Social Influence On Shopping by Kaggle | Ecommerce and Consumer Trends

About this Dataset

Social Influence On Shopping

Social Influence on Shopping

Social Survey Data from 300,000 Millennials and Gen Z Members

By Adam Halper [source]

About this dataset

This dataset offers a comprehensive look into the shopping habits of millennials and Gen Z members, including valuable insights about how their choices are influenced by social media. By exploring the responses given to survey questions related to this topic, we can gain an understanding of how these generations' interests, beliefs and desires shape their decisions when it comes to retail experiences. With 150 million survey responses from our 300,000+ millennial and Gen Z participants, we can uncover powerful insights that could help influencers, businesses and marketers more accurately target this demographic. Our data includes important information such as questions asked during the survey, segment types targeted by those questions and corresponding answers gathered with detailed counts/percentages - making this dataset incredibly useful for anyone wanting an in-depth understanding of what drives the purchasing behavior of today's youth

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How to use the dataset

The first step in using this dataset is to take a look at each column: Question, Segment Type, Segment Description, Answer, Count & Percentage. The Question column will provide background on what exactly each survey question was asking - allowing you to get an overall view of what kind of topics were being surveyed in relation to millennials' shopping habits & social media influence. You will then be able to follow up with analysis based on the respective Segment Types & Descriptions given (such as income levels), which leads us into analyzing answers from both Count & Percentage columns combined - providing absolute numbers vs relative ones for further analysis (such as percentages).

Afterwards you'll need an advanced data analysis program such as SPSS or R-Studio - depending on your technical ability - though all most basic spreadsheet programs should suffice, excluding Matlab supported ones due its excessive complexity for something simple like this.. After selecting your preferred program inputting our file with all 150 million survey responses may take some time based on your computers processing capabilities but once loaded you'll be ready for endless possibilities! Now it's time get running with pulling out key insights you require utilizing various different tools found within these platforms whether it be linear regression or guided ANOVA testing which ever technique fits best should help lead navigate through uncovering deeper meaning in your ultra specific question!

As a final precaution while diving through waters filled surprises also keep note any adjustments needed potentially due overfitting or multicollinearity otherwise could cause major issues skew end results unfit requiring start whole process anew! Good luck delving deep discovering millennial behavior related digital world!

Research Ideas

Identifying which type of segment is most responsive to engaging shopping experiences, such as influencer marketing, social media discounts and campaigns, etc.

Analyzing the answers given to survey questions in order to understand millennial and Gen Z's opinion about social influence on their shopping habits - what do they view positively or negatively?

Using the survey responses to uncover any interesting trends or correlations between different segments - is there a particular demographic that values or uses certain types of social influence on their shopping habits more than others?

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

You are free to:

Share - copy and redistribute the material in any medium or format for any purpose, even commercially.

Adapt - remix, transform, and build upon the material for any purpose, even commercially.

You must:

Give appropriate credit - Provide a link to the license, and indicate if changes were made.

ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: WhatsgoodlyData-6.csv

Column name	Description
Question	This column contains a list of questions asked in our survey regarding topics such as shopping habits, trust in online retailers, influences from friends and family at purchase time, etc. (Text)
Segment Type	This section indicates whether or not an individual was part of a larger segmented group which was surveyed exclusively for that specific question. (Text)
Segment Description	Here you will find a description of the segment population who were surveyed for each question listed in “Question” above. (Text)
Answer	The Answer column includes all the possible answers given by each respondent per question asked on our survey (i.e., Yes/No/Unsure). (Text)
Count	The Count column contains number values representing the total amount of respondents uniquely corresponding with each Answer option to its respective Question (i.e., 10 respondents answered No). (Numeric)
Percentage	This last column provides percentage values that interpret count data found in Count while including all answers among any segments specified across all Questions posed by our survey platform (i.e., 30% responded No). (Numeric)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Adam Halper.

Tables

Whatsgoodlydata 6

@kaggle.thedevastator_uncovering_millennials_shopping_habits_and_socia.whatsgoodlydata_6

24.06 KB
1450 rows
7 columns


CREATE TABLE whatsgoodlydata_6 (
  "index" BIGINT,
  "question" VARCHAR,
  "segment_type" VARCHAR,
  "segment_description" VARCHAR,
  "answer" VARCHAR,
  "count" BIGINT,
  "percentage" DOUBLE
);