Mailing smarter to catalog customers

Gönül, Füsun F.; Kim, Byung-Do; Shi, Mengze

doi:10.1002/(sici)1520-6653(200021)14:2<2::aid-dir1>3.0.co;2-n

Cited by 77 publications

(25 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The notion that periods with increased purchases may enhance the accessibility of the LP and thereby produce a spillover effect on behavior is in line with the literature on RFM models, direct mailings, and decay effects in both advertising recall and purchase history in household scanner data (Leone, 1995;Gönül, Kim, & Shi, 2000).…”

Section: Operationalization Of Main Explanatory Factorssupporting

confidence: 74%

Reward redemption effects in a loyalty program when customers choose how much and when to redeem

Dorotić

Verhoef²,

Fok

et al. 2014

International Journal of Research in Marketing

View full text Add to dashboard Cite

The redemption of loyalty program (LP) rewards has an important impact on LP members' behavior, particularly on purchase behavior before and after redeeming a reward. However, little is known about the interplay between members' purchase and redemption behavior when members are not pressured with points expiration and they choose for themselves when and how much to redeem. In this context, the effects of redemption are not straightforward, as little additional effort is required from an LP member to obtain the reward. Analyzing the behavior of 3,094 members in such an LP, we find that the mere decision to redeem a reward significantly enhances purchase behavior before and after the redemption event, even when members redeem just a fraction of their accumulated points. Conceptually, we refer to this enhancement as the redemption momentum, which is an alternative and novel explanation of the existence of pre-reward effects that does not depend on points-pressure. In addition to the overall impact of redemption on purchases, prior purchase behavior also enhances redemption decisions. Finally, we find a number of moderating effects on purchase and redemption behavior that derive from the length of LP membership, age, income and direct mailings. Our study's most important managerial implication is that firms can avoid imposing binding thresholds and/or points expiry and still enhance members' purchase behavior.

show abstract

Section: Operationalization Of Main Explanatory Factorssupporting

confidence: 74%

Reward redemption effects in a loyalty program when customers choose how much and when to redeem

Dorotić

Verhoef²,

Fok

et al. 2014

International Journal of Research in Marketing

View full text Add to dashboard Cite

show abstract

“…Hazard function models are increasingly recognised in the marketing literature. 5,6 The advantage of the hazard function model is twofold: the ability to incorporate right-censored observations and the ability to allow for tend to stay longer. Similarly, all four variables (DUTY, PAYMENT, HPBRAND and HPCHANGE) negatively influence the attrition rate.…”

Section: Estimation Resultsmentioning

confidence: 99%

“…More sophisticated techniques such as the finite mixture or the Bayesian model can be employed to incorporate unobserved customer heterogeneity in model parameters. 12,13 …”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Encouraging customers to pay less for mobile telecommunication services

Joo

Jun

2002

J Database Mark Cust Strategy Manag

View full text Add to dashboard Cite

“…For customer churn, Bolton [1] and Gonul et al [5] used proportional hazards model to predict the probability of a customer switching to competitors. For recommendation systems, Wang and Zhang [15] used a similar method to decide the right time for recommending a product to a user.…”

Section: Related Workmentioning

confidence: 99%

A Probabilistic Multi-Touch Attribution Model for Online Advertising

Wang

Zhang

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

It is an important problem in computational advertising to study the effects of different advertising channels upon user conversions, as advertisers can use the discoveries to plan or optimize advertising campaigns. In this paper, we propose a novel Probabilistic Multi-Touch Attribution (PMTA) model which takes into account not only which ads have been viewed or clicked by the user but also when each such interaction occurred. Borrowing the techniques from survival analysis, we use the Weibull distribution to describe the observed conversion delay and use the hazard rate of conversion to measure the influence of an ad exposure. It has been shown by extensive experiments on a large realworld dataset that our proposed model is superior to stateof-the-art methods in both conversion prediction and attribution analysis. Furthermore, a surprising research finding obtained from this dataset is that search ads are often not the root cause of final conversions but just the consequence of previously viewed ads.Keywords computational advertising, multi-touch attribution, survival analysis INTRODUCTIONInternet increasingly becomes the leading advertising medium, where online users generate a tremendous amount of feedback information including clicks and conversions. The feedback data reveal the needs/preferences of users, and thus enable online advertising systems to deliver ads to those who are most likely to respond. Nowadays companies spare no effort to attract consumers to visit their websites through various advertising channels, among which display ads and search ads are two dominant types.Recently, researchers from both academia and industry have become more and more interested in analysing the contribution of each advertising channel to user conversion which is known as the "attribution" problem. An accurate attribution model would be of great help for advertisers to interpret the effects of different advertising channels and make informed decisions to optimize their advertising campaigns (e.g., by reallocating advertising budgets). An online advertising campaign is usually launched across multiple channels such as display ads, paid search ads, social media ads, and so on. In most cases, users would have been exposed to the ads from a particular advertising campaign many times before their final conversion, as illustrated in Figure 1. Suppose that a brand X delivers ads through three channels: display, social and paid search: user 1 saw X's display ad at t 1 1 when browsing a webpage, and then saw X's social ad at t 1 2 ; later, she searched for X's products and clicked its paid ad link at t 1 3 ; finally, she made a purchase on X's website at time T 1 . In this case, how should we assess the contribution of those three advertising channels to that user's conversion?A number of attribution models have been proposed and utilized in recent years. Figure 2 shows some representative ones. Most of the existing attribution models widely used in practice are rule-based, and their effectivenesses are limited by thei...

show abstract

Mailing smarter to catalog customers

Cited by 77 publications

References 25 publications

Reward redemption effects in a loyalty program when customers choose how much and when to redeem

Reward redemption effects in a loyalty program when customers choose how much and when to redeem

Encouraging customers to pay less for mobile telecommunication services

A Probabilistic Multi-Touch Attribution Model for Online Advertising

Contact Info

Product

Resources

About