{"id":14129,"date":"2020-11-11T10:01:42","date_gmt":"2020-11-11T10:01:42","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=11200"},"modified":"2023-10-05T14:10:38","modified_gmt":"2023-10-05T14:10:38","slug":"smes-machine-learning-edge-during-the-pandemic","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/smes-machine-learning-edge-during-the-pandemic\/","title":{"rendered":"SMEs Embrace Machine Learning to Develop Competitive Edge During the Pandemic"},"content":{"rendered":"\t\t
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Over the last few years, there has been a clear discrepancy between the data strategies of large and small businesses. One report from IDG found that 78% of large companies<\/a> believed a data strategy was crucial to their business models. However, their smaller counterparts have been more likely to procrastinate investing in data-driven business models.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This is changing in the wake of the ongoing pandemic. Businesses across the nation are struggling to keep their heads above water. Back in September, economists determined that nearly 100,000 companies permanently shuttered. Data from Yelp showed that 60% of companies that temporary closed later announced that they would never open their doors again.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Fortunately, companies still have a strong chance of succeeding if they invest in sound, data-driven strategies. NetworkNewswire published a report<\/a> back in June about the state of big data during the current economic crisis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The report emphasized that data analytics technology has clearly saved some companies from bankruptcy. Some businesses are actually doing better than ever, which is largely due to their clever utilization of big data. This is something people need to understand if they are asking how do I become a successful entrepreneur during the pandemic.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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However, big data strategies are not guaranteed to yield optimal results. Companies are still likely to encounter various challenges, which they need to address their contingency plans. Nevertheless, the utilization of sound data strategies significantly increases their likelihood of success in the midst of the pandemic.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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How Companies Are Using Data Analytics to Carve Out A Survival Strategy During the COVID-19 Crisis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Various companies have found different strategies to survive the current crisis. Some of these strategies are listed below.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Responding to Rapid Changes in Consumer Behavior<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Researchers from McKinsey found that one of the biggest reasons that companies were pivoting to big data during the pandemic was to better adapt to customer behavioral changes. Data analytics has helped them adjust their marketing strategies to new customer attitudes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Some companies didn\u2019t see the merit of big data before the pandemic. Companies that were run by older owners were the most resistant for two reasons. First of all, they tended to be less technologically sophisticated and less likely to see the dividends that technology could bring. Also, they had the benefit of years of experience understanding their local market.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Unfortunately, prior experiences with customers became less valuable during the pandemic. Consumer behavior changed dramatically in response to new social distancing guidelines. Some demographics were more likely to make nonessential purchases, while others increased their demand for such items and services.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Companies with a strong data infrastructure were more responsive to these changes. They could use their data analytics platform to carefully monitor those trends<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Better Oversight of Remote Workers<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Strict social distancing measures forced companies to transition to remote workplaces. As of May, 57% of companies were entirely remote. Around two-third of those employers said they intended to stay entirely remote for the for seeable months.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Companies had to be more diligent about monitoring employee performance. Fortunately, data analytics made it easier for them to assess performance across multiple metrics. They could keep track of the active minutes that employees logged, which enabled them to objectively determine their workplace engagement rates. They could also use quality assurance measures to rank performance across various departments.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Of course, some of these models could have been instituted before the pandemic. However, many companies didn\u2019t see the need, since they could have managers oversee employees more directly and use their own judgment. Data analytics provided in extremely useful quality control measures to ensure employees maintained strong productivity.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Streamlining supply-chains<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Companies also need to reevaluate their supply chains during the pandemic. There are several challenges that they face:<\/p>\n\n