Marketing: Real People, Real Choices, 8e

Solomon, Marshall, and Stuart

Copyright © 2016 Pearson Education, Inc.

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Chapter Objectives

Explain how marketers increase long-term success and profits by practicing customer relationship management

Understand Big Data, data mining and how marketers can put these techniques to good use

Describe what marketing analytics include and how organizations can leverage both marketing analytics and predictive analytics to improve marketing performance

Identify how organizations can use marketing metrics to measure performance and achieve marketing control

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Real People, Real Choices: Decision Time at Teradata Corporation

Which option should be pursued?

Option 1: Continue on current course and focus on short-term product launch in order to provide more time and resources for future re-launch

Option 2: Launch the product and brand in a “two-prong” release

Option 3: Delay the cloud product launch, accelerate efforts to rebrand Aprimo, then launch cloud product and new brand together

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DISCUSSION NOTE:

Explain to students that you want them to think about each option, and that you will return to this question at the end of the chapter.

CRM: A Key Decision Tool for Marketers

Customer relationship management (CRM) involves systematic tracking of consumers preferences and behaviors over time in order to tailor individualized value propositions

Allows firms to get “up close and personal”

A process by which firms enact their customer orientation

Capture information at each customer touchpoint

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LECTURE NOTES:

Customer relationship management has been embraced by many successful firms. Customer relationship management, or CRM as it usually called in conversation, is defined on this slide. Marketers know that one way to more finely segment consumers is to allow them to personalize products. That’s the idea behind this ad, and one of the reasons why marketers value CRM. A systematic tracking of consumers’ preferences and behaviors over time in order to tailor the value proposition as closely as possible to each individual’s unique wants and needs

CRM facilitates one-to-one marketing

Following the link above leads to the Customer Lifetime Value Tool, which is available from Harvard Business Online. This resource begins with an introduction and conceptual overview (which are a bit long for an in-class demo). However, instructors can skip straight to the Sample Problem to illustrate how lifetime value of the customer is calculated, or use the Tool to work an example interactively during class.

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CRM Facilitates One to One Marketing

One-to-one marketing includes several steps

Identify customers and get to know them in as much detail as possible

Differentiate among these customers in terms of both their needs and their value to the company

Interact with customers and find ways to improve cost efficiency and the effectiveness of the interaction

Customize some aspect of goods or services offered to each customer

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LECTURE NOTES:

CRM facilitates one-to-one marketing, a process which is composed of four interrelated steps:

Step 1: Identify customers and get to know them in as much detail as possible.

Step 2: Differentiate customers by their needs and value to the company.

Step 3: Interact with customers; find ways to improve cost efficiency and the effectiveness of the interaction.

Step 4: Customize some aspect of the products you offer each customer.

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Table 5.1: Four Steps of One-to-One Marketing

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Lecture Notes:

Slide provides examples of each of the activities associated with the one to one marketing process.

Discussion Notes:

Can you think of any activities other than those shown in Table 5.1 which could be added to any of these steps?

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Examples of CRM in Action

USAA

Amazon.com

Disney’s MyMagic +

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Discussion Note:

To fully appreciate the value of a CRM strategy, consider the experience of USAA, which began as an insurance company catering to the military market and today is a leading global financial services powerhouse. Unlike State Farm, Allstate, and other traditional insurance providers, USAA does not provide field agents with an office; in fact, USAA’s employees conduct business almost entirely over the phone. But ask any USAA member how they feel about the service and you’ll get a glowing report.

USAA’s success is built largely upon its state-of-the-art CRM system. No matter where on the globe you are, no matter what time of day or night, a USAA representative is able to pull up your profile. Its well trained staff expertly use CRM technology to strengthen their long-term customer relationships and, more importantly, get customers to move many or all of their business over to USAA, including banking, credit cards, money management, investments, and financial planning.

Amazon.com is another company who has mastered the use of CRM to enhance customer relationships. For loyal users, Amazon tracks visits so that it can customize advertisements, product promotions, and discounts for each shopper. This helps keep customers engaged during each of their visits and helps ensure that they continue to come back for more.

In 2014, Disney launched MyMagic+, a new system that allows Disney World visitors to more efficiently plan out their vacation experience and reduce the need to carry around tickets and other items previously necessary to tour the park. Visitors can book events in advance, reserve times on rides, and review the park activities that they have experienced in the past, to name a few of the main features.

MyMagic+ is designed to be partnered with a wearable computer called the Disney Magic Band, which enables users to verify all of the actions they have taken through the MyMagic+ system without carrying around receipts or other forms of proof.

In addition, they can use the wearable Magic Band to make transactions while in the park. The benefits and convenience for visitors is obvious, but for Disney another big advantage is the amount of data it can collect on visitors’ behavior and actions. These data better enable the firm to understand how to communicate with each customer and manage each relationship more effectively.

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Figure 5.1: Characteristics of CRM

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LECTURE NOTES:

Firms that successfully make use of CRM have a different mind-set, different goals, different measures of success, and generally look at customers in a different way when compared to firms that don’t use CRM. Four critical characteristics of CRM are shown in Figure 7.8.

Share of customer: Because it is always easier and less expensive to keep an existing customer than to get a new one, CRM firms try to increase their share of customer, not share of market; this is the percentage of an individual customer’s purchase of a product over time that is the same brand. For this reason, CRM focuses on increasing a brand’s share of customer.

Lifetime value of the customer: Lifetime value is the potential profit a single customer’s purchase of a firm’s products generates over the customer’s lifetime. Estimating LVC requires that the firm first estimate future sales across all products for the next 20 or 30 years, and then that the firm attempt to figure out what profit the company could make from this customer in the future.

CRM firms view customers differently – as assets. Customer equity is defined as the financial (net) value of a customer throughout the lifetime of the relationship.

The final characteristic that makes CRM unique is the fact that organizations focus on high-value customers. This means the firm prioritizes its customers and customizes communications accordingly.

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Share of Customer

Its easier and less expensive to keep a current customer than it is to acquire a new one.

Many firms look to increase share of customer, instead of share of market.

Share of customer is the percentage of a given customer’s purchases in a category over time

Enables company to grow sales and profits at a lower cost, relative to new customer acquisition

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Lecture Notes:

Because it is always easier and less expensive to keep an existing customer than to get a new one, CRM firms try to increase their share of customer, not share of market; this is the percentage of an individual customer’s purchase of a product over time that is the same brand.

For example, if Amazon’s database records indicate that a given patron has purchased three books by a certain author, the CRM aspect of this system would try to increase share of customer by automatically sending an email to that individual offering him or her the opportunity to preorder a new book which has been written by the author, but not yet released.

If the firm can get the consumer to buy additional books from favored authors, it has increased its share of customer. This is where CRM’s ability to customize and personalize marketing promotions to individual consumers can be helpful.

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Customer Equity and Lifetime Value

Lifetime value of a customer is how much profit a firm will make on a customer

Customer equity is financial value of a customer relationship

Takes into account monetary investments to acquire and maintain relationship

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LVC Tool

Discussion Note:

To better illustrate the lifetime value of the customer concept, instructors can visit the link provided on this slide during class, or create an assignment that makes use of this resource (http://hbsp.harvard.edu/multimedia/flashtools/cltv/index.html).

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Customer Prioritization

Not all customers are equal … at least, not in terms of profitability!

CRM systems enable marketers to identify priority customers and customize communications and special offers accordingly

For example, a firm may emphasize personal selling for contacting high-volume customers, while using direct mail or telemarketing to communicate to low-volume customers

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Lecture Notes:

Using a CRM approach, the organization prioritizes its customers and customizes its communications to them accordingly.

For example, some bank customers generate a lot of revenue because they pay interest on loans or credit cards, while others simply use

the bank as a convenient place to store a small amount of money and take out a little bit each week to buy beer.

Banks use CRM systems to generate a profile of each customer based on factors such as value, risk, attrition, and interest in buying new financial products. This automated approach helps marketers decide which current or potential customers it will target with certain communications or how much effort it will expend to retain an account—all the while cutting its costs by as much as a third.

For example, customers who patronize casinos frequently will receive more direct mail offers from the casino, and higher value perks or incentives in an attempt to entice them to stay, compared to the that which would be sent to casual or infrequent gamblers. Remember that 80 /20 rule . . .in a CRM world, 80% of the profits often come from 20% of the customers.

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CRM: Transforming Customers into Corporate Assets

CRM leverages database technologies to customize customer interactions based on:

Share of customer

Lifetime value

Customer equity

Customer prioritization

Are their limitations, or even dangers, to viewing customers as financial assets?

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Lecture Note:

Summary slide for Chapter 5, Learning Objective 1

Discussion Note:

Insights from CRM are largely based on transactional data that reflects past behaviors of current customers.

What about non-customers?

What if attitudes underlying behaviors change in ways that aren’t picked up in customer transactional data?

What about the human side of the relationship? Trust? Commitment?

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Big Data: Terabytes Rule

Big data is a popular term to describe the exponential growth of structured and unstructured data

Internet data can be hard to analyze using traditional approaches

Internet of Things

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Lecture Notes:

As more consumer experiences shift into the digital space and new means of connecting and interacting with both individuals and corporations becomes

possible and widely accepted, the amount of data available to marketers is increasing exponentially. According to SAS, a leading provider of data analytics software, “Big Data refers to the ever-increasing volume, velocity, variety, variability and complexity of information.”

Each action you take online leaves a digital imprint, and all of those imprints have the potential to yield valuable insights for a wide range of stakeholders within society.

As new technologies continue to enhance the ways we connect to people, machines, and organizations, the volume and complexity of Big Data will continue to increase.

The Internet of Things is a term that is increasingly used in articles and stories on technology trends to describe how everyday objects (e.g., cars, refrigerators, etc.) are connected to the Internet and in turn are able to communicate information throughout an interconnected system.14 Areas that would become part of this network include medical devices, cars, toys, video games—the list goes on and on.

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Insights from Big Data

Big Data can provide competitive advantages in three main areas:

Identifying new opportunities

Transforming insights into better products

Delivering timely information more efficiently

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Lecture Notes:

Marketers can create competitive advantages based upon their use and analysis of Big Data through three main mechanisms:

Identifying new opportunities through analytics that yield greater return on marketing investment

Transforming insights into products and services that are better aligned with desires of consumers

Delivering communications on products and services to the marketplace more efficiently and effectively

These insights can enhance firm profits, but may also benefit society as a whole (see next slide on Google Flu)

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Big Data Predicts Infectious Disease

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Discussion Note:

Students may have heard of You may have heard of Google Flu, which maps possible levels of flu activity around the world in real time—all based on search terms. You search for “flu symptoms,” and Google assumes that you or someone you know is probably sick. Google then combines this information with other, similar searches in your area and, bam, your location gets added to the map as a known flu outbreak.

Unfortunately, it turns out that Google Flu isn’t all that accurate, mainly because people who think they have the flu and search for it actually tend to have something else.

Enter BioMosaics. The company combines airline records and disease reports with demographic data to help public health official visualize health risks, such as West Nile virus or SARS.

With this information, health officials can then deploy preventive measures where they’re most needed, be it cities, counties, or even hospitals.

For example, a cholera epidemic broke out after a major earthquake in Haiti in 2010. BioMosaics was able to show where Haitian-born residents in the U.S. were most likely to live, and, when combined with air and sea routes to and from Haiti, it could pinpoint where cholera outbreaks were possible in the U.S., making preventive measures possible.

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Big Data Creation, Sources, and Usage

Millions of pieces of information that make up Big Data originate from two source categories:

Direct path

Indirect path

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Lecture Notes:

The millions of pieces of information that make up Big Data originate from both direct and indirect paths. Here are two examples to illustrate how this works:

1. Direct path: You shop for a car online and see a model that you like. You submit a request for information form in which you supply personal information, including features of the car that appeal to you. That information is stored in the car dealership’s database, and a salesperson pulls it up later on before she contacts you about the car.

2. Indirect path: On the other hand, data creation can be a by-product of another action. A company that uses Big Data might know, for example, that consumers who purchase green detergent products, register as Democrats, and hold college degrees are more likely than average to purchase a hybrid vehicle. A person who fits this profile might receive a communication about a Honda Prius or other hybrid car even though he or she has not (yet) specifically requested information about these vehicles.

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Figure 5.2: Sources of Big Data for Marketers

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Lecture Note:

Big Data can come from many sources. These sources can be both within and outside of the organization and created and compiled from different groups.

The following slides provide more detail on these various sources.

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Social Media Sources

Web scraping

Sentiment analysis

Measuring brand attitude by assessing the context or emotion of online comments

Brand mapping

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Lecture Notes:

With an increasing array of social media sites that boast large number of consumers interacting with each other, with brands, and with other entities, a wealth of information is being produced about how individuals feel about products and just about everything else in their lives. It is not uncommon today

for consumers to either praise or condemn a product online.

Hint: If your brand’s name appears a lot of time with terms like “awful” or “sucks,” you probably have a problem.

Many companies engage in Web scraping, using computer software known as web crawlers to extract large amounts of data from websites.

Sentiment analysis is a process by which analysts seek to identify changes in customers’ attitude toward a brand by assessing the context or emotion of comments provided.

Discussion Note:

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Nielsen Brand Association Map

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Discussion Notes:

Nielsen’s BAM (Brand Association Maps) analyzes online consumer conversations and plots the words and phrases most commonly associated with a given brand.

The closer a word appears to the map’s center, the stronger the association.

Similarly, the proximity of words to each other reflects the strength of their relationships in the online posts.

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Figure 5.3: Corporate IT Data Sources

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Lecture Notes:

Corporate sources that live within the organization might include CRM databases, back-end websites, Web analytic databases (e.g., Google Analytics), enterprise resource planning databases, and even accounting-related databases.

While each of these and more can contain a treasure trove of information on an organization’s consumers, unfortunately these systems live in departmental “silos”; one group in the company may not share this information with others in the firm, so each group gets only an incomplete picture of its customers.

Marketing needs to be the function within the organization that cuts across these groups in order to mine these databases and connect the dots.

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Government and NGO Data

Increased types and amounts of government-generated data are accessible to enterprising marketers.

U.S. Census

Index of Economic Freedom

Bureau of Transportation Statistics

Recalls.gov

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Lecture Notes:

Provided by the government and non-governmental organizations.

These types of data could be most anything from extracted U.S. Census results to data on the economic conditions in developing countries that allow marketers to better understand the demographics of consumers at home and the opportunities for global expansion.

Discussion Notes:

Slide provides links to various governmental and NGO-based data sources that may be used by marketers to gain a better understanding of current and prospective markets.

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Commercial Entities

Many companies today collect data in large quantities to sell to other organizations

Credit card purchase data

Supermarket scanner data

Data sold in aggregate form

May be a primary or secondary source of revenue for the firm

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Discussion Notes:

Many companies today collect data in large quantities to sell to organizations that can derive value from them.

For some provider firms, this activity is their primary source of revenue; for others, it is a nice additional source of revenue over and above their principal business activities.

The data are sold in aggregated form so that it’s not possible to identify the actions of a specific consumer, but scanner data still provide extremely useful information to both manufacturers and retailers about how much shoppers buy in different categories and which brands they choose.

Discussion Note:

It may surprise students to know that many credit card companies, such as American Express and MasterCard, sell purchase data to advertisers so that they can better target their ads. Supermarkets like Safeway and Krogers’ have for many years sold scanner data—data derived from all those items that are scanned at the cash register when you check out with your loyalty card (which just happens to have your demographic profile information in its record!).

How do students feel about their data being sold to other firms in aggregate form, even if their anonymity is preserved?

If students view this practice negatively, ask them whether they would be willing to trade off this information in order to receive added customer benefits?

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Partner Databases

Two-way information exchange between purchasing organization and suppliers

Provides benefits to buyers and sellers

Real-time demand signals

Replace inventory with information

Fewer stock-outs

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Lecture Notes:

Many firms today have adopted a channel partner model in which there is a two-way exchange of information between purchasing organizations and their vendors through shared information technology systems.

If you’re the producer of a product that is sold by a large retailer such as Walmart, think about the information and insights you could gain from access to the consumer information that a key retailer may gathers from its interactions with shoppers in its stores.

Discussion Note:

Walmart in particular is already well known for employing this approach through its vendor management system known as Retail Link, which provides real-time purchase data to suppliers, making it possible for them to track purchase data for their products in real time.

Vendors are able manage the process of replenishment so that they can ensure that their products are available for consumers exactly when and where they need them.

For marketers, this provides a valuable source of purchase data in real time that they can use to analyze purchase patterns within different Walmart locations.

Walmart saves on the costs of having to manage this process themselves.

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Data Mining

The biggest data challenge for many firms is determining what to do with it all!

Data mining refers to process by which analysts sift through Big Data to identify unique patters of behavior

Data warehouses

Data brokers

Reality mining

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Lecture Notes:

Big Data can easily exacerbate the problem of information overload, in which the marketer is buried in so much data that it becomes nearly paralyzing to decide which of it provides useful information and which does not.

Most marketing information systems include internal customer transaction databases, and many include acquired databases. Often, these databases are extremely large.

To take advantage of the massive amount of data now available, a sophisticated analysis technique called data mining is now a priority for many firms. This refers to a process in which analysts sift through Big Data (often measured in terabytes) to identify unique patterns of behavior among different customer groups.

In a marketing context, data mining uses computers that run sophisticated programs so that analysts can combine different databases to understand relationships among buying decisions, exposure to marketing messages, and in-store promotions.

These operations are so complex that often companies need to build a data warehouse (which can cost more than $10 million) simply to store and process the data.

Data brokers specialize in collecting and selling personal information about consumers.

Discussion Note:

Cellular operators have begun signing deals with business partners who are eager to market products based on specific phone users’ location and calling habits.

Such reality mining is the collection and analysis of machine-sensed environmental data pertaining to human social behavior with the goal of identifying predictable patterns of behavior. Reality mining was declared to be one of the “10 technologies most likely to change the way we live” by Technology Review Magazine.

If reality mining catches on, phone companies’ calling records will become precious assets. And these records will only grow in value as customers use their phones to browse the Web, purchase products, pay bills, and update their Facebook pages.

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Figure 5.4: Structured and Unstructured Data Examples

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Discussion Notes:

Data derived from data mining efforts can be broadly classified as structured and unstructured data.

Structured data are data that (1) are typically numeric or categorical; (2) can be organized and formatted in way that is easy for computers to read, organize, and understand; and (3) can be inserted into a database in a seamless fashion.

Conversely, unstructured data are often qualitative in nature and do not possess all three of these properties.

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Unstructured Data

Data analysts have traditionally focused on structured data

More readily obtainable

Computers today can easily analyze a large number of data points.

Deriving meaning from unstructured data is more difficult, but potentially more valuable

New technologies are making this process easier

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Lecture Notes:

Unstructured data contain nonnumeric information that is typically formatted in way that is meant for human eyes and is not easily understood by computers.

A good example of unstructured data is the body of an e-mail message. The e-mail carries a lot of meaning to a human but poses a greater challenge for a machine to understand or organize.

Discussion Notes:

Have students look up the Facebook/Twitter/Instagram profiles of popular consumer brands, like M&M’s, or popular retailers, like H&M.

Discuss the quantity and types of information that is available through these sources. Note that relevant comments on these brands may also be bound on the pages of consumers and even non-consumers.

Ask the student to:

Imagine that you are the social media manager for a consumer brand.

You are fortunate to have a lot of likes and followers and a high level of interaction as well, but you believe that this is only the tip of the iceberg.

You know that all of these comments from your customers could be a source of a lot of great information—the only problem is that there are thousands of them flooding in, and you’re only one person.

How could you possibly find the time to effectively analyze their contents and discern valuable patterns from all of this information?

Significant advances in data-analytic technologies make the process of unstructured data analysis easier through the development of computer logic that can search through and extract patterns from large amounts of textual data. It also makes it more cost effective through the use of automated processes as opposed to manual intervention.

These types of technologies give unstructured data a “structure,” enabling it to be shared and leveraged when combining it with data sources held elsewhere in an organization.

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Ethical/Sustainable Decisions in the Real World

Data brokers are companies that collect and sell personal information about consumers, including:

Religion and ethnicity

User names

Income

Medication they take, and more …

Acxiom acknowledges it has on average 1,500 pieces of information on 200 million+ Americans

Should it be legal for companies to collect and sell your personal information without your knowledge?

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Discussion Notes:

Instructor should first ask students:

Have you ever traded personal information online in return for access to “free” reports or other information sources?

Do you always read carefully through privacy disclosure statements when registering on a new website or social media platform? Or do you simply click on “agree?”

The instructor should then follow up with the question on the slide.

While students are likely to disagree with companies selling their personal information, they should realize that the online behaviors of individuals like themselves is what makes this possible (and even lucrative!).

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Data Scientists: Transforming Big Data into Winning Insights

Being able to transform data into insights is a challenging proposition!

Requires understanding of advanced analytics as well as way companies interact with consumers

Data scientists search through disparate data sources to discover hidden insights

Advanced degrees, often Ph.D.’s

Six-figure starting salaries

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Lecture Notes:

A data scientist is someone who searches through multiple, disparate data sources in order to discover hidden insights that will provide

a competitive advantage.

These individuals frequently have PhDs, often command six-figure starting salaries (according to Glassdoor.com, the median salary as of 2014 was

$115,000), and are becoming an increasingly important source of competitive advantage for organizations that want to leverage Big Data.

Traditional data analysts often looked at one data source, whereas data scientists typically look at multiple sources of data across the organization

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Figure 5.5: Uses of Data Mining

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Lecture Notes:

Data mining has four important applications for marketers:

1. Customer acquisition: Many firms include demographic and other information about customers in their database. For example, a number of supermarkets offer weekly special price discounts for store “members.” These stores’ membership application forms require that customers indicate their age, family size, address, and so on. With this information, the supermarket determines which of its current customers respond best to specific offers and then sends the same offers to noncustomers who share the same demographic characteristics.

2. Customer retention and loyalty: The firm identifies big-spending customers and then targets them for special offers and inducements other customers won’t receive. Keeping the most profitable customers coming back is a great way to build business success because—here we go again!—keeping good customers is less expensive than constantly finding new ones.

3. Customer abandonment: Strange as it may sound, sometimes a firm wants customers to take their business elsewhere because servicing them actually costs the firm too much. Today, this is popularly called “firing a customer.” For example, a department store may use data mining to identify unprofitable customers—those who don’t spend enough or who return most of what they buy. For example, data mining has allowed Sprint to famously identify its customers as “the good, the bad, and the ugly.”

4. Market basket analysis: Develops focused promotional strategies based on the records of which customers have bought certain products. Hewlett-Packard, for example, carefully analyzes which of its customers recently bought new printers and targets them to receive e-mails about specials on ink cartridges and tips to get the most out of their machines.

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Big Data: Summary

Mining of Big Data can provide marketers with valuable new insights …

But also presents difficult new challenges!

Technological challenges

Analytic challenges

Ethical challenges

Does knowing how companies seek to use personal information change your perspective of marketing?

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Lecture Notes:

Summary slide for Chapter 5, Learning Objective 2.

Discussion Notes:

Instructor should ask the extent to which students were aware of data mining practices by marketers.

How does increased knowledge of these topics and business practices impact their view of marketing?

How will increased knowledge of these topics and business practices influence their own online consumption behaviors?

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Marketing Analytics

“Half the money I spend on advertising is wasted – I just don’t know which half.”

– John Wannamaker,

19th century Philadelphia Retailer

Marketing analytics comprises technologies and processes that enable marketers to collect, measure, analyze, and assess marketing effectiveness

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Lecture Note:

The famous Wannamaker quote highlights the need to be able to tie specific actions in advertising to measureable results.

Increased emphasis on marketing analytics enables marketers to increase accountability and justify investments into marketing activities.

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Connecting Digital Channels to Marketing Analytics

Marketers have long faced challenges in determining campaign and channel effectiveness

Digital marketing has become an increasingly important element of the marketer’s toolbox

More and more people spending increasing time online

Much easier to track consumer behavior in response to digital marketing actions

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Figure 5.6: Major Digital Marketing Channels

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Lecture Notes:

The options for investment in digital marketing channels are diverse with consumers spending large amounts of time on traditional websites, social networking sites, and search engines, to name a few areas.

Digital marketing channels are typically broken up into four main categories. Within these, there are multiple types of marketing efforts and campaigns that marketers can develop and track.

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Comparing Value of Digital Marketing Investments

Cost-per-click

Advertiser is charged only when user clicks on ad

More expensive, requires greater interaction

Cost-per-impression

Advertiser is charged each time ad shows up on user page

Less expensive, but not as easy to measure

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Lecture Notes:

For marketers, investments in digital marketing are especially attractive because their cost is often directly tied to specific actions users take.

For instance, Google’s paid search ads can be purchased or bid on based on a cost per click in which the cost of the advertisement is charged only each time an individual clicks on the advertisement and is directed to the Web page that the marketer placed within the advertisement. This method of charging for advertisements is common for online vendors of advertisement space.

Other methods of purchasing advertisements digitally include cost per impression, in which the cost of the advertisement is charged each time the advertisement shows up on a page that the user views.

Companies that sell online advertising space commonly use both of these methods of charging for advertisements. Cost-per-click purchases of advertisements are typically more expensive, as they demand a higher level of interaction from the user. Cost-per-impression ad purchases can provide a good value. However, the cost-per-impression structure requires a greater leap of faith because it’s not so easy to measure the value of an impression (or view of an advertisement).

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Predictive Analytics

Up to now, discussion of marketing analytics has focused on validating prior investments

Focus on understanding current performance

Predictive analytics use large quantities of data to more accurately predict future outcomes

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Lecture Notes:

Up to this point, we’ve looked how marketing analytics can be leveraged to better understand how current marketing channels and initiatives are performing.

Another intriguing area for any marketer is the ability to actually predict the future and thus better understand the value of their marketing campaigns even before they implement them.

Predictive analytics techniques use large quantities of data and variables that the analysts know relate to one another to more accurately predict specific future outcomes

Discussion Note:

Vodafone Netherlands is the second-largest mobile carrier in the Netherlands. As the organization’s senior information architect for business intelligence noted, “We have a reasonably large number of customers, a limited marketing budget, and the need to understand how to apply the money effectively and get the best results.”

Vodafone had a wealth of information and wanted to have the capabilities to identify opportunities in order to more effectively predict consumer

behavior and better tailor service offerings to consumers based on the information.

One way that Vodafone was able to create value from predictive analytics was through the understanding of winter roaming patterns and, in particular, which of their customers were most likely to go skiing.

Through the firm’s analysis, they were better able to identify and predict which customers would fall within the category of going skiing in the winter and

target them exclusively with a campaign that was tailored to offer great value for winter roamers.

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Marketing Metrics and Predictive Analytics

Marketing metrics enable firms to assess performance of current initiatives.

Predictive analytics is a “crystal ball” through which marketers can predict the success of future initiatives.

What factors might a bank card issuer use to help predict student customers’ spring break location choices?

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Lecture Notes:

Summary slide for Chapter 5, Learning Objective 3.

Discussion Notes:

What types of purchase patterns and behaviors (available to banks and credit card issuers) are likely to be associated with whether or not a student goes away for spring break? Are there indicators of preferred locations? How could marketers use this information to come up with targeted offers and customized services?

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Metrics for Marketing Control

Marketing control means the ability to identify deviations in expected performance – both positive and negative – as soon as they occur

Enable marketers to adjust their actions before greater losses or inefficiencies are accumulated

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Lecture Notes:

In a data rich and data-driven world, organizations have the ability to gain a more detailed understanding than ever before of what’s going on both inside and outside their operations.

For marketers, this means having the ability to show more clearly a return on their various investments and to use this knowledge to develop and execute marketing plans and strategies.

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Key Marketing Metrics

Click-through rate

(Click-throughs /Impressions) X 100

Conversion rate

# of goal achievements/# of website visitors

Cost per order

Advertising costs/Orders

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Real People, Real Choices: Decision Made at Teradata

Lisa chose option 2

Implementation: Lisa’s team launched the cloud product at a major industry trade show, even as they applied data from their research to the creative development of the Teradata brand.

Measuring Success: The market took notice . One major analyst firm welcomed the new approach so much that it renamed the annual category research report “Integrated Marketing Management.”

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Copyright Information

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America

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