New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System
Abstract
:1. Introduction
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- To propose a new RFI model that is generally applicable to measure the audience behaviors in Wi-Fi advertising system.
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- To segment the audience behaviors into well-defined groups based on the RFI model.
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- To create a dynamic-characteristics range table to interpret the segmented behavioral characteristics of the audience based on their respective RFI values.
2. Related Works
2.1. Behavioral Analysis
2.2. Audience Segmentation
2.3. Performance Evaluation Metrics
3. Proposed Framework
3.1. Dataset
3.2. Data Cleaning
3.3. Data Transformation
4. Experiments and Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Domain | Year | Behavioral Analysis | Behavioral Segmentation | Purpose of Application |
---|---|---|---|---|
e-Commerce | 2021 [4] | RFM model | K-means clustering | Purchasing behavioral segmentation - Segment the customers according to their purchasing behaviors as the marketing reference. |
2021 [5] | RFMT model | Agglomerative hierarchical clustering (AHC) | Online shopping behavioral segmentation - Segment the customers based on their shopping behaviors to discover their online shopping patterns. | |
2022 [6] | RFM model | K-means clustering | Customer classification - Segment the customers according to their behaviors for improving sales. | |
2022 [7] | RFM model | Gaussian Mixture Model (GMM) | Supermarket customer segmentation - Segment the customers according to their purchasing behaviors. | |
Banking | 2021 [8] | RFM model | RFM score | Customer segmentation - Segment the customer behaviors in bank activities in increasing bank average balances. |
2022 [9] | RFMT model | K-means clustering, Agglomerative hierarchical clustering (AHC) | Mobile banking behavioral segmentation - Segment the customers to discover the customer’s transaction patterns in banking. | |
Insurance | 2020 [10] | RFM model | RFM score | Policyholder segmentation - Segment the policyholders according to their claiming patterns. |
2022 [11] | RFM model | K-means clustering | Client segmentation - Segment the clients according to their behaviors for the needs of the policy. |
Categories | Algorithm | Key Characteristic |
---|---|---|
Centroid based | K-means [29] | Partitioning the data into k clusters based on the centroid of cluster |
Hierarchical based | Agglomerative Hierarchical Clustering [30] | Recursively merging the nearest pair of data or clusters to generate a hierarchy of clusters |
Model based | Gaussian Mixture Model (GMM) [31] | Estimating the probabilities of each data belonging to each cluster |
Case | Campaign | Algorithm | Elbow Method | Silhouette Score | CH Index | Dunn Index |
---|---|---|---|---|---|---|
Normal case | 764 | K-means | 5 | 5 | 5 | 3 |
Agglomerative hierarchical | 5 | 5 | 5 | 4 | ||
Gaussian Mixture Model | 3 | 5 | 3 | 3 | ||
776 | K-means | 5 | 5 | 5 | 3 | |
Agglomerative hierarchical | 5 | 4 | 5 | 3 | ||
Gaussian Mixture Model | 3 | 4 | 3 | 3 | ||
Special case | 764 | K-means | 5 | 5 | 5 | 3 |
Agglomerative hierarchical | 4 | 4 | 4 | 4 | ||
Gaussian Mixture Model | 3 | 5 | 3 | 3 | ||
776 | K-means | 5 | 5 | 5 | 3 | |
Agglomerative hierarchical | 5 | 5 | 5 | 3 | ||
Gaussian Mixture Model | 3 | 3 | 3 | 3 |
Case | Campaign | Clustering Algorithm | Elbow Method ↓ | Silhouette Score ↑ | CH Index ↑ | Dunn Index ↑ |
---|---|---|---|---|---|---|
Normal case | 764 | K-means | 17,179.932 | 0.512 | 12,127.681 | 0.337 |
Agglomerative hierarchical | 23,695.748 | 0.459 | 9911.987 | 0.734 | ||
Gaussian Mixture Model | 34,949.082 | 0.143 | 6897.579 | 0.771 | ||
776 | K-means | 16,297.157 | 0.511 | 12,658.076 | 0.393 | |
Agglomerative hierarchical | 17,555.738 | 0.495 | 11,400.780 | 0.375 | ||
Gaussian Mixture Model | 34,077.653 | 0.419 | 7015.656 | 0.785 | ||
Special case | 764 | K-means | 17,737.915 | 0.501 | 11,590.687 | 0.320 |
Agglomerative hierarchical | 19,004.798 | 0.490 | 10,488.531 | 0.399 | ||
Gaussian Mixture Model | 38,536.754 | 0.367 | 5750.592 | 0.472 | ||
776 | K-means | 17,198.727 | 0.496 | 11,738.731 | 0.349 | |
Agglomerative hierarchical | 19,000.083 | 0.480 | 10,163.169 | 0.400 | ||
Gaussian Mixture Model | 37,381.868 | 0.379 | 5901.983 | 0.499 |
Campaign | Audience Number | Audience Percentage | Cluster | Recency ↓ (Day) | Frequency ↑ (Time) | Interest ↑ (Second) |
---|---|---|---|---|---|---|
764 (Total audience 19,777) | 1693 | 8.56% | 1 | 2.3156 | 4.1784 | 0.1110 |
8457 | 42.76% | 2 | 3.1211 | 1.2157 | 0.0000 | |
7511 | 37.98% | 3 | 10.1687 | 1.0570 | 0.0000 | |
122 | 0.62% | 4 | 2.5039 | 5.3934 | 3.6148 | |
1994 | 10.08% | 5 | 6.4750 | 1.3661 | 1.0587 | |
776 (Total audience 19,524) | 7483 | 38.33% | 1 | 10.2028 | 1.0588 | 0.0000 |
1601 | 8.20% | 2 | 2.3031 | 4.1537 | 0.0906 | |
8268 | 42.35% | 3 | 3.0987 | 1.2124 | 0.0000 | |
131 | 0.67% | 4 | 2.4740 | 5.2595 | 3.7023 | |
2041 | 10.45% | 5 | 6.3460 | 1.3988 | 1.0642 |
Campaign | Audience Number | Audience Percentage | Cluster | Recency ↓ (Day) | Frequency ↑ (Time) | Interest ↑ (Second) |
---|---|---|---|---|---|---|
764 (Total audience 19,777) | 1764 | 8.92% | 1 | 2.2631 | 4.1508 | 3.9372 |
8458 | 42.77% | 2 | 3.1215 | 1.2158 | 0.0000 | |
7514 | 37.99% | 3 | 10.1718 | 1.0568 | 0.0112 | |
127 | 0.64% | 4 | 2.7980 | 4.8110 | 115.7868 | |
1914 | 9.68% | 5 | 6.6500 | 1.3161 | 27.4169 | |
776 (Total audience 19,524) | 7570 | 38.77% | 1 | 10.2471 | 1.0577 | 0.2763 |
1647 | 8.44% | 2 | 2.2784 | 4.1299 | 3.0429 | |
8274 | 42.38% | 3 | 3.1014 | 1.2127 | 0.0000 | |
138 | 0.71% | 4 | 2.8428 | 4.8696 | 115.9700 | |
1895 | 9.70% | 5 | 6.0973 | 1.3858 | 28.2536 |
Criteria | Quartile Range | Actual Range | Characteristics | |
---|---|---|---|---|
Campaign 764 | Campaign 776 | |||
Recency (R) | R = 0 | value = 0.0000 | value = 0.0000 | Audience with no gap time from the last engagement |
0 < R ≤ 25% | 0 < value ≤ 3.2551 | 0 < value ≤ 3.2566 | Audience with shortest gap time from the last engagement | |
25% < R ≤ 50% | 3.2551 < value ≤ 6.0630 | 3.2566 < value ≤ 6.0915 | Audience with medium gap time from the last engagement | |
50% < R ≤ 75% | 6.0630 < value ≤ 8.8710 | 6.0915 < value ≤ 8.9265 | Audience with long gap time from the last engagement | |
R > 75% | value > 8.8710 | value > 8.9265 | Audience with longest gap time from the last engagement | |
Frequency (F) | F ≤ 0 | value = 0.0000 | value = 0.0000 | Audience with no frequency |
0 < F ≤ 25% | 0 < value ≤ 2.2271 | 0 < value ≤ 2.2105 | Audience with low frequency | |
25% < F ≤ 50% | 2.2271 < value ≤ 3.0900 | 2.2105 < value ≤ 3.0348 | Audience with medium frequency | |
50% < F ≤ 75% | 3.0900 < value ≤ 3.9530 | 3.0348 < value ≤ 3.8592 | Audience with high frequency | |
F > 75% | value > 3.9530 | value > 3.8592 | Audience with highest frequency | |
Interest (I) | I = 0 | value = 0.0000 | value = 0.0000 | Audience with no interest in the advertisement |
0 < I ≤ 25% | 0 < value ≤ 18.0680 | 0 < value ≤ 29.2051 | Audience with low interest in the advertisement | |
25% < I ≤ 50% | 18.0680 < value ≤ 54.2039 | 29.2051 < value ≤ 58.4103 | Audience with medium interest in the advertisement | |
50% < I ≤ 75% | 54.2039 < value ≤ 176.1219 | 58.4103 < value ≤ 188.3255 | Audience with high interest in the advertisement | |
I > 75% | value > 176.1219 | value > 188.3255 | Audience with highest interest in the advertisement |
Campaign | Case | Cluster | Audience Percentage | Characteristics |
---|---|---|---|---|
764 | Normal case | 1 | 8.56% | Audience with shortest gap time from the last engagement, highest frequency, and low interest in the advertisement |
2 | 42.76% | Audience with shortest gap time from the last engagement, low frequency, and no interest in the advertisement | ||
3 | 37.98% | Audience with longest gap time from the last engagement, low frequency, and no interest in the advertisement | ||
4 | 0.62% | Audience with shortest gap time from the last engagement, highest frequency, and highest interest in the advertisement | ||
5 | 10.08% | Audience with long gap time from the last engagement, low frequency, and medium interest in the advertisement | ||
Special case | 1 | 8.92% | Audience with shortest gap time from the last engagement, highest frequency, and low interest in the advertisement | |
2 | 42.77% | Audience with shortest gap time from the last engagement, low frequency, and no interest in the advertisement | ||
3 | 37.99% | Audience with longest gap time from the last engagement, low frequency, and low interest in the advertisement | ||
4 | 0.64% | Audience with shortest gap time from the last engagement, highest frequency, and high interest in the advertisement | ||
5 | 9.68% | Audience with long gap time from the last engagement, low frequency, and medium interest in the advertisement | ||
776 | Normal case | 1 | 38.33% | Audience with longest gap time from the last engagement, low frequency, and no interest in the advertisement |
2 | 8.20% | Audience with shortest gap time from the last engagement, highest frequency, and low interest in the advertisement | ||
3 | 42.35% | Audience with shortest gap time from the last engagement, low frequency, and no interest in the advertisement | ||
4 | 0.67% | Audience with shortest gap time from the last engagement, highest frequency, and highest interest in the advertisement | ||
5 | 10.45% | Audience with long gap time from the last engagement, low frequency, and medium interest in the advertisement | ||
Special case | 1 | 38.77% | Audience with longest gap time from the last engagement, low frequency, and low interest in the advertisement | |
2 | 8.44% | Audience with shortest gap time from the last engagement, highest frequency, and low interest in the advertisement | ||
3 | 42.38% | Audience with shortest gap time from the last engagement, low frequency, and no interest in the advertisement | ||
4 | 0.71% | Audience with shortest gap time from the last engagement, highest frequency, and high interest in the advertisement | ||
5 | 9.70% | Audience with long gap time from the last engagement, low frequency, and medium interest in the advertisement |
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Lim, S.-T.; Ong, L.-Y.; Leow, M.-C. New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System. Future Internet 2023, 15, 351. https://doi.org/10.3390/fi15110351
Lim S-T, Ong L-Y, Leow M-C. New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System. Future Internet. 2023; 15(11):351. https://doi.org/10.3390/fi15110351
Chicago/Turabian StyleLim, Shueh-Ting, Lee-Yeng Ong, and Meng-Chew Leow. 2023. "New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System" Future Internet 15, no. 11: 351. https://doi.org/10.3390/fi15110351