Data mining techniques used by restaurants

Restaurants use several data mining techniques for various purposes and these mainly fall into 3 categories:

  1. Association and sequences: Data mining techniques help to detect any association between isolated data sets. For e.g, a customer ordering restaurant specialty might order a salad and a wine too. This pairing of items together, where an affinity is identified helps to prepare great menu item sets to ensure customer satisfaction. As Kasavana (2010) states “Data associations are often credited with a means for influencing customers to spend more than anticipated or upselling.”
  2. Clusters: The detection of recurring patterns helps the restaurants to prepare customer profiles and in the CRM strategies. “ Clustering can also alert a restaurant to the need for e-mail promotions, frequent diner programming, gift cards, oakland cleaners or other incentives (Kasavana,  2010).”
  3. Forecats: It helps the restaurant to prepare better menus, planning, and purchasing and supports effective staffing strategies.



Kasavana, M.L. (2010) Mining restaurant data: Know your customer. Available at: http://www.hospitalityupgrade.com/_magazine/magazine_Detail-ID-522-Mining-Restaurant-Data:-Know-your-customer..asp (Accessed: 13 June 2017)


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