Powering retailers’ digitization through analytics and automation
Retailers face significant pressure to improve revenue, margins and market share by applying price optimisation models. These are mathematical models that calculate how demand varies at different price levels, then combine that data with information on costs and inventory levels to recommend prices...
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| Format: | Article |
| Language: | en_US |
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Taylor & Francis
2018
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| Online Access: | http://hdl.handle.net/1721.1/119184 https://orcid.org/0000-0002-4650-1519 |
| _version_ | 1826207334683115520 |
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| author | Simchi-Levi, David Wu, Michelle Xiao |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Simchi-Levi, David Wu, Michelle Xiao |
| author_sort | Simchi-Levi, David |
| collection | MIT |
| description | Retailers face significant pressure to improve revenue, margins and market share by applying price optimisation models. These are mathematical models that calculate how demand varies at different price levels, then combine that data with information on costs and inventory levels to recommend prices that will improve revenue and profits. These models have been around for a while-so what is different now? We have identified three important changes: (1) Data: availability of internal and external real-time data such as traffic to a website, consumers making buy/no buy decisions and competitor pricing strategies; (2) Analytics: advances in machine learning and ease of access (R, Python) have enabled the development of systems that learn on the fly about consumer behaviour and preferences and generate effective estimates of demand-price relationships; and (3) Automation: increase in computing speed enables real-time optimisation of prices of hundreds of competing products sold by the same retailer. We take advantage of these new opportunities by showing how they were applied at Boston-based flash sales retailer Rue La La, online market maker Groupon, and the largest online retailer in Latin America, B2W Digital (B2W). While all these examples are of on-line businesses which have readily available data and can change prices dynamically, we have also implemented similar methods for brick-and-mortar retailed in applications such as promotional pricing, new product introduction, and assortment optimisation with similar business impacts. Thus, beyond applications to price optimisations, these new trends enable companies to revolutionise their business from procurement to supply chain all the way to revenue management. Keywords: analytics, machine learning, price theory, online retail, forecasting |
| first_indexed | 2024-09-23T13:48:00Z |
| format | Article |
| id | mit-1721.1/119184 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:48:00Z |
| publishDate | 2018 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | mit-1721.1/1191842022-10-01T17:14:09Z Powering retailers’ digitization through analytics and automation Simchi-Levi, David Wu, Michelle Xiao Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Institute for Data, Systems, and Society Massachusetts Institute of Technology. Operations Research Center David Simchi-Levi Simchi-Levi, David Retailers face significant pressure to improve revenue, margins and market share by applying price optimisation models. These are mathematical models that calculate how demand varies at different price levels, then combine that data with information on costs and inventory levels to recommend prices that will improve revenue and profits. These models have been around for a while-so what is different now? We have identified three important changes: (1) Data: availability of internal and external real-time data such as traffic to a website, consumers making buy/no buy decisions and competitor pricing strategies; (2) Analytics: advances in machine learning and ease of access (R, Python) have enabled the development of systems that learn on the fly about consumer behaviour and preferences and generate effective estimates of demand-price relationships; and (3) Automation: increase in computing speed enables real-time optimisation of prices of hundreds of competing products sold by the same retailer. We take advantage of these new opportunities by showing how they were applied at Boston-based flash sales retailer Rue La La, online market maker Groupon, and the largest online retailer in Latin America, B2W Digital (B2W). While all these examples are of on-line businesses which have readily available data and can change prices dynamically, we have also implemented similar methods for brick-and-mortar retailed in applications such as promotional pricing, new product introduction, and assortment optimisation with similar business impacts. Thus, beyond applications to price optimisations, these new trends enable companies to revolutionise their business from procurement to supply chain all the way to revenue management. Keywords: analytics, machine learning, price theory, online retail, forecasting 2018-11-19T15:49:00Z 2018-11-19T15:49:00Z 2017-11 2017-09 Article http://purl.org/eprint/type/JournalArticle 0020-7543 1366-588X http://hdl.handle.net/1721.1/119184 Simchi-Levi, David and Michelle Xiao Wu. “Powering Retailers’ Digitization through Analytics and Automation.” International Journal of Production Research 56, 1–2 (November 2017): 809–816 © 2018 Informa UK Limited https://orcid.org/0000-0002-4650-1519 en_US http://dx.doi.org/10.1080/00207543.2017.1404161 International Journal of Production Research Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Taylor & Francis Prof. Simchi-Levi via Elizabeth Soergel |
| spellingShingle | Simchi-Levi, David Wu, Michelle Xiao Powering retailers’ digitization through analytics and automation |
| title | Powering retailers’ digitization through analytics and automation |
| title_full | Powering retailers’ digitization through analytics and automation |
| title_fullStr | Powering retailers’ digitization through analytics and automation |
| title_full_unstemmed | Powering retailers’ digitization through analytics and automation |
| title_short | Powering retailers’ digitization through analytics and automation |
| title_sort | powering retailers digitization through analytics and automation |
| url | http://hdl.handle.net/1721.1/119184 https://orcid.org/0000-0002-4650-1519 |
| work_keys_str_mv | AT simchilevidavid poweringretailersdigitizationthroughanalyticsandautomation AT wumichellexiao poweringretailersdigitizationthroughanalyticsandautomation |