Evaluation of the Aeroplan distinction status program desirability

Aeroplan Distinction Status计划由我们的合作伙伴Aimia Canada Inc.于2014年推出。给予 ** 进一步的奖励和认可，以该计划的最参与和积极的成员。根据该计划，** 会员有资格根据上一日历年的合格里程累积获得三个等级之一。**每个等级都提供奖励和促销等福利。我们的合作伙伴希望确定状态 ** 程序是否对会员的赚取行为有影响，如果有影响，则影响哪种类型的会员。** 本项目的困难在于检测具有自然可变性的时间序列中的具体变化，这种自然可变性可能包括 ** 非平稳成分、趋势和季节成分。受状态影响的盈利率（ER）局部变化 ** 的检测可以被视为两步过程。首先，必须检测ER时间序列中的局部变异性 ** 变化。第二，必须表明这种变化的时间与状态资格阈值相关的关键时间一致（即接近）。为了确定这种巧合不是偶然的，应该在几个关键时刻反复观察它们。获得统计上显著的 ** 结果可能需要将具有相似ER行为的几个成员分组。该项目有两个 ** 目标：**1）通过局部统计指标检测ER局部变异性的局部变化，并将其与 ** 鉴定阈值时间相关联。2)通过与最佳线性 ** 预测的偏差检测ER局部变异性的局部变化，并将其与鉴定阈值时间关联。我们将通过寻找当地统计措施（待 ** 制定）和与状态资格阈值相关的关键时间之间的一致性来实现目标1）。我们将通过 ** 首先开发ER系列的线性预测器，然后通过检查每个ER系列的预测偏差（预测 ** 减去实际值）来实现目标2）。该偏离应揭示ER变异性的变化，其可能 ** 指示行为变化。

The Aeroplan Distinction Status program was launched in 2014 by our partner Aimia Canada Inc. to give**further rewards and recognition to the program's most engaged and active members. Under that program,**members qualify for one of three tiers based on eligible mileage accumulation from the previous calendar year.**Each tier offers benefits such as rewards and access to promotions. Our partner wants to determine if the status**program has an influence on members earn behaviour, and if so, for what type of members. The difficulty in**this project is to detect specific changes in a time series that has a natural variability that may include**non-stationary components, trends and seasonal components. The detection of Earning Rate (ER) local changes**that are influenced by status can be regarded as a two-step procedure. First, one must detect local variability**changes in the ER time series. Second, on must show that the times of such changes coincide (i.e. are close to)**with the critical times associated with status qualification thresholds. To establish that such coincidences are**not accidental, they should be observed repeatedly for several critical times. Obtaining statistically significant**results may require the grouping of several members having similar ER behaviours. The project has two**objectives:**1) Detect local changes in the ER local variability via local statistical indicators and correlate them with**qualification threshold times. 2) Detect local changes in the ER local variability via deviations from best linear**prediction and correlate them with qualification threshold times.**We will achieve objective 1) by searching for coincidences between local statistical measures (to be**developed) and critical times associated with status qualification thresholds. We will achieve objective 2) by**first developing a linear predictor of the ER series, and then by examining the prediction deviation (prediction**minus actual value) for each ER series. This deviation should reveal changes in the ER variability that could be**indicative of behaviour change.