Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph
Website morphing infers latent customer segments from clickstreams and then changes websites' look and feel to maximize revenue. The established algorithm infers latent segments from a preset number of clicks and then selects the best “morph” using expected Gittins indices. Switching costs, pot...
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| Format: | Article |
| Language: | en_US |
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Institute for Operations Research and the Management Sciences (INFORMS)
2017
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| Online Access: | http://hdl.handle.net/1721.1/111141 https://orcid.org/0000-0001-8510-8640 https://orcid.org/0000-0002-9983-4237 |
| _version_ | 1826209730278719488 |
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| author | Liberali, Guilherme (Gui) Hauser, John R Urban, Glen L |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Liberali, Guilherme (Gui) Hauser, John R Urban, Glen L |
| author_sort | Liberali, Guilherme (Gui) |
| collection | MIT |
| description | Website morphing infers latent customer segments from clickstreams and then changes websites' look and feel to maximize revenue. The established algorithm infers latent segments from a preset number of clicks and then selects the best “morph” using expected Gittins indices. Switching costs, potential website exit, and all clicks prior to morphing are ignored. We model switching costs, potential website exit, and the (potentially differential) impact of all clicks to determine when to morph for each customer. Morphing earlier means more customer clicks are based on the optimal morph; morphing later reveals more about the customer's latent segment. We couple this within-customer optimization to between-customer expected Gittins index optimization to determine which website “look and feel” to give to each customer at each click. We evaluate the improved algorithm with synthetic data and with a proof-of-feasibility application to Japanese bank card loans. The proposed algorithm generalizes the established algorithm, is feasible in real time, performs substantially better when tuning parameters are identified from calibration data, and is reasonably robust to misspecification. |
| first_indexed | 2024-09-23T14:28:07Z |
| format | Article |
| id | mit-1721.1/111141 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:28:07Z |
| publishDate | 2017 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1111412022-09-29T09:35:24Z Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph Liberali, Guilherme (Gui) Hauser, John R Urban, Glen L Sloan School of Management Hauser, John R Urban, Glen L Website morphing infers latent customer segments from clickstreams and then changes websites' look and feel to maximize revenue. The established algorithm infers latent segments from a preset number of clicks and then selects the best “morph” using expected Gittins indices. Switching costs, potential website exit, and all clicks prior to morphing are ignored. We model switching costs, potential website exit, and the (potentially differential) impact of all clicks to determine when to morph for each customer. Morphing earlier means more customer clicks are based on the optimal morph; morphing later reveals more about the customer's latent segment. We couple this within-customer optimization to between-customer expected Gittins index optimization to determine which website “look and feel” to give to each customer at each click. We evaluate the improved algorithm with synthetic data and with a proof-of-feasibility application to Japanese bank card loans. The proposed algorithm generalizes the established algorithm, is feasible in real time, performs substantially better when tuning parameters are identified from calibration data, and is reasonably robust to misspecification. 2017-09-06T20:03:44Z 2017-09-06T20:03:44Z 2014-05 2012-08 Article http://purl.org/eprint/type/JournalArticle 0025-1909 1526-5501 http://hdl.handle.net/1721.1/111141 Hauser, John R. et al. “Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph.” Management Science 60, 6 (June 2014): 1594–1616 © 2014 Institute for Operations Research and the Management Sciences (INFORMS) https://orcid.org/0000-0001-8510-8640 https://orcid.org/0000-0002-9983-4237 en_US http://dx.doi.org/10.1287/mnsc.2014.1961 Management Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) Prof. Hauser via Shikha Sharma |
| spellingShingle | Liberali, Guilherme (Gui) Hauser, John R Urban, Glen L Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph |
| title | Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph |
| title_full | Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph |
| title_fullStr | Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph |
| title_full_unstemmed | Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph |
| title_short | Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph |
| title_sort | website morphing 2 0 switching costs partial exposure random exit and when to morph |
| url | http://hdl.handle.net/1721.1/111141 https://orcid.org/0000-0001-8510-8640 https://orcid.org/0000-0002-9983-4237 |
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