Vocab Blog Post #4- Marketing Analytics

I am back everyone!! I am here today to explain some of the vocab words that stuck out to me through chapters 9, 10 and 11. I am going to do the vocab words that I struggled with understanding or words that I did not know.

Before-After Experimental Design

A before and after study measures at two different times. The first time point is before the study starts. The second time point is after the study starts. The goal of this design is to examine if the exposure has changed over time.

The first point is the “before” and the second point is the “after”

For this experiment design you compare subjects before and after they get treatment. This is the most popular evaluative method that is used. This is popular in clinical studies to see what a treatment does before and after.

  • Overcome ethical concerns
  • Low cost
  • Convenient
  • Simple
  • Lack of comparison or control group

Within-Subject Design

This a type of experimental design in which participants are exposed to every treatment or condition. This is also know as a repeated- measures experimental design. It compares two or more different treatments.

  • Group differences
  • High Variance

A within- subject design is generally more powerful and more likely to detect a treatment effect.

A within-subject design can also help reduce errors associated with individual difference. With this design all individuals are exposed to all levels of treatments, so individual differences will not change the results. Each person serves as his or her own baseline.

Data Integration

Is the combination of technical and business processes used to combine data from sources into meaningful and valuable information. A complete data integration delivers trusted data from various sources.

Integration begins with the ingestion process and includes steps like cleansing, ETL mapping and transformation. Data Integration enables analytic tools to produce effective business intelligence.

  • Improves collaboration
  • Saves time
  • Efficient
  • Reduces errors
  • Delivers more valuable data
  • Data from legacy systems
  • Data from newer businesses
  • External Data
  • Keeping up

Swarm Intelligence

Is a branch of intelligence that discusses the collective behavior emerging within self organizing societies. This is a field of biologically inspired intelligence that is based on the behavior of social insects like ants, bees, wasps and termites.

  • Flexible- They respond to internal and external challenges
  • Scalable- From a few agents to a million
  • Self-organized- The solutions are pre-defined
  • Speed- Changes can be fast
  • Behavior- Difficult to predict the behavior from the individual
  • Sensitivity- Even a small change can lead to different group behavior
General Swarm Principles:
  1. Proximity- The basic unit of the swarm should be able to respond back to changes to interactions.
  2. Quality- Swarm should be able to respond to quality factors like safety of location
  3. Diverse- Swarm should not be concentrated in a narrow region.
  4. Stability- Population should not change its mode of behavior every time the environment changes.
  5. Adaptability- Swarm is sensitive to changes in the environment that result in different swarm behavior.

Box-and-Whisker Plot

A box-and-whisker plot is a convenient way of visually displaying data through quartiles. The lines that extend parallel from the boxes are known as the “whiskers”. Which are used to indicate variability outside the quartile. Outliers are maybe sometimes plotted as just individual dots that are inline with the whiskers. Box-and whisker plots can be drawn horizontal or vertical.

Some observations to look for are…

  • What the key values are.
  • If there are any outliers and what the values are.
  • Is the data symmetrical?
  • How tight is the data grouped?
  • If the data is skewed and in what direction

The best time to use a box-and-whisker plot is when you have multiple data sets from independent sources that are related like..

  • Test scores between schools or classrooms
  • Data from before and after a process change
  • Data from duplicate machines manufacturing the same products.

The procedure to develop a box-and-whisker plot comes from 5 different statistics

  1. Minimum value- smallest value in the data set
  2. Second quartile- the value below which the lower 25% of the data are contained
  3. Median value- the middle number in a range of numbers
  4. Third quartile- the value above which the upper 25% of the data are contained
  5. Maximum value- largest number in the data set.

“Marketing without data is like driving with your eyes closed”

Dan Zarella

Overall, all the chapters had some great terminology that was used and I learned a lot with just getting to understand the vocab and just knowing what the words means makes the chapters easier to understand.

Vocab Blog Post #4- Marketing Analytics

2 thoughts on “Vocab Blog Post #4- Marketing Analytics

  1. hannahneecenwtc says:

    Hello! I enjoyed your post and the visuals you provided to dive deeper into some of the terms. They helped me understand them a lot better. Thank you!


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