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Machine Learning and Generative AI for Marketing
Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing: Take your data-driven marketing strategies to the next level using Python

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Profile Icon Yoon Hyup Hwang Profile Icon Nicholas C. Burtch
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$49.99
Paperback Aug 2024 482 pages 1st Edition
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Arrow left icon
Profile Icon Yoon Hyup Hwang Profile Icon Nicholas C. Burtch
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$49.99
Paperback Aug 2024 482 pages 1st Edition
eBook
$27.98 $39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$27.98 $39.99
Paperback
$49.99
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Free Trial
Renews at $19.99p/m

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Machine Learning and Generative AI for Marketing

Decoding Marketing Performance with KPIs

Every data-driven or AI/ML-driven marketing strategy starts with a set of optimization and enhancement goals that have key performance metrics tied to them. These key performance metrics or measures are often termed the key performance indicators (KPIs) that help you track the performance of your marketing campaigns and define the success criteria of your marketing efforts. Some of the frequently used KPIs in digital and data-driven marketing that we will define and explore in this chapter are conversion rate, cost per acquisition (CPA), customer lifetime value (CLV), and return on investment (ROI).

In this chapter, we will discuss a frequently used set of marketing KPIs, what they tell you about your marketing campaigns, and how they can be used to further improve and optimize your marketing strategies and business outcomes. We will use an insurance product marketing dataset as an example to highlight how Python and its data analysis and...

Understanding marketing KPIs

As a marketer or marketing data scientist, KPIs are the keys to measuring the successes and failures of any marketing efforts. The proper usage and definition of KPIs ahead of embarking on any marketing campaign is crucial for the effective measurement of marketing successes and failures. However, improper usage of KPIs can result in misinformation being gathered and often wasting significant financial and human resources that have a direct negative impact on the business.

For example, if your marketing goal is to raise brand awareness of a targeted group (i.e., Generation Alpha) but you are not properly breaking down the sources of marketing content impressions (i.e., text vs. image vs. video, or YouTube vs. Instagram vs. TikTok, or influencer referrals vs. paid ads), it may tell a completely different story than what is actually happening. We will review some of the frequently used KPIs in this section and move on to the implementation and real-world...

Computing and visualizing KPIs from data with Python

With the foundation of various marketing KPIs that we have discussed so far, let us dive into a potential real-world use case of some of these KPIs, how to compute and visualize them, and how to interpret them for further data-driven decision-making. We will be using an insurance product marketing dataset for our example.

From a financial point of view, the success of marketing is to bring in more revenue with less spending. The most straightforward approach in tracking and measuring this is to look at conversion rate, CLV, and CPA. The conversion rate tells us the percentage of leads converted into paying customers, CLV tells us the estimated revenue being generated per acquired customer, and CPA tells us how much it costs to acquire a paying customer. By combining these three KPIs, we can understand the ROI and contribution to the net income.

Using the above-mentioned example dataset, let’s discuss how these KPIs...

Tracking marketing performance with KPIs

Computing and building KPIs is one key aspect of decoding marketing performances, but staying on top of the progressions and changes in KPIs is another equally important aspect of having a good grasp of your marketing performance. There are numerous ways to visualize KPIs and countless ways of tracking and sharing the KPIs with key stakeholders. In this section, we are going to share some of the common approaches to visualizations and ongoing KPI tracking.

Choosing the right visualization for KPIs

When presenting KPI data, the choice of visualization plays a crucial role in conveying insights effectively. Here are some guidelines to help choose the most appropriate type of visualization:

  • Bar charts: Ideal for comparing quantities across distinct categories, such as sales performance across different regions or products.
  • Pie charts: For illustrating a composition or proportion of a whole, like market share distribution...

Summary

In this chapter, we have laid a concrete foundation for building data and AI/ML-driven marketing models and strategies. We have discussed commonly used marketing KPIs that not only help the business measure the marketing performance but also provoke action items for improvements based on the strengths and weaknesses discovered through multilevel analyses. Using an insurance product marketing dataset as an example, we have seen how these KPIs can be measured and analyzed with Python for further improvements and optimizations in future marketing efforts.

We have also discussed how various dashboarding tools, such as Tableau, Power BI, and Looker, can be used for reusable real-time KPI tracking. As everyone emphasizes, AI/ML starts with data and deep exploratory analysis to decide what to train AI/ML models with and what to optimize for. The items and KPIs covered in this chapter will come in handy when designing and developing advanced AI/ML models for marketing in the future...

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Key benefits

  • Enhance customer engagement and personalization through predictive analytics and advanced segmentation techniques
  • Combine Python programming with the latest advancements in generative AI to create marketing content and address real-world marketing challenges
  • Understand cutting-edge AI concepts and their responsible use in marketing

Description

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience. This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales. Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards By the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.

Who is this book for?

This book targets a diverse group of professionals: Data scientists and analysts in the marketing domain looking to apply advanced AI ML techniques to solve real-world marketing challenges Machine learning engineers and software developers aiming to build or integrate AI-driven tools and applications for marketing purposes Marketing professionals, business leaders, and entrepreneurs who must understand the impact of AI on marketing Reader are presumed to have a foundational proficiency in Python and a basic to intermediate grasp of ML principles and data science methodologies

What you will learn

  • Master key marketing KPIs with advanced computational techniques
  • Use explanatory data analysis to drive marketing decisions
  • Leverage ML models to predict customer behaviors, engagement levels, and customer lifetime value
  • Enhance customer segmentation with ML and develop highly personalized marketing campaigns
  • Design and execute effective A/B tests to optimize your marketing decisions
  • Apply natural language processing (NLP) to analyze customer feedback and sentiments
  • Integrate ethical AI practices to maintain privacy in data-driven marketing strategies
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Length: 482 pages
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Language : English
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Table of Contents

15 Chapters
The Evolution of Marketing in the AI Era and Preparing Your Toolkit Chevron down icon Chevron up icon
Decoding Marketing Performance with KPIs Chevron down icon Chevron up icon
Unveiling the Dynamics of Marketing Success Chevron down icon Chevron up icon
Harnessing Seasonality and Trends for Strategic Planning Chevron down icon Chevron up icon
Enhancing Customer Insight with Sentiment Analysis Chevron down icon Chevron up icon
Leveraging Predictive Analytics and A/B Testing for Customer Engagement Chevron down icon Chevron up icon
Personalized Product Recommendations Chevron down icon Chevron up icon
Segmenting Customers with Machine Learning Chevron down icon Chevron up icon
Creating Compelling Content with Zero-Shot Learning Chevron down icon Chevron up icon
Enhancing Brand Presence with Few-Shot Learning and Transfer Learning Chevron down icon Chevron up icon
Micro-Targeting with Retrieval-Augmented Generation Chevron down icon Chevron up icon
The Future Landscape of AI and ML in Marketing Chevron down icon Chevron up icon
Ethics and Governance in AI-Enabled Marketing Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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