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first_imgShare Facebook Twitter Google + LinkedIn Pinterest Under sunny skies for three days, visitors to the 56th annual Farm Science Review took a break from harvest to learn about the latest innovations in agriculture.Farm Science Review, held Sept. 18-20, drew 108,074 visitors, who came to admire new machinery and learn about techniques and trends, test-drive all-terrain vehicles, and talk about soybean tariffs and taxes. Though it didn’t rain this year as it did during much of last year’s show, clear skies kept some farmers in the field harvesting.Water coolers drained as the mercury rose each day of the farm show sponsored by the College of Food, Agricultural, and Environmental Sciences (CFAES).While farm income nationwide is projected to dip, plunging 50% in 2018 compared to the 2013 level, and soybean tariffs are squeezing out markets, there are always new tractors, combines and equipment to see that might offset any pessimism. The Review offered that plus a range of educational presentations to help growers weather tough financial times.“As farmers and producers pay closer attention to their bottom lines this year, Farm Science Review was a good resource for them,” said Nick Zachrich, manager of the Review. “Along with showcasing the latest trends and technologies in agriculture, CFAES and other experts led sessions on profitability, trade, tariffs and the farm bill.”This year’s show attracted 636 exhibitors in an area that was expanded by 20 acres.At a show where farmers learned about reducing input costs, administering antibiotics to their livestock and marketing local foods, among other topics, avid gardeners gathered tips on growing fruit in their backyards, attracting hummingbirds and beekeeping.“Visitors were able to experience everything from test-driving utility vehicles to seeing the latest equipment run in field demonstrations,” Zachrich said. “There was truly something here for everyone in agriculture to improve their operations.”Among the new offerings at this year’s show were experts who taught beef quality assurance, a certification that’s increasingly becoming important for beef producers.More combines spread across fields this year, harvesting corn 12 rows at a time in one of the many field demonstrations of cutting-edge equipment. Attendees at the show perused components of autonomous tractors and heard talks about farm estate planning and the effect climate change is having on agriculture.last_img read more

first_imgTags:#Big Data#Facebook#Microsoft#social#twitter Facebook is Becoming Less Personal and More Pro… Guide to Performing Bulk Email Verification Related Posts The Dos and Don’ts of Brand Awareness Videoscenter_img markhachman A Comprehensive Guide to a Content Audit Ten days before Hollywood hands out its Oscar statuettes, a pair of studies – one by Microsoft’s in-house “Nate Silver,” and another measuring social influence – have already picked the winners.Microsoft Research’s David Rothschild, who, like Silver, used early polls to correctly predict the outcome of the presidential elections in all but Florida, has used the predictive nature of the early awards shows to place his bets on who will be winning the various Academy Awards. Meanwhile, an English analyst firm, Brandwatch, has attempted to slice social media data in a couple of new ways (by both critical reaction and popular acclaim) to anticipate the winners.And who are those winners? The envelopes, please…Best Picture:ArgoBest Director: Steven Spielberg (Rothschild, Brandwatch popular) / David O. Russell (Brandwatch critics)Best Actor: Daniel-Day LewisBest Actress: Jennifer Lawrence (Rothschild, Brandwatch popular) / Jessica Chastain (Brandwatch critics)Best Supporting Actor: Tommy Lee Jones (Rothschild) / Christoph Waltz (Brandwatch popular) / Robert de Niro (Brandwatch critics)Best Supporting Actress: Anne HathawayBest Animated Film:BraveBest Original Song: Adele’s “Skyfall“The remaining categories, including best makeup, screenplay, documentary shorts, and others, can be found on the respective sites: Predictwise for Rothschild’s predictions, and the Brandwatch Oscars site.Why Will They Win?In November, Rothschild used the same methodologies employed by quant hero Nate Silver to determine the outcome of the 2012 Presidential election: examining polling information collected before the election to determine the outcome. In February 2012, Rothschild wrote that Obama would win, well before election season got underway. As it turned out, of course, he was right.(See also Why Nate Silver Won, And Why It Matters and Nate Silver’s Model A Stunning Portrait Of Logic Over Punditry) “I approach forecasting the Oscars the same way I approach forecasting anything, including politics,” Rothschild said in a blog post. “I look for the most efficient data, and I create statistically significant models without any regard for the outcomes in any particular year. All models are tested and calibrated on historical data, with great pains taken to ensure that the model is robust to ‘out-of-sample’ outcomes, not just what has happened in the past. The models predict the future, not just the past.“Thus, the science is identical, but there are differences in which data prove most useful,” Rothschild wrote.The predictive models that Rothschild could tap into are the ones that most people are now using to handicap Oscar races: previous awards shows like the BAFTA awards, Screen Actors Guild (SAG) awards, and the Golden Globes. Some data he tossed out: For elections, fundamental data such as past election results and economic indicators can be used as predictive tools. But in movies, box-office figures and even ratings are not statistically effective, he said.“I focus even more heavily on prediction markets, which are very robust, but I also include some user-generated data that helps me learn more about correlations within movies and between categories, such as, ‘How many categories will Lincoln win?” Rothschild added.Finally, he updates his results in real time. Naturally, there’s a way to tap into these results yourself: the Oscars Ballot Predictor app for Microsoft Excel, one of the few apps to provide real-time data for Microsoft’s Office suite. The app allows users to vote, and includes the real-time, up-to-date Oscar predictions.What could be Rothschild’s next step? “Sports is something we’re looking at,” a Microsoft spokeswoman said via email.Whose Opinion Matters: Critics, Or Audiences?Brandwatch has taken a more “traditional” approach: pull together mentions of each actor, director, movie, or other category across a broad swath of social media to look for positive, relevant references that can indicate a good chance of winning.Brandwatch taps into the Twitter firehose, and to date relevant Oscar mentions have totaled 304,550 mentions, with about 1,400 to 1,600 per day being added at the end of January. Naturally, that number will go up. Twitter makes up about 40% of the data that Brandwatch samples, according to a FAQ provided to ReadWrite.One surprise, it found, was that Lincoln was the early odds-on favorite to win Best Picture. But sentiment flipped after Argo started winning the title at the Producers Guild of America, British Academy Film Awards, LA Film Critics Association, the Golden Globes and others.What Brandwatch tries to do – differently, it says, from other studies – is pull together the volume of positive predictions. There are two variables: the number of mentions, as well as the sentiment behind them. This tries to ensure that a large number of comments on Helen Hunt’s red-carpet dresses, for example, won’t be factored in any more than a smaller number of positive comments for rival Jessica Chastain’s performance.However, the study also breaks down the projected winners by two categories: Critics, both “professionals” at major papers, plus semi-pro bloggers at enthusiast sites, and the general public. The skews show both sides of the acting industry, who aren’t paid critics but know their business presumably more than the average joe.Brandwatch was hired to perform the study by the Motion Picture Association of America (MPAA), most known for its aggressive stance on copyright and attacks on file-sharing networks. But Brandwatch suggests another use for the data: “The findings hold wider implications for the film industries. If winners diverge from viewer favorites, this could indicate a greater need to relate to target audiences. Further qualitative analysis can uncover why film titles are recommended online: vital information for gaining endorsement and boosting box office takings. Key actors and directors can be correlated with film titles: To what extent does an established cast boost online reputation (and by extension sales)?”It’s not quite clear why Brandwatch’s critic/public split is a better gauge than Rothschild’s single number. But if you’re running a betting pool, the smart money is on Argo, Daniel Day-Lewis and Anne Hathaway going home with Oscar on their arm.Image source: flickr/ebbandflowphotography.last_img read more

first_imgWith This One Question, You’ll Never Need an Ic… Why is it that customer service never seems to get any innovation attention? For decades, providing great customer service has been a constant challenge and expense, yet relatively few technologies exist to ease the difficulty or cost. Sales and marketing see new software arrive every year, yet customer support has scarcely changed in the last half-century, save for better issue-tracking tools. Technology has brought an expectation of immediacy from the consumer — an expectation impossible to fulfill in customer service. According to LivePerson, 34 percent of consumers wouldn’t rate a customer experience as excellent if it took the company more than a minute to respond. The average customer service response times? Almost three minutes for chat and 17 hours for email. SuperOffice reports that one in five companies fail to regularly respond to chat requests entirely. Over a decade ago, chatbots promised to solve these problems with 24/7 support. But, as we can see from the dearth of chatbot deployments in the industry, that promise hasn’t been fulfilled. Theories as to why vary, but most agree that chatbot interactions are unintelligent, frustrating, and obviously not human; at the end of the day, they didn’t resolve the customer’s need. Increasingly, the strategy to make service better is to not provide any service at all. Instead, companies try to “enable” customers to find their own answers and solutions by deflecting their requests to an FAQ page or a form. While this saves time for support agents, it rarely provides a satisfactory end-customer experience. If the customer is fixing his own problems, the company gets none of the loyalty (or retention stats) that result from a customer feeling “looked after.” There’s an enormous difference between resolving a problem and deflecting it. What Customers WantCustomer loyalty is the fuel that drives successful businesses. Keeping loyal customers is far more valuable than finding new ones — increasing customer retention by as little as 5 percent can lead to a 95 percent profit spike. And from the opposite perspective, NewVoice Media’s latest Serial Switchers report found that, in 2018, bad customer service cost businesses more than $75 billion. In other words, companies that figure out the customer service equation and generate loyalty could collectively add billions to their bottom line. So what’s keeping customer service teams from claiming that lost revenue? What Forbes contributor and customer experience expert Stan Phelps calls “the customer expectation gap.” Phelps defines this gap across three dimensions, grounding each in data from IBM Institute for Business Value report:Speed. More than eight in 20 consumers want faster response times, according to the report.Consistency. Sixty-eight percent of those surveyed said they want customer service teams to harmonize their experiences across all channels of communication.Personalization. Of those surveyed, 76 percent expect customer service teams to understand and address their individual needs.What can customer service teams do to close the gap? Support agents can only help so many customers per hour, and pushing them to work faster cuts consistency and personalization. Instead, companies like Thankful are taking on the challenge with fresh AI technology and solutions, enabling companies to provide service that’s quick, personable, and consistent. In essence, Thankful hopes to fulfill the long-forgotten promise of customer service: giving customers what they want.The Tech Customer Service NeedsThankful’s mission to bring artificial intelligence to bear on shoddy customer service began when CEO Ted Mico met co-founder and CTO Evan Tann while he was developing an AI-powered wine recommendation tool for customers.“I’d had a procession of bad customer experiences earlier that week,” Mico explains, “so I jokingly asked Evan, ‘Why are we working on fixing wine recommendations when customer service is so broken?’ Thankful was founded that week with the mission of making help human.” Realizing many of Mico’s service issues could’ve been handled without human intervention, Mico and Tann went to work on an AI platform. Tann released the first version of the software on GitHub the same day as Microsoft’s BotBuilder. It blew up quicker than the big-budget build, and Tann’s radical approach soon made it to the front page of Hacker News. Despite the initial acclaim, Tann’s team had to radically rewrite the codebase over several years before the platform could attain the 99 percent out-of-the-box accuracy rate the brand used as a benchmark before it could launch to businesses.“Most [early] bots couldn’t provide correct responses after a couple of tries, which frustrated early-adopting consumers, businesses, and influencers in the space,” Madhu Mathihalli writes in industry magazine TotalRetail. “We’re not a bot,” Mico stresses. “In fact, more than 90 percent of incoming queries we’re dealing with are email, not chat. Thankful is the brain that governs service via any text-based channel.” “The key to great service is understanding what the customer wants and being able to deliver what the customer needs,” Mico adds. “At Thankful, we talk a lot about the five pillars that make up great customer service — speed, knowledge, accuracy, empathy, and thoroughness. Any of these pillars is hard for technology to emulate — getting all five to work together took almost three years of programming.” Faster Is FirstOnce Mico and Tann had an accurate model, they set their sights on the most glaring of the three customer service gaps: speed. “Consumers’ expectation for immediate service was created by tech, and it can only be solved by tech,” Mico argues. “We wanted technology to deliver on the promise of solving problems for the customers, delivering a human-like experience that makes them feel as though they’re being properly looked after.”  “We currently average 40-50 percent resolution rates for our e-commerce clients,” he says. Without an agent in the loop, Thankful still strives to provide a high level of service. This allows a company’s human agents to focus and dedicate more time to the remaining issues, which are often more complex.  Consistency Is CriticalThe second piece of the customer service puzzle, consistency, is the one that Mico and Tann think has been most absent from midmarket online retailers. Gladly’s 2018 Customer Service Expectations Survey revealed that 76 percent of customers receive conflicting answers when they ask different support agents the same question. Mico says that the replicable nature of e-commerce customers’ challenges is partially what led him and Tann to focus on the space. “It’s mostly repetitive issues like shipping, exchanges, returns, and product information: perfect for machine learning,” he says, “but now Thankful is also capable of much more complex e-comm-related actions.”The Proof Is in PersonalizationConsistency, of course, can be a double-edged sword. Customers rightly expect to have their individual circumstances considered, which most rules-based AI platforms fail to do. Mico acknowledges that Thankful can’t honor every customer request, but he explains that it can make exceptions. “We remember who you are — if you’re a longtime customer or VIP member, Thankful takes this information into account and responds appropriately,” he says. “Additionally, our AI is smart enough to understand context such as key information, like an order number, so you won’t have to repeat yourself later on — it will retain information conversationally just like a human, but with a better memory.”In the future, Mico hopes to make Thankful even more “human” in its personalization skills. “We get tons of thank-you responses from customers with smileys. Customers assume that because the problem is being solved in a human-like way that a human is responsible. Customers don’t tend to send heart emojis to robots,” he says.But if more companies start adopting a similar approach to customer service, customers just might. Tags:#chatbot#customer service#customer service technology#Personalization Related Posts How Self-Service Technology Can Boost Startup G… Brad AndersonEditor In Chief at ReadWrite AI is Not the Holy Grail of Sales, at Least Not… Brad is the editor overseeing contributed content at ReadWrite.com. He previously worked as an editor at PayPal and Crunchbase. You can reach him at brad at readwrite.com. Man or Machine? For Better Customer Service, Us…last_img read more

first_imgBonus: Microsoft initially hid the Windows Photo Viewer from Windows 10, but you can bring it back with a simple behind-the-scenes edit. Browsing RW2 photos and videos on the same GH5 memory card can be a problem for Windows 10 users. Here’s the solution.Cover image via Shutterstock.If you’re like me when returning from a day of casual filming with the GH5, I’m sure your media folder gets filled with as many photographs as video files. Even on professional shoots, I’m prone to taking the odd behind-the-scenes snap.The only problem is that Microsoft hasn’t issued an RW2 codec that works on the 64bit Windows 10 OS. As a result, you’ll have a folder that looks like the following.As such, you’ll find plenty of disgruntled Panasonic users flocking to the Microsoft forums hoping for a codec. Microsoft did issue a camera RAW codec back in 2013 that would display the RW2 files, but when a Windows 10 user attempts to install that codec, they see the following error.There hasn’t been a further update, and it looks like we won’t be getting one anytime soon.It’s incredibly annoying because, to view each photograph, you have to open either Photoshop or Lightroom. Photoshop is only going to show you one image at a time, and while Lightroom, which does allow you to browse all photographs, becomes disjointed when you also have video files on your memory card. (It takes 5-10 seconds to process the video preview.) Perhaps all GH5 users who are experiencing this problem could just shoot in JPG, but where’s the fun in that?Until Microsoft offers a codec solution to Windows 10 users, we’re going to have to find a workaround for simple thumbnail browsing. One such fix is FastPictureViewer. (Side note: this is not a sponsored post. I’ve tried and tested various picture viewers and codec packs such as FastStone Image Viewer, FastRawViewer, and Aftershot Pro.)FastPictureViewerFastPictureViewer is preview software that allows you to view, review, and delete pictures in seconds. The load time is only slightly slower than the default Windows photo application, and here’s the kicker: the next image loads almost immediately. By comparison, Lightroom takes about half a second to load the next image. At full resolution, I flicked through the images so fast it felt like I was watching a stop-motion animation.For those on a budget, the $49 price tag for software just to view your RAW GH5 photos easily may be a little too much. However, you can also purchase the codec pack for just $9.99, which will allow you to open your RW2 files in Windows Photo Viewer. This will make your media folder look like the following.After installing the codec pack, simply right-click on the RW2 file and select the photos app, or Windows photo viewer, as the default. You can now browse the RW2 files just like they’re JPEGs.last_img read more