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Retrofitting Beer Keg Trackers in Australia




Beer Keg Tracking
Illustration: © IoT For All

An estimated 12% of the world’s kegs go missing each year. While some might end up at a warehouse or in the wrong brewery, others are stolen and sold for scrap. As metal prices rise, brewers expect to lose millions of dollars on lost kegs. Unlike bottles and cans, kegs are expensive assets. Priced around USD100 each, kegs are returned and reused by the brewers. This created the keg rental business as it allows small and medium-sized breweries flexibility in managing their kegs supply according to seasonal demands. 

Konvoy started in July 2019 as a keg rental company based in Australia. Founded by a team with over 40 years of combined beverage and keg pooling experience, the company currently has in circulation 70,000 beer kegs across its Australia and New Zealand operations.

Representing over 200 brewers in Australia and New Zealand, Konvoy’s clients include well-known craft breweries such as Moon Dog, Fixation, Parrot Dog, and Hawkers.

When creating the company, Konvoy knew that they needed to address the problem of lost kegs to protect their customers’ bottom line and differentiate themselves in the rental market. It is hard for their clients to hit their ROI when they keep losing kegs. Konvoy decided to invest in a full-fledged monitoring solution that makes operational and economic sense to replace the time-consuming tracking solutions.

A Brewing Problem

At the core of Konvoy’s business, the need for a simple, tracking solution was essential to entering the keg pooling market. QR code and RFID scanning solutions are common in the market but inefficient as each keg has to be scanned individually when kegs are moved from venue to venue. If anyone fails to scan the kegs promptly, misses a keg, or has a problem with a damaged QR code, there will be gaps and lags in the system. This creates inaccuracy in keg fleet data, leading to inefficient resource allocation.

Real-Time Operational and Economical Solution

To address the problem, Konvoy commissioned a Kegfox beacon, a full-fledged, purpose-fit tracking device, and a software system that reports the keg’s location throughout its life cycle. The tracker is based on Sigfox’s 0G network, which Thinxtra operates in Australia.

The beacon is programmed to record data of the kegs at specific intervals or events with an algorithm that detects different types of movement, such as when the kegs are moved or cleaned. Data is then transmitted to the cloud via Thinxtra’s 0G Sigfox network in Australia and New Zealand. Thanks to the low-power consumption of the network, the beacon can last up to 7 years, transmitting information on its location and temperature several times a day.

“While we focused initially on tracking, this solution has given us the opportunity to also monitor the kegs’ temperature. Because most of the producers have unpasteurized beverages, leaving a keg at too high a temperature actually spoils the product. Being able to send an alert to our customer saying their keg has been transported at the right temperature is an important benefit for them,” said Adam Trippe-Smith, Founder of KONVOY.

The average keg is built to deliver 20 to 30 years of service, notwithstanding rough treatment and temperature extremes. The tracker needs to endure harsh treatment from filling, distribution, stacking in inventory, dispensing at the local pub, collecting and returning to the brewery, rigorous cleaning, sterilization, and then back to the filling station start again.

Supply Chain Relief

Konvoy’s go-to-market urgency was a challenge. It was important to make the solution and data accessible to Konvoy as soon as possible beyond hardware. To value-add to hardware, the team also created a mobile application Konvoy and its partners now use throughout the chain for provisioning and pairing the beacon and kegs to accelerate the deployment of Kegfox. The ease of use and access to live location data will help Konvoy define its position in the keg rental market.

Accountability has always been a big challenge. While someone has to be held accountable if the kegs go missing, it is hard to penalize clients and expect a good long-term relationship with them. Ensuring financial stability while maintaining a good relationship with clients can be a difficult line to straddle. By shifting the responsibility for tracking kegs from the customers to Konvoy, a huge amount of stress is removed from the keg supply chain.

Thirsty for More

After a few rounds of beta testing and deployment in the local market, Konvoy was happy with the results. Over 20,000 devices have been fitted to date. The rest of the trackers are expected to be fitted on their fleet in Australia and New Zealand by the end of the year.

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Digital Twins Come of Age




Digital Twin
Illustration: © IoT For All

While the concept of digital twins has been around a while, their use in specific use cases, such as monitoring employee or customer safety, and more traditional strategies such as predictive maintenance and resource management has seen adoption rocket. 

The concept of digital twins in an IIoT context and beyond has been a popular talking point for many years. The basic strategy is that an enterprise creates a digital replica of a physical asset, which can then model future events. Michael Grieves conceived the idea at the University of Michigan in 2002, where he initially dubbed it ‘Doubleganger’ before setting on the more prosaic “Conceptual Ideal for PLM.” From this starting point, the Industrial IoT industry has seen a complex evolution in digital twins, where increasingly complex systems are digitally mapped and modeled, then managed by machine learning and AI tools. 

Seeing Into the Future

The addition of AI to a digital twin can deliver powerful insights into future performance, not just in heading off inefficiencies (such as those created by materials shortages, or even more subtly, changes in the quality of raw materials being used in processes), but also flagging up equipment failures in advance. This opens the door to truly predictive maintenance, letting businesses automatically schedule the repair of multiple pieces of equipment, minimizing disruption to production schedules, but also enabling ‘just-in-time’ spare parts procurement, itself minimizing storage and other logistics costs. 

Safety Breaches Mitigated 

Another vital deliverable of the digital twin strategy is highlighting potential safety breaches in advance of them actually occurring, ensuring compliance and worker safety at a far higher level than might be possible otherwise. These benefits combine into a compelling package, so much so that a recent Gartner report found that the development of 5G and improved implementation of digital twins have combined with being partly responsible for a 47% increase in IIoT investment despite the global pandemic. The survey also found that 31% of respondents said they use digital twins to improve their employee or customer safety, with 27% of companies saying that they plan to use digital twins to manage autonomous equipment, robots, or vehicles. Gartner expects that by 2023, one-third of mid-to-large-sized companies that implemented IoT will have implemented at least one digital twin associated with a COVID-19-motivated use case.

Driving Manufacturing Success

One of the core challenges in using the digital twin approach in manufacturing is that once mission-critical systems are connected to the digital twin, any delay in the data collection, network, and processing can allow developing issues to spiral out of control. Thus getting as near as possible to real-time data flows is essential. The burgeoning rollout of 5G is beginning to pay dividends here, as equipment manufacturer Lacroix Group demonstrates in France. Partnered with Orange, it is actively exploring the benefits of 5G combined with a digital twin approach at its electronics plant in Montrevault-sur-Evre, monitoring external data sources such as suppliers, vendors, market forecasts, and combining them with flexible internal processes to maximize efficiency. 

“We are taking a bit of a digital twin approach to this facility. For electronic manufacturing, we need to have highly flexible assembly lines where we can change set-ups from one line to the next or tooling setups, and we need to monitor suppliers, vendors, market forecasts, etc., in real-time,” he says. “Today, we don’t have all the vital data sets in real-time. But having access to this data should enable us to better address base yield and base efficiency in what we’re going to do with our manufacturing. It’s about being informed enough to change the set up very quickly and adapt to quantity demands effectively”, Stéphane Gervais, executive vice president of strategic innovation at Lacroix Electronics, told IndustryWeek in a recent interview.

Explosive Engineering Growth

Engineering has been a plentiful avenue for digital twins, sometimes using the digital twin as the first incarnation of a complex product or process, created before the physical version. This can then be overlaid with IIoT data from comparable products and sensors in the field to iterate and improve constantly. Aerospace firm Boeing uses digital twins to design new aircraft, running simulations to establish the performance of various parts through the lifecycle of the plane. According to the company, this technique has delivered a 40% improvement rate in the first-time quality of parts. 

Vast Scale Deployment

Continuing up the physical scale, another interesting emerging market for digital twin technologies is entire cities, or ‘city digital twins.’ Research from ABI Research has predicted that the number of urban digital twins will exceed 500 by 2025, rapidly expanding from pilot projects to widespread deployments. Real-time 3D models of city environments can be enormously useful in increasing resilience, optimizing resource management, and driving energy saving. The ability to model new infrastructure deployments – anything from 5G masts to new lighting systems – and then monitor effectiveness over an extended period can deliver substantial practical and cost-saving benefits for minimal logistical investment. Cities that have already deployed digital twins include Singapore, Dublin, Helsinki, Jaipur, and Boston. 

Hot Topic

Another digital twin industry bellwether can be found in the shape of the Digital Twin Consortium, founded in May 2020, which in October 2020 issued a press statement announcing that membership had already hit 150 enterprises in just four months. Founding members include Autodesk, GE Digital, and Northrop Grumman Corporation, Ansys, Dell Technologies, Lendlease, and Microsoft. The organization aims to drive the adoption of digital twins in core industry sectors of aerospace and defense, infrastructure, natural resources, and manufacturing.

In short, the digital twin may be almost hitting double figures in age but has never been more relevant to the future of IIoT, as well as increasingly playing an invisible but substantial role in our cities and supply chains alike. As Industry 4.0 principles and technologies continue to expand into new verticals and attract investment, it seems that the age of the digital twin is only just beginning in earnest.

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Riders on the Storm: A True Story About Testing a GPS Location-Based Apps




Illustration: © IoT For All

What is it like chasing a tornado, and how did I find myself testing software on the border of Canada? With the car trunk packed to the brim and AC/DC playing at full power, you head central-east to Ontario. The rebellious sky changes color as you follow the route—blooming thunder signals that you are slowly approaching the epicenter. The storm is brewing, and you feel it coming. 

It’s half an hour away at most.

After a sharp turn, you hear a flip sound out of the trunk—it seems like a tripod made a flight above the other parts of your video equipment. Hopefully, there’s no damage, but you don’t have time to figure it out—your adrenaline is high, and dozens of breathtaking images are at stake.

Apart from a couple of cameras, power supply units, and all the accessories you carry, you’ve got a special chaser’s mobile app that’s running in the background. It collects the logs of your route and shares your updates with other chasers by regularly changing your location on the map and uploading all the media files with recently caught pictures.

The app also allows you to chat with other chasers and remain connected as you’re moving on the map. Sharing your location keeps you safe and other storm chasers updated on the weather conditions and the road’s condition.

You’re heading another 200 miles towards the whirling gale when the app suddenly crashes—giving you no opportunity to continue your chase. All your efforts and your ride logs are wiped out from the live map and lost forever, both for you and the other chasers.

The latter can be described as “hardcore testing” of the app, which I also witnessed in my practice. At the same time, testing an app at its max before a big release remains any software testers’ main priority. And this can be achieved by reproducing the most realistic conditions and playing out several scenarios.

Testing Location-Based Apps

I’ve been into developing mobile apps and testing GPS navigation software for quite a while. And what I love most about testing location-based apps is how they behave in real conditions. Simply put, the main challenge is reproducing the real speed a chaser reaches when driving the expected distance. 

But let’s start from the beginning.

Before the first version of the chaser app was deployed in production and went life, we spent hours testing it in several ways, including:

  • Functionality testing: This includes testing the basic parameters of a location-based app, such as its responsiveness, UI, screen size, and orientation.
  • Usability testing: Aims to test your app’s UX and answers the question of whether it’s user-friendly and comprehensive.
  • Localization testing: Checking location-specific features and parameters. This includes ensuring that the date and time, speed, and distance on the map are displayed properly.
  • Testing networks and airplane mode: With location-based apps, it is essential to test how the app performs with different network connections (Wi-Fi, 4G or 5G, LTE, etc.). Additionally, we checked the app’s operation in airplane mode as some of our chasers accomplish their distance by air.
  • Interrupt testing: Aims at testing how the app reacts to incoming calls and messages, signal loss, or switching tracks in a music player. It also detects if the app is back to where it was before the interruption.
  • Testing geolocation blocking: The usage of some apps may be restricted in certain areas for political or other reasons. That’s why it’s important to check that user access is not restricted in the regions where the app is expected to be used.   

Once all the tests were completed and appeared successful, and the app remained stable during hours of continuous use—we officially released the beta version. Testing in real conditions was expected to detect more bugs to fix in the next release. And it actually did. 

Once real chasers made a couple of rides using the app, they noticed that a crash occurred on an iOS device roughly 5-6 hours after the chase, making all of the data impossible to restore after re-launching the app. For other chasers, the user who experienced the crash was stuck on the map and shown as “offline.”

Conducting Lab Testing

Now, the goal was to investigate the bug, reproduce, and fix it. We sat down for another round of tests. One problem. All we knew about the crash was that it occurred on iOS version 13.6. 

We launched the app on a similar device and conducted a couple of “chases” that lasted 10 and 20 hours with the app running in the background. 

And guess what? No crash happened. 

We understood that another ride should be longer and take place out of our lab conditions, and we needed to hit the road as if we were genuine storm chasers. 

Simulating Real Conditions

Sometimes software testing in real conditions is impossible in the area where it takes place. In our case, we quickly understood that the speed at which real storm chasers are moving somewhere in Tornado Alley is impossible to reach within our location. The traffic laws of the country simply prohibit it. 

Our average speed in the city was about 30 miles per hour, while other chasers experienced the crash when driving on the highway at 55 miles per hour. The sky-chasers who are piloting an aircraft are also quite difficult to reproduce by an average QA tester sitting in front of the computer at the office. 

We needed to find a way to reproduce not just the crash but the exact conditions in which it appeared for the chasers by gradually increasing the speed of the ride, the distance, and the route.

That meant we needed to be able to react to the crash in an informative way. We saw that as the only way to reproduce the steps and conditions that caused the app to crash.

Using a Mock Location for Testing a Location-Based App

So, our goal was to simulate a continuous ride following real chasers’ routes while gradually increasing our speed. The expected result was catching the crash, getting the logs, and receiving the exact speed values, distances, and route coordinates that caused the app to become unstable. 

GPS Navigation apps didn’t appear overnight. Testing software builds available with various apps for location simulation like iMyFone, AnyTo, EaseUS, MobiAnyGo, iSpoofer, Wondershare’s Dr. Fone, Xcode, and more. 

We tried all of them and found that the main restriction was exactly the speed we were (or better) could not simulate. The limit was only 22 mph with iMyFone and EaseUS and about 31 mph with Wondershare’s Dr. Fone, which basically brought us back to the lab conditions and made it impossible to reproduce the crash. 

Once this became clear, we switched to Xcode, where we could play by our rules and try chasing the crash at different speed values we set ourselves. 

Simulating a chase with Xcode meant we needed to set the desired speed and geographic coordinates in a text file. This data format is known as .gpx and can be generated with a GPX Generator. 

Uploading this type of file format to Xcode would allow us to simulate a continuous ride across the spots indicated on the map of the USA while being physically located in front of the PC. With Xcode, we also managed to simulate losing internet connection and changing connection types to 2G, 3G, EDGE, and more. After all, a GPX file with all the necessary information could be uploaded directly from the debug build of our app. 

All that remained was to connect the iOS device to the PC, launch Xcode, and upload the GPX file.

Generating a GPX file according to the chase route

This allowed us to use mock locations, simulate traveling the U.S., and test possible routes at different speeds similar to what tornado chasers could reach before the app crashed. In our case, the iOS build was down after 6 hours of continuous riding at 30 mph, which makes a total distance of only about 180 miles. 

If the speed increased to 50 mph, the time of continuous app work decreased to 4 hours. The higher the speed, the quicker we reproduced the crash. It always appeared at a distance of 180 miles, no matter if the app was running in the foreground or background and if any media files had been uploaded to the app by the chaser. 

Now it was time to fix the crash. We downloaded the container with files containing the information about the latest chase that was interrupted from the debug build using Xcode installed on macOS. 

This allowed us to receive the most accurate information about the steps and conditions for reproducing the crash. In addition, with these files, it was possible to continue the chase on another device using Xcode at the developers’ side to investigate the app performance during the chase where the crash appeared.

Using Xcode to test a location-based app appeared to be an effective and relatively easy way to gather sufficient data for applying the necessary fixes. My journey to Tornado Alley was completed when developers fixed the crash and minor bugs, and the final version of the app went live!

What’s Important to Know When Testing Location-Based Apps

Now, when storm chasers are getting ready for the tornado season, which is forecasted to pass with no crashes, it’s time to share the points we learned when testing a location-based app:

  1. The state of the app when receiving or sending the data (background/foreground): In our case, the state didn’t matter, as the app crashed both in the foreground and background.
  2. Type of Internet connection, its quality, and possible interruptions quality: Mobile data or WiFi—both are available for simulation in Developer Options. It’s important to simulate all the available connection types, including EDGE, 2G, or 3G, as the person who navigates with the map may stop at the petrol station and connect to the WiFi signal. At the same time, the app is expected to continue working smoothly in the background.
  3. The format of geolocation coordinates: Those should be precise and include latitude and longitude, altitude, speed, time, directions, and all the data that helps to reproduce the route as closely as possible.
  4. The time and frequency of sending geolocation data: Again, we need to reproduce the whole route. We tried increasing the distance (the interval of sending geolocation of the chaser) between each point of the map to decrease their number to offload the app. But this way causes the route to becoming less precise, which can prevent locating and reproducing the bug. In other words, “the line” you draw on the map should correspond to the route and the ride conditions.
  5. Continuous work of the app in the background mode: When collapsing the app or locking the device, the app is expected to continue working smoothly.
  6. App recovery after reboot: The app should withstand any interruptions and recover the whole route after it was closed, force closed, or if a crash occurs.
  7. Using real conditions in line with location simulation: Luckily, several tools for location simulation are available for software testers both on Android and iOS. 

By going through all these tests, we finally reached a way to properly test the location-based app to ensure it will work for even the most daring tornado chasers.

And there you have it: Tornadoes, a QA engineer, and a story for the ages.

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Artificial Intelligence

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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Argo AI Develops LiDAR to Advance Autonomous Delivery




The delays in getting autonomous cars on the road are mentioned often on this site. A “breakthrough” LiDAR technology developed by Argo AI could change all that. It means we could be seeing commercial autonomous vehicles on the road finally, making deliveries and offering side-sharing.

Argo LiDAR Technology

Argo AI introduced Argo LiDAR, which allows the technology to bypass the shortcomings that parked the ideas of autonomous delivery and ride-sharing until now.

Argo’s Self-Driving System (SDS) allows driverless cars to be aware of their surroundings – 360 degrees, day or night. Cars with Argo LiDAR are safe on city streets, suburban neighborhoods, and highways, according to the company’s blog post.

Argo Ai Lidar Highway

The breakthrough in the Argo AI LiDAR came about when Argo acquired a company developing long-range radar. The resulting LiDAR has a range of 400 meters. This allows dark objects to be detected, and ultra-high resolution perception allows for photorealistic imaging, which allows small objects to be identified.

“Argo Lidar takes us to a whole new level of self-driving technology, unlocking our ability to power both delivery and ride-hail services,” said Argo AI CEO and founder Bryan Salesky. “The Argo Self-Driving System delivers the safety, scale, and service experience that businesses want and their customers demand, especially coming out of the pandemic.”

Applications of the LiDAR Technology

The Argo AI blog post shared the advantages the new LiDAR technology could bring to commercial autonomous driving.

  • Safe for use in cities, suburbs, and highways, allowing an easy connection to a warehouse.
  • Scaled use in six U.S. cities and Europe will take place this year.
  • Service between addresses in urban and suburban areas would help with deliveries.
Argo Ai Lidar Ford2
AI Argo in the Strip District on Monday April 26, 2021 in Pittsburgh, Pennsylvania. (Photo by Jared Wickerham/For AI Argo)

Argo AI has partnered with Ford Motor Company and Volkswagen Group to develop commercially-available autonomous vehicles.

“We have unparalleled autonomous driving technology and operations capabilities,” added Salesky. “Proving out these abilities every day, across six cities, from our nation’s capital to Miami to Silicon Valley, we are ready to enable the next phase of growth for delivery, retail, and ride-sharing partners.”

Argo Ai Lidar Fleet

The LiDAR sensor, as part of the Argo SDS, joins custom sensors to allow commercial autonomous cars to:

  • See dark vehicles that reflect less than 1 percent of light, even at long range and at night.
  • Navigate left turns into oncoming traffic with a 360° view.
  • Transition instantly from darkness to bright light.
  • Distinguish between small, moving animals and vegetation.

The Argo AI Hardware Development team is working with a manufacturer for production of the LiDAR sensor. The first to be
produced are being road-tested. There are plans with Ford and Volkswagen for widespread commercialization.

To show how long this technology has been in the works, read how Apple was working on it two years ago . And yet, it has no autonomous car on the roads.

Image Credit: Argo AI Newsroom


Laura Tucker Laura Tucker

Laura hails from the Chicago area and has been a writer and editor covering news, entertainment, and technology for nearly 20 years and has been with Onlinetivity since its inception, editing and covering news. In her spare time, she enjoys exploring new devices and mobile apps.

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