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| Title | Sales Goals Planning using Evidence Regression |
| Authors | Nelson Baloian, Belisario Panay, Sergio Peñafiel, José Pino, Jonathan Frez, Cristóbal Fuenzalida |
| Publication date | 2022 |
| Abstract | Sales planning is a recurrent activity for retail stores in order to provide the necessary resources for a good operation. This is usually based on prediction models which take as input diverse parameters. Modern technology has made possible to easily and economically register three important parameters which are important for sales planning: foot traffic (number of visitors entering a store), conversion rate (which proportion of the visitors make a purchase), and average value of sales ticket. In this paper we present a model for helping retail managers to plan their future sales based on these three simple parameters. The model has been implemented as a sales planning tool which allows them to answer questions like "how much should the foot traffic improve to attain a certain a certain sales goal and how difficult will it be to achieve this goal". The model is based on the Dempster Shaffer plausibility theory which allows and easy interpretation of the results. |
| Pages | 112-120 |
| Conference name | Online Workshop on Collaborative Technologies and Data Science in Smart City Applications |
| Publisher | Logos Verlag (Berlin, Germany) |
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