Customer Analytics for Growth Using Machine Learning, AI, and Big Data
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Artificial intelligence and machine learning could be rocket fuel for your business, adding tremendous value to the entire enterprise, but only if you know how to harness and leverage them. With AI and machine learning reshaping the business landscape for numerous industries, there is increasingly high demand to bring data to life, going beyond the raw numbers to link them to strategic business initiatives.
Customer Analytics for Growth Using Machine Learning, AI, and Big Data will sharpen your analytics mindset, enabling you to bridge any knowledge gap that may exist between your data science teams and the C-suite. Here you will learn how to convert model based recommendations into actionable insights and better managerial decisions.
Program Highlights & Benefits
In Customer Analytics for Growth, you will:
- Master how to frame managerial questions around big data and analytics
- Select the right tools for predicting future customer behavior
- Explore and understand the latest AI applications, including their pros and cons
- Discover the companies that are using these new technologies most effectively
- Gain insights into best practices for recruiting and managing data-science teams
Many companies are swimming in data, and they are spending millions to collect more. But even with new tools and algorithms to analyze and make predictions based on consumer data, it’s often still not being used effectively. Customer Analytics for Growth is for business leaders who want to cultivate an analytics-based mindset throughout their organization, and gain a deep understanding of emerging AI technologies that are rapidly changing businesses today.
In Customer Analytics for Growth, you will explore the upside — and the downside — of complex data models, and understand the importance of transparency in data collection and analysis.
The program examines customer analytics using three foundational pillars:
- Descriptive Analytics examines the different types of customer data and how they can be visualized, ultimately helping you leverage your findings and strengthen your decision making.
- Predictive Analytics explores the potential uses of the data once collected and interpreted. You’ll learn to utilize different modeling tools, such as regression analysis, be exposed to the latest machine learning algorithms, and estimate relationships among variables to predict future end-user behavior.
- Prescriptive Analytics takes you through the final step: formulating concrete recommendations based on your data. These recommendations can be directed toward a variety of efforts, including pricing and social-platform outreach.
A distinctive highlight of Customer Analytics for Growth is engaging in discussions with expert practitioners from a range of industries who have experience with both business-to-consumer and business-to-business customer models. They will reveal their real-time challenges and best practices, sharing their experience with the three most common hurdles of analytics strategy — tools, talent, and metrics — discussing what tools to use when, how to build analytics teams, and what to track about your customers. Each session also includes a short, highly interactive case study that allows you to explore real-world applications.
Customer Analytics for Growth brings together a powerhouse team of Wharton faculty from operations, information, and decisions; legal studies and business ethics; marketing; and statistics. They guide you through the most current theories and best practices for designing and implementing a data-analysis strategy, while continuously linking the learning to your real-life challenges.
The exceptional multidisciplinary learning journey will also give you a front-row seat to the powerful research and thought leadership of Wharton Customer Analytics (WCA), the world’s preeminent academic research center focusing on the practice of data-driven business decision making. This offers an advantage you will not find anywhere else.
Capstone Project: A Live Case Study
The Customer Analytics for Growth Capstone gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge. Working in groups, you will leverage skills within your group to identify how to successfully use data to create cutting-edge, customer-focused marketing practices.
In these sessions, groups will use real-world data to apply customer analytics to marketing challenges, starting with data collection and data exploration, and continuing all the way to data-driven decisions. After completing your group project, you will be asked to reflect on how to identify scenarios from your own company or business, where there could be benefits from the innovative and effective data-driven practices learned during the week.
Session topics include:
- The Future of Marketing Science: Big Data, New Data, Better Science
- How AI and Machine Learning Are Changing Customer Analytics
- How Blockchain Can Impact Customer Analytics
- Data Science: A Team Sport (Building the Analytics Team)
- Customer Value Analysis
- Business Experiments
- Predictive Analytics with Machine Learning
- Pricing Analytics
Who should attend
Senior-level managers in both B-to-C and B-to-B organizations who are responsible for influencing business decisions across marketing, finance, operations, and strategy will benefit from Customer Analytics. Specific job titles include CEO, CMO, CTO, COO, and digital officers. Additionally, executives who are responsible for data science and the teams that collect data, those who are beginning to use available data to inform strategy and operating decisions, and those who are new to analytics will benefit from the program.
Participants are not required to have a strong math or technical background. Customer Analytics focuses instead on the managerial issues that intersect with analytics, including how best to convey insights from data to decision-makers.
Industries that are currently exploiting business analytics include, but are not limited to, consumer packaged goods, financial services, health care/pharmaceuticals, manufacturing, media/communications technology, hardware/software technology, transportation, and logistics.