Introduction

Supposing I worked in a company that sells specific watch products which can report daily weather, to promote our products, I want to determine where can I place my advertimsement, target customers and destinct features that can appeal to majority of respondents to buy.

Import data from Github

## # A tibble: 6 x 9
##   RespondentID Check_weather How_check Specific Watch_check Age   Gender Income
##          <dbl> <fct>         <fct>     <fct>    <fct>       <fct> <fct>  <fct> 
## 1   3887201482 Yes           The defa… <NA>     Very likely 30 -… Male   $50,0…
## 2   3887159451 Yes           The defa… <NA>     Very likely 18 -… Male   Prefe…
## 3   3887152228 Yes           The defa… <NA>     Very likely 30 -… Male   $100,…
## 4   3887145426 Yes           The defa… <NA>     Somewhat l… 30 -… Male   Prefe…
## 5   3887021873 Yes           A specif… Iphone … Very likely 30 -… Male   $150,…
## 6   3886937140 Yes           A specif… AccuWea… Somewhat l… 18 -… Male   $100,…
## # … with 1 more variable: USRegion <fct>

Demographic description

*Firstly examine the demographic data for RespondentID, looking at Age, Gender, and USRegion one by one.

**We find that there’s nearly no imbalance for age and gender. And for different age groups, there’s still no obvious gender imbalance. We can say that this is a successful comprehensive and rational survey that data collected can reflect most groups of people. Then any conclusion from this survey can be dependable.

Examine Different Age group whether check weather

*We find elder people (45-more) are more lileky to check the weather so the difference between check weather or not is increasing largely with the increasing of age. Checking daily weather for females is significantly more frequent than males in the same age group, especially when they are in young age.

Examine different Income Group whether check weather

Here, we created a categorical variables named wealth based on their income, indicating the wealth status of each respondents. The divided details showed below:
$0 to $9,999~“Extremely Poor”
$25,000 to $49,999,$10,000 to $24,999 ~ “Poor”
$50,000 to $74,999,$75,000 to $99,999,$100,000 to $124,999 ~ “Medium”
$125,000 to $149,999,$150,000 to $174,999,$175,000 to $199,999 ~ “Rich”
$200,000 and up ~ “Extremely Rich”,
Prefer not to answer,NA ~ “Other”
Then we examine the relationshipo between weather checking and wealth level.

We can see that there’s no big difference between different income groups checking wheather or not.

Examine different region whether check weathers

*We can find that people in different regions have different tendency to check the daily weather report. In New England and East South Central, people there are most likely to check weather everyday however respondents in Mountain, Pacific and South Atlantic are not so willing to check weather report everyday. Many possilbe factors can explain this phenomenon like the climate of New England varies greatly across its 500 miles (800 km) span from northern Maine to southern Connecticut So that people there are more likely to check daily weather.

Examine different checking ways

*We can find that the default weather app on cell-phone rank 1st among different checking ways. Although nowadays people more and more rely on their cell-phones, there are still large amount of respondents choose local TV news for everday weather report and Weather Channel still wins Respondents favor.

Exmaine different age groups choose which ways to check daily weather.

Here we only consider top 3 ways (“Local TV News”,“The default weather app on your phone”,“The Weather Channel”) to check weather everyday.

We can find that Local TV News wins majority elder respondents favors and people whose age under 45 are more likely to check daily weather with their cell-phone app.

Business Intelligence Analysis and Insights

We firstly give score for each level of how likely would you be to check the weather on watch. The criteria showed below: -‘Very unlikely’ ~ 0 -‘Somewhat unlikely’ ~ 1 -‘Somewhat likely’ ~ 2 -‘Very likely’ ~ 3 Then we calculated average score for different regions. And we analysis interested on watch for respondents of different age groups and wealth status.

##                    Very unlikely Somewhat unlikely Somewhat likely Very likely
## Middle Atlantic               15                 3              10          35
## East North Central            18                 6              22          43
## South Atlantic                20                 3              27          44
## Pacific                       19                 6              35          31
## West South Central            15                 6              12          25
## East South Central             5                 4               6           9
## New England                    8                 2              12          10
## West North Central             9                 2              14          11
## Mountain                      12                 6              11          18
##                    AvgScore
## Middle Atlantic    2.031746
## East North Central 2.011236
## South Atlantic     2.010638
## Pacific            1.857143
## West South Central 1.810345
## East South Central 1.791667
## New England        1.750000
## West North Central 1.750000
## Mountain           1.744681

Here is a facet grid of the frequencey of watch checking facetted by Age Group (columns) and Wealth Groups (rows). We can see that if respondents own a smartwatch, elder respondents’ interstes on checking weather on smartwatch appears to be extremenly high. Only when they in extremely poor status, they show little interests.Therefore, elder customers can be our target customers and we can introduce some usefull features like fall detection on our watch products which more fit on the elder.
We can confident our pricing can be very flexible for that diffierent income group all express their favors. And we can adopt a product portfolio management approach to our product feature mix and investors manage their own investment portfolio.
And from the region analysis, we can put our more effort to advestise our watch product on Local TV news and the weather channel in Middle Atlantic, East North Central and South Atlantic.