Empirical Study of Omnichannel Purchasing Pattern with Real Customer Data from Health and Lifestyle Company
Abstract
:1. Introduction
2. Literature Review
2.1. Concept of Omnichannel and Channel Categorization
2.2. Customer Experience in Digital Era and Omnichannel
2.3. Behavior Analysis of Omnichannel Usage
3. Research Design and Methodology
3.1. Hypothesis
3.1.1. Theoretical Background
3.1.2. Research Model and Hypotheses
3.2. Sample and Data Collection
3.3. Sample Characteristics
3.4. Definition of Variable for Resarch
3.5. Descriptive Statistics
3.6. One-Way ANCOVA
4. Analysis of Results
4.1. Hypothesis 1
4.2. Hypothesis 2
4.3. Hypothesis 3, Hypothesis 4
5. Discussion
6. Conclusions
6.1. Managerial Implication
6.2. Practical/Social Implications
6.3. Limitations
Author Contributions
Funding
Conflicts of Interest
References
- Sopadjieva, E.; Dholakia, U.M.; Benjamin, B. A Study of 46,000 Shoppers Shows That Omnichannel Retailing Works Omnichannel customers are avid users of retailer touchpoints. Harv. Bus. Rev. 2017, 3, 1–2. [Google Scholar]
- Fisher, M.; Gallino, S.; Netessine, S. Does Online Training Work in Retail? Available online: https://ssrn.com/abstract=2670618 (accessed on 14 December 2019).
- Pine, J., II. Shoppers Need A Reason to Go to Your Store—Other Than Buying Stuff. Harv. Bus. Rev. 2017, 12, 2–5. [Google Scholar]
- Nash, D.; Armstrong, D.; Robertson, M. Customer Experience 2.0: How Data, Technology, and Advanced Analytics are Taking an Integrated, Seamless Customer Experience to the Next Frontier. J. Integr. Mark. Commun. 2013, 1, 32–39. [Google Scholar]
- Rigby, D. The Future of Shopping. Harv. Bus. Rev. 2011, 89, 64–75. [Google Scholar]
- Herhausen, D.; Binder, J.; Schoegel, M.; Herrmann, A. Integrating Bricks with Clicks: Retailer-Level and Channel-Level Outcomes of Online–Offline Channel Integration. J. Retail. 2015, 91, 309–325. [Google Scholar] [CrossRef]
- Mascarenhas, O.A.; Kesavan, R.; Bernacchi, M. Lasting customer loyalty: A total customer experience approach. J. Consum. Mark. 2006, 23, 397–405. [Google Scholar] [CrossRef]
- Kleinberger, H.; Morrison, G.P. Turning Shoppers into Advocates: The customer-focused enterprise. Eur. Retail Dig. 2007, 54, 58. [Google Scholar]
- Rose, S.; Clark, M.; Samouel, P.; Hair, N. Online Customer Experience in e-Retailing: An empirical model of Antecedents and Outcomes. J. Retail. 2012, 88, 308–322. [Google Scholar] [CrossRef]
- Klaus, P.; Maklan, S. Towards a better measure of customer experience. Int. J. Mark. Res. 2013, 55, 227–246. [Google Scholar] [CrossRef] [Green Version]
- Sathish, A.S.; Venkatesakumar, R. Customer experience management and store loyalty in corporate retailing—With special reference ton Sony World. Annamalai Int. J. Bus. Stud. Res. 2011, 3, 67–77. [Google Scholar]
- Neslin, S.A.; Grewal, D.; Leghorn, R.; Shankar, V.; Teerling, M.L.; Thomas, J.S.; Verhoef, P.C. Challenges and opportunities in multichannel customer management. J. Serv. Res. 2006, 9, 95–112. [Google Scholar] [CrossRef]
- Sousa, R.; Voss, C.A. Service quality in multichannel services employing virtual channels. J. Serv. Res. 2006, 8, 356–371. [Google Scholar] [CrossRef]
- Verhoef, P.C.; Neslin, S.A.; Vroomen, B. Multichannel customer management: Understanding the research-shopper phenomenon. Int. J. Res. Mark. 2007, 24, 129–148. [Google Scholar] [CrossRef]
- Ahmed, S.; Kumar, A. Opportunities and Challenges of Omni Channel Retailing in the Emerging Market. J. Retail Manag. Res. 2015, 1, 1–16. [Google Scholar]
- Ansari, A.; Mela, C.F.; Neslin, S.A. Customer channel migration. J. Mark. Res. 2008, 45, 60–75. [Google Scholar] [CrossRef]
- Avery, J.; Steenburgh, T.J.; Deighton, J.; Caravella, M. Adding bricks to clicks: Predicting the patterns of cross-channel elasticities over time. J. Mark. 2012, 76, 96–111. [Google Scholar] [CrossRef]
- Brynjolfsson, E.; Hu, Y.J.; Rahman, M.S.; Piotrowicz, W.; Cuthbertson, R.; Herhausen, D.; Binder, J.; Schoegel, M.; Herrmann, A.; Verhoef, P.C.; et al. Association for Information Systems AIS Electronic Library (AISeL) Channel Integration Towards Omnichannel Management: A Literature Review. J. Retail. 2015, 91, 5–16. [Google Scholar]
- Beck, N.; Rygl, D. Categorization of multiple channel retailing in Multi-, Cross-, and Omni-Channel Retailing for retailers and retailing. J. Retail. Consum. Serv. 2015, 27, 170–178. [Google Scholar] [CrossRef]
- Verhoef, P.C.; Kannan, P.K.; Inman, J.J. From Multi-Channel Retailing to Omni-Channel Retailing: Introduction to the Special Issue on Multi-Channel Retailing. J. Retail. 2015, 91, 174–181. [Google Scholar] [CrossRef]
- Payne, A.; Frow, P. The role of multichannel integration in customer relationship management. Ind. Mark. Manag. 2004, 33, 527–538. [Google Scholar] [CrossRef]
- Reynolds, T.J.; Gutman, J. Advertising Is Image Management. J. Advert. Res. 1984, 24, 27. [Google Scholar]
- Keller, K.L. Conceptualizing, Measuring, Managing Customer-Based Brand Equity. J. Mark. 1993, 57, 1–22. [Google Scholar] [CrossRef]
- Tuominen, P. Pekka Tuominen Managing Brand Equity. Fin. J. Bus. Econ. 1999, 48, 65–100. [Google Scholar]
- Lewnes, A.N.N.; Keller, K.L. 10 Principles of Modern Marketing. MIT Sloan Manag. Rev. 2019, 60, 1–10. [Google Scholar]
- Schmitt, B. Experiential Marketing. J. Mark. Manag. 1999, 15, 53–67. [Google Scholar] [CrossRef]
- Brakus, J.J.; Schmitt, B.H.; Zarantonello, L. Brand Experience: What Is It? How Is It Measured? Does It Affect Loyalty? J. Mark. 2009, 73, 52–68. [Google Scholar] [CrossRef]
- Edelman, D.C. Branding in The Digital Age. Harv. Bus. Rev. 2010, 88, 62–69. [Google Scholar]
- Financial Times. Definition of Digital Marketing. Available online: http//:markets.ft.com/research/Lexicon/Term?term=digital-marketing (accessed on 10 October 2019).
- Verhoef, P.C.; Lemon, K.N.; Parasuraman, A.; Roggeveen, A.; Tsiros, M.; Schlesinger, L.A. Customer Experience Creation: Determinants, Dynamics and Management Strategies. J. Retail. 2009, 85, 31–41. [Google Scholar] [CrossRef] [Green Version]
- Klaus, P.; Nguyen, B. Exploring the role of the online customer experience in firms’ multi-channel strategy: An empirical analysis of the retail banking services sector. J. Strateg. Mark. 2013, 21, 429–442. [Google Scholar] [CrossRef]
- Anne Coughlan, E.A.; Louis, W.; Stern, L.W.; El-Ansary, A. Marketing Channel, 7th ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2006. [Google Scholar]
- Pauwels, K.; Leeflang, P.S.H.; Teerling, M.L.; Huizingh, K.R.E. Does Online Information Drive Offline Revenues?: Only for Specific Products and Consumer Segments! J. Retail. 2011, 87, 1–17. [Google Scholar] [CrossRef] [Green Version]
- Van Baal, S. Should retailers harmonize marketing variables across their distribution channels? An investigation of cross-channel effects in multi-channel retailing. J. Retail. Consum. Serv. 2014, 21, 1038–1046. [Google Scholar] [CrossRef]
- Raj, S.P. Striking a Balance between Brand ‘Popularity’ and Brand Loyalty. J. Mark. 1985, 49, 53–59. [Google Scholar]
- Wallace, D.W.; Giese, J.L.; Johnson, J.L. Customer retailer loyalty in the context of multiple channel strategies. J. Retail. 2004, 80, 249–263. [Google Scholar] [CrossRef]
- Dholakia, R.R.; Zhao, M.; Dholakia, N. Multichannel retailing: A case study of early experiences. J. Interact. Mark. 2005, 19, 63–74. [Google Scholar] [CrossRef]
- Schramm-Klein, H.; Wagner, G.; Steinmann, S.; Morschett, D. Cross-channel integration—is it valued by customers? Int. Rev. Retail. Distrib. Consum. Res. 2011, 21, 501–511. [Google Scholar] [CrossRef]
- Kwon, W.-S.; Lennon, S.J. What induces online loyalty? Online versus offline brand images. J. Bus. Res. 2009, 62, 557–564. [Google Scholar] [CrossRef]
- Balasubramanian, S.; Raghunathan, R.; Mahajan, V. Consumers in a multichannel environment: Product utility, process utility, and channel choice. J. Interact. Mark. 2005, 19, 12–30. [Google Scholar] [CrossRef] [Green Version]
- Moore, M.; Carpenter, J.M.; Fairhurst, A. Strategic Integration of Multi-Channel Retailing in the Softgoods Sector. J. Mark. Channels 2005, 12, 3–21. [Google Scholar] [CrossRef]
Groups according to Omnichannel Purchasing Pattern | N | N% |
---|---|---|
Group 1 1 | 181 | 10.1 |
Group 2 2 | 1513 | 77.3 |
Group 3 3 | 65 | 3.3 |
Group 4 4 | 183 | 9.3 |
TOTAL | 2000 | 100 |
Variables | Type | Type (2) | Definition |
---|---|---|---|
(1) Type of omnichannel purchasing pattern | Independent Variables | Nominal | Customer categorized into groups according to different purchasing patterns in on/offline channel |
(2) number of transaction | Dependent Variables (1) | Numeric | Total number of transaction made by customer during the period from joining membership to current in on/offline channel |
(3) total purchasing amount | Dependent Variables (2) | Numeric | Total purchasing amount made by customer during the period from joining membership to current in on/offline channel |
(4) average purchasing amount per transaction | Dependent Variables (3) | Numeric | Average purchasing amount per transaction made by customer during the period from joining membership to current in on/offline channel (2)/(3) |
(5) trading period | Co-variables | Numeric | Period counted from the day of joining membership to current for each customer |
Four Different Groups Categorized by Omnichannel Purchasing Pattern | ||||||
---|---|---|---|---|---|---|
Group 1 | Group 2 | Group 3 | Group 4 | |||
Gender | Male | N | 63 | 258 | 13 | 23 |
N% | 33.0% | 16.8% | 19.4% | 11.1% | ||
Female | N | 127 | 1276 | 54 | 185 | |
N% | 66.5% | 83.2% | 80.6% | 88.9% | ||
N | 1 | 0 | 0 | 0 | ||
N% | 0.5% | 0% | 0% | 0% | ||
Age | Mean | 43.1 | 46.8 | 43.2 | 43.6 | |
Var | 76.7 | 120.4 | 63.4 | 64.5 | ||
SD | 8.8 | 11.0 | 8.0 | 8.0 | ||
Max | 72.0 | 88.0 | 68.0 | 69.0 | ||
Trading Period | Mean | 6.0 | 5.8 | 6.6 | 6.7 | |
Var | 12.8 | 9.0 | 11.8 | 8.2 | ||
SD | 3.6 | 3.0 | 3.4 | 2.9 | ||
Max | 17.0 | 16.0 | 16.0 | 17.0 | ||
Number of Purchasing | Mean | 3.9 | 3.2 | 6.5 | 6.2 | |
Var | 17.7 | 10.9 | 23.4 | 22.9 | ||
SD | 4.2 | 3.3 | 4.8 | 4.8 | ||
Max | 28.0 | 30.0 | 30.0 | 30.0 | ||
Purchasing Amount (KRW) | Mean | 409,960 | 309,141 | 644,733 | 549,651 | |
SD | 687,896 | 444,764 | 675,099 | 631,785 | ||
MAX | 4,985,000 | 6,318,500 | 4,617,950 | 5,083,540 | ||
Average Purchasing Amount per transaction (KRW) | Mean | 88,958 | 98,003 | 93,825 | 82,744 | |
SD | 69,982 | 78,741 | 48,213 | 55,425 | ||
MAX | 450,400 | 690,000 | 228,298 | 529,700 |
Source | Type III Sumof Squares | df | Mean Square | F | Significance | Partial Eta Squared |
---|---|---|---|---|---|---|
Corrected Model | 4192.052 a | 4 | 1048.013 | 85.414 | 0.000 | 0.146 |
Intercept | 2308.666 | 1 | 2308.666 | 188.159 | 0.000 | 0.086 |
Trading Period | 1935.943 | 1 | 1935.943 | 157.782 | 0.000 | 0.073 |
Group | 1859.695 | 3 | 619.898 | 50.523 | 0.000 | 0.071 |
Error | 24,478.140 | 1995 | 12.270 | |||
Total | 55,286.000 | 2000 | ||||
Corrected Total | 28,670.192 | 1999 |
Prameter | B | Std.Error | t | Sig. | 95% Confidence Interval | Partial Eta Squared | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Intercept | 4.029 | 0.297 | 13.575 | 0.000 | 3.447 | 4.612 | 0.085 |
Trading period | 0.322 | 0.026 | 12.561 | 0.000 | 0.271 | 0.372 | 0.073 |
[Group = 1] | −2.089 | 0.351 | −5.944 | 0.000 | −2.779 | −1.400 | 0.017 |
[Group = 2] | −2.735 | 0.260 | −10.528 | 0.000 | −3.245 | −2.226 | 0.053 |
[Group = 3] | 0.370 | 0.492 | 0.752 | 0.452 | −0.595 | 1.335 | 0.000 |
[Group = 4] | 0 a |
Group(I) | Group(J) | Mean Difference (I–J) | Std.Error | Sig. a | 95% Confidence Interval | |
---|---|---|---|---|---|---|
Lower Bound | Upper Bound | |||||
1 | 2 | 0.646 | 0.269 | 0.098 | −0.064 | 1.356 |
3 | −2.459 * | 0.498 | 0.000 | −3.773 | −1.145 | |
4 | −2.089 * | 0.351 | 0.000 | −3.017 | −1.161 | |
2 | 1 | −0.646 | 0.269 | 0.098 | −1.356 | 0.064 |
3 | −3.105 * | 0.438 | 0.000 | −4.261 | −1.950 | |
4 | −2.735 * | 0.260 | 0.000 | −3.421 | −2.049 | |
3 | 1 | 2.459 * | 0.498 | 0.000 | 1.145 | 3.773 |
2 | 3.105 * | 0.438 | 0.000 | 1.950 | 4.261 | |
4 | 0.370 | 0.492 | 1.000 | −0.929 | 1.670 | |
4 | 1 | 2.089 * | 0.351 | 0.000 | 1.161 | 3.017 |
2 | 2.735 * | 0.260 | 0.000 | 2.049 | 3.421 | |
3 | −0.370 | 0.492 | 1.000 | −1.670 | 0.929 |
Source | Type III Sumof Squares | df | Mean Square | F | Significance | Partial Eta Squared |
---|---|---|---|---|---|---|
Corrected Model | 5.909 × 1010 | 4 | 14,772,331,135.863 | 2.627 | 0.033 | 0.005 |
Intercept | 2.439 × 1012 | 1 | 2.439 × 1012 | 433.773 | 0.000 | 0.179 |
Trading Period | 7,285,127,364.465 | 1 | 7,285,127,364.465 | 1.296 | 0.255 | 0.001 |
Group | 48,255,461,925.717 | 3 | 16,085,153,975.239 | 2.861 | 0.036 | 0.004 |
Error | 1.122 × 1013 | 1995 | 5,622,696,630.596 | |||
Total | 2.948 × 1013 | 2000 | ||||
Corrected Total | 1.128 × 1013 | 1999 |
Prameter | B | Std.Error | t | Sig. | 95% Confidence Interval | Partial Eta Squared | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Intercept | 86,901.891 | 6354.356 | 13.676 | 0.000 | 74,440.021 | 99,363.761 | 0.086 |
Trading period | −624.045 | 548.239 | −1.138 | 0.255 | −1699.226 | 451.136 | 0.001 |
[Group = 1] | 5777.803 | 7524.475 | 0.768 | 0.443 | −8978.849 | 20,534.456 | 0.000 |
[Group = 2] | 14,709.550 | 5561.562 | 2.645 | 0.008 | 3802.472 | 25,616.628 | 0.003 |
[Group = 3] | 11,011.608 | 10,533.601 | 1.045 | 0.296 | −9646.405 | 31,669.620 | 0.001 |
[Group = 4] | 0 a |
Group(I) | Group(J) | Mean Defference (I–J) | Std.Error | Sig. a | 95% Confidence Interval | |
---|---|---|---|---|---|---|
Lower Bound | Upper Bound | |||||
1 | 2 | −8,931.747 | 5754.428 | 0.725 | −24,128.567 | 6265.073 |
3 | −5,233.804 | 10,651.914 | 1.000 | −33,364.355 | 22,896.746 | |
4 | 5,777.803 | 7524.475 | 1.000 | −14,093.518 | 25,649.125 | |
2 | 1 | 8,931.747 | 5754.428 | 0.725 | −6265.073 | 24,128.567 |
3 | 3,697.943 | 9368.267 | 1.000 | −21,042.633 | 28,438.518 | |
4 | 14,709.550 * | 5561.562 | 0.049 | 22.069 | 29,397.031 | |
3 | 1 | 5233.804 | 10,651.914 | 1.000 | −22,896.746 | 33,364.355 |
2 | −3697.943 | 9368.267 | 1.000 | −28,438.518 | 21,042.633 | |
4 | 11,011.608 | 10,533.601 | 1.000 | −16,806.491 | 38,829.706 | |
4 | 1 | −5777.803 | 7524.475 | 1.000 | −25,649.125 | 14,093.518 |
2 | −14,709.550 * | 5561.562 | 0.049 | −29,397.031 | −22.069 | |
3 | −11,011.608 | 10,533.601 | 1.000 | −38,829.706 | 16,806.491 |
Source | Type III Sumof Squares | df | Mean Square | F | Significance | Partial Eta Squared |
---|---|---|---|---|---|---|
Corrected Model | 4.586 × 1013 | 4 | 1.147 × 1013 | 47.925 | 0.000 | 0.088 |
Intercept | 1.494 × 1013 | 1 | 1.494 × 1013 | 62.440 | 0.000 | 0.030 |
Trading Period | 2.856 × 1013 | 1 | 2.856 × 1013 | 119.355 | 0.000 | 0.056 |
Group | 1.335 × 1013 | 3 | 4.451 × 1012 | 18.606 | 0.000 | 0.027 |
Error | 4.773 × 1014 | 1995 | 2.393 × 1011 | |||
Total | 7.753 × 1014 | 2000 | ||||
Corrected Total | 4.586 × 1013 | 4 | 1.147 × 1013 | 47.925 | 0.000 | 0.088 |
Prameter | B | Std.Error | t | Sig. | 95% Confidence Interval | Partial Eta Squared | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Intercept | 289,307.892 | 41,450.185 | 6.980 | 0.000 | 208,017.704 | 370,598.080 | 0.024 |
Trading period | 39,070.185 | 3576.225 | 10.925 | 0.000 | 32,056.658 | 46,083.712 | 0.056 |
[Group = 1] | −112,336.728 | 49,083.003 | −2.289 | 0.022 | −208,596.045 | −16,077.411 | 0.003 |
[Group = 2] | −206,080.865 | 36,278.697 | −5.680 | 0.000 | −277,228.970 | −134,932.761 | 0.016 |
[Group = 3] | 99,428.030 | 68,711.876 | 1.447 | 0.148 | −35,326.528 | 234,182.588 | 0.001 |
[Group = 4] | 0 a |
Group(I) | Group(J) | Mean Defference (I–J) | Std.Error | Sig. a | 95% Confidence Interval | |
---|---|---|---|---|---|---|
Lower Bound | Upper Bound | |||||
1 | 2 | 93,744.137 | 37,536.786 | 0.076 | −5,386.442 | 192,874.716 |
3 | −211,764.758 * | 69,483.645 | 0.014 | −395,263.528 | −28,265.989 | |
4 | −112,336.728 | 49,083.003 | 0.133 | −241,959.613 | 17,286.156 | |
2 | 1 | −937,44.137 | 37,536.786 | 0.076 | −192,874.716 | 5,386.442 |
3 | −305,508.895 * | 61,110.265 | 0.000 | −466,894.476 | −144,123.314 | |
4 | −206,080.865 * | 36,278.697 | 0.000 | −301,888.968 | −110,272.763 | |
3 | 1 | 211,764.758 * | 69,483.645 | 0.014 | 28,265.989 | 395,263.528 |
2 | 305,508.895 * | 61,110.265 | 0.000 | 144,123.314 | 466,894.476 | |
4 | 99,428.030 | 68,711.876 | 0.888 | −82,032.581 | 280,888.641 | |
4 | 1 | 112,336.728 | 49,083.003 | 0.133 | −17,286.156 | 241,959.613 |
2 | 206,080.865 * | 36,278.697 | 0.000 | 110,272.763 | 301,888.968 | |
3 | −99,428.030 | 68,711.876 | 0.888 | −280,888.641 | 82,032.581 |
Hypotheses | Remark | |
---|---|---|
H1 | It is speculated the total number of purchase during the trade period by the omnichannel users who cross-use both on/offline channels, will be higher. | Accept |
H2 | It is speculated the average purchase amount per transaction by the omnichannel users who cross-use both on/offline channels, will be higher during the trading period. | Reject |
H3 | It is speculated the total purchase amount by the omnichannel users who cross-use both on/offline channels, will be higher during the trading period. | Accept |
H4 | It is speculated the total purchase amount by a group of customer who visits offline channel first and then join online channel will be higher compared to the total purchase amount by a group of customer who joins online channel first and visit offline channel. | Reject |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kang, J.; Majer, M.; Kim, H.-J. Empirical Study of Omnichannel Purchasing Pattern with Real Customer Data from Health and Lifestyle Company. Sustainability 2019, 11, 7185. https://doi.org/10.3390/su11247185
Kang J, Majer M, Kim H-J. Empirical Study of Omnichannel Purchasing Pattern with Real Customer Data from Health and Lifestyle Company. Sustainability. 2019; 11(24):7185. https://doi.org/10.3390/su11247185
Chicago/Turabian StyleKang, Jongsoo, Marko Majer, and Hyun-Jung Kim. 2019. "Empirical Study of Omnichannel Purchasing Pattern with Real Customer Data from Health and Lifestyle Company" Sustainability 11, no. 24: 7185. https://doi.org/10.3390/su11247185