Cluster analysis

[timol]

Dear all,

I have the dataset below

The first column contains my independent variable. The first column contains string characteristics of a company that are separated by a comma. The second column contains my dependent variable which is the revenue of a company (numeric variable).

Is there a way in Alteryx to compute which of the single charateristics has what kind of impact on the revenue? In addition is there also a way to see what kind of combinations work best (for instance "Footprint in Asia" with "Headquarter in UK")?

I thought about some kind of Cluster analysis, maybe also Conjoint analysis. Maybe there are also more advanced tools in Alteryx that could be applied here that I am not aware of.

Any help would be appreciated!

Characteristic	Revenue
Headquarter in UK	287
Female CFO	284
Male CEO, Footprint in Asia, Headquarter in UK	344
Footprint in Asia	194
Footprint in Asia, Headquarter in UK, Female CFO	10
Male CEO, Female CFO	357
Headquarter in UK	212
Headquarter in UK, Female CFO	269
Footprint in Asia	248
Footprint in Asia, Headquarter in UK, Female CFO	104
Footprint in Asia	300
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	156
Footprint in Asia, Headquarter in UK, Female CFO	194
Female CFO	374
Male CEO, Female CFO	297
Headquarter in UK	5
Footprint in Asia	72
Footprint in Asia, Headquarter in UK, Female CFO	353
Headquarter in UK, Female CFO	25
Footprint in Asia	114
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	125
Headquarter in UK, Female CFO	206
Male CEO	235
Male CEO, Footprint in Asia	298
Footprint in Asia	287
Male CEO	273
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	70
Headquarter in UK, Female CFO	255
Headquarter in UK	85
Male CEO, Footprint in Asia	357
Footprint in Asia	142
Headquarter in UK	175
Footprint in Asia	145
Footprint in Asia	135
Male CEO, Footprint in Asia, Headquarter in UK	320
Footprint in Asia	311
Footprint in Asia	382
Headquarter in UK	351
Footprint in Asia, Headquarter in UK, Female CFO	88
Male CEO	244
Footprint in Asia, Headquarter in UK, Female CFO	311
Headquarter in UK, Female CFO	237
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	16
Male CEO	354
Male CEO	187
Headquarter in UK, Female CFO	314
Female CFO	376
Male CEO	89
Male CEO, Female CFO	218
Female CFO	310
Footprint in Asia	229
Footprint in Asia, Headquarter in UK, Female CFO	164
Footprint in Asia	165
Footprint in Asia	38
Headquarter in UK	305
Headquarter in UK	322
Headquarter in UK, Female CFO	368
Headquarter in UK, Female CFO	16
Male CEO, Footprint in Asia, Headquarter in UK	159
Headquarter in UK	363
Male CEO, Female CFO	117
Male CEO	43
Footprint in Asia, Headquarter in UK, Female CFO	142
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	68
Headquarter in UK	393
Footprint in Asia, Headquarter in UK, Female CFO	277
Male CEO	348
Male CEO, Female CFO	267
Headquarter in UK	344
Headquarter in UK	131
Male CEO, Female CFO	245
Male CEO	95
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	172
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	202
Male CEO, Female CFO	337
Male CEO, Footprint in Asia	147
Male CEO, Footprint in Asia	205
Male CEO, Female CFO	64
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	65
Headquarter in UK, Female CFO	288
Female CFO	257
Footprint in Asia, Headquarter in UK, Female CFO	271
Male CEO, Footprint in Asia, Headquarter in UK	108
Male CEO, Footprint in Asia, Headquarter in UK	5
Headquarter in UK	15
Headquarter in UK	204
Footprint in Asia	312
Headquarter in UK, Female CFO	223
Footprint in Asia	35
Male CEO, Footprint in Asia	225
Footprint in Asia, Headquarter in UK, Female CFO	112
Male CEO, Footprint in Asia, Headquarter in UK	195
Male CEO	2
Male CEO, Female CFO	269
Male CEO, Footprint in Asia	68
Headquarter in UK, Female CFO	110
Headquarter in UK	179
Headquarter in UK	238
Male CEO	203
Female CFO	246
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	52
Male CEO, Footprint in Asia, Headquarter in UK	45
Male CEO, Footprint in Asia, Headquarter in UK	376
Male CEO, Footprint in Asia, Headquarter in UK	59
Headquarter in UK, Female CFO	213
Male CEO, Footprint in Asia	207
Footprint in Asia, Headquarter in UK, Female CFO	88
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	127
Headquarter in UK	132
Male CEO, Female CFO	262
Male CEO, Footprint in Asia	312
Headquarter in UK	307
Footprint in Asia	67
Footprint in Asia	335
Footprint in Asia, Headquarter in UK, Female CFO	288
Male CEO, Footprint in Asia	232
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	88
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	255
Male CEO, Female CFO	250
Male CEO, Female CFO	283
Headquarter in UK	396
Male CEO	105
Male CEO, Footprint in Asia, Headquarter in UK	27
Footprint in Asia	51
Male CEO, Footprint in Asia	382
Male CEO, Footprint in Asia	101
Headquarter in UK	233
Male CEO, Female CFO	133
Footprint in Asia	331
Male CEO, Female CFO	369
Headquarter in UK, Female CFO	125
Male CEO, Female CFO	375
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	166
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	360
Female CFO	216
Headquarter in UK, Female CFO	242
Footprint in Asia, Headquarter in UK, Female CFO	244
Male CEO, Footprint in Asia	287
Male CEO, Footprint in Asia, Headquarter in UK	372
Male CEO, Footprint in Asia	218
Footprint in Asia	94
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	350
Male CEO, Footprint in Asia, Headquarter in UK	286
Male CEO, Footprint in Asia, Headquarter in UK	107
Male CEO, Footprint in Asia, Headquarter in UK	62
Footprint in Asia, Headquarter in UK, Female CFO	148
Footprint in Asia, Headquarter in UK, Female CFO	272
Male CEO, Footprint in Asia, Headquarter in UK	85
Headquarter in UK, Female CFO	276
Male CEO, Footprint in Asia, Headquarter in UK	82
Headquarter in UK, Female CFO	110
Male CEO, Female CFO	305
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	131
Footprint in Asia	369
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	345
Male CEO, Female CFO	323
Headquarter in UK, Female CFO	75
Male CEO, Female CFO	205
Footprint in Asia	1
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	317
Female CFO	372
Male CEO	353
Headquarter in UK	330
Male CEO	160
Female CFO	304
Footprint in Asia	240
Male CEO, Footprint in Asia	108
Female CFO	197
Male CEO, Footprint in Asia, Headquarter in UK	146
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	304
Male CEO	101
Female CFO	79
Footprint in Asia	119
Headquarter in UK, Female CFO	194
Female CFO	338
Footprint in Asia	314
Male CEO	137
Female CFO	91
Headquarter in UK, Female CFO	356
Footprint in Asia, Headquarter in UK, Female CFO	311
Headquarter in UK	296
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	98
Headquarter in UK, Female CFO	144
Female CFO	275
Male CEO, Footprint in Asia, Headquarter in UK	153
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	256
Footprint in Asia, Headquarter in UK, Female CFO	179
Male CEO	21
Headquarter in UK	341
Headquarter in UK	36
Male CEO, Footprint in Asia, Headquarter in UK	233
Male CEO, Footprint in Asia, Headquarter in UK	181
Headquarter in UK, Female CFO	374
Footprint in Asia	24
Male CEO, Female CFO	120
Female CFO	47
Footprint in Asia	209
Headquarter in UK, Female CFO	75
Headquarter in UK, Female CFO	188
Headquarter in UK	49
Footprint in Asia, Headquarter in UK, Female CFO	236
Male CEO	379
Male CEO, Footprint in Asia	203
Male CEO, Footprint in Asia, Headquarter in UK	120
Female CFO	306
Female CFO	202
Footprint in Asia, Headquarter in UK, Female CFO	388
Male CEO, Footprint in Asia, Headquarter in UK	254
Footprint in Asia	182
Headquarter in UK, Female CFO	239
Male CEO, Footprint in Asia	241
Footprint in Asia	101
Headquarter in UK, Female CFO	376
Footprint in Asia, Headquarter in UK, Female CFO	252
Male CEO, Female CFO	218
Footprint in Asia	13
Male CEO, Female CFO	234
Headquarter in UK, Female CFO	177
Male CEO, Female CFO	214
Headquarter in UK, Female CFO	71
Headquarter in UK	277
Headquarter in UK	102
Male CEO, Footprint in Asia	370
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	370
Female CFO	165
Male CEO, Footprint in Asia	223
Male CEO, Female CFO	236
Male CEO	33
Headquarter in UK, Female CFO	83
Male CEO	292
Footprint in Asia	46
Footprint in Asia	112
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	278
Footprint in Asia	251
Footprint in Asia, Headquarter in UK, Female CFO	287
Headquarter in UK	237
Male CEO	74
Footprint in Asia	181
Footprint in Asia, Headquarter in UK, Female CFO	18
Female CFO	143
Footprint in Asia, Headquarter in UK, Female CFO	265
Footprint in Asia	395
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	146
Footprint in Asia	275
Headquarter in UK, Female CFO	115
Headquarter in UK, Female CFO	158
Headquarter in UK, Female CFO	346
Male CEO	94
Male CEO, Footprint in Asia	222
Footprint in Asia, Headquarter in UK, Female CFO	63
Male CEO, Footprint in Asia	16
Female CFO	202
Footprint in Asia	163
Male CEO	374
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	311
Female CFO	387
Male CEO, Footprint in Asia	192
Footprint in Asia	308
Male CEO, Footprint in Asia	79
Female CFO	399
Male CEO, Footprint in Asia, Headquarter in UK	5
Male CEO	38
Male CEO, Footprint in Asia, Headquarter in UK	226
Footprint in Asia	314
Male CEO, Female CFO	144
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	120
Headquarter in UK, Female CFO	356
Footprint in Asia, Headquarter in UK, Female CFO	275
Male CEO, Footprint in Asia, Headquarter in UK	62
Headquarter in UK, Female CFO	364
Female CFO	361
Male CEO, Female CFO	163
Footprint in Asia, Headquarter in UK, Female CFO	226
Headquarter in UK	98
Headquarter in UK	349
Male CEO	136
Male CEO, Footprint in Asia, Headquarter in UK	134
Male CEO, Female CFO	130
Male CEO, Footprint in Asia	377
Male CEO, Footprint in Asia	324
Headquarter in UK	73
Footprint in Asia	272
Male CEO, Female CFO	93
Footprint in Asia	365
Headquarter in UK, Female CFO	351
Male CEO, Footprint in Asia, Headquarter in UK	228
Footprint in Asia	143
Footprint in Asia, Headquarter in UK, Female CFO	54
Female CFO	74
Footprint in Asia	33
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	153
Male CEO	118
Footprint in Asia, Headquarter in UK, Female CFO	68
Footprint in Asia	200
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	279
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	386
Male CEO	365
Male CEO, Footprint in Asia, Headquarter in UK	290
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO	73

[timol]

Does noone have an idea?

[dougperez]

Hello @timol , I'm not an expert with data clustering (I'm still on college here hahaha), have you tried the predictive tools? I used some of them in some exercices

[timol]

Thank you

No I have not tried the tools

Any suggetions how to or which kind of tools to use are more than welcomed.

Thank you!!!

[dougperez]

I used the clustering predictive (kmeans for my exercises) and worked very well!

[atcodedog05]

Hi @timol 

Your use case seems like a market basket analysis link use case (analysis on which 2 products are bought together).

You can refer to this weekly challenge where they have a market basket analysis use case and link to resources. 

https://community.alteryx.com/t5/Weekly-Challenge/Challenge-254-DATA-DRIVEN-COCKTAIL-RECIPE-PART-1/t...

Hope this helps 🙂

[dougperez]

Whoa @atcodedog05 that's a wonderful content, I'm always learning with your responses!

[timol]

Thank you!!!

I am still a little bit confused, sorryy

Could you please tell me how you would use this method on my data?

[CharlieS]

I've put together an example workflow to show a few steps we can take here and a few tool examples for the future.

I started out with a little parsing and a plot of means. Predictive modeling is cool, but never underestimated the power of basic sample statistics. 

Now that we have an idea of the base characteristics, we can transform the data and create dummy variables for each. Once we have dummy variables, we can create interaction variables from these dummies to test the combined effects of these characteristics.  

This is the format of data that could be used for predictive analysis. I include an example of a training a Forest model, scoring the model, and producing a scatterplot of the results. These tools are an example of how binary/dummy variables could be used to predict a continuous variable, Revenue. However, I do not recommend using this due to the limited variance, distribution, and observations of this dataset. 

Hopefully the example workflow and explanation here will help you move forward in this analysis. Check out the attached workflow and let me know if you have any questions. 

[timol]

Thank you so much!

Is there a way to see the coefficient of each Characteristic and characteristic interaction with the respective significance level?