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Web Site Example

Web Site Example

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Web Site Example

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  1. Web Site Example • Web site for clothing catalogue company • Company has customer data on purchases from site, but wants to know more about all visitors to their web site • Buys web panel data • from Nielsen//NetRatings or Media Metrix (not in NZ) • E.g. Nielsen//NetRatings universe for the At Home Internet audience measurement is all individuals aged 2+ living in homes that have access to the Internet via a PC owned or leased by a household member and using a Windows operating system

  2. Respondent Data

  3. Frequency Distribution

  4. Fit Poisson Model • R Code: visit.dist <- c(2046,318,129,66,38,30,16,11,9,10,55) lpois <- function(lambda,data) { visits <- 0:9 sum(data[1:10]*log(dpois(visits,lambda))) + data[11]*log(ppois(9,lambda,lower.tail=FALSE)) } optimise(function(param){-lpois(param,visit.dist)},c(0,10)) • Result: maximum value of log-likelihood is achieved at λ=0.72

  5. Simple Poisson Model

  6. Nature of Heterogeneity • Unobserved (or random) heterogeneity • The visiting rate λ is assumed to vary across the population according to some distribution • No attempt is made to explain why people differ in their visiting rates • Observed (or determined) heterogeneity • Explanatory variables are observed for each person • We explicitly link the value of λ for each person to their values of the explanatory variables • E.g. Poisson regression model

  7. Poisson Regression Model • Let Yi be the number of times that individual i visits the web site • Assume Yi is distributed as a Poisson random variable with mean λi • Suppose each individual’s mean λi is related to their observed explanatory characteristics by • Take logs of household income and age first • R code, using glm function for Poisson regression: glm.siteVisits <- glm(Visits ~ logHouseholdIncome + Sex + logAge + HH.Size, family=poisson(), data=siteVisits) summary(glm.siteVisits)

  8. Poisson Regression Estimates Can also fit model using maximum likelihood as for simple Poisson model, but this will not give standard errors

  9. Poisson vs Poisson Regression • The simple Poisson model (model B) is nested within the Poisson regression model (model A) • So we can use a likelihood ratio test to see whether model A fits the data better • Compute the test statistic and reject the null hypothesis of no difference if

  10. Expected Number of Visits • So person 2 should visit the site less often than person 1

  11. Poisson Regression Model

  12. Poisson Regression Fit • Poisson regression model improves fit over simple Poisson model • But not by much • Try introducing random heterogeneity instead of, or as well as, observed heterogeneity • Possibilities include: • Zero-inflated Poisson model • Zero-inflated Poisson regression • Negative binomial distribution • Negative binomial regression

  13. Zero-inflated Poisson Regression • Assume that a proportion π of people never visit the site • However other people visit according to Poisson model • Probability distribution:

  14. Zero-inflated Poisson Model • Note that Poisson model predicts too few zeros • Assume that a proportion π of people never visit the site • Remaining people visit according to Poisson distribution • No deterministic component • R code: lzipois <- function(pi,lambda,data) { visits <- 1:9 data[1]*log(pi + (1-pi)*dpois(0,lambda)) + sum(data[2:10]*log((1-pi)*dpois(visits,lambda))) + data[11]*log((1-pi)*ppois(9,lambda,lower.tail=FALSE)) } optim(c(0.5,1),function(param){-lzipois(param[1],param[2],visit.dist)}) • Likelihood maximised at π=0.73, λ=2.71

  15. Zero-Inflated Poisson Model

  16. Zero-inflated Poisson Regression • Can add deterministic heterogeneity to zero-inflated Poisson (ZIP) model • Again assume that a proportion π of people never visit the site • However other people visit according to Poisson regression model • Probability distribution:

  17. Fit ZIP Regression Model • R code: siteVisits <- read.csv(“visits.csv”) lzipreg <- function(param,data) { zpi <- param[1] lambda <- exp(param[2] + data[,3:6] %*% param[3:6]) sum(log(ifelse(data[,2] == 0,zpi,0) + (1-zpi)*dpois(data[,2],lambda))) } optim(c(.7,2,0,-0.1,0.1,0),function(param){-lzipreg(param,as.matrix(siteVisits))},control=list(maxit=1000)) • Likelihood maximised at π=0.74, β=(1.90,-0.09,-0.13, 0.11,0.02)

  18. ZIP Regression Predictions

  19. Simple NBD Model • Recall the negative binomial distribution • The number of visits Y made by each individual has a Poisson distribution with rate λ • λ has a Gamma distribution across the population • At the population level, the number of visits has a negative binomial distribution

  20. Fitting NBD Model • R code: lnbd2 <- function(alpha,beta,data) { visits <- 0:9 prob <- beta/(beta+1) sum(data[1:10]*log(dnbinom(visits,alpha,prob))) + data[11]*log(1-pnbinom(9,alpha,prob)) } optim(c(1,1),function(param) {-lnbd2(param[1],param[2],visit.dist)}) • Likelihood maximised for α=0.157 and β=0.197

  21. NBD Model Predictions

  22. NBD Regression • Can also add deterministic heterogeneity to NBD model • Again assume that a proportion π of people never visit the site • However other people visit according to an NBD regression model • Probability distribution: • Reduces to simple NBD model when g=0

  23. NBD Regression Estimates Can also fit model using maximum likelihood, but this will not give standard errors

  24. NBD Regression Fit

  25. Covariates In General • Choose a probability distribution that fits the individual-level outcome variable • This has parameters (a.k.a. latent traits) θi • Think of the individual-level latent traits θi as a function of covariates x • Incorporate a mixing distribution to capture the remaining heterogeneity in the θi • The variation in θi not explained by x • Fit this model (e.g. using maximum likelihood)

  26. New Concepts • How to incorporate covariates in probability models • Poisson, zero-inflated Poisson and NBD regression models for count data • However, getting the outcome variable distribution right was more crucial here than introducing covariates • Importance of covariates is often exaggerated

  27. Reach and Frequency Models • Advertising is a major industry • NZ Ad expenditure reached $1.5bn in 2000 • Many companies spend millions each year • Crucial to understand the effects of this expenditure • Major outcomes include how many people are reached by an ad campaign, and how many times • Known as reach and frequency (R&F) • Typically analysis is limited to calculating media exposure, not advertising exposure

  28. Reach and Frequency Models • Data on TV viewing, newspaper and magazine, radio listening etc is routinely gathered • Ratings and readership figures determine the price of space in these media • However this data typically does not enable detailed reach and frequency analysis • E.g. readership questions ask about the last issue read, and how many read out of average 4 issues • Longitudinal data is collected on TV viewing, but item non-response causes problems with direct analysis • Models are needed to derive complete reach and frequency analyses from the collected data

  29. R&F Analysis Examples

  30. R&F Analysis Examples

  31. Beta-Binomial Model for R&F • If an advertiser has placed an ad in each of 10 issues of a magazine, the beta-binomial model assumes that: • Each person has a probability p of reading each issue • These probabilities follow a beta distribution • Each issue is read independently, between and across individuals • Distribution of # issues read for each person is binomial • The resulting aggregate exposure distribution is the beta-binomial • Applied to R&F analysis by Metheringham (1964) • Still widely used • But not very accurate

  32. Typical Exposure Distribution

  33. Modified BBM • One problem with the beta-binomial model is that it does not model loyal viewers/readers/listeners well • By adding a point mass at 1 to the beta distribution of exposure probabilities, the BBM can be modified to accommodate loyal readers etc • Derived by Chandon (1976); improved by Danaher (1988), Austral. J. Statist.

  34. Multiple Media Vehicles • The BBM (and modified BBM) focus on exposure to one media vehicle (e.g. one magazine) over the course of an ad campaign • Need to extend to multiple vehicles • Model both reading choice and times read, in one combined model • Could assume independence • E.g. Dirichlet-multinomial model • Assumes independence of irrelevant alternatives (IIA) • But there are known to be correlations between different media vehicles • E.g. women’s magazines, business papers, programmes on TV1 vs TV3

  35. Multiple Media Vehicles • Models need to take correlations between media vehicles into account • Log-linear models have been used • But these are computationally intensive for moderately large advertising schedules • Canonical expansion model (Danaher 1992) • Uses Goodhardt and Ehrenburg’s “duplication of viewing” law to minimise need for multivariate correlations • Data on pairwise correlations used, but higher order joint probabilities are derived using this law • Higher order interactions are assumed to be zero • Canonical expansions are used for the joint probabilities to minimise computations

  36. FMCG Sales/Purchasing • Retail sales figures for fast moving consumer goods • Have good aggregate weekly sales figures • Data available down to SKU level • Data collected at store level • Know when total sales are changing over time • Can also investigate overall response to promotions • Using store level data can give more accurate results, and even allow some segmentation by chain or region • However sales figures cannot show us who is buying more when sales increase, or who is affected by promotions • Heavy buyers? Light buyers? New buyers? • Households with kids? Retired couples? Flatters? • Even when overall sales are flat, there may be hidden changes • Marketing activities could be made more effective using this sort of information, so how can we find out about this?

  37. Household Purchasing Data • Data about FMCG purchases collected from a panel of households • Can be collected through diaries • Or even weekly interviews, based on recall • Best method is currently to equip panel with scanners • This is used by each household member to record all items bought • ACNielsen (NZ) runs a scanner panel of over 1000 households • Data includes amount purchased, price, date, product details down to SKU level • Also have demographic characteristics of household

  38. Common Research Questions • Who buys my product? • Perhaps better answered by U&A (usage and attitudes) study • How much do they buy? • How often? • Who are my heavy buyers? Light buyers? Frequent buyers? • How many are repeat buyers? • How does this compare to my other brands? How about my competitors? • Are my results normal? • How do they compare to similar products in other categories?

  39. Example of Purchase Data

  40. Results for 4-Week Months

  41. Observations • Usually there will be a wide range of purchasing intensity among buyers of each brand • Also a proportion who do not buy the brand • Instead of a whole brand, we can also look at a brand/package size combination • Similar findings apply at both levels

  42. Another Example • Data gathered from a panel of 983 households • Purchases of Lux Flakes over a 12 week period • Various summary measures shown below

  43. Example (continued) • Low penetration overall • 22 buyers, about 2% of panel • More than half the purchases were by “new” buyers • The cumulative purchasing distribution looks similar to the cumulative reach distributions from the last lecture

  44. Negative Binomial Model • Fit NBD model – assumes Poisson process for purchase occasions, with Gamma heterogeneity • R code: purchase.dist <- c(961,17,3,2) lnbd3 <- function(alpha,beta,data) { visits <- 0:3 prob <- beta/(beta+1) sum(data[1:4]*log(dnbinom(visits,alpha,prob))) } optim(c(1,1),function(param) {-lnbd3(param[1],param[2],purchase.dist)}) • Likelihood maximised for α=0.045 and β=1.514

  45. Negative Binomial Model • Can also fit the model based on the observed values of two quantities • The proportion of people p0 making no purchases during the study period • The mean number of purchases made m (assuming that only one item is purchased at each purchase occasion) • Then solve for α and βnumerically

  46. Multivariate NBD • Generalise to multiple time periods with durations Ti, i=1,…,t • Various partitionings of the Ti lead to variables that are also NBD • E.g. divide into the first s time periods and the remaining t - s • The values for the latter t - s periods, conditional on those for the first s, are multivariate NBD • α is incremented by the total purchases from the first s periods, and the mean is updated as a weighted average of the original mean and the observed mean. • So can easily apply empirical Bayes techniques using this model

  47. NBD Model for Longer Periods • Another property of the NBD is that purchases over a longer time period are also NBD (assuming that the purchasing process remains the same) • The mean number of purchases increases in proportion to the length of the period • But the parameter α remains fixed

  48. NBD Model • The NBD model has been applied to products in a wide range of categories • It generally fits very well • The main exception (for diary data) is when the recording period is too short compared to the purchase frequency • Often people record shopping once in each period, rather than multiple times • Can cause problems if many people are expected to purchase once or more each period

  49. α is Usually Constant • Typically α will be relatively constant across different products in the same category • This means that the heterogeneity in purchasing rates is similar across products • However β will vary to reflect the penetrations of the different products