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Cluster analysis

timol
8 - Asteroid

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!

 

 

 

CharacteristicRevenue
Headquarter in UK287
Female CFO284
Male CEO, Footprint in Asia, Headquarter in UK344
Footprint in Asia194
Footprint in Asia, Headquarter in UK, Female CFO10
Male CEO, Female CFO357
Headquarter in UK212
Headquarter in UK, Female CFO269
Footprint in Asia248
Footprint in Asia, Headquarter in UK, Female CFO104
Footprint in Asia300
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO156
Footprint in Asia, Headquarter in UK, Female CFO194
Female CFO374
Male CEO, Female CFO297
Headquarter in UK5
Footprint in Asia72
Footprint in Asia, Headquarter in UK, Female CFO353
Headquarter in UK, Female CFO25
Footprint in Asia114
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO125
Headquarter in UK, Female CFO206
Male CEO235
Male CEO, Footprint in Asia298
Footprint in Asia287
Male CEO273
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO70
Headquarter in UK, Female CFO255
Headquarter in UK85
Male CEO, Footprint in Asia357
Footprint in Asia142
Headquarter in UK175
Footprint in Asia145
Footprint in Asia135
Male CEO, Footprint in Asia, Headquarter in UK320
Footprint in Asia311
Footprint in Asia382
Headquarter in UK351
Footprint in Asia, Headquarter in UK, Female CFO88
Male CEO244
Footprint in Asia, Headquarter in UK, Female CFO311
Headquarter in UK, Female CFO237
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO16
Male CEO354
Male CEO187
Headquarter in UK, Female CFO314
Female CFO376
Male CEO89
Male CEO, Female CFO218
Female CFO310
Footprint in Asia229
Footprint in Asia, Headquarter in UK, Female CFO164
Footprint in Asia165
Footprint in Asia38
Headquarter in UK305
Headquarter in UK322
Headquarter in UK, Female CFO368
Headquarter in UK, Female CFO16
Male CEO, Footprint in Asia, Headquarter in UK159
Headquarter in UK363
Male CEO, Female CFO117
Male CEO43
Footprint in Asia, Headquarter in UK, Female CFO142
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO68
Headquarter in UK393
Footprint in Asia, Headquarter in UK, Female CFO277
Male CEO348
Male CEO, Female CFO267
Headquarter in UK344
Headquarter in UK131
Male CEO, Female CFO245
Male CEO95
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO172
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO202
Male CEO, Female CFO337
Male CEO, Footprint in Asia147
Male CEO, Footprint in Asia205
Male CEO, Female CFO64
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO65
Headquarter in UK, Female CFO288
Female CFO257
Footprint in Asia, Headquarter in UK, Female CFO271
Male CEO, Footprint in Asia, Headquarter in UK108
Male CEO, Footprint in Asia, Headquarter in UK5
Headquarter in UK15
Headquarter in UK204
Footprint in Asia312
Headquarter in UK, Female CFO223
Footprint in Asia35
Male CEO, Footprint in Asia225
Footprint in Asia, Headquarter in UK, Female CFO112
Male CEO, Footprint in Asia, Headquarter in UK195
Male CEO2
Male CEO, Female CFO269
Male CEO, Footprint in Asia68
Headquarter in UK, Female CFO110
Headquarter in UK179
Headquarter in UK238
Male CEO203
Female CFO246
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO52
Male CEO, Footprint in Asia, Headquarter in UK45
Male CEO, Footprint in Asia, Headquarter in UK376
Male CEO, Footprint in Asia, Headquarter in UK59
Headquarter in UK, Female CFO213
Male CEO, Footprint in Asia207
Footprint in Asia, Headquarter in UK, Female CFO88
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO127
Headquarter in UK132
Male CEO, Female CFO262
Male CEO, Footprint in Asia312
Headquarter in UK307
Footprint in Asia67
Footprint in Asia335
Footprint in Asia, Headquarter in UK, Female CFO288
Male CEO, Footprint in Asia232
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO88
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO255
Male CEO, Female CFO250
Male CEO, Female CFO283
Headquarter in UK396
Male CEO105
Male CEO, Footprint in Asia, Headquarter in UK27
Footprint in Asia51
Male CEO, Footprint in Asia382
Male CEO, Footprint in Asia101
Headquarter in UK233
Male CEO, Female CFO133
Footprint in Asia331
Male CEO, Female CFO369
Headquarter in UK, Female CFO125
Male CEO, Female CFO375
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO166
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO360
Female CFO216
Headquarter in UK, Female CFO242
Footprint in Asia, Headquarter in UK, Female CFO244
Male CEO, Footprint in Asia287
Male CEO, Footprint in Asia, Headquarter in UK372
Male CEO, Footprint in Asia218
Footprint in Asia94
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO350
Male CEO, Footprint in Asia, Headquarter in UK286
Male CEO, Footprint in Asia, Headquarter in UK107
Male CEO, Footprint in Asia, Headquarter in UK62
Footprint in Asia, Headquarter in UK, Female CFO148
Footprint in Asia, Headquarter in UK, Female CFO272
Male CEO, Footprint in Asia, Headquarter in UK85
Headquarter in UK, Female CFO276
Male CEO, Footprint in Asia, Headquarter in UK82
Headquarter in UK, Female CFO110
Male CEO, Female CFO305
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO131
Footprint in Asia369
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO345
Male CEO, Female CFO323
Headquarter in UK, Female CFO75
Male CEO, Female CFO205
Footprint in Asia1
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO317
Female CFO372
Male CEO353
Headquarter in UK330
Male CEO160
Female CFO304
Footprint in Asia240
Male CEO, Footprint in Asia108
Female CFO197
Male CEO, Footprint in Asia, Headquarter in UK146
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO304
Male CEO101
Female CFO79
Footprint in Asia119
Headquarter in UK, Female CFO194
Female CFO338
Footprint in Asia314
Male CEO137
Female CFO91
Headquarter in UK, Female CFO356
Footprint in Asia, Headquarter in UK, Female CFO311
Headquarter in UK296
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO98
Headquarter in UK, Female CFO144
Female CFO275
Male CEO, Footprint in Asia, Headquarter in UK153
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO256
Footprint in Asia, Headquarter in UK, Female CFO179
Male CEO21
Headquarter in UK341
Headquarter in UK36
Male CEO, Footprint in Asia, Headquarter in UK233
Male CEO, Footprint in Asia, Headquarter in UK181
Headquarter in UK, Female CFO374
Footprint in Asia24
Male CEO, Female CFO120
Female CFO47
Footprint in Asia209
Headquarter in UK, Female CFO75
Headquarter in UK, Female CFO188
Headquarter in UK49
Footprint in Asia, Headquarter in UK, Female CFO236
Male CEO379
Male CEO, Footprint in Asia203
Male CEO, Footprint in Asia, Headquarter in UK120
Female CFO306
Female CFO202
Footprint in Asia, Headquarter in UK, Female CFO388
Male CEO, Footprint in Asia, Headquarter in UK254
Footprint in Asia182
Headquarter in UK, Female CFO239
Male CEO, Footprint in Asia241
Footprint in Asia101
Headquarter in UK, Female CFO376
Footprint in Asia, Headquarter in UK, Female CFO252
Male CEO, Female CFO218
Footprint in Asia13
Male CEO, Female CFO234
Headquarter in UK, Female CFO177
Male CEO, Female CFO214
Headquarter in UK, Female CFO71
Headquarter in UK277
Headquarter in UK102
Male CEO, Footprint in Asia370
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO370
Female CFO165
Male CEO, Footprint in Asia223
Male CEO, Female CFO236
Male CEO33
Headquarter in UK, Female CFO83
Male CEO292
Footprint in Asia46
Footprint in Asia112
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO278
Footprint in Asia251
Footprint in Asia, Headquarter in UK, Female CFO287
Headquarter in UK237
Male CEO74
Footprint in Asia181
Footprint in Asia, Headquarter in UK, Female CFO18
Female CFO143
Footprint in Asia, Headquarter in UK, Female CFO265
Footprint in Asia395
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO146
Footprint in Asia275
Headquarter in UK, Female CFO115
Headquarter in UK, Female CFO158
Headquarter in UK, Female CFO346
Male CEO94
Male CEO, Footprint in Asia222
Footprint in Asia, Headquarter in UK, Female CFO63
Male CEO, Footprint in Asia16
Female CFO202
Footprint in Asia163
Male CEO374
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO311
Female CFO387
Male CEO, Footprint in Asia192
Footprint in Asia308
Male CEO, Footprint in Asia79
Female CFO399
Male CEO, Footprint in Asia, Headquarter in UK5
Male CEO38
Male CEO, Footprint in Asia, Headquarter in UK226
Footprint in Asia314
Male CEO, Female CFO144
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO120
Headquarter in UK, Female CFO356
Footprint in Asia, Headquarter in UK, Female CFO275
Male CEO, Footprint in Asia, Headquarter in UK62
Headquarter in UK, Female CFO364
Female CFO361
Male CEO, Female CFO163
Footprint in Asia, Headquarter in UK, Female CFO226
Headquarter in UK98
Headquarter in UK349
Male CEO136
Male CEO, Footprint in Asia, Headquarter in UK134
Male CEO, Female CFO130
Male CEO, Footprint in Asia377
Male CEO, Footprint in Asia324
Headquarter in UK73
Footprint in Asia272
Male CEO, Female CFO93
Footprint in Asia365
Headquarter in UK, Female CFO351
Male CEO, Footprint in Asia, Headquarter in UK228
Footprint in Asia143
Footprint in Asia, Headquarter in UK, Female CFO54
Female CFO74
Footprint in Asia33
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO153
Male CEO118
Footprint in Asia, Headquarter in UK, Female CFO68
Footprint in Asia200
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO279
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO386
Male CEO365
Male CEO, Footprint in Asia, Headquarter in UK290
Male CEO, Footprint in Asia, Headquarter in UK, Female CFO73
10 REPLIES 10
timol
8 - Asteroid

Does noone have an idea?

dougperez
12 - Quasar

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
8 - Asteroid

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
12 - Quasar

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

atcodedog05
22 - Nova
22 - Nova

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
12 - Quasar

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

timol
8 - Asteroid

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
17 - Castor
17 - Castor

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. 

 

20210517-RevenueAnalysis1.JPG

 

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.  

 

20210517-RevenueAnalysis2.JPG

 

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
8 - Asteroid

Thank you so much!


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

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