5/28/2023 0 Comments Compute geodist for each row stata![]() Though not always, one conventionally assumes such random effects to be normally distributed. Clustering is often accommodated through the inclusion of random subject-specific effects. The first issue is dealt with through a variety of overdispersion models such as the beta-binomial model for grouped binary data and the negative-binomial model for counts. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the variability in the data is not adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of hierarchical structure in the data, stemming from clustering in the data which, in turn, might result from repeatedly measuring the outcome, for various members of the same family, and so on. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Although we specifically present the contract expiration case, this model can easily be adapted for customer acquisition scenarios as well. Thereafter, an optimization model is used to find the optimal trade-off between margin and customer lifetime. We estimate customer life using discrete-time survival models with time varying covariates related to contract expiration and product changes. The model optimizes the number of MTM customers to be swapped to fixed-term contracts, as well as the number of contract renewals that should be pursued, at various term lengths and price points, over a period of time. The model is presented in the context of a cohort of existing customers, some of whom are MTM customers and others who are approaching contract expiration. In this paper, we present a mathematical model that optimizes the CLV against this tradeoff between margin and lifetime. This tradeoff must be evaluated not only at the time of customer acquisition, but throughout the customer's tenure, particularly for fixed-term contract customers whose contract is due for renewal. The objective, of course, is to maximize the Customer Lifetime Value (CLV). Below is a simplified version of the code that will yield the exact same results as above.Companies that offer subscription-based services (such as telecom and electric utilities) must evaluate the tradeoff between month-to-month (MTM) customers, who yield a high margin at the expense of lower lifetime, and customers who commit to a longer-term contract in return for a lower price. Further in the latest versions of Stata we can combine sort and by into a single statement. We can make use of the “*” wildcard to indicates that we wish to use all the variables. If you have a lot of variables in the dataset, it could take a long time to type them all out twice. Finally, we list the observations for which _N is greater than 1, thereby identifying the duplicate observations. Then we use all of the variable in the by statement and set set n equal to the total number of observations that are identical. In this example we sort the observations by all of the variables. Now let’s use _N to find duplicate observations.īy id score x1 x2 y1 y2 z1 z2: generate n = _N Let’s use _n to find out if there are duplicate id numbers in the following data:Īs it turns out, observations 6 and 7 have the same id numbers and but different score values. To list the highest score for each group use the following: To list the lowest score for each group use the following: ![]() Now n1 is the observation number within each group and n2 is the total number of observations for each group. Of course, to use the by command we must first sort our data on the by variable. ![]() Using _n and _N in conjunction with the by command can produce some very useful results. ![]() Let’s see how _n and _N work.Īs you can see, the variable id contains observation number running from 1 to 7 and nt is the total number of observations, which is 7. _N is Stata notation for the total number of observations. _n is 1 in the first observation, 2 in the second, 3 in the third, and so on. _n is Stata notation for the current observation number. Stata has two built-in variables called _n and _N. ![]()
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