{"id":573,"date":"2014-11-05T04:22:23","date_gmt":"2014-11-05T04:22:23","guid":{"rendered":"http:\/\/rovdownloads.com\/blog\/?p=573"},"modified":"2014-11-21T17:17:50","modified_gmt":"2014-11-21T17:17:50","slug":"markov-chains","status":"publish","type":"post","link":"https:\/\/rovdownloads.com\/blog\/markov-chains\/","title":{"rendered":"Markov Chains"},"content":{"rendered":"

Theory<\/strong>
\nA Markov chain exists when the probability of a future state depends on a previous state and when linked together forms a chain that reverts to a long-run steady state level. This Markov approach is typically used to forecast the market share of two competitors. The required inputs are the starting probability of a customer in the first store (the first state) returning to the same store in the next period versus the probability of switching to a competitor\u2019s store in the next state.<\/p>\n

Procedure<\/strong><\/p>\n

  • Start Excel and select Risk Simulator | Forecasting | Markov Chain.<\/li>\n
  • Enter the required input assumptions (see Figure 1 for an example) and click OK to run the model and report.<\/li>\n

    Note<\/strong>
    \nSet both probabilities to 10% and rerun the Markov chain, and you will see the effects of
    \nswitching behaviors very clearly in the resulting chart as shown at the bottom of Figure 1.<\/p>\n

    \"Untitled-1\"<\/p>\n

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