website or store.
  • Research and decision influence. Examine whether it’s easy for the user to find products of interest and “gather the information and confidence they need to move that product into a shopping cart,” Charness said.
  • Decision confidence. From browsing to buying, companies should make it easy for the shopper to say yes to a purchase. “Organizations should have this part of the conversation with their shoppers and build their confidence to press ‘buy,’”Charness noted.
  • Delivery/collection. “Speed of delivery (with ease of return implied) comes next, and the cost to deliver or collect a product becomes one of the most relevant associations any retailer will have to profitability and customer satisfaction,” Charness said. Assess your supply chain and fulfillment capabilities, and benchmark them to competitors and best-in-class companies.
  • Post-sales service, resales and loyalty. “Your conversation with your customer doesn’t end with the shipment,” Charness Consider what else you can say to or advise your customer on to keep the relationship alive and productive.
  • Personalization everywhere. “Put yourself in your shopper’s shoes, and play back the conversations you’ve had during the entire shopper journey,” Charness said. “Was it always relevant, interesting and useful? Or did you communicate with mass marketing techniques, treating everyone the same?” Develop a strategy for using AI and machine learning across “the end-to-end interaction chain” with customers to enhance personalized service.

The future of e-commerce

Many businesses have been on their e-commerce journey for years, adapting business processes to the customer predilection for digital transactions. However, few were well prepared for the rapid and wholesale shift to digital transactions driven by the pandemic. A record-breaking 11,100-plus stores closed in the U.S. last year, and 40 major retailers filed for Chapter 11 bankruptcy protection, according to CoStar Group, a collector of retail real estate data. More stores are expected to shutter in the upcoming years, with some analysts predicting 100,000 stores — mostly apparel retailers — could close by 2025.

Yet, despite their struggles and challenges, many organizations are on their way to success. The awareness that challenges must be faced and addressed indicates that organizations understand that data-driven, personalized and secure customer transactions are the future.

How these transactions happen — whether online, via a mobile device, through some combination of digital and physical channels or by some augmented reality lens not yet imagined — will depend on circumstances and customer preferences, but they will increasingly involve digital technologies.

Indeed, Gartner has advocated replacing the term e-commerce with digital commerce to better reflect the convergence of all the digital systems that go into transactions today.

As customers increasingly decide that the frictionless experiences they have when buying online from leading digital vendors are the norm, the semantic distinction between e-commerce or digital commerce or any other kind of buying and selling transaction will disappear.

“When it’s all said and done,” Gillespie said, “we’ll just call it commerce.”

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France’s Shift Technology, an SaaS Provider of AI based Decision Automation for Insurance, Secures $220M via Series D




France-based Shift Technology, an SaaS provider of AI-enhanced decision automation and optimization solutions for the insurance sector, recently revealed that it has finalized a $220 million Series D funding round.

Shift Technology‘s latest investment round brings total investment in the company to $320 million along with a market valuation of over $1 billion. This investment reportedly marks Advent’s sixth growth equity investment in 2021. Shift’s round was led by Advent International, via Advent Tech, along with contributions from Avenir and other investors.

Previous Series C investors Accel, Bessemer Venture Partners, General Catalyst, and Iris Capital also took part in Shift’s Series D round.

With this latest funding, Shift said it would use the capital to expand its business operations into the US, Europe, and Asia as well.

In the United States, the firm will be penetrating the property and casualty (P&C) insurance sector and will also expand into the health insurance industry, an area in which the company sees a great opportunity.

The funds raised by Shift Technology will also be used to support researach and development (R&D) work in the implementation of new solutions to cater to innovative decision automation and optimization needs for insurers.

Initially known for its fraud detection and claims automation solutions, in January 2021 Shift Technology launched its Insurance Suite to enable insurance providers to leverage AI-powered decision automation and optimization tech to a wider array of critical processes (related to policy lifecycle, including underwriting, subrogation, and compliance).

The firm currently serves over 100 clients in 25 countries and has reportedly analyzed almost 2 billion claims so far.

Thomas Weisman, a Director on Advent’s technology investment in London, stated:

“Since its founding in 2014, Shift has made a name for itself in the complex world of insurance.Shift’s advanced suite of SaaS products is helping insurers to reshape manual and often time-consuming claims processes in a safer and more automated way. We are proud to be part of this exciting company’s next wave of growth.”

Jeremy Jawish, CEO and co-founder, Shift Technology, remarked:

“We are thrilled to partner with Advent International, given their considerable sector expertise and global reach and are taking another giant step forward with this latest investment. We have only just scratched the surface of what is possible when AI-based decision automation and optimization is applied to the critical processes that drive the insurance policy lifecycle.”

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PR Newswire3 days ago

Polystyrene Foam Market worth $32.2 billion by 2026 – Exclusive Report by MarketsandMarkets™

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Chiliz Price Prediction 2021-2025: $1.76 By the End of 2025

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Teamsters Lead Historic Defeat of CEO Pay at Marathon Petroleum

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Apple is giving a laser company that builds some of its AR tech $410 million

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Launch of Crypto Trading Team by Goldman Sachs

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Beyond the fanfare and SEC warnings, SPACs are here to stay

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How to Become a Cryptographer: A Complete Career Guide

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G20 TechSprint Initiative invites firm to tackle green finance

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Ripple’s XRP Price
Blockchain4 days ago

Charted: Ripple (XRP) Turns Green, Here’s Why The Bulls Could Aim $2