3 族群分析與市場區隔

3.1 族群分析 (K-means)

Within R, segmenatation is simply a function call

set.seed(123)
S = kmeans(W,3)$cluster
table(S)

## S
##  1  2  3 
## 34 28 38

這裡的區隔變數是什麼？
除此之外，還有哪一些常用的區隔變數呢？
它們是最理想的區隔變數嗎？為什麼？

3.2 Average Part Worth per Segment (APWPS)

sapply(1:max(S), function(i) colMeans(W[S==i,]))

##               [,1]    [,2]     [,3]
## intercept  4.46106  4.0602  2.36787
## low        0.44326  0.2668  0.03905
## medium     0.29612 -0.7620 -0.08003
## high      -0.73918  0.4952  0.04103
## black     -0.26476 -0.1239  1.94629
## green      1.10582  0.4047 -1.19582
## red       -0.84115 -0.2808 -0.75058
## bags      -0.03909 -0.3239  0.63403
## granu     -0.40962 -2.0666 -0.45229
## leafy      0.44915  2.3904 -0.18176
## yes       -0.08138  0.8737  0.51003
## no         0.08138 -0.8737 -0.51003

APWPS的定義是什麼？它的策略意涵是什麼？

Let’s build a function Seg(k, seed)

Seg = function(k, seed, seeding=F) {
  P = matrix(rep(0, k*4 ), ncol=4)
  U = matrix(rep(0, k*nrow(W)), ncol=k )
  lx = c(1,2,2)
  for(i in 2:k) lx = c(lx, (i-1)*2 + c(1,2,2) )
  h = rep.int(1,k)
  if(k==3) h[1]=2 else if(k>=4) h[1]=3
  layout(matrix(c(1,2,2,2+lx),k+1,3,byrow=T), heights=h, widths=c(1.5,1,1) )
  sd = ifelse(seeding, sample.int(1000,1), seed)
  set.seed(sd)
  S = kmeans(W,k)$cluster
  n = as.vector(table(S))
  # cat(k, 'segments, seed =', sd, ', N =', n, '\n')
  m = apply(W[,2:ncol(W)], 2, function(x) tapply(x ,S, mean))
  par(mar=c(3,2,3,2))
  pie(table(S),radius=1,col=col1[1:k],font=2)
  dc = discrcoord(W, S)$proj[,1:2]
  par(mar=c(1,2,1,2))
  plot(dc[,1],dc[,2],type='p',col=col1[S],pch=19,cex=1.5)
  for(i in 1:k) {
    par(mar=c(2,2,2,1))
    barplot(m[i,],las=2,axes=F,axisnames=F,col=col1[i],
            width=.5,space=1,border=NA)
    abline(h=0, v=c(0,3,6,9,11)+0.25 )
    P[i,1] = which.max(m[i,1:3])
    P[i,2] = which.max(m[i,4:6])
    P[i,3] = which.max(m[i,7:9])
    P[i,4] = which.max(m[i,10:11])
    U[,i] = rowSums(W[, c(1, P[i,]+c(1,4,7,10)) ])
    mtx = sapply(1:k, function (k) sapply(-4:13, function(x)
      sum(U[S==k,i] >= x & U[S==k,i] < x+1 ))  )
    mtx = t(as.matrix(mtx))
    par(cex=0.6,mar=c(2,3,2,2))
    barplot(mtx,las=2,ylim=c(0,30),col=col1)
    abline(h=seq(5,25,5),col='lightgrey',lty=3)
    z = paste(P[i,],collapse=', ')
    text(0,28,sprintf("Produnt %d: {%s}, %.1f%%",
                      i,z,100*n[i]/nrow(W)),cex=1.2,pos=4,col=col1[i])
  }}

As shown bloew, Seg(k, seed) makes k segments with seed, and plots:

a pie charts that shown the propotions of segments
a scatter chart that marks every respondents on the Reduced Product Attribute Space
and for each segment:
- derive its corresponding APWPS
- defind a product with the Worthiest Parts, and
- draw the product’s Distribution of Utility, colored by segments

Seg(3,779)

SIM-2: 族群分析與市場區隔

在 RStudio 裡面，做模擬程式其實是很容易的：

manipulate( Seg(k, 123, seeding),
  k = slider(2,5,2,step=1),
  seeding = button("Reset Seed") )

試使用這個模擬程式，回答下列問題：

同一族群的消費者，是否會有相似的價值判斷呢？為甚麼？
你可以從從儀錶板上面觀察到這種現象嗎？
分別在\(k=2,3\)，找出你覺得最好的區隔？把seed記下來，大家來比看看。
從儀錶板上面，你如何判斷某一區隔的優劣呢？
適合做產品設計的市場區隔，也會適合拿來做訂價策略、通路策略、媒體策略嗎？
價值係數之外，受測者的人口統計或生活型態變數對我們的行銷策略會有什麼幫助？
我們要如何利用這些資訊呢？

Wrap Up - Segmentation

族群分析不等於市場區隔
產品屬性空間的族群區隔不一定會產生方便訂價的效用分布
方便產品設計和訂價的區隔不一定方便用來做通路或媒體策略
市場區隔的難處在於，無限可能的區隔變數；族群分析通常只能在某種前提下，幫我們找到方向