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.

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.

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

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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