CRISA, a well-known market research company, tracks about 60-70 brands within 30 product categories in order to best determine marketing strategies for their clients. To do this, CRISA conducts household panels in India, and has data covering about 50,000 urban and 25,000 rural Indian households. Optimal households for research are selected using stratified sampling, and in urban areas data captures information from 80% of the market.

CRISA uses this data to provide market research services to their clients. These clients include two main groups:

- Advertising Agencies: These agencies receive monthly data from CRISA. They utilize this database to make recommendations and decisions for their own clients' marketing and advertising strategies.
- Goods Manufacturers: This group of clients are able to monitor changes in their market share with CRISA's data.

For a long time now, CRISA has implemented segmentation algorithms which cluster consumers based on their demographic characteristics. There is now a demand for CRISA to segment the market further in order to better capture brand loyalty and the consumer purchasing process. Two sets of variables which CRISA wants to implement clustering on are:

- Purchase Behavior: This includes how often consumers purchase, volume purchased, use of discounts, and other variables related to the purchase process
- Basis of Purchase: This set includes price and selling proposition

The objective of this additional clustering is to gain insight on purchase behaviors and brand loyalty, and identify the most important attributes which help to identify this behavior. This way, CRISA's clients can better use the information provided to make decisions. The goal is to develop unique strategies targeting different segments, in order to better reach individuals in each cluster and increase brand loyalty of consumers. This is more cost-effective than implementing a general marketing strategy which may only appeal to a fraction of consumers.

We converted several categorical variables into dummy variables by applying one hot encoding to understand difference between clusters such as Mother Tongue, Gender, Children and Education.

Firstly, we built a clustering model by only using variables are related to 'Purchase behavior'. Purchasing behavior can be identified based on these attributes: the number of brands purchased, brand loyalty, the number of transactions, the number of runs purchasing same brand, volume of product and average price. We built clustering models by changing some parameters such as centers, nstart and iter.max in Kmeans model. It is shown that when we changed parameters of k-means model, there is no significant difference between models. Our baseline model is created by selecting 25 random sets and using 12 iterations.

KMeans3 | SumOfSquare |
---|---|

Cluster 1 | 1242.74 |

Cluster 2 | 1141.76 |

Cluster 3 | 1585.15 |

Total Within | 3969.64 |

Between | 2020.36 |

Total | 5990 |

Clusters | Size |
---|---|

Cluster 1 | 259 |

Cluster 2 | 166 |

Cluster 3 | 175 |

Then, we checked characteristics of clusters to understand differences between clusters. Households size of cluster 3 is higher than other clusters. This increases the consumption such as number of brands, transactions and volume. Moreover, households in cluster 3 have higher brand loyalty than other clusters (generally consume products of same brand) and their affluence index is lower than others. Education level is greatest in cluster 1 and smallest in cluster 3.

In light of this information, marketing strategies must vary from cluster to cluster. For example, if you aim to reach people who have lower economic class and higher brand loyalty, you should consider households in cluster 3 and shape your marketing planning regarding patterns of cluster 3.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.339768 | 3.474903 | 16.60618 | 0.2193920 | 3.200772 | 23.91120 | 13.509652 | 7778.097 |

2 | 2.409639 | 5.108434 | 20.99398 | 0.2350520 | 5.138554 | 50.01205 | 27.180723 | 16856.536 |

3 | 2.822857 | 4.382857 | 13.86286 | 0.7250765 | 2.857143 | 23.98286 | 8.228571 | 13349.429 |

Next, we drew cluster plot by using the fviz package. It can be clearly seen that this clustering model is not optimal since clusters are heavily overlapping. This model was not able to segment households successfully in the intersection area.

Then, we applied both the elbow and silhouette method to decide the number of clusters in the model. According to elbow method, we can say that best k value is 6. Alternatively, the silhouette method determined the number of clusters as 4. As a result, we selected the number of clusters as 4 since if we increase the number of clusters, the scope of the business cannot be easily managed by marketing teams.

This plot shows the clusters when we apply 4 different clusters regarding elbow and silhoutte model. This model is slightly better than previous model, but still not best because clusters are overlapping a decent amount.

KMeans3 | SumOfSquare |
---|---|

Cluster 1 | 1170.81 |

Cluster 2 | 502.28 |

Cluster 3 | 879.76 |

Cluster 4 | 875.11 |

Total Within | 3427.96 |

Between | 2562.04 |

Total | 5990 |

Secondly, we applied k-means clustering by using different variables. Basis of purchase variables obtains percent of volume purchased not on promotion, on promo code 6 and other than 6, proposition of beauty, health and baby products.

KMeans3 | SumOfSquare |
---|---|

Cluster 1 | 1693 |

Cluster 2 | 229.32 |

Cluster 3 | 2500.69 |

Total Within | 4423.01 |

Between | 1566.99 |

Total | 5990 |

Clusters | Size |
---|---|

Cluster 1 | 335 |

Cluster 2 | 73 |

Cluster 3 | 192 |

The graph below indicates that basis for purchase variables are not sufficient to segment households in consumption of consumer goods using 3 clusters. Clusters overlapped and so variance between clusters is small.

Then, we tried to learn behavior differences between clusters. Households in cluster 2 have higher brand loyalty (77%) than other clusters (meaning they generally consume products of same brand) and their affluence index is lowest among all others. Also, social economic status of cluster 2 is the lower than others (almost 3.4). People in cluster 3 are more educated than others. Overall, households in cluster 1 and 3 shows similar patterns in consumption.

Based on these insights, marketing strategies must vary from cluster to cluster. For instance, if you work on launching products which have medium price, you should target households in cluster 2 and build your marketing strategies matching with characteristics of cluster 2.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.591045 | 4.483582 | 17.14030 | 0.3670430 | 3.800000 | 31.60597 | 15.853731 | 13008.752 |

2 | 3.356164 | 4.150685 | 8.90411 | 0.7642848 | 2.904110 | 25.46575 | 8.506849 | 13279.315 |

3 | 2.015625 | 3.697917 | 19.89583 | 0.2290487 | 3.630208 | 32.52604 | 18.328125 | 9487.188 |

Then, we applied both elbow and silhouette methods to decide the number of clusters in the model. According to the elbow method, we can say that the best k value is 7. Besides of this, silhouette method determined the number of clusters as 8. As a result, instead of using 7 or 8 for the number of clusters, we determined the number of clusters as 4 since this help to manage marketing plans systematically.

This plot shows the clustering model when we ran 4 different clusters considering elbow and silhoutte above. This model is slightly better than previous model using only 3 clusters, but still not best because the clusters are overlapping.

KMeans3 | SumOfSquare |
---|---|

Cluster 1 | 1170.81 |

Cluster 2 | 502.28 |

Cluster 3 | 879.76 |

Cluster 4 | 875.11 |

Total Within | 3427.96 |

Between | 2562.04 |

Total | 5990 |

Lastly, we applied k-means clustering by using combined variables in part a and part b. Combined variables includes both purchase behavior and basis for purchase variables. The tables below give information about clustering model when we selected k as 3.

KMeans3 | SumOfSquare |
---|---|

Cluster 1 | 878.75 |

Cluster 2 | 3440.63 |

Cluster 3 | 5068.85 |

Total Within | 9388.23 |

Between | 2591.77 |

Total | 11980 |

Clusters | Size |
---|---|

Cluster 1 | 71 |

Cluster 2 | 243 |

Cluster 3 | 286 |

The graph below indicates that combined variables are slightly better than previos models, yet it is still not sufficient to segment households. Clusters overlapped (intersections are not easy to identify) and so variance between clusters is small.

Then, we checked characteristics of clusters whether we see vital differences between clusters. Household sizes of clusters are similar to each other. Moreover, households in cluster 1 have higher brand loyalty than other clusters (hence transaction of brand runs is also highest) and their affluence index is lower than others. Order of educational level is cluster 3 > cluster 2 > cluster 1. People in cluster 3 are more educated than others. Overall, households in cluster 1 and 3 shows similar pattern in consumption.

Based on this information, marketing strategies must vary from cluster to cluster. For example, if you launch premium products into the market, you have to reach people who have higher affluence index and education level which corresponds to cluster 3. For cluster 3, purchasing power is higher and not price oriented, but rather quality oriented.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 3.450704 | 4.140845 | 8.098591 | 0.7808103 | 2.732394 | 23.91549 | 7.492958 | 13055.14 |

2 | 2.588477 | 4.242798 | 15.395062 | 0.4660238 | 3.193416 | 24.25103 | 11.251029 | 12173.96 |

3 | 2.188811 | 4.160839 | 20.615385 | 0.1889798 | 4.237762 | 38.81469 | 21.625874 | 11411.45 |

Then, we applied both elbow and silhouette method to do benchmarking among clusters. According to the elbow method, we can say that best k value is 6. Alternatively, silhouette method determined the number of clusters as 9. As a result, we determined the number of clusters as 6 due to the business implications of having too many clusters.

This plot shows the clustering model when we apply 6 different clusters regarding benchmarking analysis above. Within clusters SSQ is higher and between clusters SSQ is lower than the previous model. This model is slightly better than previous models, but still not best because clusters are overlapping.

KMeans3 | SumOfSquare |
---|---|

Cluster 1 | 1506.44 |

Cluster 2 | 1526.87 |

Cluster 3 | 733.63 |

Cluster 4 | 790.09 |

Cluster 5 | 1743.87 |

Cluster 6 | 1095.17 |

Total Within | 7396.07 |

Between | 4583.93 |

Total | 11980 |

Several issues occur when using the k-means clustering. For example, the k-means algorithm is very sensitive to outliers and noise since the mean statistic is sensitive to these occurrences. In order to address these issues, we chose to explore k-medoids clustering as a second clustering technique.

First, we will apply this clustering using the purchasing behavior. We started by using 3 clusters again. Here is a plot of the clusters using this choice.

size | max_diss | av_diss | diameter | separation |
---|---|---|---|---|

160 | 6.474649 | 2.044774 | 9.059644 | 0.4692172 |

242 | 8.784529 | 2.255817 | 11.178010 | 0.6147253 |

198 | 10.019243 | 2.553799 | 13.270996 | 0.4692172 |

This plot does not vary much visually from the k-means algorithm. Once again, though, we wanted to verify what the optimal number of clusters actually is. Therefore, we chose to use both the elbow and silhouette methods to check for the optimal number of clusters. The elbow method does not demonstrate a clear elbow, but the silhouette method suggests 4 clusters to be optimal. Because of this, we will run the k-medoids again using 4 clusters instead of 3.

The graph below indicates that k medoids model works slightly better than k mean regarding basis for purchase variables, yet there is a still overlapping so distance between clusters is small.

size | max_diss | av_diss | diameter | separation |
---|---|---|---|---|

156 | 6.474649 | 2.005804 | 9.059644 | 0.4692172 |

212 | 7.349266 | 2.017348 | 9.071292 | 0.6147253 |

180 | 8.425903 | 2.324993 | 11.441277 | 0.4692172 |

52 | 7.794822 | 3.004423 | 12.126794 | 1.0068283 |

According to table, cluster 2 has higher affluence index and lower brand loyalty than other clusters. On the other hand, households in cluster 3 is the most loyal customers in this market and brand runs metric is the lowest among all households. Cluster 1 and Cluster 4 have similar consumption patterns with slight differences. Household size of cluster 4 is the highest; hence total consumption vary significantly from other clusters.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.391026 | 3.141026 | 13.98077 | 0.1591571 | 2.634615 | 20.59615 | 11.185897 | 6841.955 |

2 | 2.316038 | 4.551887 | 21.52830 | 0.2478214 | 5.014151 | 44.36792 | 25.268868 | 12592.934 |

3 | 2.727778 | 4.005556 | 13.92222 | 0.7224212 | 2.866667 | 22.88889 | 8.205556 | 10747.722 |

4 | 2.788461 | 6.519231 | 18.48077 | 0.2947516 | 3.692308 | 37.55769 | 16.769231 | 28408.173 |

Now, just like with k means, we will cluster on the basis for purchase variables. Once again, three clusters will be used as a baseline. Here is the result of the three clusters:

size | max_diss | av_diss | diameter | separation |
---|---|---|---|---|

290 | 9.565754 | 1.967946 | 12.687231 | 0.4508119 |

237 | 15.534372 | 2.951697 | 21.109567 | 0.4508119 |

73 | 6.857316 | 1.564375 | 7.758894 | 0.6283877 |

The silhouette method clearly indicates that 2 clusters would be optimal in the case of clustering on basis for purchase variables.

Running the pam algorithm with only two clusters yields clusters of very different sizes, as is visible below.

size | max_diss | av_diss | diameter | separation |
---|---|---|---|---|

254 | 9.825695 | 1.717100 | 12.687231 | 0.4625144 |

179 | 11.236392 | 2.662583 | 13.730433 | 0.4625144 |

70 | 6.857316 | 1.466505 | 7.758894 | 0.6932850 |

97 | 15.234251 | 2.775529 | 21.109567 | 0.6949584 |

This table shows differences between clusters. For example, if we want to launch premium soap, we should try to understand cluster 2 and cluster 4. Nonetheless, when we want to increase sales of low priced products, we have to focus cluster 1 and cluster 3.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.622047 | 4.433071 | 16.374016 | 0.4110557 | 3.610236 | 30.13780 | 14.606299 | 13184.57 |

2 | 2.039106 | 3.905028 | 20.061453 | 0.2254913 | 3.765363 | 34.11173 | 17.944134 | 10513.85 |

3 | 3.385714 | 3.928571 | 8.471429 | 0.7709884 | 2.857143 | 24.92857 | 8.114286 | 12940.21 |

4 | 2.391753 | 4.278351 | 19.268041 | 0.2473120 | 4.030928 | 32.84536 | 20.216495 | 10434.90 |

Lastly, we will impliment the k medoids algorithm on the combined variables. First, we will start with the baseline of three clusters.

size | max_diss | av_diss | diameter | separation |
---|---|---|---|---|

308 | 10.502413 | 3.628277 | 14.88709 | 1.296149 |

72 | 8.068223 | 3.358211 | 11.94597 | 1.656396 |

220 | 16.028957 | 4.176661 | 21.67919 | 1.296149 |

The three clusters are visualized below. The above table shows that the size differences in clusters are large. Instead, we will search for the optimal cluster size in an attempt to balance this.

The silhouette method indicates the optimal number of clusters as 8. This is a large number of clusters, though, given the business implications - 8 separate marketing plans would be very intensive to develop and implement. Therefore, we will select 4 as the best number of clusters given the result of the elbow method. 4 clusters also do not look too bad in the silhouette graph.

size | max_diss | av_diss | diameter | separation |
---|---|---|---|---|

177 | 10.327528 | 3.317120 | 13.87253 | 0.8590833 |

196 | 9.397880 | 3.490261 | 14.05870 | 1.0664848 |

65 | 8.068223 | 3.176974 | 11.94597 | 1.2785782 |

162 | 16.028957 | 4.023063 | 21.67919 | 0.8590833 |

The clusters are somewhat closer in size than just using 3 groups, though cluster 3 is still a lot smaller than the others. In the table below, we can see the differences between the 4 clusters on some of the key variables. Cluster three has higher brand loyalty than the other clusters. The other three, larger clusters vary on some important variables such as brand runs, volume, and household size. Cluster 4 has the lowest household size, and purchases the smallest volume.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.728814 | 4.711864 | 16.050847 | 0.4815375 | 3.112994 | 25.58757 | 10.949153 | 14096.186 |

2 | 2.403061 | 4.821429 | 21.066326 | 0.2102490 | 5.000000 | 46.08673 | 26.132653 | 14036.668 |

3 | 3.446154 | 3.938461 | 7.953846 | 0.7977172 | 2.646154 | 23.92308 | 7.123077 | 13018.692 |

4 | 1.987654 | 2.962963 | 16.820988 | 0.2743024 | 2.956790 | 22.06790 | 11.901235 | 6521.204 |

As a third clustering algorithm, we chose to use hierarchical clustering.

We implemented agglomerative hierarchical clustering by using different distance techniques such as weighted, complete and Ward's method.

We chose the clustering based on Ward's method rather than complete method. The sizes of clusters based on complete and weighted measures vary significantly. Most of households are in cluster 1 (86%). However, clustering with Ward's method creates more balanced clusters. We can check the agglomerative coefficient, which measures the amount of clustering structure found (values closer to 1 suggest strong clustering structure). We can clearly say that Ward's method is better than others on the basis of this dataset.

Method | Value |
---|---|

Agg. Coef of Weighted | 0.89 |

Agg. Coef of Complete | 0.93 |

Agg. Coef of Ward | 0.98 |

Cluster label based on weighted method | Size |
---|---|

1 | 328 |

2 | 269 |

3 | 3 |

Cluster label based on Complete method | Size |
---|---|

1 | 533 |

2 | 53 |

3 | 14 |

Cluster label based on Ward's method | Size |
---|---|

1 | 217 |

2 | 241 |

3 | 142 |

We checked for the optimal number of clusters using the hierarchical method.

This determines 5 clusters to be good based on both the elbow and silhouette methods. However, we determined the number of clusters as 4 since when we increase the number of clusters, size of several clusters is smaller. This does not add any significant value to business, because adding value of focusing some households in small clusters is not worth it.

Cluster label k=4 | Size |
---|---|

1 | 217 |

2 | 202 |

3 | 142 |

4 | 39 |

Cluster label k=5 | Size |
---|---|

1 | 193 |

2 | 202 |

3 | 142 |

4 | 39 |

5 | 24 |

When we check features of the households, we can say that cluster 2 has much higher affluence index but less brand loyalty, meaning households in cluster 2 purchase a wider variety of brands in the market. On the other hand, cluster 1 has higher brand loyalty in this market. The average household size of cluster 4 is much higher than others, which is why volume of consumption is also higher.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.645161 | 3.903226 | 14.12903 | 0.6618342 | 3.082949 | 22.84332 | 9.336405 | 10251.530 |

2 | 2.326733 | 4.678218 | 21.40594 | 0.2316532 | 5.039604 | 45.64356 | 25.950495 | 13423.292 |

3 | 2.500000 | 3.436620 | 14.50000 | 0.1445449 | 2.598591 | 22.75352 | 11.450704 | 7752.676 |

4 | 2.589744 | 6.025641 | 19.56410 | 0.3023641 | 3.230769 | 32.92308 | 14.282051 | 28510.128 |

The first table shows cluster distribution of observations visually and the second one indicates the dendogram of the hierarchical clustering method which was built by using Ward's method with 4 clusters.

Secondly, we applied hierarchical clustering by using different variables. Basis of purchase variables obtains percent of volume purchased not on promotion, on promo code 6 and other than 6, proposition of beauty, health and baby products.

We chose the clustering based on Ward's method rather than the complete method. The sizes of the clusters based on complete and weighted measure vary significantly. Most of households are in cluster 1 (99%). However, clustering with Ward's method creates more balanced clusters. We can clearly say that Ward's method is better than the others on the basis of this dataset.

Method | Value |
---|---|

Agg. Coef of Weighted | 0.95 |

Agg. Coef of Complete | 0.96 |

Agg. Coef of Ward | 0.98 |

Cluster label based on weighted method | Size |
---|---|

1 | 592 |

2 | 7 |

3 | 1 |

Cluster label based on Complete method | Size |
---|---|

1 | 594 |

2 | 5 |

3 | 1 |

Cluster label based on Ward's method | Size |
---|---|

1 | 419 |

2 | 66 |

3 | 115 |

Using these clustering variables, we again checked for the optimal clusters using both silhouette and elbow methods. The elbow and silhouette methods indicate 7-8 clusters as optimal, but this is a large amount from a business perspective.

Instead, we decided to try smaller numbers of clusters and compare. In the first table, cluster 1 dominates other clusters, thus, 4 clusters are better than others. On the other hand, when we increase the number of clusters, some clusters are smaller and that is not sufficient for market segmentation. As a result, we determined the number of clusters as 4 since the clusters are more balanced than others. Concentrating on feasible customer segments is much better.

Cluster label k=3 | Size |
---|---|

1 | 419 |

2 | 66 |

3 | 115 |

Cluster label k=4 | Size |
---|---|

1 | 183 |

2 | 236 |

3 | 66 |

4 | 115 |

Cluster label k=5 | Size |
---|---|

1 | 25 |

2 | 236 |

3 | 66 |

4 | 115 |

5 | 158 |

The first table shows the cluster distribution of observations, and the clusters are overlapping. The features were not able to seperate observations properly. The second one indicates the dendogram of the hierarchical clustering method which was built by using Ward's method with 4 clusters.

The following table indicates the differences between the clusters. Cluster 3 shows significant differences from the other clusters (higher brand loyalty and less affluence index). Nonetheless, clusters 1 and 4 show similar consumption behaviors.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.065574 | 3.841530 | 19.224044 | 0.2375024 | 3.677596 | 32.89071 | 16.912568 | 10649.81 |

2 | 2.635593 | 4.559322 | 17.072034 | 0.4206826 | 3.758475 | 31.03814 | 15.211864 | 13402.30 |

3 | 3.439394 | 3.848485 | 7.636364 | 0.7899970 | 2.818182 | 24.54545 | 7.757576 | 12936.59 |

4 | 2.373913 | 4.191304 | 18.791304 | 0.2421365 | 3.791304 | 32.41739 | 19.600000 | 10288.61 |

Lastly, we applied hierarchical clustering by using combined variables in part a and part b. Combined variables includes both purchase behavior and basis for purchase variables.

We used Ward's method to measure distance between points (Ward performs well) and cut the tree by checking dendrogram. Overall, we can use 5 clusters to segment households and then concentrate on characteristics of these clusters.

Method | Value |
---|---|

Agg. Coef of Ward | 0.96 |

Cluster label k=3 | Size |
---|---|

1 | 340 |

2 | 68 |

3 | 192 |

Cluster label k=4 | Size |
---|---|

1 | 290 |

2 | 68 |

3 | 192 |

4 | 50 |

Cluster label k=5 | Size |
---|---|

1 | 109 |

2 | 181 |

3 | 68 |

4 | 192 |

5 | 50 |

We also checked the elbow and silhouette method for the combined clustering variables. Elbow doesn't give a super distinctive result in this case. Silhouette indicates that we should use 2 clusters, but this is very few, so we still feel a few more are better.

First table shows cluster distribution of observations, and it is evident that clusters are overlapping, meaning the features were not able to seperate observations properly. The second one indicates dendrogram of hierarchical clustering method which was built by using Ward's method with 4 clusters.

This table shows differences between clusters based on combined variables. It can be clearly seen that behavior of cluster 1 and 2 are totally different. Cluster 2 has the lowest affluence index and the highest brand loyalty.(focus this group for low priced products). For premium products, you have to consider households in cluster 1 and cluster 3.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.400000 | 4.465517 | 19.720690 | 0.2172261 | 4.434483 | 38.97241 | 21.748276 | 12500.06 |

2 | 3.382353 | 3.852941 | 8.397059 | 0.7820743 | 2.720588 | 23.45588 | 7.308823 | 12401.69 |

3 | 2.578125 | 4.281250 | 15.708333 | 0.4877121 | 3.088542 | 25.20833 | 11.229167 | 12566.09 |

4 | 1.580000 | 2.720000 | 18.120000 | 0.2582438 | 2.360000 | 19.10000 | 9.820000 | 5356.80 |

We implemented 3 different clustering methods: k-means, k-medoids and hierarchical clustering. The clusters obtained from these procedures are slightly different since the modeling approach varies from model to model. For example, similarity of k-means is based on means. However, k-means is sensitive to outliers and hence k-medoids calculates distances using the median. Additionally, agglomerative hierarchical clustering begins with the maximum number of clusters (the number of observations) and then continues until one cluster is left. We have a chance to cut the dendrogram at the proper level. The advantage of hierarchical clustering is that this model gives an analysis more in depth than other models.

We decided to use a low number of clusters such as 3 and 4 to segment households, because we did not find any benefit from using higher number of clusters in terms of business interpretation. Besides, the total number of households is in order of 100, a considerably small dataset, which also justifies using small number of clusters.

Overall, k-medoids performs slightly better than k-means. Hierarchical clustering and k-medoids shows similar performance by considering distribution of observations. We selected k-medoids model with 4 clusters for best segmentation and clustering. This shows the segmentation of the households in the soap market.

Cluster | SEC | HS | Affluence | maxBr | No of Brands | No of Trans | Brand Runs | Volume |
---|---|---|---|---|---|---|---|---|

1 | 2.728814 | 4.711864 | 16.050847 | 0.4815375 | 3.112994 | 25.58757 | 10.949153 | 14096.186 |

2 | 2.403061 | 4.821429 | 21.066326 | 0.2102490 | 5.000000 | 46.08673 | 26.132653 | 14036.668 |

3 | 3.446154 | 3.938461 | 7.953846 | 0.7977172 | 2.646154 | 23.92308 | 7.123077 | 13018.692 |

4 | 1.987654 | 2.962963 | 16.820988 | 0.2743024 | 2.956790 | 22.06790 | 11.901235 | 6521.204 |

The table below states that cluster 3 has the lowest affluence index and higher brand loyalty. Cluster 1 and 2 have similar households size, yet their brand loyalties are different. Cluster 4 purchases less than other clusters because the household size is lower. This model explains consumer behaviors more and adds vital value to marketing planning.

For this one best segmentation, we built a decision tree by predicting labels of observations. We used information gain as splitting criteria and determined minsplit and complexity parameters as 40 and 0.001, respectively. Then, we checked variable importance as this table shows importance score of each feature:

```
## Avg__Price Pr_Cat_3 Brand_Runs
## 154.719836 140.866784 118.814076
## Pr_Cat_1 No__of__Trans No__of_Brands
## 98.126803 45.781570 42.372578
## Others_999 Pur_Vol_No_Promo____ Pr_Cat_2
## 39.129662 36.143951 36.056754
## Vol_Tran maxBr Pur_Vol_Promo_6__
## 32.375785 31.303730 25.681229
## Trans___Brand_Runs PropCat_15 Pur_Vol_Other_Promo__
## 13.043221 10.900374 8.560410
## PropCat_12 Total_Volume PropCat_5
## 7.630262 6.009459 5.007882
```

According to this table, 'Avg__Price','Pr_Cat_3','Brand_Runs','Pr_Cat_1' and 'No__of__Trans' are the most 5 important variable to predict clustering label accurately. This shows that the most important variables are combination of purchase behavior and basis for purchase.

This table demonstrates the confusion matrix of training data and train accuracy:

```
## predTrn
## 1 2 3 4
## 1 116 7 1 3
## 2 11 120 3 3
## 3 0 0 50 0
## 4 8 20 0 78
```

Metric | Result |
---|---|

Training Accuracy | 0.8667 |

This table shows the confusion matrix of testing data and testing accuracy:

```
## predTst
## 1 2 3 4
## 1 44 4 1 1
## 2 12 44 0 3
## 3 0 0 15 0
## 4 7 12 0 37
```

Metric | Result |
---|---|

Testing Accuracy | 0.7778 |