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Strategic Decision Making with Machine Learning (ML)

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Featured Best Practice on Machine Learning

22-slide PowerPoint presentation
Technological innovation has developed Artificial Intelligence's ability to create intelligent machines that work and react like humans. Some machines have reached the performance levels of humans in performing certain specific tasks, so that artificial intelligence is now found in applications as [read more]

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Automation has a profound role in improving Operational Excellence and Decision making.  Organizations, these days, are increasingly using Machine Learning (ML) to improve the Strategic Decision making expertise of their leaders.

Global research by MIT Sloan Management Review in collaboration with Google—involving 4,700 executives and a number of Key Informant Interviews—indicates that ML is gradually transforming the way enterprises build and analyze value.  This is attributed mainly to Machine Learning’s ability to render Key Performance Indicators (KPIs) more analytical and rigorous.  More rigorous KPIs allow executives to augment business processes by using strategic measures to guide ML algorithms.

Organizations that are already spending resources on and employing ML to serve their customers are fundamentally distinct from those that don’t invest in ML.  They have a broader perspective of their customers and analyze KPI data and reports regularly.  It allows enterprises to liberate people from routine tasks and focus their attention on strategic initiatives.

Machine Learning-focused enterprises consider data as one of their most valuable assets and embrace ML and other technological tools of this age to create a Competitive Advantage.

Key findings of the MIT SMR study indicate that:

  • Around 75% of the study participants considered that investing in Machine Learning and Automation allows them achieve their functional KPIs efficiently.
  • The organizations with most exciting and impressive ML programs were quite determined in managing data as a notable asset.
  • Data-driven enterprises have a clear competitive advantage over businesses that are sluggish in adopting data or ML competencies.

The Marketing unit is typically the foremost adopter of Machine Learning in an enterprise.  Advertising, Segmentation, and Customer Intelligence are the areas where ML is most widely used.

Technology leaders employ 3 crucial Machine Learning best practices to enable Strategic Decision Making:

  1. Employ KPIs to Enable an Integrated, Single View of Customers.
  2. Analyze and Interpret Factors Driving KPIs.
  3. Evaluate KPI Reports Carefully and Regularly.

Let’s dive deeper into the details of the best practices.

Employ KPIs to Enable an Integrated, Single View of Customers

A data-powered, comprehensive customer perspective is essential for business leaders today.  Organizations that incentivize their people to use Machine Learning with relevant quantifiable metrics tend to have an all-encompassing, broader picture of their customers.

Rewarding the use of ML and establishing appropriate practical and realistic KPIs to assess that assists the businesses in developing a single view of the customers.  This enables the businesses to create proper customer segmentation and plan effective engagements with the customers.

Analyze and Interpret Factors Driving KPIs

The next best practice to enable Strategic Decision Making through ML use is to interpret the underlying factors that impact their performance indicators.  The corporations that reward the use of ML are more inclined to agree to the point that they can evaluate the factors and data that become the basis of their KPIs.  The competence to analyze and interpret the elements that make up organizational KPIs helps to enhance Operational Excellence.

This competence promotes improved common understanding of organizational data and customer requirements, thereby steering data-driven, Decision making.  Data, analytics, and ML are employed by leading enterprises to run software and applications essential for predicting clients’ requirements, manage systems needed to interpret business needs, and make these systems learn and become more intelligent.

Interested in learning more about the Machine Learning best practices to enable Strategic Decision Making ? You can download an editable PowerPoint on Strategic Decision Making with Machine Learning here on the Flevy documents marketplace.

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15-page PDF document
Learn about the most common machine learning algorithms in regression, classification, and clustering problems which include linear regression, logistic regression, naive Bayes, support vector machine, and k-means. Then an introduction to the Python code for a classic classification problem, [read more]

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About Mark Bridges

Mark Bridges is a Senior Director of Strategy at Flevy. Flevy is your go-to resource for best practices in business management, covering management topics from Strategic Planning to Operational Excellence to Digital Transformation (view full list here). Learn how the Fortune 100 and global consulting firms do it. Improve the growth and efficiency of your organization by leveraging Flevy's library of best practice methodologies and templates. Prior to Flevy, Mark worked as an Associate at McKinsey & Co. and holds an MBA from the Booth School of Business at the University of Chicago. You can connect with Mark on LinkedIn here.

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