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Correlation analysis between customer’s behaviour on website and actual purchase
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Procediaonline Computer Science 00 (2018) 000–000 Available at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 126 (2018) 1450–1456
22nd International Conference on Knowledge-Based and Intelligent Information & 22nd International ConferenceEngineering on Knowledge-Based Systems and Intelligent Information & Engineering Systems
Correlation analysis between customer’s behaviour on website and Correlation analysis between customer’s behaviour on website and actual purchase actual purchase Satoshi Yoshimarua* , Daisuke Sakamotoa, Takehiko Yazawaa, Kazuhiko Tsudab Satoshi Yoshimarua*, Daisuke Sakamotoa, Takehiko Yazawaa, Kazuhiko Tsudab a a
Honda Motor Co., Ltd., 8-1, Hon-machi, Wako-shi, Saitama-Pref, 351-0114, Japan b University of Tsukuba, Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan Honda Motor Co., Ltd., 8-1,3-29-1, Hon-machi, Wako-shi, Saitama-Pref, 351-0114, Japan b University of Tsukuba, 3-29-1, Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan
Abstract Abstract The study of demand forecasting for automobile has been tried for a long time. The demand forecasting for automobile is a quite important for business profit of view. this report, showtime. a newThe forecasting approach tofor understand theistrend of The study matter of demand forecasting forpoint automobile hasInbeen tried forwea long demand forecasting automobile a quite important for business profit point of view. In this report, we show a new forecasting approach to understand the trend of automobilematter market. automobile market. © 2018 The Authors. Published by Elsevier Ltd. © 2018 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. © This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection of KES KES International. International. Selection and and peer-review under under responsibility responsibility of Selection and peer-review under responsibility of KES International. Keywords: Type your keywords here, separated by semicolons ; Keywords: Type your keywords here, separated by semicolons ;
1. Introduction 1. Introduction The demand forecasting is very important factor for all automobile companies. If stock too much, Q)Quality issue demand forecasting is very important factormanagement for all automobile companies. stock too much, issue by The aging degradation, C)High scrap cost, D)High man-day for stockIf control might beQ)Quality caused. And if by aging degradation, C)High cost, D)High management man-day for stock control might caused. And if stock too less, Q)Quality issues scrap by quick job, C)Temporary cost, D)Opportunity lost by long due datebemight be caused. stock are too less, Q)Quality by quick job, C)Temporary cost, way, D)Opportunity lost due date might be caused. Both wanted to avoidissues to face. To consider macro statistics the number ofby thelong related element (economics, Both wanted to too face. To consider statistics way, same the number of prices. the related news,are politics andtosoavoid on) is much to pursuemacro the correct answer, as stock Alsoelement the sales(economics, results are news, andforsoexample on) is too much tocurved pursueline the design correctand answer, same as design stock prices. Alsoboth the are salesinresults are totally politics different between straight line although the same totally different for example between curved line design and straight linemodel. design although both are in the same segment. It is almost impossible to forecast including characteristics of each segment. It is almost impossible to forecast including characteristics of each model. * Corresponding author. E-mail address: [email protected] * Corresponding author. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of KES International. This is an and openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International.
1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 10.1016/j.procs.2018.08.117
2
Author name / Procedia Computer Science 00 (2018) 000–000
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456
1451
Table 1. QCD point of view Stock too less
Stock too much
Q (Quality)
Quality issues by quick job
Quality issue by aging degradation
C (Cost)
Temporary cost
High scrap cost
D (Delivery)
Opportunity lost by long due date
High management man-day for stock control
Currently, a few companies are trying to forecast for Japan automobile market. Nissan has been trying to utilize AI for forecasting with Yahoo. Isuzu started a new project to forecast for proper stock management. Honda has also been trying to forecast based on Big Data analysis. As said in the beginning, to consider macro statistics ways for demand forecasting for automobile is very difficult and also incorrect way. We introduce a new forecasting approach to utilize actual customer's motion to understand market sales trend for actual business utilization. A lot of approach by macro statistics way has been researched for a long time. However the approach by macro point of view (especially customer's behaviour on websites) is rare. This report will be a new type survey to show the relationship between actual customer's behaviour (micro) and market sales trend (macro). 2. Survey Recently, there are a lot of researches about forecasting automobile demand. About sales quantity, FK.Wang, 2011 shows the forecasting methodology to utilize current automobile sales quantity, wholesale price index and income. Birendra K. Mishra, 2009 also shows the value for manufacturer and retailer to forecast automobile demand for inventory control and cost reduction. Mainly these reports are based on macro indicator (economics, demography, and so on), so the forecasting result depends on the economic trend. There is no report to show the forecasting methodology based on micro indicator (each customer’s behaviour). 3. Analytical method 3.1. Web self-estimation Honda is a global company and has Website for each country. Japan market, the focus of this report, also has a website and the function of self estimation is provided for every customer on this website. Customers can try to estimate which they want to purchase freely on the website, and all results of self-estimation have been stock as logs. Fig. 1. Honda HP (Japan) and Self-estimation
Author name / Procedia Computer Science 00 (2018) 000–000
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Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456 Fig. 2. Self-estimation Result log
3.2. Viewpoint To utilize the logs (all results of self-estimation), we show four analysis results. 1) Timing, 2) Correlation, 3) Stability, 4) Relation of TV Commercial, Self-estimation on Website, and Sales result. 4. Result of analysis 4.1. Timing To visualize the shift of Self-estimation trend, we found a linkage of timing with actual sales result. Orbit shape of them is similar and the shift of Self-estimation trend is 0-2 months faster than that of actual sales result. Mainly millennial generation check the specifications and the price of the car which they are considering to purchase on website with self-estimation at first, and then they visit to dealership to purchase. Fig. 3. Timing linkage
4
Author name / Procedia Computer Science 00 (2018) 000–000
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456
1453
4.2. Correlation Self-estimation can show the result about combination of grade, color, option. Freed is one of the most popular models in Japan, MMC launched on 16th September, 2016. Using this model, we researched the correlation between the self-estimation trend on website and actual sales result, and we found strong correlation on select rate. Fig. 4. Correlation on FREED
4.3. Stability Self-estimation service is usually released on launch date of each new model. Highest number of self-estimation logs are gathered on this first day after launched. After this timing, the number of self-estimation logs usually decrease, however select rate doesn't change. This stability shows that we can grasp a sales forecast to see the result of selfestimation trend on first day after launched. Fig. 5. Select rate stability on Web-estimation
Author name / Procedia Computer Science 00 (2018) 000–000
1454
5
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456 Fig. 6. Select rate stability on Web-estimation (All option of FREED)
4.4. Relation of TV Commercial, Self-estimation on Website, and Sales result We researched correlation about TV-commercial and self-estimation and actual sales result of Fit, known as “Jass” in many countries. From end of June 2016, TV-commercial has started about Fit using the model colored Rouge Amethyst. Then this Rouge Amethyst rapidly became one of the most popular color on self-estimation and soon the strong correlation on select rate realized between self-estimation and actual sales result. It shows there is strong linkage with TV-commercial and self-estimation and actual sales result. Fig. 7. FIT TV-commercial on the end of June, 2017
6
Author name / Procedia Computer Science 00 (2018) 000–000
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456
1455
Fig. 8. Correlation from TV-commercial to actual sales result
5. Conclusion Through this research, we could prove the linkage between customer's behaviour on website (micro) and actual sales trend (macro). To elucidate the linkage logic more deeply, we will be able to optimize our corporate activities for example on stock control, production planning, sales strategy, and so on. Millennials will make up 60% of the global adult population by 2030, and to understand their behaviour on website will become more important to grasp the actual need of customer. We will continue the research to find out clear linkage logic.
References [1] FK Wang, KK Chang, CW Tzeng (2011) “Using adaptive network-based fuzzy inference system to forecast automobile sales” Expert Systems with Applications [2] J Berkovec (1985) “Forecasting automobile demand using disaggregate choice models” Transportation Research Part B: Methodological
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Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456 Author name / Procedia Computer Science 00 (2018) 000–000
7
[3] Birendra K. Mishra, Srinivasan Raghunathan, Xiaohang Yue (2009) “Demand Forecast Sharing in Supply Chains” Production and Operations [4] SA Abu-Eisheh, FL Mannering (2002) “Forecasting automobile demand for economies in transition: A dynamic simultaneous-equation system approach” Transportation Planning and Technology [5] S Shahabuddin (2009) “Forecasting automobile sales” Management Research News [6] FL Mannering, K Train (1985) “Recent directions in automobile demand modeling” Transportation Research Part B: Methodological [7] B Brühl, M Hülsmann, D Borscheid (2009) “A sales forecast model for the german automobile market based on time series analysis and data mining methods” Industrial Conference on Data Mining [8] U Ramanathan (2012) “Supply chain collaboration for improved forecast accuracy of promotional sales” International Journal of Operations & Production Management [9] CY Kung, CP Chang (2004) “Application of Grey Prediction Model on China Automobile Industry” Journal of Grey System [10] FJ Kovac, MF Dague (1972) “Forecasting by product life cycle analysis” Research Management [11] JS Armstrong, VG Morwitz, V Kumar (2000) “Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?” International Journal of Forecasting
ScienceDirect ScienceDirect
Procediaonline Computer Science 00 (2018) 000–000 Available at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 126 (2018) 1450–1456
22nd International Conference on Knowledge-Based and Intelligent Information & 22nd International ConferenceEngineering on Knowledge-Based Systems and Intelligent Information & Engineering Systems
Correlation analysis between customer’s behaviour on website and Correlation analysis between customer’s behaviour on website and actual purchase actual purchase Satoshi Yoshimarua* , Daisuke Sakamotoa, Takehiko Yazawaa, Kazuhiko Tsudab Satoshi Yoshimarua*, Daisuke Sakamotoa, Takehiko Yazawaa, Kazuhiko Tsudab a a
Honda Motor Co., Ltd., 8-1, Hon-machi, Wako-shi, Saitama-Pref, 351-0114, Japan b University of Tsukuba, Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan Honda Motor Co., Ltd., 8-1,3-29-1, Hon-machi, Wako-shi, Saitama-Pref, 351-0114, Japan b University of Tsukuba, 3-29-1, Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan
Abstract Abstract The study of demand forecasting for automobile has been tried for a long time. The demand forecasting for automobile is a quite important for business profit of view. this report, showtime. a newThe forecasting approach tofor understand theistrend of The study matter of demand forecasting forpoint automobile hasInbeen tried forwea long demand forecasting automobile a quite important for business profit point of view. In this report, we show a new forecasting approach to understand the trend of automobilematter market. automobile market. © 2018 The Authors. Published by Elsevier Ltd. © 2018 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. © This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection of KES KES International. International. Selection and and peer-review under under responsibility responsibility of Selection and peer-review under responsibility of KES International. Keywords: Type your keywords here, separated by semicolons ; Keywords: Type your keywords here, separated by semicolons ;
1. Introduction 1. Introduction The demand forecasting is very important factor for all automobile companies. If stock too much, Q)Quality issue demand forecasting is very important factormanagement for all automobile companies. stock too much, issue by The aging degradation, C)High scrap cost, D)High man-day for stockIf control might beQ)Quality caused. And if by aging degradation, C)High cost, D)High management man-day for stock control might caused. And if stock too less, Q)Quality issues scrap by quick job, C)Temporary cost, D)Opportunity lost by long due datebemight be caused. stock are too less, Q)Quality by quick job, C)Temporary cost, way, D)Opportunity lost due date might be caused. Both wanted to avoidissues to face. To consider macro statistics the number ofby thelong related element (economics, Both wanted to too face. To consider statistics way, same the number of prices. the related news,are politics andtosoavoid on) is much to pursuemacro the correct answer, as stock Alsoelement the sales(economics, results are news, andforsoexample on) is too much tocurved pursueline the design correctand answer, same as design stock prices. Alsoboth the are salesinresults are totally politics different between straight line although the same totally different for example between curved line design and straight linemodel. design although both are in the same segment. It is almost impossible to forecast including characteristics of each segment. It is almost impossible to forecast including characteristics of each model. * Corresponding author. E-mail address: [email protected] * Corresponding author. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 Thearticle Authors. Published by Elsevier Ltd. Selection under responsibility of KES International. This is an and openpeer-review access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International.
1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of KES International. 10.1016/j.procs.2018.08.117
2
Author name / Procedia Computer Science 00 (2018) 000–000
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456
1451
Table 1. QCD point of view Stock too less
Stock too much
Q (Quality)
Quality issues by quick job
Quality issue by aging degradation
C (Cost)
Temporary cost
High scrap cost
D (Delivery)
Opportunity lost by long due date
High management man-day for stock control
Currently, a few companies are trying to forecast for Japan automobile market. Nissan has been trying to utilize AI for forecasting with Yahoo. Isuzu started a new project to forecast for proper stock management. Honda has also been trying to forecast based on Big Data analysis. As said in the beginning, to consider macro statistics ways for demand forecasting for automobile is very difficult and also incorrect way. We introduce a new forecasting approach to utilize actual customer's motion to understand market sales trend for actual business utilization. A lot of approach by macro statistics way has been researched for a long time. However the approach by macro point of view (especially customer's behaviour on websites) is rare. This report will be a new type survey to show the relationship between actual customer's behaviour (micro) and market sales trend (macro). 2. Survey Recently, there are a lot of researches about forecasting automobile demand. About sales quantity, FK.Wang, 2011 shows the forecasting methodology to utilize current automobile sales quantity, wholesale price index and income. Birendra K. Mishra, 2009 also shows the value for manufacturer and retailer to forecast automobile demand for inventory control and cost reduction. Mainly these reports are based on macro indicator (economics, demography, and so on), so the forecasting result depends on the economic trend. There is no report to show the forecasting methodology based on micro indicator (each customer’s behaviour). 3. Analytical method 3.1. Web self-estimation Honda is a global company and has Website for each country. Japan market, the focus of this report, also has a website and the function of self estimation is provided for every customer on this website. Customers can try to estimate which they want to purchase freely on the website, and all results of self-estimation have been stock as logs. Fig. 1. Honda HP (Japan) and Self-estimation
Author name / Procedia Computer Science 00 (2018) 000–000
1452
3
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456 Fig. 2. Self-estimation Result log
3.2. Viewpoint To utilize the logs (all results of self-estimation), we show four analysis results. 1) Timing, 2) Correlation, 3) Stability, 4) Relation of TV Commercial, Self-estimation on Website, and Sales result. 4. Result of analysis 4.1. Timing To visualize the shift of Self-estimation trend, we found a linkage of timing with actual sales result. Orbit shape of them is similar and the shift of Self-estimation trend is 0-2 months faster than that of actual sales result. Mainly millennial generation check the specifications and the price of the car which they are considering to purchase on website with self-estimation at first, and then they visit to dealership to purchase. Fig. 3. Timing linkage
4
Author name / Procedia Computer Science 00 (2018) 000–000
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456
1453
4.2. Correlation Self-estimation can show the result about combination of grade, color, option. Freed is one of the most popular models in Japan, MMC launched on 16th September, 2016. Using this model, we researched the correlation between the self-estimation trend on website and actual sales result, and we found strong correlation on select rate. Fig. 4. Correlation on FREED
4.3. Stability Self-estimation service is usually released on launch date of each new model. Highest number of self-estimation logs are gathered on this first day after launched. After this timing, the number of self-estimation logs usually decrease, however select rate doesn't change. This stability shows that we can grasp a sales forecast to see the result of selfestimation trend on first day after launched. Fig. 5. Select rate stability on Web-estimation
Author name / Procedia Computer Science 00 (2018) 000–000
1454
5
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456 Fig. 6. Select rate stability on Web-estimation (All option of FREED)
4.4. Relation of TV Commercial, Self-estimation on Website, and Sales result We researched correlation about TV-commercial and self-estimation and actual sales result of Fit, known as “Jass” in many countries. From end of June 2016, TV-commercial has started about Fit using the model colored Rouge Amethyst. Then this Rouge Amethyst rapidly became one of the most popular color on self-estimation and soon the strong correlation on select rate realized between self-estimation and actual sales result. It shows there is strong linkage with TV-commercial and self-estimation and actual sales result. Fig. 7. FIT TV-commercial on the end of June, 2017
6
Author name / Procedia Computer Science 00 (2018) 000–000
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456
1455
Fig. 8. Correlation from TV-commercial to actual sales result
5. Conclusion Through this research, we could prove the linkage between customer's behaviour on website (micro) and actual sales trend (macro). To elucidate the linkage logic more deeply, we will be able to optimize our corporate activities for example on stock control, production planning, sales strategy, and so on. Millennials will make up 60% of the global adult population by 2030, and to understand their behaviour on website will become more important to grasp the actual need of customer. We will continue the research to find out clear linkage logic.
References [1] FK Wang, KK Chang, CW Tzeng (2011) “Using adaptive network-based fuzzy inference system to forecast automobile sales” Expert Systems with Applications [2] J Berkovec (1985) “Forecasting automobile demand using disaggregate choice models” Transportation Research Part B: Methodological
1456
Satoshi Yoshimaru et al. / Procedia Computer Science 126 (2018) 1450–1456 Author name / Procedia Computer Science 00 (2018) 000–000
7
[3] Birendra K. Mishra, Srinivasan Raghunathan, Xiaohang Yue (2009) “Demand Forecast Sharing in Supply Chains” Production and Operations [4] SA Abu-Eisheh, FL Mannering (2002) “Forecasting automobile demand for economies in transition: A dynamic simultaneous-equation system approach” Transportation Planning and Technology [5] S Shahabuddin (2009) “Forecasting automobile sales” Management Research News [6] FL Mannering, K Train (1985) “Recent directions in automobile demand modeling” Transportation Research Part B: Methodological [7] B Brühl, M Hülsmann, D Borscheid (2009) “A sales forecast model for the german automobile market based on time series analysis and data mining methods” Industrial Conference on Data Mining [8] U Ramanathan (2012) “Supply chain collaboration for improved forecast accuracy of promotional sales” International Journal of Operations & Production Management [9] CY Kung, CP Chang (2004) “Application of Grey Prediction Model on China Automobile Industry” Journal of Grey System [10] FJ Kovac, MF Dague (1972) “Forecasting by product life cycle analysis” Research Management [11] JS Armstrong, VG Morwitz, V Kumar (2000) “Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?” International Journal of Forecasting