MRKT 350 Exam Review

Using data to maximize marketing outcomes and improve decisions about the future company actions.

Humans are limited, biased information processors, so data helps improve decision-making.

Treating personal experience as sufficient evidence.

Overweighting a vivid example or anecdote because it is easy to remember.

Data, analytics, and visualization.

Quantitative information (numbers) or qualitative information (words, photos, graphs) used for analysis.

Using data models to understand data and relationships between data elements.

Using interactive maps and charts to help people understand information quickly and easily.

Cleaning, structuring, and integrating data.

To fix incomplete, noisy, or inconsistent data and improve the quality of results.

To organize unstructured data into a useful format for analysis.

To combine multiple sources and discover insights that one source alone cannot provide.

An abstract structure showing how data elements relate to one another.

General spreadsheets, programming tools, and proprietary marketing analytics solutions.

Structuring, transforming, analyzing, and storing data in rows and columns.

Missing data, outliers, impossible or inconsistent values, and duplicates.

Solving problems using executable computer algorithms.

Applying programming to data and statistical problems.

Free, community-developed tools such as R and Python.

Tools developed and sold by firms, such as SAS and SPSS.

It handles large data projects, offers ready-made packages and professional visualizations, and has a large support community.

A variable that stores text.

A variable that stores whole numbers.

A variable that stores numbers, including decimals.

A variable that stores TRUE or FALSE.

Massive amounts of structured and unstructured data that grow too quickly for traditional tools to store and process effectively.

Linked tables with predefined categories connected through keys; used for structured data.

A database that doesn't rely on linked tables; useful for semi-structured or unstructured data.

A language used to manage relational databases and retrieve information.

A question or request that retrieves information from a database.

Specifies which data or columns to retrieve.

Specifies which table to retrieve data from.

Filters data based on a condition.

Groups records into categories, such as by state.

Combines information from two tables

Returns only records that match in both tables.

Returns all records from the left table and matching records from the right.

Returns all records from the right table and matching records from the left.

Returns all records from both tables; unmatched fields contain NULL.

Data collected directly from your audience through your own platforms.

Another company's first-party data shared through a direct partnership.

Data collected by an outside provider without a direct relationship with consumers.

Outbound pushes products toward consumers; inbound attracts them with relevant content.

A platform for managing content, SEO, email, social media, websites, ads, and automation to generate leads and retain customers.

A platform that combines, organizes, and analyzes audience data for targeting.

An EU privacy law that gives consumers rights concerning their personal data, including the ability to object to certain tracking.

A California privacy law regulating personal information, including household-level data.

Managing digital and traditional campaigns to optimize media-mix return on investment (ROI).

Using historical data, statistics, and regression to connect marketing activities with sales.

Identifying which customer touchpoints receive credit for a conversion.

Giving all conversion credit to the final touchpoint before a customer converts.

The idea that multiple marketing touchpoints work together to influence customer outcomes.

Examining consumer data directly from social media networks.

Analyzing one aspect of a post, such as text or images.

Monitoring data across several social networks.

Analytics provided directly by a social media platform, such as reach, likes, and demographics.

Analyzing website traffic and visitor behavior to improve marketing.

Where website visitors came from.

What visitors do on a website.

Whether website activity generates revenue.

Examining digital text, photos, audio, or video to identify patterns and meaning.

Analyzes words and connects language patterns to psychological states.

Breaking text into smaller units, such as individual words, for analysis.

Analyzing language to identify positive, negative, or other emotional sentiment.

To focus limited marketing resources on groups of similar customers.

Dividing a market into groups of similar customers.

Choosing which customer segments to pursue.

Deciding how customers should perceive a brand relative to competitors.

Grouping customers by characteristics such as age, income, gender, or education.

Grouping customers by values, personality, interests, and lifestyles.

Grouping customers by usage, shopping frequency, loyalty, or coupon use

Using the same marketing mix for everyone.

Serving several segments with a different marketing mix for each

Focusing on one specific market segment.

Tailoring marketing to individual customers.

Product, Price, Place, and Promotion.

Grouping customers to maximize similarity within clusters and differences between clusters.

The sum of squared differences across variables; a smaller distance means two observations are more alike.

A method that builds a tree (dendrogram), merging similar customers first.

A tree diagram showing how observations or clusters are grouped.

A method where you choose the number of clusters, assign points to the nearest center, and update centers until assignments stabilize.

The average position of observations in a cluster.

Consider feasibility, segment size, differentiation, and stability.

Clusters should be formed from segmentation variables; outcomes such as sales and satisfaction are used afterward to evaluate segments.

Examining average cluster characteristics, naming segments, and comparing their attractiveness.

A measure used to evaluate a data-driven project and improve marketing effectiveness.

Identify, analyze, and improve.

Design, implementation, and evaluation.

Conduct exploratory research and develop hypotheses.

Monitor results and make adjustments.

Determine whether goals were met and decide what to do next.

Retrieving a brand from memory without necessarily seeing it.

Identifying a brand after prior exposure when you see or encounter it.

How easily a brand comes to mind.

The range of situations in which a brand comes to mind or is used.

The expected value of a customer relationship over time.

CLV = (Margin × Retention) / (1 + Discount Rate − Retention).

Customer revenue minus customer costs.

Renewal rates, recommendations, ratings, and service effectiveness.

The total number of times content is displayed, including repeat displays.

The number of unique people exposed to content.

A desired action, such as completing checkout or signing up.

Conversions divided by reach.

Visitors who type a website URL directly into their browser.

Visitors who arrive through a link on another website.

Visitors who arrive through unpaid search engine results.

Visitors who arrive through social media.

The percentage of visitors who leave after viewing only one page.

Clicks divided by the number of people who saw the link.

The percentage of customers who buy again.

Total marketing cost divided by new customers acquired during the same period.

Website traffic from people using mobile devices.

Low cost, customization, quality, responsiveness, and innovation.