Willingness to Pay (WTP) compared to demographics

Statistics on Demographcis effect on Willingness to pay

## 
## Call:
## lm(formula = log(wtp) ~ as.factor(marital_status) + time_married + 
##     level_worry + as.factor(lose_location) + as.factor(lost_previously) + 
##     log(curr_ring_price) + wow + as.factor(gender) + age_category, 
##     data = wtp_data_filt)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.11609 -0.51407 -0.00251  0.43673  2.06133 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                  3.12480    3.38076   0.924   0.3645  
## as.factor(marital_status)2   4.00783    2.28092   1.757   0.0917 .
## as.factor(marital_status)3   2.66368    2.57104   1.036   0.3105  
## time_married                 0.47782    0.52193   0.915   0.3690  
## level_worry                  0.83701    0.67614   1.238   0.2277  
## as.factor(lose_location)2    0.73316    0.78591   0.933   0.3602  
## as.factor(lose_location)3    0.70691    0.71596   0.987   0.3333  
## as.factor(lose_location)4    0.61781    0.92478   0.668   0.5105  
## as.factor(lose_location)5    1.34444    0.86217   1.559   0.1320  
## as.factor(lost_previously)2 -0.90174    0.52653  -1.713   0.0997 .
## as.factor(lost_previously)3  0.88032    1.55981   0.564   0.5777  
## log(curr_ring_price)        -0.72740    0.36605  -1.987   0.0584 .
## wow                          0.18304    0.08901   2.056   0.0508 .
## as.factor(gender)2           0.69541    0.52405   1.327   0.1970  
## age_category                -0.49912    0.41971  -1.189   0.2460  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.096 on 24 degrees of freedom
##   (29 observations deleted due to missingness)
## Multiple R-squared:  0.4808, Adjusted R-squared:  0.178 
## F-statistic: 1.588 on 14 and 24 DF,  p-value: 0.1547

Nothing crazy significant except how high people rate the idea leads them to pay more. There’s a couple other demographics that supposedly are slightly significant but I don’t too much stock in them.

Idea Rating compared to demographics

Statistics on Idea Rating

## 
## Call:
## lm(formula = wow ~ as.factor(marital_status) + time_married + 
##     level_worry + as.factor(lose_location) + as.factor(lost_previously) + 
##     log(curr_ring_price) + wow + as.factor(gender) + age_category, 
##     data = wtp_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9259 -1.4732  0.3151  1.5363  4.5404 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   4.8753     6.5399   0.745   0.4618  
## as.factor(marital_status)2   -1.4965     4.6795  -0.320   0.7513  
## as.factor(marital_status)3    1.7365     5.2724   0.329   0.7442  
## time_married                 -1.6941     1.0123  -1.673   0.1046  
## level_worry                  -1.4728     1.4226  -1.035   0.3088  
## as.factor(lose_location)2     1.1827     1.6974   0.697   0.4913  
## as.factor(lose_location)3    -0.8236     1.5355  -0.536   0.5957  
## as.factor(lose_location)4    -0.4454     1.6430  -0.271   0.7882  
## as.factor(lose_location)5    -2.3358     1.8124  -1.289   0.2073  
## as.factor(lost_previously)2   0.9621     1.0011   0.961   0.3442  
## as.factor(lost_previously)3   8.0163     3.0906   2.594   0.0145 *
## log(curr_ring_price)          0.6187     0.6781   0.912   0.3688  
## as.factor(gender)2            0.8152     1.1064   0.737   0.4669  
## age_category                  0.8392     0.8403   0.999   0.3260  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.426 on 30 degrees of freedom
##   (35 observations deleted due to missingness)
## Multiple R-squared:  0.4348, Adjusted R-squared:  0.1899 
## F-statistic: 1.775 on 13 and 30 DF,  p-value: 0.09534

Nothings Too significant. I wouldn’t make any claims saying that different demographics rate the idea better or worse. At most I would talk about people who answered “3” to the lost previously question will rate the idea higher on average.

Profitability Analysis

I am assuming you have a fixed cost (overhead, engineering, administrative cost) of roughly $1 Million Dollars, a per unit cost of $200 and that your survey represents 1 Million Adult Americans. Feel free to ask me to change those assumptions.

Really good fit of the data, we got 99% of the data with the model