The adoption of machine learning to business processes has led to higher acceleration, growth, and adaptability than ever before. Building a successful machine learning capability, however, is dependent on a business's digital foundation from an organizational and technical perspective. Further, for businesses to deploy machine learning effectively, they require access to large amounts of data which is a crucial ingredient to make machine learning possible.
Acquiring the right amount of data can demand a sizable investment requirement from revenue cycle leaders. Collecting data, whether structured or unstructured, could translate to significant upfront costs. Aside from the storage of input data, model files, and prediction output, supplementary costs associated with data pretreatment necessitate revenue cycle leaders to carefully consider and evaluate risks, dependencies, and benefits when embarking on machine learning projects.
Achieving the Right Strategy Mix
Data scientists know how to build models; they know how to clean data and operationalize the data pipelines for maximum returns. It's challenging, however, to connect between the business strategy and the model. Achieving the right mix of this partnership can help revenue cycle leaders scale their operations without losses in efficiency or quality of service.
David Frigeri, Practice Area Lead, Advanced Analytics and Visualization at Slalom, rightfully addresses this in the Adopting Machine Learning to Drive Revenue and Market Share webinar. He says, "successful businesses build strategies that answer before competitors- the kind of time the market needs in what models and predictions and insights to be delivered to the right people at the right time." David envisions that for ML to realize its full potential, businesses have to get better at helping the management and data scientists connect the strategy with their capabilities.
Whether it's decreasing machine downtime, increasing efficiency, or saving energy, predictive maintenance with IoT analytics improves an organization's decision-making methods, thereby optimizing its revenue cycles. This gives the C-suite a way to balance their decisions against larger volumes of data than previously possible, leading to a wealth of insights and new opportunities that were historically out of reach on a manual scale. These data-driven insights spur revenue growth in many ways, from predicting business outcomes and improving business strategy to driving better customer engagement.
Acquiring new customers is a critical component of any company's revenue growth strategy. The more the number of customers, the greater is its revenue generation avenues. It is no surprise that businesses rely on applying machine learning philosophies to their CRM data with the aim to discover better selling strategies that could eventually attract new buyers. Additionally, machine learning can help identify quality leads for both marketing and sales teams and support these teams to send personalized campaigns to garner interest from prospects.
Automating Workflows for Growth
Machine learning automation capabilities present an excellent opportunity to improve cross-sell prospects and target greater market share. Automating workflows not only helps to reduce costs but also can offer massive growth benefits. With intelligent automation, chances of errors and human mistakes become negligible, and hence business machinery can operate continuously uninterrupted. In addition, automation lets business managers know which customers they should engage when they log off from the website or visit the website, leading to better utilization of resources and greater productivity.
Identifying Opportunities for ML Applications
Start-ups and tech behemoths are no longer the only ones focusing on machine learning. Every business sector has numerous use cases where machine learning and cognitive computing can address some of the most significant pain points in revenue cycle management, leading to increased revenue capture for early adopters of the technology.
ML business cases that offer tremendous customer revenue growth, reduced costs, and greater financial visibility have the highest priority of being funded inside any enterprise today. The combination of improving customer experiences, automating processes (to reduce costs), and generating financial insights (for enhanced financial visibility) is the ideal combination to drive revenue and market share. As a result, machine learning can be the next big game-changer in business growth. With a guaranteed increase in revenue, this technology will be a crucial driver of enterprise revenue in the coming years, primarily because it focuses on bettering the end consumer experience.
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