BUS607 Study Guide

Navigating this Study Guide

Study Guide Structure

In this study guide, the sections in each unit (1a., 1b., etc.) are the learning outcomes of that unit. 

Beneath each learning outcome are:

• questions for you to answer independently;
• a brief summary of the learning outcome topic; and
• and resources related to the learning outcome. 

At the end of each unit, there is also a list of suggested vocabulary words.

How to Use this Study Guide

1. Review the entire course by reading the learning outcome summaries and suggested resources.
2. Test your understanding of the course information by answering questions related to each unit learning outcome and defining and memorizing the vocabulary words at the end of each unit.

By clicking on the gear button on the top right of the screen, you can print the study guide. Then you can make notes, highlight, and underline as you work.

Through reviewing and completing the study guide, you should gain a deeper understanding of each learning outcome in the course and be better prepared for the final exam!

Unit 1: Introduction to Data-Driven Decision-Making

1a. Explain what data-driven information is and how it assists in business decision-making

• What is the four-step method for guiding data-driven decision-making (DDDM)?
• What are the 5-steps in the data-driven decision-making process?
• What is big data analytics, and how is it used to help make better decisions?

Data-driven decision-making is collecting data, extracting patterns and facts from that data, and using those facts to guide decisions. Utilizing data in decision-making is superior to using a person's, group's, or organization's intuition and can result in making better decisions, generating new business insights, and identifying new business opportunities.

To begin making data-driven decisions, the organization must start with a clear objective as to what they are trying to accomplish. It could be increased sales, reduced manufacturing costs, improved process efficiency, or any number of measurable outcomes. Once the objective(s) have been determined, the organization would gather and analyze the available data to make decisions from. After the decision is implemented, it is important to determine if the results were validated by the analysis.

The clear objective needs to be a well-defined business challenge or query, such as determining if there is a relationship between x and y. The organization would then determine its data needs. This data has to be very robust and provide outcomes over a variety of scenarios, over a consistent period of time. The results create the knowledge the organization needs to make its data-driven decision on how to proceed in the future.

Big data is data that is so large or complex that traditional data processing methods are inadequate. How it is used differs based on the situation, but in all cases, it has the following characteristics: volume, which is the amount of data; velocity, which is the speed of data delivered; and variety, which is the different types of data. When properly harnessed, big data can provide insights that a typical organization's internal data may not.

To review, see Data-Driven Decisions.

1b. Examine the steps in the data-driven decision-making process and how each step is effectively executed

• What are the differences between big data produced intentionally or unintentionally and that produced by humans and by machines?
• What are the possible outcomes of the decision-making process?
• What are the categories of decision-making styles?
• What are the primary decision-making approaches?

DDDM can be utilized to uncover hidden patterns, unexpected relationships, and market trends or reveal preferences that may have been difficult to discover previously. Armed with this information, organizations can make better decisions about production, financing, marketing, and sales than they could before.

Decision-making boils down to a choice between alternatives against a defined set of selection criteria. There are usually costs versus benefits, advantages versus disadvantages, and alignment with preferences. The more factors to consider, the more challenging the decision will be. Adding time limits and personal emotions further complicates the process. Utilizing data to help optimize the alternatives informs the decision maker with much more support to make their decision.

Decision-making styles can generally be defined into three categories:

1. Psychological, which is based on needs, desires, preferences, and/or values of the decision-maker.
2. Cognitive, which involves an integrated feedback system between the decision-maker and the environment's reaction to those decisions. This style includes iterative cycles and regular reassessments to measure the impacts of the decision.
3. Normative, which derives decisions based on the communication and sharing of logic within an organization's normative construct. The decision has to fit in and support the organization's mission and goals.

There are three primary decision-making approaches:

1. Avoidance is the absence of making a decision altogether. This can happen when there is insufficient information to reach a decision, the negative impact could outweigh the benefits, no immediate need to make a decision, or if the person considering the alternatives does not have the decision-making authority.
2. Problem-solving is when the activities result in a satisfactory solution being reached. This typically reaches a definite goal from a present condition.
3. Problem-seeking occurs when the problem-solving process itself questions the focus or scope of the original problem itself. The problem could be ill-defined, too large, too small, or missing a key component to reach a satisfactory solution.

To review, see Decision-Making in Management.

1c. Analyze how data-driven decision-making techniques across business domains guide decision-making to create new opportunities

• What are some of the ways digitalization has changed the way businesses operate and make decisions?
• What is business intelligence, and how does it impact businesses today?
• What are the 5 processes that comprise business intelligence architecture?

Digitalization is capturing all areas of life and the typical management task of decision-making. Businesses will begin moving toward becoming more autonomous in their decision-making using machines and cyber systems. The important step toward autonomous decisions or decision support (cyber systems will prepare a decision, but finally executed by a human) will be the next development step for decision making.

This has led to a rapid increase in data volumes in companies which has meant that momentous and comprehensive information gathering is barely possible by manual means. Business intelligence solutions help facilitate this by providing tools and appropriate technologies to assist with the collection, integration, storage, editing, and analysis of existing data.

Business intelligence (BI) is the concepts and methods that support decision-making through information analysis, delivery, and processing. In many cases, BI is considered the data analysis, reporting, and query tools that help users derive valuable information.

The following processes comprise BI architecture:

1. Data collection – the operational systems that provide the required BI data
2. Data integration – involves the ETL (extract-transform-load) functions needed to transfer the data from the original source into a format compatible with other data stored in the data warehouse
3. Data storage – the data warehouse or data mart in which the data is stored
4. Data processing – includes the concepts and tools utilized in the evaluation and analysis of the data
5. Data presentation – is the process of preparing and presenting the analysis results

The following figure provides an overview of the individual processes and the components which belong to each process step.

To review, see The Effects of Using Business Intelligence Systems.

Unit 1 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• avoidance
• big data analytics
• business intelligence
• business intelligence architecture
• cognitive
• data collection
• data integration
• data presentation
• data processing
• data science
• data storages
• data-driven decision-making
• data-driven decisions
• decision-making approaches
• decision-making process
• decision-making styles
• data variety
• data velocity
• data volume
• digitalization
• measurable
• metadata
• normative
• open data
• problem-seeking
• problem-solving
• psychological
• validation

Unit 2: Transforming to a Data-Driven Decision-Making Enterprise

2a. Differentiate between the continuums of the data-driven decision-making implementation process and recognize the milestones that must be completed along each continuum

• What does the term analytics mindset mean?
• What are the 3 continuums in the Data-Driven Decision-Making Change Model?
• Why should each stage of the continuums of the Data-Driven Decision-Making Change Model be implemented at the same time?

To make informed decisions based on data, managers must have an analytics mindset that enables them to understand how the data is derived, interpreted, and communicated. Therefore, they need to develop an analytical skill set that helps them know what makes sense and understand where analytics adds value. This enables them to be confident enough to ask pertinent questions of the analyst. Read this article to learn the benefits of managers developing an analytical mindset.

The three continuums in the DDDM change model are:

1. Organization / people
2. Data / technology
3. Process / workflows

All three of these continuums must be completed in stages for a successful DDDM implementation. If a continuum surpasses or lags behind the others, the implementation may not have the proper processes in place from the other continuums to support effective decision-making.

Implementing DDDM requires more than just data and technology. It requires 2 other continuums, along with data and technology, to be successful.

To review, see Data-Driven Decision-Making Change Model.

2b. Evaluate the critical success factors for building an analytics-focused organization

• What are the six critical success factors for implementing DDDM?
• Why are these success factors important for DDDM?
• What are the differences between a business problem that must be measurable and the operation repeatable over a specific time period?

There are a number of critical success factors in order for the DDDM program to be effective and have the proper impact on the organization. These include:

1. Executive support for the mandates required for the analytics, sponsors, and champions
2. A well-defined business challenge or query
3. Lots of data from internal and sometimes external sources
4. The right team and skillsets supporting the initiative, including a champion, technical resources, and subject matter expert
5. Integration into the organization's overall operations and processes to capture the right information
6. The ability to track results and update models to determine if predicted outcomes were supported by the analysis

For a business problem to be well-defined, it must be measurable and the operation repeatable over a specific time period. To be measurable, the results must be able to be measured or counted to determine if the prediction was accurate. A repeatable operation requires that the chosen attribute to measure must occur regularly and have a repeatable pattern. A specific time period requires the variable to have a specific beginning and end, (week, month, quarter, etc.).

To review, see Critical Success Factors.

2c. Examine how management uses data outcomes to guide organizational decision-making

• What are the differences between the 4 different analytical models to frame tactical and strategic questions?
• What are the various analytics domains that could be deployed in an organization?

Analytics can be used to assess and visualize decisions, describe the implications of historical data, predict and model future expectations, and optimize internal processes. The ability to navigate and derive value from big data is critical to successful organizational management.

There are four primary tactical and strategic analytical models managers may use to frame analytics:

1. Descriptive analytics: what happened
2. Decision / diagnostic analytics: why it happened
3. Predictive analytics: what will happen
4. Prescriptive analytics: how we can make it happen

As the organization progresses from one stage to another, the benefits of the analysis increase while the implementation complexity increases. The focus also shifts from what happened in the past to what we can do to impact the future. Stage 5, cognitive analytics, includes artificial intelligence analytics.

Analytics can impact nearly every domain in an organization, including finance, marketing, talent, customers, risk management, transportation, and sales.

To review, see Developing an Analytical Mindset.

Unit 2 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• analytical model
• analytics
• analytics mindset
• business challenge
• change model
• cognitive analytics
• continuum
• critical success factors
• data / technology
• descriptive analytics
• diagnostic analytics
• measurable
• organizational / people
• predictive analytics
• prescriptive analytics
• process / workflow
• repeatable
• specific time period

Unit 3: The Role of Leadership

3a. Explain why leadership is considered the leading critical success factor in becoming a data-driven decision-making organization

• What are the two forces that are defining the next generation of leaders?
• What are 3 or more things the leader of the 21st century will need to be for success?

The world of business is ever-changing, which brings new challenges for leaders. To continue to be effective, they will have to learn and embrace new leadership models based on globalization, decentralization, and diversity needs.

Effective leaders in the 21st century will have to be many things, including:

• Strategic opportunists, to find strategic opportunities before competitors
• Globally aware, to face the significant foreign competition
• Managing a decentralized organization, to meet the environmental demands for organizational speed, flexibility, learning, and leanness increase
• Sensitivity to diversity, as the shift to a more diverse workforce continues
• Interpersonal competence, for awareness and sensitivity to the multicultural expectations of a diverse workforce
• Organizational community builders, to help members develop a sense of ownership of the organization and its mission

To review, see Leadership Needs in the 21st Century.

3b. Contrast the role of leadership with other success factors, such as a well-defined business challenge, the right personnel, or integrating data findings into the organization

• What are the four cultural requirements leaders must embrace to become a big-data-enabled organization?
• What are the four distinct leadership roles that are taking on the challenges of navigating big data and analytics for organizations?

Leadership is a critical success factor in many disciplines, including project management, six sigma, and data analytics. Leaders across a variety of industries realize the benefits of data analytics in their organization's decision-making support. Many realize that the greatest challenge is not the technology or even the personnel, but a lack of leadership.

Becoming a big-data-enabled organization requires a culture of empowerment, trust, transparency, and inquiry. These qualities enable analytics to become pervasive throughout the organization. In most transformations, leadership outranks any technical or personnel challenges the organization may face.

Leadership is the biggest indicator of analytics success. Leaders need to position and promote the use of data-driven decisions and analytics as critical to the success of the organization. The four leadership roles that are needed to take on the challenges of implementing big-data analytics in an organization include:

1. Chief Data Officer: the data owner and architect who sets the data definitions and strategies
2. Chief Analytics Officer: has a board-level realm and responsibilities to maintain forward-thinking progress
3. Data Scientists: provide high technical skills and are proficient in their understanding of the business
4. Data Manager: serves as the organizer and architect of the data

To review, see Embracing Big Data and Data Analytics.

3c. Summarize what comprises good leadership in implementing data-driven decision-making

• What are some of the common traits that typically identify effective leaders?
• How are Contrast the differences between task-centered and employee-centered styles of leadership behavior different?
• How are autocratic, democratic, and laissez-faire styles of leadership different?

People often evaluate effective leaders based on personality or leadership traits. These traits can vary from leader to leader. There are many common characteristics that are indicators of effective leadership. There are also differences between task-centered versus employee-centered leadership behavior and the various leadership styles.

Some of the more common personality traits companies look for in making their hiring and promotion decisions include:

Most behavioral leadership styles fall within two categories:

A leader's style can often be identified in the way they make decisions, especially in the degree to which they seek employee involvement. This aspect of leadership is divided into a spectrum of three broad categories:

• An Autocratic leader makes decisions without any significant employee involvement
• A Democratic leadership approach significantly involves the employee team in the process
• A Laissez-Faire approach is very hands-off, where the employees make decisions on their own.

To review, see Effective Leaders.

Unit 3 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• autocratic
• business knowledge
• chief analytics officer
• chief data officer
• data manager
• data scientist
• decentralized organization
• democratic
• desire to lead
• diversity
• drive
• employee-centered
• empowerment
• extraversion
• global awareness
• honesty
• inquiry
• integrity
• intelligence
• interpersonal competence
• laissez-faire
• leadership
• open-mindedness
• organizational community
• self-confidence
• self-esteem
• strategic opportunist
• task-centered
• transparency
• trust

Unit 4: Types of Data

4a. Distinguish between different types of quantitative and qualitative data

• How are quantitative and qualitative data different?
• How and when can you use quantitative versus qualitative data?

Data is at the heart of DDDM. Numerous types of data and sources must be included in a robust DDDM initiative. Every type of data must be extracted from a source, transformed into a standard format acceptable for the data warehouse, and then made available for analysis. Once data is prepared, analytics are deployed to create both hindsight and foresight analytics. You will need to understand these types of analytics and how they relate to successful DDDM initiatives.

Quantitative data is based on counting or measuring the attributes of a population. They are always numbers that specify weight, height, length, population, etc. Quantitative data can be discrete, resulting from counting with only certain numerical values, or continuous, resulting from measuring with a variety of values.

Qualitative data is based on categories or descriptions of a population. They are usually words or letters, such as color, street name, automobile name, etc. Qualitative data includes the color of hair, year in college, month, etc.

Quantitative and qualitative data can both be used to summarize frequency distributions. Since quantitative data are always numeric, it is more often utilized for descriptive (summary) statistics than qualitative data. Quantitative data can also be used to inform a broader understanding of a population through inferential statistics.

To review, see Qualitative versus Quantitative Data and Characteristics of Qualitative and Quantitative Data.

4b. Analyze the role of big data

• What are the benefits of utilizing big data in new product development?
• How is big data different from other types of data or data processing?

Big Data refers to data that is so large, fast, or complex that it's difficult or impossible to process using traditional methods. It contains extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Technology has turned the average customer into a robust source of a variety of data. The magnitude, rapidity, and richness of the data generated have had a profound effect on new product development (NPD). Three NPD phases, in particular, can be affected by big data, including:

1. Idea and concept generation, through the collection of large amounts of customer feedback and suggestions
2. Design and engineering, through market research input from customers on their needs, wants, and opinions
3. Testing and launch, through timely feedback on new and improved product designs and applications

To review, see Big Data in New Product Development.

4c. Compare different types of analytics and their role in building a data-driven decision-making enterprise

• What are the five stages of analytics development, and what question can be answered at each stage?
• How do the stages of analytics development integrate into the DDDM Change Model?

There are four primary stages of analytics development plus cognitive analytics for a total of five stages. A different question can be answered at each stage, with the early stages delivering hindsight and the later stages delivering foresight. The value of the results improves as an organization moves along the spectrum along with the complexity of implementation.

There are five stages of analytics development are:

1. Descriptive analytics
2. Decision / diagnostic analytics
3. Predictive analytics
4. Prescriptive analytics
5. Cognitive analytics

At each stage of analytics, certain questions can be answered.

These stages of analytics development can be implemented as an organization proceeds along the continuums of the DDDM Change Model. The less complex and past-focused stages can usually be implemented in the early phases, while the more complex and future-focused stages can usually be performed by organizations in the advanced phases of implementation.

To review, see Big Data Analytics and Examples of Analytical Development.

Unit 4 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• analytics development
• descriptive statistics
• frequency distributions
• inferential statistics
• qualitative data
• quantitative data

Unit 5: Deriving Data Insights

5a. Describe the mean, median, and mode of a set of data

• What are the differences between the mean, median, and mode of a data set?
• How do you differentiate between nominal, ordinal, interval, and ratio scales?

The 'center' or midpoint of a data set helps describe a location. The mean and median are the two most widely used measures of the data 'center'. The mean, also called the average, is the total values divided by the number of values. The median is the middle number that splits the ordered data set into two equal parts. It is used most often when there are extreme values or outliers in the data since it is not impacted by precise numerical values. Another measure of the center is the mode, which is the most frequent value. There can be more than one mode in a data set as long as the values have the same frequency.

Frequency is the number of times an event or a value occurs in a dataset. A frequency table lists each item and the number of times the item appears. The level of measurement is how a data set is measured and can vary with the type of data being analyzed. The four levels of measurement include:

1. Nominal scale, which is qualitative, includes categories such as colors, names, labels, etc.
2. Ordinal scale, which is similar to the nominal scale, but listed in an ordered fashion, like the top restaurants in a city or the best beaches in a country
3. Interval scale, which measures data that is in a definite order but does not necessarily have a starting point, like weather temperature
4. Ratio scale, which can be very informative since it has a 0 starting point and can be calculated and ordered

To review, see Frequency, Frequency Tables, and Levels of Measurement.

5b. Analyze data presented in frequency tables, frequency distributions, and graphics

• How do you differentiate between frequency, relative frequency, and cumulative relative frequency?
• How are frequency distributions utilized to analyze data?
• What are the criteria for selecting the best graphics to display data to a particular audience?

When organizing data, it is important to know how many times a value appears. Questions like, the number of hours students study or the percentage of families with multiple pets. Frequency (also called, absolute frequency), relative frequency, and cumulative relative frequency are measures that answer questions like these.

The absolute frequency is the number of times a value occurs in the data. The relative frequency is the ratio of the number of times a value occurs in the total number of values. The cumulative relative frequency is the summation of all of the relative frequencies and totals to 1 or 100%.

When displaying data to an audience, it's important to make the right choice to help them quickly understand the point being made. Some simple charts that can be used include:

• Line charts for comparing trends, multiple datasets over time, or correlations
• Area charts for comparing change over time from two or more variables
• Column charts for showing frequency distribution and comparing datasets
• Bar charts for ranking datasets or comparing datasets
• Pie charts for comparing datasets as percentages of a whole.

To review, see Frequency, Frequency Tables, and Levels of Measurement and Presenting Data.

5c. Analyze relative frequencies and the relationship with frequency tables

• How are relative frequencies different from absolute frequencies?
• What are some of the ways a frequency distribution can be displayed?
• What are the features of a histogram versus the features of a bar chart?

Frequency distributions are visual displays that organize and present frequency counts so that the information can be interpreted more easily. They can be shown as absolute frequencies or relative frequencies, such as proportions or percentages. A frequency distribution can be shown in a table or graph. Some common methods of showing frequency distributions include frequency tables, histograms, or bar charts.

A histogram displays the distribution of all observations in a quantitative dataset. It can be used for describing the shape, center, and spread to better understand the data distribution. The height of each column shows the frequency for the specific range of values. The columns are usually of equal width. The values of each column must be mutually exclusive (no spaces between columns). There should be no ambiguity in the x-axis label.

The columns in a bar chart represent categorical variables or discrete ungrouped numeric variables. It is primarily used to compare the frequency (count) of a category or characteristic against another. The bar height (vertical or horizontal) shows the frequency for each category or characteristic. The data distribution is not important since each column represents an individual category or characteristic. Therefore, gaps are included between each bar, and the bars can be arranged in any order without impacting the data.

To review, see The Statistical Language of Frequency Distribution and Frequency Tables.

5d. Interpret cumulative frequency distribution and explain its use in decision-making

• What are the differences between cumulative relative frequency and relative or absolute frequencies?
• What should the last entry in a cumulative distribution be equal to?

Cumulative relative frequency is the accumulation of the previous relative frequencies. To find the cumulative relative frequencies, add all the previous relative frequencies to the relative frequency for the current row. This distribution helps the analyst know all entries have been accounted for.

The absolute frequency is the number of times a value occurs in the data. The relative frequency is the ratio of the number of times a value occurs in the total number of values. The cumulative relative frequency is the summation of all of the relative frequencies and totals to 1 or 100%.

To review, see Frequency, Frequency Tables, and Levels of Measurement and The Statistical Language of Frequency Distribution.

Unit 5 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• absolute frequency
• area chart
• bar chart
• column chart
• cumulative relative frequency
• frequency
• frequency distributions
• frequency tables
• histogram
• interval scale
• level of measurement
• line chart
• mean
• median
• mode
• nominal scale
• ordinal scale
• pie chart
• ratio scale
• relative frequency

Unit 6: Creating Effective Visualizations

6a. Examine visualization best practices for different audiences

• How do visualizations make large amounts of data easier to understand?
• How do visualizations reveal data at several levels of detail from a broad overview to the fine structure?

Well-crafted data visualizations not only present data in easily understood images, but when done well, they enable the viewer to quickly perceive insights they may have missed if presented in summary tables and spreadsheets. A good data visualization does not only convert large amounts of data into images, but when done well, it engages the viewer and tells a story.

Well-crafted visualizations present complex ideas or results and communicate them with clarity, precision, and efficiency. Visualizations should:

• Show the data
• Have the viewer on the substance instead of the methodology
• Avoid distorting the data
• Present many numbers in a small space
• Make large data sets easier to understand
• Present the data at several levels of detail, from a high-level overview to a deep data dive

To review, see Data Visualization.

6b. Identify why creating effective visualizations is an iterative process

• Why are data visualizations essential for exploratory data analysis and data mining?
• How do presentation graphics and exploratory graphics differ?

With the advent of better software, faster processors, and cheaper memory, it has become easier to create and iterate visualizations. With this power comes responsibility, as it is very important to create good visualizations that clearly articulate the point the analyst is trying to make. Visualizations can be effective or ineffective, which can generate very strong feelings either way.

Data visualizations are useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining to check data quality and to improve an analyst's familiarity with the structure and features of the data before them.

Presentation graphics are usually a select number of graphics created for any number of people and need to be well-designed and well-created with an effective explanatory text, either verbally or textually. They are used to convey known or summarized information.

Exploratory graphics can include several graphics created for an individual such as yourself. They don't need to be perfect but provide alternate views and additional information.

To review, see Importance of Data Visualization.

6c. Explain how data visualizations can be used to tell stories

• Why is reducing the need for an audience to interpret the key to creating an effective presentation?
• How are linear, user-directed, parallel, and random-access storytelling different?

Reducing the need for the audience to interpret the findings in a visualization is the key to an effective presentation. This can be accomplished by the type of chart used or by highlighting key points through color choices.

Storytelling has been a useful tool to communicate information and knowledge over time. Using visualizations to tell a story with data helps make the information more concise and memorable. The most effective storytelling helps the audience reach the right conclusion and take the appropriate action.

The sequence or order of events makes a big difference in storytelling and refers to the path the viewer is taken through in the visualization. Stories can be presented in a number of ways, including:

To review, see Presenting Data and Storytelling and Visualization.

Unit 6 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• clarity
• efficiency
• exploratory graphics
• linear storytelling
• parallel storytelling
• precision
• presentation graphics
• random access storytelling
• sequence
• storytelling
• user-directed path storytelling
• visualization

Unit 7: Database Marketing and Customer Relationship Management

7a. Differentiate between customer lifetime value (CLV) and customer relationship management (CRM)

• How does customer lifetime value differ from customer relationship management (CRM)?
• How is customer lifetime value used as a component of customer relationship management?
• What is the importance of customer lifetime value to customer relationship management?

Database marketing is a direct marketing technique that uses customer data to create direct communication.

Database marketing requires interaction with customers. This relationship is known as customer relationship management (CRM).

The lifetime value of a customer, also known as customer lifetime value (CLV), is the total amount of money a customer is expected to spend on a business' products or services during their lifetime. CLV is an important metric to measure customer relationship management (CRM). CLV reveals how well customers like your product or service. Decision-makers can make informed decisions and grow their business using CLV metrics.

CLV is an important component of CRM because it helps establish a value for each customer relationship. With this knowledge, the organization can determine where to invest its resources based on how valuable the customer is to the organization.

To review, see Customer Relationship Management and Customer Lifetime Value.

7b. Analyze how businesses use database marketing to improve CRM

• What are the top benefits associated with database marketing?
• How does customer relationship management (CRM) differ from database marketing?

Database marketing does more than build relationships with customers. It reduces marketing costs, increases profit margins, and improves product development. There are various customer database technologies utilized to build business-to-customer relationships.

The top benefits associated with database marketing include:

1. Improved customer understanding
2. Improved operational efficiency through expense reductions
3. Improved revenues through better performance
4. Greater visibility of business performance
5. Greater confidence and predictability

CRM is a customer-centric approach to business, which requires a deep understanding of the customers' needs and behavior. Database marketing is a subset of CRM that enables the CRM system to gather and analyze customer contacts through all business functional areas.

To review, see The 5 Money-Making Benefits of Database Marketing and Customer Relationship Management.

7c. Evaluate the practical factors of database marketing that contribute to understanding consumer wants and needs

• What are the differences between customer profiles, customer activity, and customer management?
• What are the top business strategies supported by a database marketing system?

A database marketing system contains detailed information about individual customers and their activities, which is essential to being able to foster a deeper relationship with them. These systems typically store three primary categories of information, which include customer profiles, customer activities/behavior, and customer management.

Customer profile data includes the customer's name, contact information, birthday, etc. Customer activity or behavior includes purchase history, such as what is purchased, how often purchases are made, and how much is being spent. Customer management enables the business to keep track of all of the customer contact points made through automated outreach programs, loyalty programs, and cross-marketing between physical and virtual stores.

The key strategies enabled by a database marketing system include lead generation, customer acquisition, customer retention, customer growth, customer referrals, and customer loyalty, which all support the CRM system. Thus, CRM technology establishes connections to customers through direct marketing, sales, customer service, and support.

To review, see Customer Database and The 5 Money-Making Benefits of Database Marketing.

Unit 7 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• customer activity
• customer-centric
• customer lifetime value
• customer management
• customer profile
• customer relationship management
• database marketing

Unit 8: Data-Driven Uses and Misuses

8a. Relate the consequences of improperly using or implementing analytics

• Why is data collection a crucial step in the research or marketing process?
• How are primary and secondary sources of data different?

Before there can be any data-driven decision-making, there has to be some data. This data is 'fed' into the system to help generate business insights and be collected from various sources. Data collection methods vary based on the purpose of the research, the availability of data, and its suitability for a particular use. As we've previously discussed, quantitative data and qualitative data are very different and fulfill specific needs.

Data collection methods are just as important as the collected data. Without a firm understanding of what is to be collected and how it is collected, the organization risks collecting incorrect or unusable data. Even though the actual data may not take place until the fourth step in the market research process, it needs to be an important consideration in the first step of Problem Definition.

Primary research data is data collected directly by the organization and could be prior purchase data or direct survey data obtained through their research processes. Secondary data is data collected or compiled by third parties that can be acquired by the organization. This includes Nielsen Ratings data, Gallup Poll data, or U.S. Census data.

To review, see Collecting Data and Data Collection Methods.

8b. Examine how organizations have benefitted from properly using DDDM to grow their business

• What are some sources of primary data to support an organization's DDDM?
• What are some sources of secondary data to support an organization's DDDM?
• What are the different types of data businesses collect for their DDDM?

Businesses collect a wide variety of data to help inform their decision-making. To be successful, they have to differentiate between the types of data, prioritize the relevance and determine how much is relevant to their decision-making process.

Primary research is usually data collected directly by the organization. Many times, it is used to fill in gaps found in secondary research data. The typical primary research includes:

Secondary research is gathering information from other's primary research, such as journals, books, or other sources. Secondary research is usually less costly, more efficient, and less time-consuming since it is already developed by other sources.

Organizations typically collect five types of data for their DDDM efforts, including:

To review, see Types of Data Sources and Business Data.

8c. Analyze the key questions management must ask to determine the best decisions to make in response to an analysis result

• Why is it important to avoid bias when interpreting data for analysis?
• What are the components of a management information system?
• How does a management information system support the DDDM?

Before any decisions can be made from the data collected, it must be analyzed and summarized to be comprehended by the organization's management. To avoid bias, personal opinions should not be introduced into the decision-making process. Still, it is essential to interpret the analysis results in light of their impact on the organization. That is, does it make sense in light of the current situation? Blindly following the analysis can also result in making bad decisions.

The task of managing a company's information needs falls to the management information system, which is comprised of the users, hardware, and software that support decision-making. These information systems collect and store key company data and produce the information managers need for analysis, control, and decision-making.

To review, see Analyzing Data and Management Information Systems.

Unit 8 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• analysis
• business process data
• data collection
• interviews
• observations
• personal data
• physical-world observations
• primary research
• primary sources
• public data
• secondary research
• secondary sources
• surveys