As retail continues to adopt algorithms and AI, it is clear that human involvement and emotion are important elements in a successful data strategy—especially when connecting with customers.
, “Winning decisions are increasingly driven by analytics more than instinct, experience, or merchant ‘art’. By leveraging smarter tools—those beyond backward-looking, ‘hind sighting’ analysis—retailers can increasingly make forward-looking predictions that are quickly becoming the ‘table stakes’ necessary to keep up.
” Data has been available to retailers for a long time. Now, having watched Amazon pave the way, retailers are starting to see all the possibilities technology can offer. Amazon has more data on more customers than any other retailer, and they use these data to target them effectively to drive conversions. For Amazon, there is little to no intuition – it’s all data. The example that they continue to set will drive retailers even more towards the science of retail. Artificial Intelligence is powerful and is one of the most relevant trends in today’s retail industry. Retailers are looking for places to apply this burgeoning technology in ways that will move the needle, with many companies leveraging AI to improve customer service by anticipating their behavior. What retailers and brands often don’t realize is that one of the most important data signals in predicting customer behavior is the voice of the customer itself. Retailers often build advanced AI-driven models based on historical data, but without knowing the customers’ wants and needs directly, the data is not very useful. The question is, how can retailers make the data come to life and impact the customer on an emotional level to drive the business? As the speed of technology adoption continues to accelerate, the idea of machines taking over creeps in. AI and machine learning have been around for many years and are included in many solutions. There is no need to be afraid of the power of computing. But I think it’s safe to say there is still and will always be a need for a human component. Human-based computation is when a machine performs its function by outsourcing certain steps to humans to solve a problem.states that “AI and machine learning are designed to do much of the work that humans can do.” Although these algorithms can be quick, efficient and sophisticated, and they are beginning to “evolve to handle increased complexity, there always remains pieces of work for which an efficient algorithm does not yet exist. These tasks, usually ‘microwork,’ require some kind of human intervention, because they generally involve illogical, emotional or ethical elements that can't be made sense of by a machine.” The repetitious work can now be automated, but there will also always be a need for emotion and human involvement. IBM Watson markets their ability to help companies build competitive advantage with AI. According to their, “Watson helps you unlock the value of your data in entirely new, profound ways. By freeing your employees from repetitive tasks, you can empower your teams to focus on more creative, higher-value work. With insights from Watson, you can predict and shape future business outcomes, while rethinking your practices and workflows.” IBM Watson is a machine learning system, trained by data.Business Insider , the drug discovery program was introduced in 2014 to help pharmaceutical companies develop drugs, but Watson struggled to prove its efficacy to health systems. The problem was that while Watson quickly learned to absorb and process massive quantities of data, it had trouble accurately identifying patient information. reported that “if data wasn’t formatted properly, or even chronologically arranged, the software had trouble understanding patient histories. And the system was incapable of comparing new cancer patients against databases of previous patients to discover hidden treatment patterns, because such practices would not be considered evidence-based.” Human error is inevitable, but in the case of the IBM Watson tool for drug discovery the margin for machine error was just too large. Human oversight was still needed. Another great example of human-based computation is Google’s Image Labeler. In 2006, Luis von Ahn of Carnegie Mellon University developed the ESP game, a human-based computation game focused on the challenge of attaching keywords to the millions of images on the Web. The game started by matching the player with a random partner with whom he or she cannot communicate. Once matched, both partners were shown the same image. Their task was to agree on a word that would be an appropriate label for the image. Both partners enter possible words, and once a word is entered by both partners, that word was agreed upon and became a label for the image. This process would continue for a total of 15 images over a 2.5 minute period. Von Ahn licensed the game to Google, which became the first version of Google’s Image Labeler. Millions of images were labeled between 2006 and 2011 using this approach of combining human input with technology. Today, similar approaches are being used to apply AI to retail to help merchants make better decisions than they could on their own. By incorporating human input with advanced analytics , retailers are seeing the results in terms of increased success rates on new assortments. The key is finding the right balance and incorporating these inputs at the right time in the process. While algorithms may not fully take control in 2019, Amazon is setting the bar high, and other industry players can’t afford to sit on the sidelines any longer.From early on Greg realized there was a better way for retailers to make decisions, and devoted himself to revolutionizing the retail industry. With decades of experienc...
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