Recommended
Recommended
More Related Content
Similar to Csat random walk opportunity cost
Similar to Csat random walk opportunity cost (20)
Recently uploaded
Recently uploaded (20)
Csat random walk opportunity cost
- 1. Confidential and Proprietary CSAT Opportunity Cost Why we need to act fast
- 2. What is Random Walk Theory 2 It is one of the simplest but important time series forecasting model It assumes that for every time period, a variable takes a random step away from its previous position and these steps are independently and identically distributed in size Fx currency market and US stock forecasting are two examples of application of this model
- 3. Can Customer Satisfaction be explained by Random Walk? 3 Customer Satisfaction as measured on industry benchmark such as JD Power is based on perception of customers of US utilities( As stock values represents general public perception of a company) It is not easy to predict how different parameters such as rate cases, Utility’s actual performance and market conditions are going to affect customer satisfaction
- 4. CSAT Random Walk: Mathematical proof 4 We looked at JD Power quarterly CSAT data from 2010 to date and ran the quarterly CSAT values for large utilityTop Quartile and 6 major utilities over this period We ran the various tests to verify seasonality, autocorrelation and random walk thru’ various standard tests As we can see the results, CSAT data supports random walk theory JDP Top Q Utl 1 Utl 2 Utl 3 Utl 4 Utl 5 Utl 6 Overall Original Series Plot Y Y Y Y Y Y Y Y First Different Plot Y Y Y Y Y Y Y Y Standard Deviation Check Y Y Y Y Y Y N Y Random Walk Statistic Test (SAS Default) Y Y Y Y Y Y Y Y Seasonality There is NO seasonality in the series Regress CSAT scores on season dummy variablesY Y Y Y Y Y Y Y AutocorrelationThere is NO autocorrelation in the seriesSAS Autoreg procedure Y N Y Y Y N N Y Conclusion: The data series follows a random walk pattern in general, so we can use random walk model to predict the CSAT scores Test name Test Methods Section Y=Support hypothesis N= Not Support hypothesis Hypothesis There is random walk in the seriesRandom Walk
- 5. Comparing fundamentals and predict CSAT behavior with very different probable results 5 Year 2 Year 3 Growth Rate Std Dev Min Average Max Min Average Max Top Q 0.58% 1.52% 734 742 751 748 760 772 Utl 1 0.49% 2.86% 705 720 736 714 735 756 Utl 2 0.51% 2.30% 715 728 741 726 743 760 Utl 3 0.85% 3.48% 703 722 741 721 747 773 Utl 4 0.49% 2.88% 726 742 758 734 756 778 Ult 5 0.56% 4.50% 711 736 761 719 753 787 Utl 6 0.12% 6.75% 647 682 716 639 685 731 Utl 1 and Utl 3 have similar growth rate and Std deviation but produce different results
- 6. Probabilistic Range of Utl 3 and Utl 1relative to the Top Q range 6 734 748751 772 660 680 700 720 740 760 780 800 2017 2018 Top Q Utl3 Utl1 RandomWalk Theory helps to predict customer satisfaction and its relative position toTop Quartile
- 7. Random Walk helps to calculate the opportunity Cost of the Delayed Actions to improve CSAT 7 Utl1 Utl2 Utl3 Utl4 Utl5 Utl6 Positive Drift # 14 12 16 16 13 10 Negative Drift# 10 12 8 8 11 14 Positive Drift Avg 2.28% 2.47% 2.83% 2.03% 4.03% 6.59% Negative Drift Avg -2.03% -1.45% -3.11% -2.60% -3.53% -4.50% EconomicValue 0.49% 0.51% 0.85% 0.49% 0.56% 0.12% Positive Drift Probability 0.58 0.50 0.67 0.67 0.54 0.42 Negative Drift Probability 0.42 0.50 0.33 0.33 0.46 0.58 Cost of Delayed Action Min (CSAT Rate) 1.80% 1.96% 1.98% 1.54% 3.47% 6.47% Cost of Delayed Action Max (CSAT Rate) 4.31% 3.92% 5.93% 4.62% 7.56% 11.09% Cost of Delayed Action Min (CSAT Points) 13 14 15 12 26 44 Cost of Delayed Action Max (CSAT Points 32 29 44 35 57 76