## 1. Introduction

## 2. Theoretical Framework

#### 2.1. Industrial Market Segmentation

- (1)
- Demographics—industry, company size, and customer location.
- (2)
- Operating variables—technology, user status, and customer capabilities.
- (3)
- Purchasing approaches—purchasing function organization, power structures, buyer-seller relationships, and purchase policies/criteria.
- (4)
- Situational factors—urgence of order fulfillment, product application, and size of order.
- (5)
- Buyers’ personal characteristics—buyer–seller similarity, attitudes toward risk, and buyer motivation/perceptions.

- (1)
- Infrastructure barriers: (This concern the culture, structure, and resources which can prevent the segmentation process from starting or being completed successfully).
- (2)
- Process barriers: These barriers reflect a lack of experience, guidance, and expertise concerning the way in which segmentation is undertaken and managed.
- (3)
- Implementation barriers: These are practical barriers concerning a move to a new segmentation model.

#### 2.2. Artificial Neural Networks

- ${x}_{i}\left(k\right)$ is input value in discrete time $k$ where $i$ goes from 0 to $m$,
- ${w}_{i}\left(k\right)$ is weight value in discrete time $k$ where $i$ goes from 0 to $m$,
- $b$ is bias,
- $F$ is a transfer function,
- ${y}_{i}\left(k\right)$ is output value in discrete time $k$.

#### 2.3. Learning Algorithms

#### 2.4. Literature Review on Spatial Clustering

## 3. Data and Methodology

- 1.
- Initialize network:

- 2.
- Present new input:

- 3.
- Calculate distances:

- 4.
- Update weights:

- 5.
- Repeat from step 2.

## 4. Results and Discussions

## 5. Implications

## 6. Conclusions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Kohonen model [42].

**Table 1.**Summary of clustering classifications and the common algorithms used to achieve partitioning, hierarchical, density-based, or grid-based approaches [66].

Clustering Classification | Description | Common Algorithms |
---|---|---|

Partitioning | Partitions the data into a user specified number of groups. Each point belongs to one group. Does not work well for irregularly shaped clusters. | k-means, k-medoids, clustering large applications-based randomized search (CLARANS), and expectation-maximization (EM) algorithm. |

Hierarchical | Decomposes data into a hierarchy of groups, each larger group contains a set of subgroups. Two methods: agglomerative (builds groups from the observation up), or divisive (start with a large group and separate). | Balanced iterative reducing and clustering using hierarchies (BIRCH), Chameleon, Ward’s method, nearest neighbor, (dendrograms are used to visualize the hierarchy). |

Density-based | Useful for irregularly shaped clusters. Clusters grow based on a threshold for the number of objects in a neighborhood. | DBSCAN, ordering points to identify cluster structure (OPTICS) and density based clustering (DENCLUE), SNN. |

Grid-based | Region is divided into a grid of cells, and clustering is performed on the grid structure. | statistical information grid (STING), Wave Cluster, and clustering in quest (CLIQUE). |

Research | Objective | Methods | Reference |
---|---|---|---|

Visualizing self-organizing maps with GIS | Demonstrate how SOM can be imported into GIS | Self-organizing maps (SOM), GIS | [68] |

Marketing Analytics: Methods, Practice, Implementation, and Links to Other Fields | To provide a practical, implementation-based overview of marketing analytics methodology | Theoretical review | [69] |

Local News Online: Aggregators, Geo-Targeting and the Market for Local News | Examines how placement of geo-targeted local news links on Google News affected local news consumption | Empirical analysis is to identify the effect of adding geo-targeted local news links to Google | [70] |

Optimal Segmentation in Platform Strategy: Evidence from Geotargeted Advertising | Provides a model to improve ad targeting | Regression discontinuity | [71] |

Customer Recognition and Mobile Geo-Targeting | Investigates how combining behavior-based marketing with mobile geo-targeting influences profits and welfare in a competitive environment | Geo targeting | [72] |

Competitive Mobile Geo Targeting | Investigates in a competitive setting the consequences of mobile geo targeting, the practice of firms targeting consumers based on their real-time locations. | Mobile targeting, Geo-Targeting analytical models | [73] |

3D Geomarketing segmentation: A higher spatial dimension planning perspective | Reduce the overlapping issue during the process of cluster segmentation | Case studies, k-means algorithm | [67] |

Application of k-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services | Investigate the utility of k-means algorithm in customer segmentation | MATLAB, k-means algorithm | [74] |

Customer Portfolio Analysis Using the Self-Organizing Map | Investigate how the self-organizing map (SOM) can be used for one category of CRM, customer portfolio analysis (CPA) | Customer portfolio analysis (CPA), data-driven market segmentation, self-organizing map (SOM) | [75] |

Clustering With GIS: An Attempt to Classify Turkish District Data | Compare different clustering techniques for spatial classification and perform a classification for Turkey’s districts | GIS, spatial clustering techniques, SOM algorithm | [65] |

Variable | Description | N° of Observations |
---|---|---|

Customer Number | Unique customer ID | 2881 |

Latitude | Y-Coordinate of customer locations | 2881 |

Longitude | X-Coordinate of customer locations | 2881 |

Product A-2019 | Product A Revenue in 2019 | 2881 |

Product B-2019 | Product B Revenue in 2019 | 2881 |

Product C-2019 | Product C Revenue in 2019 | 2881 |

Product D-2019 | Product D Revenue in 2019 | 2881 |

Product E-2019 | Product E Revenue in 2019 | 2881 |

Ttl. Revenue (2019) | Total Revenue in 2019 | 2881 |

Industry_Type | Aerospace, Automotive, Machining | 2881 |

Product_Category | Finished Goods, Repair, Spare Parts | 2881 |

Clusters | Centers/Geo-Coordinates | Spatial Distribution |
---|---|---|

1 | 40.360841; −78.890987 | Ohio, Pennsylvania, New York, Massachusetts, Connecticut, Maryland |

2 | 41.379610; −77.218788 | Pennsylvania, New York, Massachusetts |

3 | 41.950033; −82.890716 | Ontario, Michigan, Illinois, Indiana, Wisconsin |

4 | 42.442861; −87.512295 | Michigan, Wisconsin, Illinois, Ohio, Indiana |

5 | 41.934342; −86.685673 | Michigan, Indiana, Wisconsin |

6 | 41.486035; −84.331184 | Ohio, Indiana |

7 | 40.740315; −82.233762 | Ohio |

8 | 41.604746; −80.024125 | Pennsylvania; Iowa, Ohio, Indiana |

9 | 39.968397; −91.513968 | Missouri, Illinois, Iowa, South Dakota |

10 | 39.830666; −92.607506 | Kentucky, Missouri, Virginia, Tennessee |

11 | 37.242167; −86.926336 | Kentucky, Virginia, Maryland, Pennsylvania, Ohio |

12 | 37.824302; −80.553858 | Texas, Louisiana, New Mexico, Arizona, Colorado |

13&14 | 36.126735; −108.637728 | Arizona, Utah, San Francisco, Nevada |

15 | 32.760898; −96.915516 | Texas, Arkansas, Oklahoma, Tennessee |

16 | 32.102329; −86.820285 | Florida, Georgia, Alabama, Louisiana, Mississippi, Tennessee |

Clusters | Number of Customers | % in Total | Total Sales 2019 | Industry Type | Product Category | ||||
---|---|---|---|---|---|---|---|---|---|

USD | Automotive | Aerospace | Machining | Finished Goods | Spare Parts | Repair | |||

1 | 202 | 7% | 1,677,728 | 104 | 31 | 67 | 88 | 44 | 70 |

2 | 141 | 5% | 367,479 | 63 | 30 | 48 | 111 | 0 | 30 |

3 | 133 | 5% | 961,058 | 62 | 28 | 43 | 133 | 0 | 0 |

4 | 143 | 5% | 1,143,886 | 75 | 30 | 38 | 0 | 92 | 51 |

5 | 104 | 4% | 849,350 | 42 | 19 | 43 | 104 | 0 | 0 |

6 | 136 | 5% | 618,963 | 76 | 24 | 36 | 119 | 0 | 17 |

7 | 44 | 2% | 244,528 | 26 | 7 | 11 | 0 | 0 | 44 |

8 | 111 | 4% | 677,657 | 55 | 20 | 36 | 0 | 111 | 0 |

9 | 418 | 15% | 3,368,942 | 216 | 78 | 124 | 418 | 0 | 0 |

10 | 65 | 2% | 418,376 | 29 | 17 | 19 | 65 | 0 | 0 |

11 | 186 | 7% | 2,308,062 | 93 | 35 | 58 | 0 | 0 | 186 |

12 | 270 | 10% | 3,893,410 | 137 | 60 | 73 | 0 | 270 | 0 |

13 & 14 | 330 | 12% | 2,567,582 | 170 | 62 | 98 | 330 | 0 | 0 |

15 | 129 | 5% | 1,380,176 | 52 | 29 | 48 | 0 | 0 | 129 |

16 | 294 | 11% | 3,011,615 | 154 | 62 | 78 | 0 | 0 | 294 |

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