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When you think of a company that you love, who comes to mind? It’s probably a company that you are so loyal to that it would take a seismic shift to get you to move to a competitor. As far as love goes, you probably can only think of a handful of companies that you would describe like that. But have you ever thought about why that is? In this article, we will break down the three levels of customer retention and what every company can do to move closer to the customer. Not so long ago, in a place close to here… You love and are passionate about the companies you have a personal connection with. love as “strong affection for another arising out of kinship or personal ties.” In the age of self-service, it's difficult to remember the days when companies were intensely personal. We used to buy products and services face-to-face with a company’s representative. Often, this individual was already in the buyer’s social circle. This meant that the buyer made their buying decision based on their personal connection, which the company ultimately benefited from in customer loyalty. It is much harder to switch to a competitor when a buyer has to have a conversation with someone they care about. Mirriam-Webster describes When things digitized in the 1980s, companies cut front-line staff and automated their jobs with computers. While this was an effective cost-cutting strategy, building a personal relationship with each customer became harder. Over time, companies lost touch with prospective and existing customers. You can directly correlate the rise of Google and its ads in the early 2000s with companies trying to become top-of-mind again after years of neglecting the relationship with customers. Businesses became removed from people’s social circles, so Google allowed them to do the next best thing: targeted advertising in the comfort of people’s homes. Digital advertising replaced the door-to-door sales force, but within 10 years, ad blockers reduced the effectiveness of ad marketing. Just as ads supplanted local representatives, ads are giving way to the next evolution of ‘relationship building at a distance’: content marketing. Content marketing is and works by building trust between buyers and sellers. Appealing to your logic, rather than emotion, companies can build a connection with those they want to purchase their products. However, just like with digital advertising, there will come a time in the not-so-distant future when content marketing stops being effective. highly effective If introducing new technology to replace old technology to replace interpersonal connections in sales is feeling circular, it is. But no marketing will ever supersede the trust built when people bought products and services from people in their social network. The best this technology can do is come close. The reason is that current marketing (and its supporting technology) takes a cookie-cutter approach to building trust. While scalable, you are only ever creating content that will resonate with a subset of customers. The customer loyalty pyramid We started this article by asking you to identify a company that you love. But now we want to tweak the question - how do you feel about the rest of the companies you frequent or buy from? What emotions come to mind? If you are not feeling anything, that's quite normal, as most companies are so disconnected from their customers, that indifference is the primary emotion. The customer loyalty pyramid has three tiers, divided 80%, 15%, and 5%. The bottom, where most companies live, is the ‘Service’ tier. These companies compete for loyalty based on the service they provide and are the most replaceable. Customers compare these companies with indifference, and the non-existent relationship between them and their buyers is purely convenience-based. Geico is an example of a company in this tier. Buyers decide to purchase from them based on price, which is also the defining characteristic of companies in this tier. Because of the strict transactional relationship between these companies and their customers, as soon as a lower price is available from a competitor, the customer will churn. We reserve the middle ‘Value’ tier for companies that lead with corporate values that align with those of their buyers. These companies have a stronger pull because buyers ‘like’ these companies. Customers prefer to buy from companies that align with their values. There are fewer companies in this tier and those that are, maintain a measure of differentiation between their direct ‘Value’ competitors and those in the lower ‘Service’ tier. An example of a company that now lives in ‘Value,’ but was recently in ‘Service’ is Southwest Airlines. They rejuvenated their corporate brand, which is encompassed in their move from a slogan of ‘THE Low Fare Airline’ to their values-based ‘One Heart.’ By aligning with the values of customers who want excellent service and cheap fares, they have attracted a wide fan base that pays more for their services than their lower-cost competitors. Their retention is much higher and customer lifetime value is longer than other airlines in the ‘Service’ tier. An interesting phenomenon is that while companies can move from the ‘Service’ tier to the ‘Values’ tier, it is extremely difficult to move from ‘Values’ to ‘Interpersonal Relationship’ (it is possible to drop, though). The handful of companies in the top tier started there. A little later in this article, we will unpack why that is. The interpersonal relationship tier is exactly that - buyers feel that they have a personal connection beyond service or values. Another way to think about it is customers who buy from companies in this tier may feel that they have something at stake. Because of that loyalty, customers will stay indefinitely. So how do you get yourself into the top category? Moving toward the customer Jeffrey Hall, a communications professor at the University of Kansas who researches interpersonal communication, calculated just how long you have to spend in the tunnel before you reach the light at the end: On average, it takes about 50 hours of time with someone before you consider them a casual friend, 90 hours before you feel comfortable upgrading them to just “friend,” and around 200 hours of quality time before you’d consider the two of you to be close. These interactions used to be made by the company’s sales representatives, who would build trust over hundreds of hours. We use how normal social interactions work between two people and apply it to building relationships between companies and their buyers. By merely existing, companies are in the ‘Service’ tier. Moving to the ‘Values’ level takes at least 90 highly personalized interactions. As mentioned above, it is almost impossible to move from ‘Values’ to ‘Interpersonal Relationship’ - doing so requires rebuilding the relationship from scratch. What is easier is starting with personal relationships. Machine Learning to Building Loyalty Replicating a face-to-face relationship using digital technology is difficult. Relationships are highly variable, and that variability makes it difficult for most software to adapt to an individual. Salespeople used to have intimate knowledge of someone’s life and with that knowledge, market-relevant products and services to help them. Machine learning (ML) allows us to communicate with customers much the same way as if they had a personal relationship with the company’s representative. Better yet, ML can signal to account managers and the retention desk when a personal touch, in the form of a text, phone call, or in-person visit, is necessary. In that same vein, ML can provide recommendations for what a customer’s real-time needs are, and what messaging would be most effective in addressing their concerns. This technology pays dividends when it comes time for retention. Regardless of why someone buys from you, it's 5-25x cheaper to keep their business than to sell to a new customer. Rather than sending automated, depersonalized communications, further the personal relationship that a buyer has with your company by providing them with relevant and timely information. Conclusion It is becoming harder to compete on service and values alone, as these customers have proven that price sensitivity is their most important consideration. If this continues, companies will be forced to cut additional costs (likely in customer service) in order to offer lower and lower prices. Competing on the personal relationship tier isn't just about building customer loyalty on a more competitive level - it's about building customer loyalty that is out of this universe. Building interpersonal relationships with customers at scale is challenging. It requires a different approach, strategic outlook, and the adoption of novel technology like machine learning. For most companies who are used to competing on cost alone, the investment required to compete in the relationship tier may seem staggering. But for the companies that are already there, the results (and dividends) more than pay for themselves. Also Published Here

‘Maslow's Hierarchy of Customer Retention’: The Secret to Improving Churn

BURLINGTON, United Kingdom, May 2nd, 2024/Chainwire/ $SNUKE, a new meme coin inspired by the humor of the popular TV show "South Park" and based on the Solana blockchain, has recently achieved significant milestones in its presale. These achievements underscore its potential among the array of Solana-based meme coins poised for success. Early Success in the $SNUKE Presale $SNUKE, a meme coin with vibrant community backing, has raised over 300 $SOL in the first four days of its 25-day token presale. The $SNUKE Presale has a total allocation of 600 Million $SNUKE Tokens for early Presale participants. According to the SNUKE team, the initial progress with both retail investors and prominent Solana stakeholders highlights the interest in their community-focused approach and transparent fundraising model. $SNUKE is setting a new standard for fairness in the crypto space with no insider allocation. SNUKE's team is optimistic that the presale, which commenced on April 25th, will encourage widespread participation by offering every early adopter an opportunity to secure tokens. $SNUKE Presale: Early Access to 60% of Tokens and Zero Transaction Tax The $SNUKE Presale allocates 60% of its total supply of 1 Billion #SNUKE Tokens to early adopters. The $SNUKE Token offers a commitment of 0% tax on transactions, with liquidity pool (LP) tokens burnt to ensure security and the minting function permanently disabled to prevent the creation of new tokens How to Participate in the $SNUKE Presale To participate in the $SNUKE Presale, users can follow these simple steps: Wallet Preparation: Users should ensure they have a compatible Solana wallet, such as Phantom or Solflare, set up for participation.
$SOL Acquisition: Users should acquire $SOL from Binance or Coinbase, considering applicable transaction fees, and then transfer it to their Solana Compatible self-custodial wallet.
Contribution: Users should visit the $SNUKE presale page and adhere to the provided instructions to transfer $SOL to the designated address.
Token Tracking: Users can utilize the user-friendly dashboard to monitor the $SNUKE tokens entitled based on the total $SOL contributed.
Token Receipt: After the presale, $SNUKE tokens will be airdropped to the user's wallet in proportion to their contribution. Joining the $SNUKE Community Joining $SNUKE is an opportunity to be part of a movement set to redefine meme coins on Solana. With a growing social media presence and a surge in community engagement, the $SNUKE team sees a great future for $SNUKE in the industry. Users can stay connected with the $SNUKE community and receive updates or information about partnerships by joining the $SNUKE Telegram group and following us on Twitter. About $SNUKE $SNUKE is a pioneering project launched on the Solana blockchain, inspired by the beloved "South Park" series. It blends humor with investment potential, designed to captivate meme lovers and crypto investors alike. Users can stay updated by following $SNUKE on social media: Website: https://snuke.wtf Twitter: https://twitter.com/snukecoin Telegram: https://t.me/snukecoin Contact Ms Barbara Reece Snuke contact@snuke.wtf This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. BURLINGTON, United Kingdom, May 2nd, 2024/Chainwire/ $SNUKE, a new meme coin inspired by the humor of the popular TV show "South Park" and based on the Solana blockchain, has recently achieved significant milestones in its presale. These achievements underscore its potential among the array of Solana-based meme coins poised for success. Early Success in the $SNUKE Presale $SNUKE, a meme coin with vibrant community backing, has raised over 300 $SOL in the first four days of its 25-day token presale. The $SNUKE Presale has a total allocation of 600 Million $SNUKE Tokens for early Presale participants. According to the SNUKE team, the initial progress with both retail investors and prominent Solana stakeholders highlights the interest in their community-focused approach and transparent fundraising model. $SNUKE is setting a new standard for fairness in the crypto space with no insider allocation. SNUKE's team is optimistic that the presale, which commenced on April 25th, will encourage widespread participation by offering every early adopter an opportunity to secure tokens. $SNUKE $SNUKE $SNUKE Presale: Early Access to 60% of Tokens and Zero Transaction Tax The $SNUKE Presale allocates 60% of its total supply of 1 Billion #SNUKE Tokens to early adopters. The $SNUKE Token offers a commitment of 0% tax on transactions, with liquidity pool (LP) tokens burnt to ensure security and the minting function permanently disabled to prevent the creation of new tokens How to Participate in the $SNUKE Presale To participate in the $SNUKE Presale, users can follow these simple steps: Wallet Preparation: Users should ensure they have a compatible Solana wallet, such as Phantom or Solflare, set up for participation. $SOL Acquisition: Users should acquire $SOL from Binance or Coinbase, considering applicable transaction fees, and then transfer it to their Solana Compatible self-custodial wallet. Contribution: Users should visit the $SNUKE presale page and adhere to the provided instructions to transfer $SOL to the designated address. Token Tracking: Users can utilize the user-friendly dashboard to monitor the $SNUKE tokens entitled based on the total $SOL contributed. Token Receipt: After the presale, $SNUKE tokens will be airdropped to the user's wallet in proportion to their contribution. Wallet Preparation: Users should ensure they have a compatible Solana wallet, such as Phantom or Solflare, set up for participation. $SOL Acquisition: Users should acquire $SOL from Binance or Coinbase, considering applicable transaction fees, and then transfer it to their Solana Compatible self-custodial wallet. Contribution: Users should visit the $SNUKE presale page and adhere to the provided instructions to transfer $SOL to the designated address. presale page presale page Token Tracking: Users can utilize the user-friendly dashboard to monitor the $SNUKE tokens entitled based on the total $SOL contributed. Token Receipt: After the presale, $SNUKE tokens will be airdropped to the user's wallet in proportion to their contribution. Joining the $SNUKE Community Joining $SNUKE is an opportunity to be part of a movement set to redefine meme coins on Solana. With a growing social media presence and a surge in community engagement, the $SNUKE team sees a great future for $SNUKE in the industry. Users can stay connected with the $SNUKE community and receive updates or information about partnerships by joining the $SNUKE Telegram group and following us on Twitter . Telegram Telegram Twitter Twitter About $SNUKE $SNUKE is a pioneering project launched on the Solana blockchain, inspired by the beloved "South Park" series. It blends humor with investment potential, designed to captivate meme lovers and crypto investors alike. Users can stay updated by following $SNUKE on social media: Website: https://snuke.wtf https://snuke.wtf https://snuke.wtf Twitter: https://twitter.com/snukecoin https://twitter.com/snukecoin https://twitter.com/snukecoin Telegram: https://t.me/snukecoin https://t.me/snukecoin https://t.me/snukecoin Contact Ms Barbara Reece Snuke contact@snuke.wtf This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

$SNUKE Presale Reaches 300 $SOL Milestone

Tron price has been on an upward trajectory recently as it successfully achieved the TVL milestone. Borroe Finance made a historic presale in the crypto market, shifting investors' attention toward presale coins. As the crypto market is making significant progress, another trending crypto right now is the , which is getting the spotlight. BlockDAG coin Besides being a user-centric disruptive brand with amazing options for miners and avid investors, BlockDAG gives back to its community members by announcing a gleam event and making a 2 million coin giveaway. Hype Behind Borroe Finance Presale Recently, a new Polygon-based coin has significantly hiked in the crypto world called Borroe Finance. If you're trading in the crypto market, you'll know about this new presale coin performing exceptionally well. Borroe Finance is using AI-generated funding to merge with Web3, providing safe, secure, and efficient payment solutions for its investors. How does this benefit its investors? They can buy and sell future NFTs through a secure and transparent platform. To make it more interesting, sellers listing their NFTs can offer considerable discounts to encourage more buyers. Tron Price Surges, Hitting TVL Milestone In the past week, Tron has managed to trend in the crypto market as it achieved the TVL milestone. The Tron price is significantly increasing, becoming a trending crypto right now, after reaching the mentioned mark. Tron is one of the crypto coins that is decentralized and runs on blockchain technology. Tron helps to connect content creators and their audiences directly without using any medium. Not only that, Tron provides a platform for developers to program dApps, also known as decentralized applications. BlockDAG $2M Giveaway: Batch 2 Almost Sold Out BlockDAG is rising in 2024, with a rapid sellout of batch 1. The project has entered the presale of batch 2 at an attractive price of 0.0015. With a projected $600 million roadmap in its pipeline, BDAG looks promising. BlockDAG has attracted attention from notable investors and announced a $2 million giveaway for its fortunate investors. Community members have the chance to share in this significant prize pool, provided by BlockDAG, with the condition that participation in the presale is a prerequisite. Details on how to join the presale and enhance the probability of winning are available on the BlockDAG website. BlockDAG, now in its second batch of sales, is nearing a sell-out, presenting a valuable opportunity for investors to join in on its substantial giveaway. Positioned to make a significant impact in the cryptocurrency market, BlockDAG is projected to reach $600 million in sales by the end of 2024, setting it up as one of the most robust presale coins available. The company's commitment to its users is evident through its innovative offerings, including a range of highly efficient mining devices and a mobile app designed to minimize battery consumption. This user-centric approach has not only fostered admiration within the community but has also contributed to BlockDAG's strong standing in the crypto sphere. Future Outlook Following a notable price surge in the Tron cryptocurrency, after achieving a Total Value Locked (TVL) milestone, it appears to be an opportune moment for investment. Similarly, BlockDAG has captured legacy crypto investors' attention, paralleling Tron's success. The announcement of a substantial giveaway by BlockDAG has significantly bolstered trust within both the broader crypto community and BlockDAG's own followers. This surge in confidence is evidenced by the rapid sale of batch 2, demonstrating the strong belief investors have in BlockDAG's potential. Join BlockDAG Presale | | | Website Presale Telegram Discord This story was distributed as a release by BTCWire under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/

BlockDAG's Gleam Event Reaches TVL Milestone - How They're Celebrating With Their Investors

BlockDAG এর গ্লেম ইভেন্ট TVL মাইলস্টোন ছুঁয়েছে - তারা কীভাবে তাদের বিনিয়োগকারীদের সাথে উদযাপন করছে

BlockDAGs Gleam-Event erreicht TVL-Meilenstein – wie sie mit ihren Investoren feiern

El evento Gleam de BlockDAG alcanza un hito en TVL: cómo lo celebran con sus inversores

L'événement Gleam de BlockDAG atteint le jalon TVL - Comment ils célèbrent avec leurs investisseurs

ब्लॉकडीएजी का ग्लेम इवेंट टीवीएल माइलस्टोन तक पहुंच गया - वे अपने निवेशकों के साथ कैसे जश्न मना रहे हैं

BlockDAG の Gleam イベントが TVL マイルストーンを達成 - 投資家とともに祝う様子

BlockDAG의 Gleam 이벤트가 TVL 이정표에 도달 - 투자자들과 함께 축하하는 방법

O evento Gleam da BlockDAG atinge o marco da TVL - como eles estão comemorando com seus investidores

Мероприятие BlockDAG Gleam достигло важной вехи TVL – как они празднуют вместе со своими инвесторами

BlockDAG'ın Gleam Etkinliği TVL Dönüm Noktasına Ulaştı - Yatırımcılarıyla Nasıl Kutluyorlar

Sự kiện Gleam của BlockDAG đạt được cột mốc TVL - Cách họ ăn mừng cùng các nhà đầu tư của mình

BlockDAG 的 Gleam 活动达到 TVL 里程碑 - 他们如何与投资者一起庆祝

Around the World in Eighty Days by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . IN WHICH PHILEAS FOGG AT LAST REACHES LONDON here CHAPTER XXXIV. IN WHICH PHILEAS FOGG AT LAST REACHES LONDON Phileas Fogg was in prison. He had been shut up in the Custom House, and he was to be transferred to London the next day. Passepartout, when he saw his master arrested, would have fallen upon Fix had he not been held back by some policemen. Aouda was thunderstruck at the suddenness of an event which she could not understand. Passepartout explained to her how it was that the honest and courageous Fogg was arrested as a robber. The young woman’s heart revolted against so heinous a charge, and when she saw that she could attempt to do nothing to save her protector, she wept bitterly. As for Fix, he had arrested Mr. Fogg because it was his duty, whether Mr. Fogg were guilty or not. The thought then struck Passepartout, that he was the cause of this new misfortune! Had he not concealed Fix’s errand from his master? When Fix revealed his true character and purpose, why had he not told Mr. Fogg? If the latter had been warned, he would no doubt have given Fix proof of his innocence, and satisfied him of his mistake; at least, Fix would not have continued his journey at the expense and on the heels of his master, only to arrest him the moment he set foot on English soil. Passepartout wept till he was blind, and felt like blowing his brains out. Aouda and he had remained, despite the cold, under the portico of the Custom House. Neither wished to leave the place; both were anxious to see Mr. Fogg again. That gentleman was really ruined, and that at the moment when he was about to attain his end. This arrest was fatal. Having arrived at Liverpool at twenty minutes before twelve on the 21st of December, he had till a quarter before nine that evening to reach the Reform Club, that is, nine hours and a quarter; the journey from Liverpool to London was six hours. If anyone, at this moment, had entered the Custom House, he would have found Mr. Fogg seated, motionless, calm, and without apparent anger, upon a wooden bench. He was not, it is true, resigned; but this last blow failed to force him into an outward betrayal of any emotion. Was he being devoured by one of those secret rages, all the more terrible because contained, and which only burst forth, with an irresistible force, at the last moment? No one could tell. There he sat, calmly waiting—for what? Did he still cherish hope? Did he still believe, now that the door of this prison was closed upon him, that he would succeed? However that may have been, Mr. Fogg carefully put his watch upon the table, and observed its advancing hands. Not a word escaped his lips, but his look was singularly set and stern. The situation, in any event, was a terrible one, and might be thus stated: if Phileas Fogg was honest he was ruined; if he was a knave, he was caught. Did escape occur to him? Did he examine to see if there were any practicable outlet from his prison? Did he think of escaping from it? Possibly; for once he walked slowly around the room. But the door was locked, and the window heavily barred with iron rods. He sat down again, and drew his journal from his pocket. On the line where these words were written, “21st December, Saturday, Liverpool,” he added, “80th day, 11.40 a.m.,” and waited. The Custom House clock struck one. Mr. Fogg observed that his watch was two hours too fast. Two hours! Admitting that he was at this moment taking an express train, he could reach London and the Reform Club by a quarter before nine, p.m. His forehead slightly wrinkled. At thirty-three minutes past two he heard a singular noise outside, then a hasty opening of doors. Passepartout’s voice was audible, and immediately after that of Fix. Phileas Fogg’s eyes brightened for an instant. The door swung open, and he saw Passepartout, Aouda, and Fix, who hurried towards him. Fix was out of breath, and his hair was in disorder. He could not speak. “Sir,” he stammered, “sir—forgive me—most—unfortunate resemblance—robber arrested three days ago—you are free!” Phileas Fogg was free! He walked to the detective, looked him steadily in the face, and with the only rapid motion he had ever made in his life, or which he ever would make, drew back his arms, and with the precision of a machine knocked Fix down. “Well hit!” cried Passepartout, “Parbleu! that’s what you might call a good application of English fists!” Fix, who found himself on the floor, did not utter a word. He had only received his deserts. Mr. Fogg, Aouda, and Passepartout left the Custom House without delay, got into a cab, and in a few moments descended at the station. Phileas Fogg asked if there was an express train about to leave for London. It was forty minutes past two. The express train had left thirty-five minutes before. Phileas Fogg then ordered a special train. There were several rapid locomotives on hand; but the railway arrangements did not permit the special train to leave until three o’clock. At that hour Phileas Fogg, having stimulated the engineer by the offer of a generous reward, at last set out towards London with Aouda and his faithful servant. It was necessary to make the journey in five hours and a half; and this would have been easy on a clear road throughout. But there were forced delays, and when Mr. Fogg stepped from the train at the terminus, all the clocks in London were striking ten minutes before nine. [1] Having made the tour of the world, he was behind-hand five minutes. He had lost the wager! A somewhat remarkable eccentricity on the part of the London clocks!—TRANSLATOR. [1] About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (1994). Around the World in Eighty Days. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/files/103/103-h/103-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

IN WHICH PHILEAS FOGG AT LAST REACHES LONDON

Hey Hackers! On April 19th, Voyager an agreement with the United States Federal Government to allow the $1.02 billion deal with to move forward. announced Binance The agreed upon resolution, with the U.S. Government, will allow the Binance/Voyager deal to move forward without the exculpation provision, which would relieve Voyager officials from any liability. Without the exculpation provision, Voyager officials will now be liable to criminal charges if regulators decide to investigate them for their role in the collapse of the Voyager cryptocurrency exchange. This positive resolution comes almost a month after the Binance/Voyager by U.S. District Judge Jennifer Rearden. deal was halted The purchase agreement between Binance and Voyager was to back in December 2022, and received in early March 2023, despite regulatory complaints from the SEC and CFTC. originally agreed final approval The Official Committee of Unsecured Creditors of Voyager took to to share the amazing news. Twitter https://twitter.com/VoyagerUCC/status/1648753616931495936?embedable=true I have been covering this Binance/Voyager since the beginning, as I am one of the many Voyager customers/victims, and this has been a hell of a journey. I would love this to be the last time I write about this Binance/Voyager deal…that is until I write about finally receiving my long-awaited crypto assets! What are your thoughts on this news? Will this be the final update regarding the Voyager and Binance drama? What else could happen now to stop this deal? Are you excited to finally be getting your crypto assets back? Let me know in the comments below! Enter my on Publish0x to win yourself some free Crypto! Monthly Giveaway Check out my Socials: https://linktr.ee/zerorequiem0x See ya! :) Also published here.

The U.S. Government Reaches Agreement with Voyager: $1.02 Billion Binance Deal to Move Forward

El gobierno de EE. UU. llega a un acuerdo con Voyager: Binance $ 1.02 Billion Deal to Move Forward

Le gouvernement américain conclut un accord avec Voyager : Binance accorde 1,02 milliard de dollars pour aller de l'avant

अमेरिकी सरकार वायेजर के साथ समझौते पर पहुंची: आगे बढ़ने के लिए बिनेंस $ 1.02 बिलियन डील

米国政府はボイジャーと合意に達しました：バイナンスは前進するために10.2億ドルの取引を行います

O governo dos EUA chega a um acordo com a Voyager: o acordo de US$ 1,02 bilhão da Binance avança

Chính phủ Hoa Kỳ đạt được thỏa thuận với Voyager: Thỏa thuận trị giá 1,02 tỷ đô la của Binance để tiến lên phía trước

美国政府与 Voyager 达成协议：Binance 10.2 亿美元的交易向前推进

THE ENTERTAINMENT OF MR. DIRECK REACHES A CLIMAX

CHALLENGE Doximity sets out to improve application stability for its Android application as medical professionals rely on its services to communicate remotely. RESULTS The Android team improves app stability by aligning teams, streamlining workflow, and achieving 99.99% stability with Bugnsnag. About Doximity : Healthcare Industry : Android, iOS, Web App & Platforms : 650 Employees San Francisco, CA Headquarters: Doximity Delivers Remote Capabilities to Medical Professionals Doximity is the leading digital platform for medical professionals. Recognizing that clinicians are mobile by nature, Doximity delivers a suite of tools that enable verified members to perform their jobs on-the-go or while working remotely. The company’s network members include doctors, nurses, nurse practitioners, physician assistants, and pharmacists. Doximity and its Android mobile application serve an important need as medical professionals have turned to the multifaceted app for activities such as telemedicine, patient referral facilitation, and targeted medical news. “Just like everyone else these days, doctors are starting to work from home and need a way to contact their patients,” states Matt Rea, Android Engineering Manager at Doximity. “Any increase in usage can triple or quadruple any issues you might have in the app, so crashes and infrastructure errors can’t be taken lightly.” Focus on Stability Drives Visibility and Strong User Experiences In late 2019, the Android team implemented Bugsnag, which Matt describes as fortuitous. “Going into 2020, we had the goal to work on improving stability,” Matt explains. “Once I started looking into Bugsnag, I realized how full-featured it was. Luckily, we implemented Bugsnag, so that was perfect timing for us.” Another reason the Android team decided to implement Bugsnag was to benefit from its widespread adoption by Doximity’s backend and web teams. Approximately 75 projects and applications were already using the error monitoring solution. “We wanted to have one place, specifically Bugsnag’s Stability Center page, where we could view application health for the entire system” -Matt Rea, Android Engineering Manager If a major issue or outage happens or a database goes down, we can see everything that spikes,” Matt states. “That kind of high-level view is invaluable.” Rounding out the decision was a recognition by the Android team that they needed more than crash-free scores to ensure strong user experiences. Now with measuring application stability, Doximity gains a more reflective and holistic view of their application health. As Matt explains, “Looking at crashes is only one piece of the picture. Obviously, you never want an app to crash, but you also don’t want an app to be available but not useful. Those are the kinds of things that, looking only at crash-free ratings, for example, you would miss.” Bugsnag Fosters Productivity and Team Alignment with Remote Work Matt acknowledges that it is paramount for managers to ensure that employees are feeling confident with their work.  Finding ways to reduce burnout around tasks like error detection became a priority. “Bugsnag made it much easier to track down bugs,” Matt states. “Instead of banging your head against the wall to figure out how an error occurred, the breadcrumbs that Bugsnag attaches to errors really help. We make good use of breadcrumbs.” The team also benefits from integrating Bugsnag with Slack to receive focused error alerts. As the Android lead, Matt also appreciates the metadata that Bugsnag provides. “Android is a pretty fragmented ecosystem, so a bug might occur only on Samsung or LG devices or certain OS versions, which can be pretty hard to track down from a normal QA bug reporting perspective,” Matt explains. “Bugsnag gives the engineer more of a starting point, more of a big picture around ‘how did this occur?,’ which definitely makes things easier.” To minimize the impact of errors on its user base, the Android team takes advantage of phased rollouts, supported by Bugsnag. With two week release cycles, Doximity sends out new releases to only 10% of its user base to ensure there are no spikes in errors or crashes. “Tracking releases is one of my primary responsibilities—to make sure releases go out on time and are stable—so the Releases dashboard is one of my favorite features in Bugsnag.” -Matt Rea, Android Engineering Manager “We generally have several versions in production, so I can see the past five releases, what the adoption is on each release, and what the stability is for each release,” Matt states. Android Team Achieves Stability Score Target of 99.99% and Sets New Stability Goals for the Future After implementing Bugsnag, the Android team focused initially on resolving outstanding crashes to improve the overall stability of the app. “Thanks to Bugsnag, we were able to get on top of a few big crashes right away,” Matt explains. The team adopted Bugsnag’s stability targets in order to establish goals and achieve strong stability for users. “While 99.99% seemed pretty lofty and difficult to achieve, we got there over the course of 2019 and into 2020. I think it’s a pretty good goal to try to hit that 99.99% with each release.” -Matt Rea, Android Engineering Manager Today, the Android team benefits from streamlined workflows and a quick understanding of the source for any new errors. Next, they are starting to (application not responding) and native crashes for features such as video calling that use native libraries. Matt also plans to broaden his use of Bugsnag’s user stability score. “So far, I’ve been mainly focused on the session score, although I’ve been keeping an eye on the user stability score as well. I would like to start leveraging that in the future.” look into Android ANRs Download Customer Story PDF

Domixity Reaches 99.99% Stability on Android App with Bugsnag

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn;
(2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com;
(3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn;
(4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn;
(5) Fei Wu, Zhejiang University & wufei@zju.edu.cn;
(6) Jiwei Li, Zhejiang University &  jiwei_li@zju.edu.cn;
(7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg;
(8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. Table of Links Abstract and Introduction
Related Work
LLM Negotiation for Sentiment Analysis
Experiments
Ablation Studies
Conclusion and References 3 LLM Negotiation for Sentiment Analysis 3.1 Overview In this section, we detail the multi-LLM negotiation framework for sentiment analysis: Two LLMs perform as the answer generator and discriminator. We refer to the interaction between the generator and the discriminator as a negotiation. The negotiation will repeat until a consensus is reached or the maximum number of negotiation turns is exceeded. Illustrations are shown in Figures 1 and 2. 3.2 Reasoning-infused generator The generator is backboned by a large language model. We ask the answer generator based on the ICL paradigm through prompts, aiming to generate a step-by-step reasoning chain and a decision towards the sentiment polarity of the test input. Prompts are composed of three elements: a task description, demonstrations, and a test input.  The task description is a description of the task in natural language (e.g., "Please determine the overall sentiment of test input."); the test input is the textual input in the test set (e.g., "The sky is blue."); demonstrations are from the train set of the task. Each consists of three elements: input, reasoning chains, and sentimental decision. For each test input, we first retrieve K nearest neighbors (input, sentiment decision) from the train set as demonstrations. Then, we transform demonstrations to (input, reasoning process, sentiment decision) triplets by prompting the generator to produce a reasoning chain. After concatenating the task description, demonstrations, and the test input, we forward the prompt to the generator, which will respond with a step-by-step reasoning chain and a sentimental decision. 3.3 Explanation-deriving discriminator The discriminator is backboned by another LLM. After finishing the answer generating process, the answer discriminator is used to judge whether the decision made by the generator is correct and provide a reasonable explanation. To accomplish this goal, we first construct prompts for the answer discriminator. The prompt is composed of four elements: a task description, demonstrations, a test input, and the response from the answer generator. The task description is a piece of text that describes the task in natural language (e.g., "Please determine whether the decision is correct."). Each demonstration is composed of six elements: (input text, a reasoning chain, sentiment decision, discriminator attitude, discriminator explanations, discriminator decision) and constructed by prompting the answer discriminator to provide explanations of why the sentiment decision is correct for the input text. Then we ask the discriminator with the construct prompt. The answer discriminator will respond with a text string, containing an attitude (i.e., yes, no) that denotes whether the discriminator agrees with the generator, explanations that explain why the discriminator agrees/disagrees with the generator, and a discriminator decision that determines the sentiment of the test input. Why Two LLMs but Not One? There are two reasons for using two different LLMs separately for the generator and the discriminator rather than using a single LLM to act as two roles: (1) If an LLM makes a mistake as a generator due to incorrect reasoning, it is more likely that it will also make the same mistake as the discriminator as since generator and the discriminator from the same model are very likely to make similar rationales; (2) by using two separate models, we are able to take the advantage of the complementary abilities of the two models. 3.4 Role-flipped Negotiation After two LLMs end with a negotiation, we ask them flip roles and initiate a new negotiation, where the second LLM acts as the generator, and the first LLM acts as the discriminator. We refer the interaction of two LLMs with flipped roles as role-flipped negotiation. Likewise, the role flipped negotiation is ended until a consensus is reached or the maximum number of negotiation turns is exceeded. When both negotiations result in an agreement and their decisions are the same, we can choose either decision as the final one since they are the same. If one of the negotiations fails to reach a consensus while the other reaches a decision, we choose the decision from the negotiation  that reached a consensus as the final decision. However, if both negotiations reach a consensus but their decisions do not align, we will require the assistance of an additional Language Model (LLM), as will be explained in more detail below." Introducing a third LLM If the decision from the two negotiations do not align, we introduce a third LLM and conduct the negotiation and role-flipped negotiation with each of the two aforementioned LLMs. Subsequently, we will get 6 negotiation results and vote on these results: the decision that appears most frequently is taken as the sentiment polarity of the input test. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn; (2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com; (3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn; (4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn; (5) Fei Wu, Zhejiang University & wufei@zju.edu.cn; (6) Jiwei Li, Zhejiang University &  jiwei_li@zju.edu.cn; (7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg; (8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn; (2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com; (3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn; (4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn; (5) Fei Wu, Zhejiang University & wufei@zju.edu.cn; (6) Jiwei Li, Zhejiang University &  jiwei_li@zju.edu.cn; (7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg; (8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. Table of Links Abstract and Introduction Related Work LLM Negotiation for Sentiment Analysis Experiments Ablation Studies Conclusion and References Abstract and Introduction Abstract and Introduction Related Work Related Work LLM Negotiation for Sentiment Analysis LLM Negotiation for Sentiment Analysis Experiments Experiments Ablation Studies Ablation Studies Conclusion and References Conclusion and References 3 LLM Negotiation for Sentiment Analysis 3.1 Overview In this section, we detail the multi-LLM negotiation framework for sentiment analysis: Two LLMs perform as the answer generator and discriminator. We refer to the interaction between the generator and the discriminator as a negotiation. The negotiation will repeat until a consensus is reached or the maximum number of negotiation turns is exceeded. Illustrations are shown in Figures 1 and 2. 3.2 Reasoning-infused generator The generator is backboned by a large language model. We ask the answer generator based on the ICL paradigm through prompts, aiming to generate a step-by-step reasoning chain and a decision towards the sentiment polarity of the test input. Prompts are composed of three elements: a task description, demonstrations, and a test input.  The task description is a description of the task in natural language (e.g., "Please determine the overall sentiment of test input."); the test input is the textual input in the test set (e.g., "The sky is blue."); demonstrations are from the train set of the task. Each consists of three elements: input, reasoning chains, and sentimental decision. For each test input, we first retrieve K nearest neighbors (input, sentiment decision) from the train set as demonstrations. Then, we transform demonstrations to (input, reasoning process, sentiment decision) triplets by prompting the generator to produce a reasoning chain. After concatenating the task description, demonstrations, and the test input, we forward the prompt to the generator, which will respond with a step-by-step reasoning chain and a sentimental decision. 3.3 Explanation-deriving discriminator The discriminator is backboned by another LLM. After finishing the answer generating process, the answer discriminator is used to judge whether the decision made by the generator is correct and provide a reasonable explanation. To accomplish this goal, we first construct prompts for the answer discriminator. The prompt is composed of four elements: a task description, demonstrations, a test input, and the response from the answer generator. The task description is a piece of text that describes the task in natural language (e.g., "Please determine whether the decision is correct."). Each demonstration is composed of six elements: (input text, a reasoning chain, sentiment decision, discriminator attitude, discriminator explanations, discriminator decision) and constructed by prompting the answer discriminator to provide explanations of why the sentiment decision is correct for the input text. Then we ask the discriminator with the construct prompt. The answer discriminator will respond with a text string, containing an attitude (i.e., yes, no) that denotes whether the discriminator agrees with the generator, explanations that explain why the discriminator agrees/disagrees with the generator, and a discriminator decision that determines the sentiment of the test input. Why Two LLMs but Not One? There are two reasons for using two different LLMs separately for the generator and the discriminator rather than using a single LLM to act as two roles: (1) If an LLM makes a mistake as a generator due to incorrect reasoning, it is more likely that it will also make the same mistake as the discriminator as since generator and the discriminator from the same model are very likely to make similar rationales; (2) by using two separate models, we are able to take the advantage of the complementary abilities of the two models. Why Two LLMs but Not One? 3.4 Role-flipped Negotiation After two LLMs end with a negotiation, we ask them flip roles and initiate a new negotiation, where the second LLM acts as the generator, and the first LLM acts as the discriminator. We refer the interaction of two LLMs with flipped roles as role-flipped negotiation. Likewise, the role flipped negotiation is ended until a consensus is reached or the maximum number of negotiation turns is exceeded. When both negotiations result in an agreement and their decisions are the same, we can choose either decision as the final one since they are the same. If one of the negotiations fails to reach a consensus while the other reaches a decision, we choose the decision from the negotiation  that reached a consensus as the final decision. However, if both negotiations reach a consensus but their decisions do not align, we will require the assistance of an additional Language Model (LLM), as will be explained in more detail below." Introducing a third LLM If the decision from the two negotiations do not align, we introduce a third LLM and conduct the negotiation and role-flipped negotiation with each of the two aforementioned LLMs. Subsequently, we will get 6 negotiation results and vote on these results: the decision that appears most frequently is taken as the sentiment polarity of the input test. Introducing a third LLM

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