Introduction to Decision Science for Marketing from Coursera

What's inside

Syllabus

Introduction to Decision Science for Marketing

Decision science or data analytics for marketing (predictive marketing) are new approaches to customer relationships, using big data and machine learning techniques. It is a critical opportunity for marketers and is still in the early stages of adoption. In this module, you will learn why and how companies of all sizes adopt decision science. The early adopters have seen great value in it, and new technologies make it easy to implement.

Building Customer Profiles to Optimize Enterprise Value

In this module, you will learn that building complete and accurate customer profiles is difficult but valuable. Predictive technology can help clean up data and connect online and offline information to resolve customer identities. Having all customer data in one place and making it accessible to customer-facing personnel improves the customer experience. Optimizing customer lifetime value is the best way to optimize enterprise value and manage customers. This is similar to managing a stock portfolio, taking different actions for new and long-term customers, and adjusting budgets for profitable and unprofitable customers.

Weekly Summative Assessment: Introduction to Decision Science for Marketing and Building Customer Profiles to Optimize Enterprise Value

This assessment is a graded quiz based on the modules covered this week.

Life Cycle Marketing: Predicting the Customer Journey

In this module, you will examine the stages of a customer’s journey with a company, including acquiring new customers, fostering their growth, and retaining them. You will also explore how a company’s engagement strategy should adapt at each stage of the customer life cycle. The key to maximizing the value from customers is by building trust by providing value to the customer.

Predict Customer Value and Their Likelihood to Buy/Engage

In this module, you will learn about value-based marketing, where businesses segment and target customers based on their lifetime value. High-value customers are prioritized by investing more money in retaining and appreciating them, while medium-value customers are upsold to increase their value. Low-value or unprofitable customers are not invested in as much. The module also discusses predictive analytics, specifically models that predict a customer’s likelihood to buy, in both consumer and business marketing. These models can optimize the time and efforts of sales and customer success teams in business marketing and help consumer marketers optimize their discount strategy and email frequency.

Recommend Products to Each Customer Individually

This module provides marketers with a primer on personalized recommendations, discussing different types, such as those made at the time of purchase and those tied to specific products or customer profiles. It also highlights potential issues and the importance of merchandising rules, omnichannel orchestration, and giving customers control when making personal recommendations.

Weekly Summative Assessment: Life Cycle Marketing: Predicting the Customer Journey, Customer Value, and Their Likelihood to Buy/Engage

Predict Customer Personas and Convert More Customers

By using predictive marketing techniques, marketers should focus on allocating budgets to the right people rather than the right products or channels. This includes using clustering to discover personas or communities in the customer base and gain insight into their needs, behaviors, demographics, attitudes, and preferences. This can help differentiate and optimize marketing actions and product strategies for different groups of customers, which can lead to more cost-effective growth. This module also covers three predictive marketing strategies for acquiring more and better customers: personas, remarketing, and look-alike targeting. Remarketing is used to differentiate between customers who are likely to return and those who need an incentive. Look-alike targeting on platforms like Facebook helps find new customers similar to existing ones.

Grow Customer Value

This module covers strategies for retaining customers by nurturing the relationship from the day of acquisition. It discusses various predictive marketing strategies to grow customer value, including post-purchase campaigns, replenishment campaigns, repeat purchase programs, new product introductions, and customer appreciation campaigns. It also covers loyalty programs and omnichannel marketing in the age of predictive analytics.

Retention of Customers

The module focuses on the retention of customers in order to avoid losing money. It is important to understand that not all churn is the same;, losing an unprofitable customer is less impactful than losing a valuable one. Preventing a customer from leaving is more efficient and cost-effective than trying to reactivate them. The chapter covers different churn management programs, from untargeted to targeted, and covers proactive retention management and customer reactivation campaigns.

Weekly Summative Assessment: Predict Customer Personas and Convert More Customers, Grow Customer Value, and Retention of Customers

How to Use Predictive Analytics in Marketing

The module discusses the use of predictive marketing techniques. This requires both a change in mindset to focus on individual customers and their context, as well as technical capabilities in customer data integration, predictive intelligence, and campaign automation.

Useful Tools

The current era is both exhilarating and perplexing due to the abundance of new marketing technologies emerging annually. This module provides a general understanding of the different commercial technologies available and the steps necessary to create a predictive marketing solution internally from scratch.

What It Needs to Be a Successful Predictive Marketer

This module highlights a significant career opportunity for early adopters of new technologies and methodologies, such as predictive marketing and analytics. Business understanding is more important than math skills, and asking the right questions is the key. Consumers are willing to share preference information in exchange for benefits from personalized products and services. It is important to use common sense and consider the context of the situation when using customer data to ensure trust. Predictive analytics will continue to find new applications and real-time customer insights will shape the physical world. There are benefits for early adopters of predictive marketing for both customers and companies, and adopting a predictive marketing mindset is suggested to gain a competitive advantage.

Weekly Summative Assessment: How to Use Predictive Analytics in Marketing, Useful Tools, and What It Needs to Be a Successful Predictive Marketer

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops skills in building customer profiles, using data analytics to predict marketing outcomes, and implementing strategies for growth and retention

Taught by Prof. Lalit Pankaj, an expert in the field of decision science and marketing

Provides a practical approach to data-driven marketing

Covers essential topics including customer segmentation, predictive analytics, and personalized recommendations

May require prior knowledge of basic marketing concepts

Designed for beginners in the field of data-driven marketing

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Decision Science for Marketing with these activities:

Review Statistical Analysis Concepts

Show steps

Review this course's prerequisite statistical analyses.

Browse courses on Statistical Analysis

Show steps

Review notes from a previous statistics course.
Take a practice quiz on statistical analysis.
Watch a video tutorial on statistical analysis.

Review Decision Science for Marketing Terminology

Show steps

Decision science for marketing requires a solid foundation in marketing terminology. Review foundational concepts and jargon to ensure clarity before starting the course.

Show steps

Create a list of 10 key marketing terms.
Write a short definition for each term.
Review your list to ensure you have a clear understanding.

Practice Customer Segmentation Techniques

Show steps

Building customer profiles is a key skill in decision science for marketing.

Browse courses on Customer Segmentation

Show steps

Find a dataset that contains customer data.
Use a data analysis tool to segment the customers into different groups.
Write a report that describes the different customer segments.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Follow a Tutorial on Predictive Marketing Techniques

Show steps

Predictive marketing techniques are used to optimize the efficiency of marketing campaigns.

Browse courses on Data Analysis

Show steps

Find a tutorial on predictive marketing techniques.
Follow the tutorial and complete the exercises.
Write a summary of what you learned from the tutorial.

Practice Using Data Visualization Tools

Show steps

Data visualization is a key skill for marketers to master.

Browse courses on Data Visualization

Show steps

Find a dataset that you would like to visualize.
Choose a data visualization tool that you would like to use.
Create a data visualization that communicates the insights from the data.

Develop a Marketing Campaign for a New Product

Show steps

Create a marketing campaign for a new product to demonstrate how to determine the target market.

Browse courses on Marketing Campaign

Show steps

Choose a product that you would like to market.
Conduct market research to identify your target audience.
Develop a marketing campaign that is tailored to your target audience.
Write a report that outlines the steps you took to develop your campaign.

Enter a Data Analytics Competition

Show steps

Competitions are a great way to test your skills while learning decision science for marketing.

Browse courses on Data Analytics

Show steps

Find a data analytics competition that you would like to enter.
Form a team or work independently.
Develop a solution to the problem.
Submit your solution to the competition.

Career center

Learners who complete Introduction to Decision Science for Marketing will develop knowledge and skills that may be useful to these careers:

Search Engine Optimization (SEO) Specialist

Search Engine Optimization (SEO) Specialists typically have a background in marketing, computer science, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a SEO Specialist better understand consumer behavior online and make better decisions.

See salaries and explore the career path for Search Engine Optimization (SEO) Specialist

Email Marketing Specialist

Email Marketing Specialists typically have a background in marketing, communications, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help an Email Marketing Specialist better understand consumer behavior and make better decisions.

See salaries and explore the career path for Email Marketing Specialist

Data Analyst

Data Analysts typically have a background in computer science, statistics, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics is a fundamental skill for this role, and this course can help aspiring Data Analysts build a foundation.

See salaries and explore the career path for Data Analyst

Market Research Analyst

Market Research Analysts typically must have a bachelor's degree in marketing research, statistics, or a related field. Understanding data analytics is important for this role, and this course can help build a foundation for success. Market Research Analysts study market conditions to help companies understand consumer behavior, and data analysis can be crucial for this work.

See salaries and explore the career path for Market Research Analyst

Social Media Manager

Social Media Managers typically have a background in marketing, communications, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a Social Media Manager better understand consumer behavior online and make better decisions.

See salaries and explore the career path for Social Media Manager

Digital Marketing Specialist

Digital Marketing Specialists typically have a background in marketing, computer science, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a Digital Marketing Specialist better understand consumer behavior online and make better decisions.

See salaries and explore the career path for Digital Marketing Specialist

Content Marketing Specialist

Content Marketing Specialists typically have a background in marketing, communications, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a Content Marketing Specialist better understand consumer behavior and make better decisions.

See salaries and explore the career path for Content Marketing Specialist

Marketing Manager

Marketing Managers typically have a background in marketing, business, or a related field, and usually must have a bachelor's degree at a minimum. A course like this one can be very helpful for this role, as it builds a foundation for data analysis and marketing decision-making. Understanding data analytics can help a Marketing Manager better meet the needs of consumers.

See salaries and explore the career path for Marketing Manager

Business Analyst

Business Analysts typically have a background in business, computer science, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a Business Analyst better understand consumer behavior and make better recommendations.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers typically have a background in engineering, marketing, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a Product Manager better meet the needs of consumers.

See salaries and explore the career path for Product Manager

Marketing Consultant

Marketing Consultants typically have a background in marketing, business, or a related field, and usually must have a bachelor's degree at a minimum. This course can be helpful for this role, as it builds a foundation for data analysis and marketing decision-making. Understanding data analytics can help a Marketing Consultant better meet the needs of clients and make better recommendations.

See salaries and explore the career path for Marketing Consultant

Sales Manager

Sales Managers typically have a background in sales or a related field, and usually must have a bachelor's degree at a minimum. A course like this one can be very helpful for this role, as it builds a foundation for data analysis and marketing decision-making. Understanding data analytics can help a Sales Manager better meet the needs of consumers and make better decisions.

See salaries and explore the career path for Sales Manager

Affiliate Marketing Manager

Affiliate Marketing Managers typically have a background in marketing, business, or a related field, and usually must have a bachelor's degree at a minimum. A course like this one may be helpful for this role, as it builds a foundation for data analysis and marketing decision-making. Understanding data analytics can help an Affiliate Marketing Manager better understand consumer behavior and make better decisions.

See salaries and explore the career path for Affiliate Marketing Manager

Customer Success Manager

Customer Success Managers typically have a background in sales, marketing, or a related field, and usually must have a bachelor's degree at a minimum. This course may be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a Customer Success Manager better understand customer needs and make better recommendations.

See salaries and explore the career path for Customer Success Manager

User Experience (UX) Researcher

User Experience (UX) Researchers typically have a background in psychology, human-computer interaction, or a related field, and usually must have a bachelor's degree at a minimum. This course may be helpful for this role, as it builds a foundation for data analysis within marketing. Understanding data analytics can help a UX Researcher better understand user behavior and make better recommendations.

See salaries and explore the career path for User Experience (UX) Researcher