Becoming an AI-Augmented Marketing Analyst

From Business Questions to Evidence-Based Marketing Decisions

Dr. Jose Mendoza, Academic Director and Clinical Associate Professor

Version 1.0 · July 2026

Except where otherwise noted, this chapter is licensed under CC BY 4.0.

Chapter Information

ABSTRACT

This chapter presents marketing analytics as disciplined decision support and defines the role of the AI-augmented marketing analyst. It distinguishes business analytics, marketing analytics, and data-informed decision-making; explains descriptive, diagnostic, predictive, and prescriptive analytics; and shows how business questions become analytical questions. A seven-step workflow adapted from CRISP-DM is paired with four habits for accountable AI use: specify, predict-then-verify, explain, and document. The chapter also distinguishes artificial intelligence from generative AI assistants and reviews risks associated with generated text and code. A Google Colab lab introduces campaign data, basic marketing metrics, and verification checks. The final sections address managerial interpretation, industry practice, responsible analytics, and applied exercises.

KEYWORDS

marketing analytics; business analytics; artificial intelligence; generative artificial intelligence; data-informed decision-making; descriptive analytics; diagnostic analytics; predictive analytics; prescriptive analytics; verification

VERSION AND DATE

Version 1.0 · July 2026 · Language: English (United States)

SUGGESTED CITATION

Mendoza, J. (2026). Becoming an AI-augmented marketing analyst. In Applied business analytics for marketing decision-making: Business analytics and data visualization (Chapter 1, Version 1.0) [Open educational resource]. CC BY 4.0.

LICENSE AND RIGHTS

Copyright © 2026 Jose Mendoza. Except where otherwise noted, this work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You may share and adapt this material for any purpose, provided appropriate credit is given. Third-party trademarks, screenshots, figures, and other materials remain subject to their respective rights and licenses.

Google Colab is a product of Google LLC. “Python” and the Python logos are trademarks or registered trademarks of the Python Software Foundation. pandas is a sponsored project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. Product names are used for identification only and do not imply endorsement. StyleCraft Collective is a fictional company created for instruction.

COMPANION REPOSITORY

Datasets, notebooks, and figure sources for this chapter: https://github.com/jrmst102/businessanalytics

GENERATIVE AI USE

Generative artificial intelligence and other AI-assisted tools were used in the research, writing, revision, and production of this chapter, including literature discovery, source organization, outlining, preliminary drafts, prose revision, support for code and analytical examples, and document formatting. These tools were used under the author's direction and are not credited as authors, researchers, or sources. The author determined the chapter's scope, learning objectives, methods, interpretations, and recommendations, and reviewed and approved all AI-assisted material: factual claims and citations were checked against the underlying sources rather than accepted from AI-generated summaries, and code and analytical outputs were tested or otherwise reviewed for accuracy. Responsibility for the accuracy, originality, and final form of this chapter rests entirely with the author. A fuller statement appears in the front matter of the complete guide.

Chapter Learning Objectives

By the end of this chapter, students should be able to:

1. Describe the role of the marketing analyst in data-rich and AI-assisted business environments.
2. Distinguish among descriptive, diagnostic, predictive, and prescriptive analytics in marketing contexts.
3. Explain how business questions, analytical questions, data, evidence, and recommendations are connected.
4. Distinguish artificial intelligence broadly from generative AI assistants, and identify where each may support marketing analytics and where human judgment remains necessary.
5. Use a basic Google Colab workflow to inspect a small marketing dataset.
6. Apply simple verification checks before accepting analytic output.
7. Translate analytic output into a cautious, evidence-based marketing recommendation.

CONCEPT

What This Chapter Is Really About

This chapter is not mainly about learning code. It is about learning the professional discipline of an analyst. Code, charts, models, dashboards, and AI tools are useful only when they help a decision-maker understand a problem and act with better evidence.

The guiding question is therefore: How does a marketing professional become a better decision-maker in a world where data and AI tools are widely available?

1.1 Marketing Decision Context: The Holiday Budget Decision

Imagine that you have just joined the marketing analytics team at StyleCraft Collective, a fictional mid-sized apparel retailer that sells through its website, mobile app, email program, and selected pop-up events. The company has a loyal customer base, but sales growth has slowed. The chief marketing officer wants the team to improve campaign performance before the holiday season, and she has asked for a recommendation in three weeks, before media commitments are finalized. Whatever the team recommends will determine where the holiday budget goes and which customers hear from the brand first.

At the weekly marketing performance review, each team brings a different view of the business. The email manager reports that open rates improved after a subject-line test. The paid media specialist argues that search ads generated the highest number of conversions. The social media manager points to engagement on short-form videos. The CRM manager worries that repeat purchase rates have declined among customers who bought only once. The finance team asks whether the marketing budget should be shifted away from low-margin promotions.

Everyone has data. Not everyone has the same question. Some participants want to describe what happened. Others want to understand why performance changed. Others want to predict what is likely to happen next. Still others want to decide what action the company should take. The review stalls because reports, opinions, metrics, and recommendations are being mixed before the decision has been clarified.

Notice what is missing. No one has stated the decision. Without a stated decision, every metric looks relevant and none is decisive. The improvement in open rates is real, but it does not tell the chief marketing officer whether to move money out of paid search. The finance team’s margin concern is legitimate, but it does not identify which promotions destroy margin. Analytics cannot resolve the disagreement until someone names the choice that has to be made and the date by which it must be made.

There is also a constraint that will shape everything the team can do. Three weeks is enough time to describe what happened, compare a few segments, and assemble a defensible recommendation. It is not enough time to run a clean experiment, rebuild the customer data model, or wait for a quarter of new results. Part of the analyst’s job is to say plainly what can and cannot be answered inside the time available, and then to propose the strongest evidence that fits. A recommendation that arrives after the media commitments are signed has no value, however rigorous it may be.

This situation is common. Marketing teams often have dashboards, campaign reports, CRM exports, web analytics, social media metrics, and customer feedback. However, the availability of data does not automatically produce better decisions. The value of analytics depends on how well the team frames the problem, prepares the data, selects the method, verifies the output, and communicates the recommendation.

Over the next three weeks, the team will likely use AI tools to compare channels and draft code for promotion analysis. Those uses are reasonable. The issue is whether the results are checked and whether the person signing the recommendation can explain how each number was produced. Accountability for the evidence remains with the analyst throughout the guide.

StyleCraft returns throughout the book as a running case. The company does not need analytics because it lacks data. It needs analytics because it must decide what to do.

CONCEPT

Decision Context

In this guide, the decision context refers to the managerial situation in which an analysis will be used. It includes the decision to be made, the stakeholders, the available evidence, the constraints, the timing of the decision, and the consequences of action. This is an operational course concept rather than a standalone academic construct.

1.1.1 Opening Case Questions

1. What decision does StyleCraft need to make before the holiday season?
2. Which parts of the performance review are descriptive, diagnostic, predictive, or prescriptive?
3. What additional information would help the team decide whether to change the marketing budget?
4. Where might an AI tool help, and where would human judgment still be required?

1.2 What Does a Marketing Analyst Do?

A marketing analyst helps organizations make better decisions about customers, markets, campaigns, products, pricing, channels, and communication. This work may involve data preparation, descriptive summaries, visualization, predictive modeling, experiments, dashboards, and written recommendations. However, the analyst is not simply a person who produces charts or runs code. The analyst is responsible for disciplined reasoning.

Marketing analytics is especially challenging because marketing decisions are often made under uncertainty. Customers change their preferences. Competitors respond. Channels shift. Advertising platforms change their algorithms. Macroeconomic conditions affect spending. Metrics may be incomplete, biased, or difficult to compare. Even when a dataset is accurate, it may not fully represent the decision problem. As a result, marketing analysts must combine quantitative evidence with business context.

Furthermore, the modern analyst often works with AI tools. These tools can draft code, explain an error message, summarize customer comments, suggest visualizations, or create a preliminary managerial summary. These uses can save time. They do not remove the need for judgment. As Davenport, Guha, Grewal, and Bressgott (2020) argue, AI is likely to be most valuable in marketing when it augments, rather than replaces, human managers.

An AI-augmented analyst does not outsource thinking to software. The analyst defines the question, selects the evidence, checks the work, interprets the result, and takes responsibility for the recommendation.

1.2.1 The Analyst as Translator

One useful way to understand the marketing analyst is as a translator among three languages. The first is the language of business: revenue, customers, margins, budgets, campaigns, channels, and growth. The second is the language of data: rows, variables, distributions, missing values, relationships, models, and uncertainty. The third is the language of decision-making: alternatives, trade-offs, risks, recommendations, and next steps.

Many analyses fail because these languages are not connected. A technically correct model may not answer the manager’s question. A polished dashboard may not clarify what action should be taken. A written recommendation may go beyond what the evidence supports. The analyst’s task is to keep these languages aligned.

CONCEPT

Analytic Translation

Analytic translation is the process of converting a business problem into an analytical task, interpreting the analytical result in business language, and turning the evidence into a recommendation that a decision-maker can use.

In other words, the analyst does not merely produce output; the analyst connects output to action.

1.3 Business Analytics, Marketing Analytics, and Data-Informed Decisions

The following definitions connect the analyst’s role to marketing decision support.

DEFINITION

Business Analytics

Business analytics is the use of data, quantitative methods, models, visualization, and technology to understand business problems and support decisions. In this guide, the phrase emphasizes decision support rather than technique for its own sake.

Source: Adapted from Camm et al. (2023), Davenport (2006), and Provost and Fawcett (2013).

DEFINITION

Marketing Analytics

Marketing analytics is the application of analytic methods, data, models, and technologies to marketing decisions. These decisions may involve customer understanding, campaign evaluation, segmentation, targeting, personalization, pricing, retention, forecasting, and marketing communication.

Source: Adapted from Wedel and Kannan (2016) and France and Ghose (2019).

The distinction matters because this guide is not a general statistics or programming text. It approaches analytics from the perspective of marketing decision-making. The central question is not simply whether an analysis can be performed, but whether the analysis helps a marketer understand a business problem and choose a more defensible course of action.

CONCEPT

Data, Information, Insight, and Recommendation

Data are recorded observations, values, or symbols. Information is data organized so that it becomes meaningful for a purpose. An insight is an interpretation that explains why a pattern matters. A recommendation is a proposed action supported by evidence, judgment, and context.

For example, a file containing 50,000 email recipients and clicks is data. A table showing that the click-through rate was 4.2 percent is information. The finding that recent purchasers clicked at a higher rate is an insight. The proposal to prioritize recent purchasers in the next email sequence is a recommendation.

Source: Adapted from Ackoff (1989), Rowley (2007), Farris et al. (2015), and Provost and Fawcett (2013).

1.3.1 Why Data-Informed, Not Data-Driven?

This guide uses data-informed because metrics and models do not determine actions on their own. Marketing decisions also require judgment about brand positioning, customer trust, ethics, competitive response, resource limits, and uncertainty.

For example, if a model predicts that a steep discount will increase conversions, a manager still needs to consider margin, customer expectations, brand equity, and whether frequent discounting trains customers to wait for promotions. Analytics can improve a decision without transferring responsibility for it.

CONCEPT

Data-Informed Decision-Making

Data-informed decision-making uses data and analysis as essential inputs into managerial judgment while recognizing that context, ethics, uncertainty, and strategic priorities also matter. This wording is used as an operational concept for this guide.

Source: Adapted from Provost and Fawcett (2013) and Davenport (2006).

1.4 The Four Types of Analytics

Introductory business analytics is often organized into descriptive, predictive, and prescriptive categories. This chapter adds diagnostic analytics because marketers often need to investigate why performance changed before recommending an action.

The four categories are related, but they are not interchangeable. A dashboard that reports last month’s sales is not a forecast. A forecast is not a recommendation. A recommendation should use evidence, but it must also consider the decision context and the available alternatives. Table 1.1 summarizes the four types and their managerial value.

Table 1.1
Four Types of Analytics in Marketing

DEFINITION

Descriptive Analytics

Descriptive analytics summarizes past or current data to show what happened. In marketing, it may include sales reports, campaign dashboards, customer profiles, channel comparisons, and summary statistics.

Source: Adapted from Camm et al. (2023) and Farris et al. (2015).

CONCEPT

Diagnostic Analytics

Diagnostic analytics investigates why performance may have changed by comparing groups, segments, time periods, or possible drivers. In marketing, it may involve cohort analysis, segment comparison, or channel drill-downs.

Caution: diagnostic analytics can identify plausible drivers and narrow the investigation, but observational comparisons do not by themselves establish causality.

Source: Course concept developed for this guide. The comparative methods it relies on are discussed in Camm et al. (2023) and France and Ghose (2019).

That caution deserves emphasis, because diagnostic language invites causal claims. A drill-down showing lower conversion among first-time visitors has identified an association, not a cause. Those visitors may differ in intent, in traffic source, in device, or in what they were shopping for. Chapter 7 examines how regression describes relationships without demonstrating cause, and Chapter 11 shows what kind of evidence a causal claim actually requires. Until then, treat every diagnostic finding as a candidate explanation to be tested rather than a conclusion to be reported.

DEFINITION

Predictive Analytics

Predictive analytics uses patterns in existing data to estimate what is likely to happen for new or future observations. In marketing, it may include churn prediction, sales forecasting, lead scoring, response modeling, or conversion probability estimation.

Predicting an outcome well and explaining what causes it are different goals, and a model that does one may do the other poorly.

Source: Adapted from Shmueli (2010) and Provost and Fawcett (2013).

DEFINITION

Prescriptive Analytics

Prescriptive analytics uses evidence, models, constraints, and judgment to recommend actions. In marketing, it may support budget allocation, targeting, offer selection, pricing, promotion planning, or resource prioritization.

Source: Adapted from Camm et al. (2023) and Davenport (2006).

1.4.1 Example: Email Campaign Performance

Suppose StyleCraft sent three campaigns: a new-arrivals campaign, a loyalty discount campaign, and a clearance campaign. The descriptive question is: Which campaign had the highest open rate, click-through rate, conversion rate, and revenue? The diagnostic question is perhaps the most interesting: Why did the loyalty campaign perform better among returning customers but worse among new customers? The predictive question is: Which customers are most likely to respond to the next email? The prescriptive question is: Which customers should receive which offer next week, given budget limits and margin goals?

The same dataset may support all four types of analysis, but each type asks a different question. One of the analyst’s first responsibilities is to identify which question is being asked.

1.5 From Business Question to Analytical Question

Analytics begins with the business question, not the dataset. A business question is the managerial issue that the organization needs to address; an analytical question is the measurable version of that issue. The business question states what must be decided; the analytical question states what must be measured.

Consider the question, “How can we improve customer loyalty?” This is a reasonable business concern, but it is too broad for immediate analysis. An analyst might translate it into more specific analytical questions: Which customer segments have the highest repeat purchase rates? Which first-purchase categories are associated with a second purchase? How long does it typically take a new customer to make a second purchase? Which campaign contacts are associated with retention? Each analytical question points to different data and methods. Table 1.2 illustrates this translation for several common marketing questions.

Table 1.2
Translating Business Questions into Analytical Questions

CONCEPT

Business Questions and Analytical Questions

A business question identifies the decision or managerial issue. An analytical question identifies what must be measured, compared, modeled, or visualized to support that decision. Strong analytics requires both.

Source: Adapted from Provost and Fawcett (2013) and Chapman et al. (2000).

1.6 The Marketing Analytics Workflow

CRISP-DM, a widely cited data mining process, begins with business understanding, then moves through data understanding, data preparation, modeling, evaluation, and deployment (Chapman et al., 2000). This guide adapts that logic for marketing students and AI-assisted practice.

The adapted workflow has seven steps: define the marketing decision, translate the decision into analytical questions, identify and prepare the data, explore patterns, apply an appropriate method, verify and interpret the output, and communicate a recommendation. Table 1.3 presents the seven steps and the analyst’s work at each one. Table 1.4 then shows, for the same seven steps, where AI assistance typically helps and what must be verified before the step is trusted.

Table 1.3
A Marketing Analytics Workflow: Steps and Analyst Work

Table 1.4
A Marketing Analytics Workflow: AI Support and Verification Checks

CONCEPT

Verification

Verification is the practice of checking whether analytic output is accurate, reasonable, and aligned with the analytical question before using it in a recommendation. Verification is especially important when code, charts, or summaries are produced with AI support. In this guide, verification is treated as a professional habit rather than as a single statistical test.

Source: Adapted from Chapman et al. (2000) and Provost and Fawcett (2013).

1.7 The AI-Assisted Workflow Used in This Guide

Within the seven-step workflow, AI use follows four habits: specify, predict-then-verify, explain, and document. Together, they make AI-assisted work accountable.

First, specify the task. Before asking for help, define the business question, what one row represents, the relevant variables, the intended output, the constraints, and the verification criteria. A vague prompt such as “analyze this data” is unlikely to produce a useful result. A stronger prompt tells the tool what the data represent and what decision the analysis should support.

Second, predict-then-verify. Before running code or accepting an interpretation, write down what you expect the result to be: a number, range, shape, or direction. Then compare the actual output with that expectation. If they do not match, stop and investigate. Either the output is wrong, your expectation is wrong, or the data contain something that requires explanation.

Third, explain the work. The analyst should be able to explain what each major step does and what the output means. Fourth, document the use of AI. Documentation should record the tool used, the prompt, the output, the revisions made, the errors or limitations found, and the verification performed. The AI-use documentation template in Appendix D is the standard form for that record throughout this guide.

CONCEPT

Specify, Predict-Then-Verify, Explain, Document

Specify: define the task before asking for help.

Predict-then-verify: write your expectation, run the step, compare the output with the expectation, and investigate any mismatch.

Explain: be able to explain what each step does and what the result means.

Document: record how AI was used and how the work was checked.

Source: Author-developed framework for this guide, informed by the analytic process logic in Chapman et al. (2000) and Provost and Fawcett (2013) and by the AI-augmentation argument in Davenport et al. (2020).

1.7.1 Example: A Weak Prompt and a Stronger Prompt

The difference between the two prompts below is the specify step. Neither prompt requires special technique; the second simply says what the data are and what the analysis is for.

A weak prompt. “Analyze this campaign data and tell me what to do.”

A stronger prompt. “I am analyzing a marketing campaign dataset with one row per campaign. The columns are campaign_name, channel, impressions, clicks, conversions, revenue, and ad_spend, where ad_spend is advertising spend only. Please write pandas code to calculate click-through rate, post-click conversion rate (conversions divided by clicks), cost per conversion, and return on ad spend by campaign. Do not interpret the results yet. Include checks for missing values and for zero denominators.”

The second prompt is better because it specifies what one row represents, the variables, the calculations, the denominator for each rate, the software context, and the limits on what the assistant should do. It also asks for verification before interpretation.

1.8 Key Marketing Data Concepts

Before the first lab, students need a small set of data concepts. Chapter 3 develops them formally; here they are introduced only as working ideas.

Begin with one question: what does one row represent? In the lab that follows, each row is one marketing campaign. In a customer file, each row is one customer. In a transaction file, each row is one purchase. The answer matters because it determines what a total, an average, or a rate can legitimately describe. Unless those quantities have been aggregated into explicit columns, a campaign-level table does not reveal individual customers, and a customer-level table does not reveal individual transactions. A surprising share of analytic errors are simply one kind of row being interpreted as if it were another.

Marketing data can come from CRM systems, point-of-sale files, websites, email platforms, social media platforms, advertising accounts, surveys, loyalty programs, customer service interactions, experiments, and third-party data providers. Each source has strengths and limitations.

Because marketing data are imperfect, analysts should not treat datasets as neutral mirrors of reality. A dataset is a representation of business activity shaped by what the organization measured, what systems captured, what customers consented to share, and what processing occurred before the analyst received the file. Whether a particular dataset is fit for a particular purpose is a question this guide returns to repeatedly.

Chapter 3 develops these concepts formally. It defines the unit of analysis and the grain of a table, separates stored data types from levels of measurement, specifies the four elements of a ratio metric, and sets out the dimensions along which data quality is assessed. For now, one habit is enough: before analyzing any file, state what one row represents, and write it down where the reader of your analysis can see it.

1.9 AI in Marketing Analytics: Benefits and Risks

Davenport et al. (2020) and Huang and Rust (2021) describe broad applications of AI across marketing research, strategy, and action. This course relies mainly on generative AI assistants, which have a different risk profile from predictive and algorithmic systems.

CONCEPT

Artificial Intelligence and Generative Artificial Intelligence

Artificial intelligence is the broad category: computational systems that perform tasks associated with human intelligence, including pattern recognition, prediction, classification, and language processing.

Generative artificial intelligence refers to systems that produce new content such as text, code, images, audio, or video, in response to a prompt.

In this course, most AI-assisted exercises use generative-AI assistants such as chatbots and notebook copilots. Later chapters also examine predictive and algorithmic systems, including the scoring and targeting models that marketing organizations run without generating any text at all.

Source: Adapted from Davenport et al. (2020), Huang and Rust (2021), and National Institute of Standards and Technology (2024).

For students, the most immediate use of AI may be as a workflow aid. A tool can explain why Python code failed, suggest a pandas command, summarize the meaning of an output, or propose chart alternatives. These tools can speed up learning and routine work, but students still need to understand and verify the output.

The risks are practical, and most of the ones that will affect your coursework belong specifically to generative AI rather than to artificial intelligence in general. A generative assistant may invent sources, misread columns, assume the wrong unit of analysis, ignore missing values, or generate code that runs cleanly while computing the wrong metric. The federal risk framework for generative AI names this failure directly as confabulation, the generation and confident presentation of erroneous content in response to a prompt (National Institute of Standards and Technology, 2024). That framework also identifies data privacy, intellectual property, information integrity, and human–AI configuration as risk areas, all of which apply when confidential material is placed into an external system or when generated output is passed along unchecked. Responsible use therefore requires the analyst to supervise the assistant. Table 1.5 summarizes where AI support is useful across the workflow and what the analyst remains responsible for.

Table 1.5
AI Support and Risks Across the Marketing Analytics Workflow

CONCEPT

AI in Marketing Analytics

In this guide, AI in marketing analytics refers to the use of computational systems to automate tasks, process data, identify patterns, support predictions, personalize actions, or assist communication in marketing research, strategy, and execution.

Source: Adapted from Davenport et al. (2020) and Huang and Rust (2021).

AI IN PRACTICE

Review Before You Rely

A useful professional standard is simple: do not send work to a manager that you cannot explain. AI can help draft code, summarize output, or improve a first version of a memo. However, the analyst must still check the work, revise it, explain it, and stand behind the recommendation.

Audit routine. After an assistant returns code or an interpretation, ask four questions in order. Does the output answer the question I specified? Does one row still represent what I said it represents? Can I reproduce at least one number by hand? Can I state in one sentence what this result does not show?

If any answer is no, the work is not ready to leave your desk. When it is ready, record the tool, the prompt, the revisions, and the checks using the AI-use documentation template in Appendix D.

1.10 Hands-On Application in Python and Google Colab

This first hands-on activity is intentionally simple: open a notebook, run a code cell, inspect a small marketing dataset, and ask basic questions about it. Later chapters build on these skills.

Google Colab is a browser-based notebook environment. In this guide, notebooks combine explanation, code, output, tables, and interpretation in one place. This format is useful because the reader can connect reasoning with computation. Students who have not used a notebook before should work through the setup steps and the short Python reference in Appendix A before starting Lab 1.1.

1.10.1 Lab 1.1: Inspecting a Small Campaign Dataset

In this lab, you will create a small fictional campaign dataset directly in Python. Later chapters will use larger datasets from the companion repository. This first lab avoids file-loading issues so that the focus can remain on the workflow. The numbers are simplified for instruction and should not be treated as industry benchmarks. Note the column named ad_spend: it records advertising spend for the campaign, not total campaign cost. That distinction matters as soon as return on ad spend is calculated.

Code 1.1. Create and inspect a small marketing campaign dataset

import pandas as pd

campaigns = pd.DataFrame({
"campaign": [
"New Arrivals", "Loyalty Offer",
"Clearance", "Welcome Series",
],
"channel": [
"Email", "Email", "Paid Search", "Social",
],
"impressions": [52000, 41000, 68000, 75000],
"clicks": [2860, 2540, 3400, 3000],
"conversions": [310, 420, 390, 250],
"revenue": [27900, 33600, 23400, 17500],
"ad_spend": [1800, 1200, 9500, 5200],
})

campaigns.head()

The code begins by importing pandas, a Python library commonly used for data analysis. It then creates a small table called campaigns. Each row represents one marketing campaign. The columns describe the campaign name, channel, impressions, clicks, conversions, revenue, and advertising spend.

CONCEPT

Input, Transformation, Output

Input is the data or object used in a step. A transformation is the calculation, filter, model, or change applied to the input. Output is the result produced by the step.

A good analyst can identify the input, the transformation, and the output for each major step of an analysis.

1.10.2 Adding Basic Marketing Metrics

The next code cell creates three common campaign metrics. Click-through rate divides clicks by impressions. Post-click conversion rate divides conversions by clicks. Return on ad spend divides revenue by advertising spend.

Each of those sentences hides a choice. Conversion rate in particular has no single denominator: depending on the question, conversions may be divided by clicks, sessions, visitors, leads, or recipients, and the four results will differ. For this lab, post-click conversion rate is defined as conversions divided by clicks, and the column is named accordingly so that the definition travels with the number. Return on ad spend divides revenue by advertising spend, which is why the spend column here is named ad_spend rather than cost. A total campaign cost that also included creative production or agency fees would produce a different ratio, and one that is not comparable with the industry convention for this metric.

Code 1.2. Calculate basic campaign metrics

campaigns["click_through_rate"] = (
campaigns["clicks"] / campaigns["impressions"]
)
campaigns["post_click_conversion_rate"] = (
campaigns["conversions"] / campaigns["clicks"]
)
campaigns["roas"] = (
campaigns["revenue"] / campaigns["ad_spend"]
)

campaigns[[
"campaign", "channel", "click_through_rate",
"post_click_conversion_rate", "roas",
]]

These calculations are simple, but they illustrate an important principle: metrics are constructed. The analyst chooses a numerator and a denominator. If either is wrong, the metric will be wrong even if the code runs successfully. In other words, a metric is a choice made by the analyst, not a fact discovered in the data.

CONCEPT

Metrics Are Constructed

Every metric is built, not found. The analyst chooses what goes in the numerator, what goes in the denominator, over what period, and for which records. Change any one of those choices and the number changes, even though the label on the column stays the same.

Two campaign reports can therefore show different conversion rates without either being wrong. The habit this guide asks for is to write the definition down beside the number, every time.

Source: Course concept developed for this guide, informed by Farris et al. (2015) and Rust et al. (2004). Chapter 3 specifies the elements of a ratio metric formally.

1.10.3 Verification Checks for Lab 1.1

Before interpretation, check the dataset’s structure, missing values, denominators, rate ranges, and distributions. The first two checks print inspection results; the denominator test and the assertion stop the notebook when a required condition fails.

One note on order. The zero-denominator check logically belongs before the division in Code 1.2, and that is where it should sit in your own notebook. It appears here so that all of the chapter’s verification logic can be read in one place.

Code 1.3. Run basic verification checks

# 1. Structure: how many rows and columns?
print("rows and columns:", campaigns.shape)

# 2. Missing values in any column
print(campaigns.isna().sum())

# 3. No required denominator may be zero
denominators = campaigns[
["impressions", "clicks", "ad_spend"]
]
if (denominators == 0).any().any():
raise ValueError(
"A required metric denominator contains zero."
)

# 4. Rates expressed as proportions must fall in [0, 1]
rate_columns = [
"click_through_rate",
"post_click_conversion_rate",
]
rates = campaigns[rate_columns]
assert ((rates >= 0) & (rates <= 1)).all().all()

# 5. Inspect the distribution of the two rates
campaigns[rate_columns].describe()

VERIFICATION CHECK

Before you run: predict the number of rows and columns that campaigns.shape will report now that three metric columns have been added, and predict the range in which each rate must fall.

After you run: the frame should have 4 rows and 10 columns. Compare that with your prediction. A click-through rate or a post-click conversion rate expressed as a proportion must fall between 0 and 1, and the assertion in Code 1.3 will stop the notebook if one does not.

Investigate if: a rate is greater than 1, or the assertion fails, or the denominator check raises an error. The numerator and denominator may be reversed, the data may contain errors, or the metric may not be defined the way you assumed. Verification protects the analyst from interpreting faulty output, and an assertion protects the analyst from failing to notice.

1.11 Marketing Interpretation and Managerial Insight

Campaign metrics are not recommendations. Interpretation begins by asking what each metric suggests, which metric matters for the decision, and what trade-offs or limitations remain. Does a campaign with high revenue also have high spend? Does a high post-click conversion rate translate into high contribution?

Suppose the Loyalty Offer has the highest post-click conversion rate and the highest ROAS. A weak interpretation would say, “The Loyalty Offer is best.” A stronger interpretation would say, “Among these four campaigns, the Loyalty Offer produced the strongest combination of conversion efficiency and return on ad spend. However, the data do not show whether customers would have purchased without the offer, whether the discount reduced margin, or whether the campaign can scale to a larger audience.”

The stronger interpretation is better because it connects the result to a decision while acknowledging uncertainty. It does not confuse performance measurement with causal proof. It does not assume that the best campaign in a small dataset should automatically receive the full budget. It identifies what the evidence supports and what remains unknown.

1.11.1 Revisiting the StyleCraft Case

The StyleCraft team should begin by clarifying the holiday-season decision. If the decision is budget allocation, the team needs campaign spend, revenue, margin, incremental lift, audience size, and operational constraints. If the decision is customer targeting, the team needs customer-level response and purchase history. If the decision is message strategy, the team may need creative performance, segment behavior, and customer feedback.

Descriptive analytics could summarize past campaign performance. Diagnostic analytics could compare performance across channels, segments, customer cohorts, or offer types. Predictive analytics could estimate which customers are most likely to respond to future campaigns. Prescriptive analytics could recommend how to allocate budget or which offers to send to which customers. AI tools could help with code, summaries, and drafting. The team must still verify the results, evaluate the assumptions, and make the final recommendation.

1.12 Business Analytics in Practice

This section examines how the chapter’s habits appear in real organizations: how AI changes analysts’ work, what happens when a deliverable is not verified, and what employers test in analytics interviews.

1.12.1 When the Coding Gets Easier, the Framing Gets Harder

Consider a consumer-packaged-goods insights team of eight analysts supporting brand and shopper marketing. Two years ago, a routine request, weekly promotion performance by retailer and pack size, took an analyst most of a day: pull the data, reshape it, build the summary, format the deck. Today an assistant drafts the query and the chart code in minutes. The time saved on coding shifted to other decisions: which retailers belong in the comparison, whether the promotion window should be defined by ship date or scan date, and whether the lift being reported is lift against last year, against the non-promoted weeks, or against nothing at all.

That redistribution of effort is consistent with what field experiments have found. In a preregistered study of 758 consultants at Boston Consulting Group, participants with access to GPT-4 completed 12.2 percent more tasks, completed them 25.1 percent more quickly, and produced work of measurably higher quality. On a task deliberately designed to fall outside the model’s competence, however, participants using AI were 19 percentage points less likely to produce correct solutions than participants working without it (Dell’Acqua et al., 2026). The authors describe a jagged frontier: capability that is uneven in ways not visible from the outside. Because incorrect output can be fluent and polished, teams cannot judge reliability from presentation quality.

Teams that adapt well tend to do two things. They shift senior time toward problem framing, because framing decisions are the ones the assistant cannot make. And they make quality assurance an explicit, named step rather than something that happens if there is time. On the team described here, no recurring report leaves the group until a second analyst has confirmed the denominator, the date window, and the filter set. A review of that kind costs minutes and catches the errors a single analyst working quickly does not. As production becomes cheaper, specifying the question and checking the answer become the scarce skills.

1.12.2 The Deliverable That Could Not Be Verified

The failure mode is easiest to see in professional services, where the deliverable is the product. In October 2025, Deloitte Australia agreed to issue a partial refund on a report it had produced for the Australian government under a contract valued at approximately A$440,000, after the published version was found to contain references to academic papers that do not exist and a fabricated quotation attributed to a federal court judgment (Associated Press, 2025). The firm maintained that the substance of the review held. The citations, however, had been invented, and no one had checked them before publication.

Marketing analytics fails the same way, usually with less publicity. An agency deliverable arrives carrying a confident summary metric, say “incremental reach efficiency,” that no one on the client side can reproduce, because the assistant that drafted the deck synthesized a plausible-sounding measure rather than computing a defined one. The client cannot verify it. The agency analyst cannot fully explain it. The metric nonetheless enters the next planning cycle, and by the time someone asks where the number came from, three decisions have been built on top of it.

The federal risk framework for generative AI gives this behavior a name: confabulation, the confident presentation of erroneous content (National Institute of Standards and Technology, 2024). The naming matters, because saying that the tool made a mistake locates the problem in the software. In the cases above, the software did what generative systems do. What was missing was a person who checked. An unverifiable number is a liability owned by the person who signs the deliverable.

1.12.3 What Hiring Managers Can Test For

Hiring has moved in the same direction. Employers identify analytical thinking as the most commonly required core skill, cited by seven in ten organizations, while AI and big data rank as the fastest-growing skill through 2030 (World Economic Forum, 2025). Employers therefore need analysts who can use AI tools and judge their output.

The verification habit is now common enough to be measured. In a survey of more than 33,000 respondents, 84 percent reported using or planning to use AI tools, yet only about 3 percent expressed high trust in the accuracy of what those tools produced. Sixty-six percent named “AI solutions that are almost right, but not quite” as their leading frustration, and roughly 45 percent reported that debugging AI-generated code took more time than expected (Stack Overflow, 2025). Analysts are not developers, but the pattern transfers directly: heavy use, low trust, and real time spent on the gap between the two.

That gap is what an interview can usefully probe. Instead of asking a candidate to write code from scratch, an interviewer might hand over a short notebook or a one-page analysis and ask what is wrong with it: a reversed denominator, a silently dropped set of rows, a rate above 1, a chart whose axis begins at 40 rather than 0. A candidate who can describe how they check work, and who can name an occasion when an assistant’s output looked right and was not, is demonstrating exactly the judgment the survey evidence above suggests is scarce. Verification is therefore part of the analyst’s work, not a final defensive step.

1.12.4 In Your First Analyst Job

The course mirrors the accountability structure you will work inside: labs require a prediction before execution, AI-assisted exercises require documentation of the tool, the prompt, the revision, and the check, and recommendation exercises require one limitation and one next step. Work in this course is assessed on whether it can be defended, not on whether the code ran. That is the same standard that will apply when the deliverable carries your name and the person reading it has a budget to spend.

1.13 Ethics, Privacy, and Responsible Analytics

Marketing analytics often uses customer data, creating legal, professional, and strategic responsibilities. Analysts should consider whether the data are appropriate to use, whether customers would reasonably expect the use, whether sensitive information is involved, whether the analysis could create unfair treatment, and whether the recommendation protects customer trust.

AI also creates specific privacy and governance risks. Uploading confidential customer data into an external AI system may violate company policy, privacy rules, or customer expectations, and data privacy is one of the risk areas that generative-AI governance frameworks specifically flag (National Institute of Standards and Technology, 2024). In this course, confidential or personally identifying data are never to be placed into an external AI tool. Model recommendations may also reproduce patterns of exclusion if historical data reflect unequal treatment. A responsible analyst asks not only, “Does the model work?” but also, “Should we use this result in this way?”

CONCEPT

Responsible Analytics

Responsible analytics is the practice of using data, methods, AI tools, and recommendations in ways that are accurate, transparent, privacy-aware, fair, and accountable to the decision context.

Source: Author-developed definition for this guide, synthesizing the marketing-privacy, data-ethics, and algorithmic-accountability literatures represented by Martin and Murphy (2017), Barocas and Selbst (2016), Mittelstadt et al. (2016), and Floridi and Taddeo (2016).

1.14 Chapter Summary

This chapter introduced the role of the AI-augmented marketing analyst. The main point is that analytics is a disciplined approach to decision support. It begins with a marketing decision, translates that decision into analytical questions, uses data and methods to generate evidence, verifies the output, and communicates a recommendation.

The chapter distinguished data, information, insight, and recommendation; introduced the four types of analytics and the caution that diagnostic comparisons do not establish cause; separated artificial intelligence broadly from the generative assistants students will use most; and presented the four habits of specify, predict-then-verify, explain, and document, which were then practiced in a short Google Colab lab. It also stepped outside StyleCraft to look at how those habits appear in an insights team, in a professional-services failure, and in analytics hiring.

The rest of this guide builds from this foundation. Later chapters examine marketing data, measurement, data preparation, exploratory analysis, segmentation, regression, predictive modeling, forecasting, experiments, visualization, dashboards, and storytelling. The tools will become more sophisticated, but the principle remains the same: analytics becomes valuable when it improves decisions. The next chapter examines marketing analytics as decision support in greater depth, showing how a decision is structured, how a vague request becomes a written specification, and how the deliverable is matched to the choice being made. Chapter 3 then turns to data, measurement, and marketing variables, where the concepts this chapter treated informally are defined in full.

1.15 Exercises for Practice and Homework

These exercises practice four habits: classify the analytic question, state what one row represents, verify the output, and connect the result to a marketing decision. Core chapter practice is required, in-class activities support discussion, and extensions are optional. Each exercise carries an assignment label so that instructors can assign selectively.

1.15.1 Core Chapter Practice

Exercise 1.1 Concept Check (Required Practice)

1. Explain the difference among data, information, insight, and recommendation.
2. What is the difference between a business question and an analytical question?
3. Why might data-informed be a better phrase than data-driven for many marketing decisions?
4. A dataset has one row for each customer purchase. What does one row represent, and what can that table not tell you?
5. Why is verification especially important when using AI-assisted code or interpretation?
6. What is the difference between artificial intelligence and generative artificial intelligence, and why does the distinction matter for the risks discussed in Section 1.9?

Exercise 1.2 Classify the Analytics Type (Required Practice)

For each scenario, identify whether the example is primarily descriptive, diagnostic, predictive, or prescriptive. Then explain your reasoning in one sentence.

1. A dashboard shows monthly revenue by marketing channel for the past twelve months.
2. A model estimates which customers have the highest probability of canceling a subscription next month.
3. An analysis compares conversion rates by device type and finds that mobile users abandon the checkout page more often.
4. A system recommends sending different retention offers to different customer segments based on expected profit.
5. A report shows that average order value increased after a new free-shipping threshold was introduced.
6. A forecast estimates expected weekly demand for the next quarter.
7. An analyst recommends reducing spend on a campaign because its cost per acquisition exceeds the target margin.
8. A chart shows that repeat purchase rates are lower among customers acquired through one promotion.

Exercise 1.3 Match the Metric to the Question (Required Practice)

For each business question, explain why the suggested metric is useful, state the denominator the metric requires, and identify one thing the metric does not tell you.

1. Are people engaging with our email? Suggested metric: click-through rate.
2. Are clicks turning into purchases? Suggested metric: post-click conversion rate.
3. Are we spending efficiently? Suggested metric: cost per conversion.
4. Are we generating revenue relative to advertising spend? Suggested metric: ROAS.
5. Are we retaining customers? Suggested metric: repeat purchase rate.
6. Are customers becoming more valuable over time? Suggested metric: customer lifetime value.

Exercise 1.4 Hands-On Colab Practice (Homework Submission)

1. Open a new Google Colab notebook.
2. Copy the code from Lab 1.1 into the notebook and run it.
3. Add a new column called cost_per_conversion that divides ad_spend by conversions.
4. Sort the campaigns from highest to lowest ROAS.
5. Write a three-sentence interpretation of the results.
6. Write one verification check that would make you more confident in the output, and run it.
7. Write one limitation of the dataset.

Exercise 1.5 Verification Checklist (Required Practice)

1. List one verification check for dataset structure.
2. List one verification check for missing or unusual values.
3. List one verification check for a zero or implausible denominator.
4. List one verification check for metric formulas.
5. List one verification check for interpretation limits.
6. List one verification check for AI-assisted output.
7. Which check do you think is most important for the StyleCraft case, and why?

Exercise 1.6 AI-Assisted Practice (Homework Submission)

1. Use an AI tool to support one part of Exercise 1.4, but do not allow the tool to replace your own interpretation.
2. Complete the compact documentation record shown in Table 1.6 in your notebook, then complete the full AI-use template in Appendix D for your submission.
3. State one thing the assistant produced that you revised or rejected, and explain how you detected it.

Table 1.6
AI-Use Documentation: Compact Record

Table 1.6 is a compact record for use inside a notebook. The full AI-use documentation template, including fields for the output received, the errors or limitations found, and what you learned from the process, is in Appendix D and is the version to submit with graded work throughout this guide.

Exercise 1.7 Managerial Memo (Homework Submission)

Write a short memo of 200–300 words to the StyleCraft chief marketing officer. The memo should answer this question: What should the team examine before reallocating the holiday campaign budget? Include one descriptive question, one diagnostic question, one predictive question, one prescriptive question, and one caution about AI-assisted analysis. For the diagnostic question, state explicitly what it could and could not establish. Recommended memo structure: decision context, evidence needed, caution or limitation, and next step.

1.15.2 In-Class Activities

Exercise 1.8 Identify the Decision First (In-Class Discussion)

For each situation, answer: What decision is the manager trying to make? What evidence would help? What type of analytics would be most useful first?

1. Website traffic increased last month, but online sales did not.
2. A loyalty campaign generated many purchases, but the finance team is concerned about margin.
3. A paid social campaign produced many clicks, but few new customers.
4. Customer service complaints increased after a product launch.
5. A dashboard shows that one region is underperforming.

Exercise 1.9 Business Question Translation (In-Class Discussion)

Translate each business question into at least two analytical questions. State what one row would represent in the data you would need.

1. How can we improve the performance of our loyalty program?
2. Should we increase our investment in influencer marketing?
3. Why are customers abandoning their carts?
4. Which customers should receive a win-back offer?
5. How should we evaluate whether our new campaign improved brand engagement?

Exercise 1.10 Find the Flaw in the Output (In-Class Discussion)

1. The denominator problem. A tool calculated conversion rate as conversions divided by impressions, but the class benchmark used conversions divided by clicks. What went wrong at the specify step?
2. The averaged average. A report states that the overall conversion rate is the simple average of the four campaign conversion rates. Use the campaign data from Lab 1.1 to compare this simple average with the pooled conversion rate, calculated as total conversions divided by total clicks. When would the two figures differ most?
3. The causal overclaim. A draft says, “The Loyalty Offer caused the highest ROAS, so StyleCraft should move the full holiday budget to loyalty discounts.” Identify at least three problems.
4. The silent row drop. Cleaning code used df.dropna() and reduced a 10,000-row customer file to 6,200 rows without mentioning it. Why is the output untrustworthy even if the arithmetic is correct?
5. The zero-spend campaign. A campaign with ad_spend = 0 and revenue > 0 produces infinite ROAS. How should the analyst handle this before presenting the table, and which check in Code 1.3 would catch it?
6. The invented metric. A deck reports “incremental reach efficiency” with no formula and no source. Drawing on Section 1.12, explain what you would ask for and what you would do if no formula exists.

Exercise 1.11 Ethics and Privacy Mini-Cases (In-Class Discussion)

For each case, identify the issue, the stakeholders affected, and what a responsible analyst should do instead.

1. The convenient paste. An intern has a CRM export containing customer names, emails, and purchase histories. To save time, the intern pastes 500 rows into a public AI chatbot and asks it to clean and deduplicate the list.
2. The model that learned the past. A response model trained on historical campaign data recommends excluding several ZIP codes because past response rates were low. A colleague notes that StyleCraft has historically spent almost no marketing budget in those areas.
3. The persuasive axis. Preparing an executive slide, an analyst truncates the y-axis of a revenue chart so that a 3 percent increase fills the frame. “It is the same data,” the analyst says. “I am just making the story clearer.”

Exercise 1.12 From Output to Recommendation (In-Class Discussion)

Read the following output: “The Loyalty Offer has the highest ROAS and the highest post-click conversion rate. The Clearance campaign has the lowest ROAS. The Welcome Series has the lowest revenue and the lowest post-click conversion rate.”

1. Write a weak conclusion that overclaims.
2. Write a better interpretation that stays close to the evidence.
3. Write a recommendation that includes one limitation and one next step.

1.15.3 Extensions

Exercise 1.13 Rewrite the Prompt (Optional)

1. Rewrite the prompt “Analyze this campaign data” using the following elements: business question, what one row represents, variables, desired output, constraints, and verification check.
2. Rewrite the prompt “Tell me which campaign is best” using the same elements.
3. Rewrite the prompt “Make a chart for my boss” using the same elements.
4. Rewrite the prompt “Clean this file” using the same elements.
5. Rewrite the prompt “Write a recommendation” using the same elements.
6. After rewriting each prompt, explain what the assistant could still get wrong.

Exercise 1.14 Lab 1.2: Adding Margin (Optional)

ROAS is useful, but it ignores margin. In this lab, make that limitation concrete by adding a gross margin rate to each campaign and recomputing which campaign looks best. In this dataset, margin_rate is the gross margin rate on campaign revenue: the share of revenue remaining after cost of goods sold, before any marketing expenditure. Subtracting advertising spend from gross margin dollars therefore produces net contribution after advertising spend, not contribution margin in the accounting sense; the column is named accordingly so that the two are not confused.

Code 1.4. Add gross margin and compute net contribution

campaigns["margin_rate"] = [0.55, 0.32, 0.18, 0.50]

campaigns["gross_margin_dollars"] = (
campaigns["revenue"] * campaigns["margin_rate"]
)
campaigns["net_contribution_after_ad_spend"] = (
campaigns["gross_margin_dollars"]
- campaigns["ad_spend"]
)

campaigns[[
"campaign", "roas",
"net_contribution_after_ad_spend",
]]

1. Before running the code, predict whether the net contribution ranking will match the ROAS ranking. Explain why.
2. Run the code. Which campaign generates the highest net contribution in dollars? Which generates the lowest?
3. Hand-check one row and write a two-sentence verification note. For New Arrivals, for example, net contribution after advertising spend is $27,900 × 0.55 − $1,800 = $13,545.
4. Write a four-sentence managerial interpretation that uses both ROAS and net contribution and avoids causal language.
5. A teammate argues that the Clearance campaign should be cut entirely because of its low margin. What business considerations, not visible in this table, might justify keeping it?
6. Explain why net contribution after advertising spend is not the same as contribution margin, and what a finance colleague would need in order to reconcile the two.

Exercise 1.15 Reflection Questions (Optional)

These optional questions connect the chapter to professional practice and your development as an analyst.

1. How do you expect AI tools to change the work of marketing analysts?
2. Which part of the analytics workflow seems most important for preventing mistakes?
3. What concerns do you have about using Python or Google Colab, and how can the workflow used in this guide help you learn responsibly?
4. Think about a brand you know well. What marketing decision could be improved with better data and analysis?
5. What would make a recommendation credible to you if you were the decision-maker?
6. Section 1.12 argued that verification is now part of what an analyst is hired to do. How would you demonstrate that skill in an interview?

1.16 Glossary of Terms

This glossary includes only the terms this chapter introduces. Terms that other chapters own are used with a pointer rather than redefined here; the unit of analysis, data quality, and the formal definition of a metric belong to Chapter 3.

AI in marketing analytics. The use of computational systems to automate tasks, process data, identify patterns, support predictions, personalize actions, or assist communication in marketing research, strategy, and execution (Davenport et al., 2020; Huang & Rust, 2021).

Analytic translation. The process of converting a business problem into an analytical task, interpreting the analytical result in business language, and turning the evidence into a usable recommendation. Course concept for this guide, informed by Provost and Fawcett (2013).

Analytical question. A measurable question that can be addressed with data, comparison, modeling, visualization, or experimentation (Chapman et al., 2000; Provost & Fawcett, 2013).

Business analytics. The use of data, quantitative methods, models, visualization, and technology to understand business problems and support decisions (Camm et al., 2023; Davenport, 2006).

Business question. The managerial issue or decision that an organization needs to address (Provost & Fawcett, 2013; Chapman et al., 2000).

Confabulation. The generation and confident presentation of erroneous content by a generative AI system in response to a prompt (National Institute of Standards and Technology, 2024).

Data-informed decision-making. The use of data and analysis as essential inputs into managerial judgment while recognizing context, ethics, uncertainty, and strategy (Davenport, 2006; Provost & Fawcett, 2013).

Decision context. The managerial situation in which an analysis will be used, including the decision, the stakeholders, the evidence, the constraints, the timing, and the consequences of action. Course concept for this guide.

Descriptive analytics. Analytics that summarizes past or current data to show what happened (Camm et al., 2023; Farris et al., 2015).

Diagnostic analytics. Analytics that investigates why performance may have changed by comparing groups, segments, time periods, or possible drivers, without thereby establishing causality. Course concept for this guide; the comparative methods it relies on are discussed in Camm et al. (2023) and France and Ghose (2019).

Generative artificial intelligence. Systems that produce new content, including text, code, images, audio, or video, in response to a prompt (National Institute of Standards and Technology, 2024).

Input, transformation, and output. The three parts of any analytic step: the data or object used, the calculation or change applied, and the result produced. Course concept for this guide.

Marketing analytics. The application of analytic methods, data, models, and technologies to marketing decisions (Wedel & Kannan, 2016; France & Ghose, 2019).

Metric construction. Every metric is constructed through a definition. That definition specifies what is counted or calculated, the relevant time period, and the records included. For a ratio metric, it also specifies the numerator and denominator.

Predictive analytics. Analytics that uses patterns in existing data to estimate what is likely to happen for new or future observations (Shmueli, 2010; Provost & Fawcett, 2013).

Prescriptive analytics. Analytics that uses evidence, models, constraints, and judgment to recommend actions (Camm et al., 2023; Davenport, 2006).

Responsible analytics. The use of data, methods, AI tools, and recommendations in ways that are accurate, transparent, privacy-aware, fair, and accountable to the decision context (Martin & Murphy, 2017; Barocas & Selbst, 2016; Mittelstadt et al., 2016; Floridi & Taddeo, 2016).

Specify, predict-then-verify, explain, document. The four AI-use habits applied throughout this guide: define the task before asking for help; state an expectation and compare it with the output; be able to explain each step; and record how AI was used and how the work was checked. Author-developed framework for this guide.

Verification. The practice of checking whether analytic output is accurate, reasonable, and aligned with the analytical question before using it in a recommendation (Chapman et al., 2000; Provost & Fawcett, 2013).

1.17 Further Readings

Students who want additional background may begin with the following readings. Marketing analytics sources are listed first because this guide approaches analytics from a marketing decision-making perspective.

• Wedel and Kannan (2016) for a scholarly overview of marketing analytics in data-rich environments.
• France and Ghose (2019) for a broad review of marketing analytics methods and implementation issues.
• Davenport et al. (2020) for an accessible discussion of how AI may change marketing.
• Huang and Rust (2021) for a strategic framework for artificial intelligence in marketing.
• National Institute of Standards and Technology (2024) for an authoritative catalog of generative-AI risks, including confabulation, data privacy, and human oversight.
• Provost and Fawcett (2013) for data analytic thinking and the difference between data and decisions.

1.18 References

Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16, 3–9.

Associated Press. (2025, October 7). Deloitte to partially refund Australian government for report with apparent AI-generated errors. CTV News. https://www.ctvnews.ca/business/article/deloitte-to-partially-refund-australian-government-for-report-with-apparent-ai-generated-errors/

Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.

Camm, J. D., Cochran, J. J., Fry, M. J., & Ohlmann, J. W. (2023). Business analytics (5th ed.). Cengage.

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. CRISP-DM Consortium.

Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98–107.

Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0

Dell’Acqua, F., McFowland, E., III, Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organization Science, 37(2), 403–423. https://doi.org/10.1287/orsc.2025.21838

Farris, P. W., Bendle, N. T., Pfeifer, P. E., & Reibstein, D. J. (2015). Marketing metrics: The manager’s guide to measuring marketing performance (3rd ed.). FT Press.

Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), Article 20160360. https://doi.org/10.1098/rsta.2016.0360

France, S. L., & Ghose, S. (2019). Marketing analytics: Methods, practice, implementation, and links to other fields. Expert Systems with Applications, 119, 456–475. https://doi.org/10.1016/j.eswa.2018.11.002

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9

Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45, 135–155. https://doi.org/10.1007/s11747-016-0495-4

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679

National Institute of Standards and Technology. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile (NIST AI 600-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.600-1

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508

Rowley, J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180. https://doi.org/10.1177/0165551506070706

Rust, R. T., Ambler, T., Carpenter, G. S., Kumar, V., & Srivastava, R. K. (2004). Measuring marketing productivity: Current knowledge and future directions. Journal of Marketing, 68(4), 76–89. https://doi.org/10.1509/jmkg.68.4.76.42721

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330

Stack Overflow. (2025). 2025 developer survey: AI. https://survey.stackoverflow.co/2025/ai

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413

World Economic Forum. (2025). The future of jobs report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/