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Q 71. Some of the commonly used measures of customer satisfaction are given below - NPS (Net Promoter Score) - Loyalty and referral check. C-SAT (Customer Satisfaction Index) - Satisfaction attained by the use of product/service. Churn (Customer Churn Rate) - Customer loss assessment. CAC (Customer Acquisition Cost) - CAC is all the costs spent on acquiring more customers (marketing expenses) divided by the number of customers acquired in the period the money was spent. CES (Customer Effort Score) - Customer effort assessment in getting work done/ issues resolved. What will be your ranking for the five metrics (NPS, C-SAT, Churn, CAC, CES) as per order of importance for performance of an App based cab service provider and why? Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening as per Indian Standard Time. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term.

About Baseline One of the requirements of the Measure Phase in Six Sigma DMAIC cycle is the Baseline measurement, sometimes expressed as Baseline Sigma. In fact it is hard to tell whether the baseline data is required as part of the Define phase or Measure phase. Ideally, if we need to give the problem statement, which is expected to cover What, When, Magnitude and Impact. The ‘When’ portion is expected to show the metrics related to the problem for a time period as a trend chart, so that we can see the magnitude of the problem and the variation over a period of time – and acts as a baseline. Baseline certainly helps to act as reference to compare and assess the extent of improvement. Baseline is important to get a good measure of the quantum of improvement and in turn to quantify the benefits in tangible terms. However, the following discussion brings out certain practical challenges related to Baseline. 1. Baseline metric did not exist, but is it worth post-creating it? Suppose we are trying to improve an electronic product, based on certain customer complaints, our project objective will be to ensure that the incidents of customer complaints should be reduced or eliminated. Upon subjecting the product to a special lab evaluation, we could simulate the failure. However, a reasonable baseline metric will be possible only if we subject a set of sample units for a certain period of time. This could prove quite costly and time consuming. On the other hand the solution to the problem is known and we may proceed with the actions. Since our goal is to ensure zero failure, under the given conditions and duration, comparison with a baseline is not important here. Many a time, when the company is anxious to implement the improvement to get the desired benefits, be It cost or Quality, it may not make much sense to build up a baseline data, unless, it is readily available. 2. New measurement methodology evolved as part of improvement Let’s take an example of Insurance Claims processing, where the payment / denial decisions are taken based on a set of rules and associated calculations. The improvement being sought is to reduce the rate of processing errors. However it was only as part of the improvement actions that an appropriate assessment tool was evolved to identify and quantify the errors by the processors. By this time, the improvement has already begun and it is not practically possible to trace backwards to use this tool and get a baseline measurement. 3. When improvement is for ‘Delight factors’ Often we introduce enhancement features on product, for example, new models / variants of smart phones. In such cases, the emphasis is more on the delight factors for customers, for features that they haven’t experienced earlier and any baseline comparison may not have much relevance. 4. Integrated set of modifications Let’s examine another scenario where a series of modifications were implemented on a software application and was released together as a new version. Here, the set of actions taken influenced multiple factors, including performance improvement, elimination of bugs and inclusion of new innovative features. In such situations, any comparison with a baseline performance to the current will be very difficult and would have overlapping impacts. If we still need to do a comparison before vs after, we may have to do so after factoring and adjusting for such interaction effects on the pre / post improvement outcomes. To conclude, in general, a baseline metric is an important information that we require to compare the post improvement results – However, it has to be borne in mind that certain situations challenge the feasibility and relevance of using a baseline measurement.