ML and data-driven approach to maximize your profits
Did you know that an estimated 1.7 megabytes of data are generated every second for every person on Earth?
Almost every piece of that massive amount of information could be used in one way or another and prove valuable to some external party, most likely a business company. A medical company can better interpret the results of its trial of oncology patients, a telecommunications company will choose the perfect spot for their new tower, and an appliances retailer can send you a technical assistant before you realize your refrigerator is broken - you get the idea.
Analytics of various data sets can play a major role in increasing revenues. Recent research from Boston Consulting Group (BCG) commissioned by Google found that leading businesses that are growing their customer base, ROI, and competitive advantage focus on adopting a path to full data-driven marketing and attribution. Those that succeed are seeing significant benefits – up to 30% in cost efficiency savings and a 20% increase in revenue.
Retail data analytics can provide retailers with information about customer data, like product search inquiries, time spent on a website, shopping steps, and engagement. Retail analytics help companies understand the specific qualities consumers are looking for in a given product and subsequently use them to quickly and effectively streamline sales.
Where data-driven approach and business intelligence can increase sales and savings thanks to past and current data, with Machine Learning and predictive models, we’re approaching the future. Businesses incorporate ML into their core processes for a variety of strategic reasons. ML can deliver benefits such as discovering patterns and correlations, improving customer segmentation and targeting, and ultimately increasing a business's revenue, growth, and market position. Furthermore, in terms of capital savings, implementing ML projects can lead to optimizing production inputs, maintaining capital assets, and quality control. According to a 2017 study conducted by Deloitte and sponsored by Google Cloud on the Business impacts of machine learning, the return on investment on most standard machine learning projects in the first year is 2-5 times the cost. However, as previously outlined, many companies already use data analytics to forecast demand for their products and services. Demand forecasting allows businesses to manage their inventory stock, evaluate economic returns on promotions and reduce costs. ML models are more powerful than traditional statistical forecasting because they can incorporate feedback into the loop, which constantly tunes the model, making it more accurate over time - the study mentioned above states.
Do you think Machine Learning can increase your revenue?
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