Channel switching behavior during the modernization of grocery retailing in Turkey

(a) Purpose: Considering the increasing competition and channel diversity due to retail modernization we aim to study the channel switching behavior progress in a long horizon and the customer-related factors as influencers of channel switching. (b) Design/Methodology/Approach: Major channel switching behaviors of households are analyzed during six years, using household panel data covering grocery purchases from supermarket, local market and traditional channels in Turkey. Household demographics and behavioral variables are induced from dataset and Logistic regression model is employed to measure the effects of customer-related variables on the probability of channel switching. (c) Findings: Traditional channel still keeps the highest loyal customer base in Turkey. Loyalty to both traditional channel and supermarkets decreases, while to local markets increases by time. Households loyal to traditional channel transfer either a small amount of their purchases to modern channels or make modern channels their major channel occasionally. In any case, there is a transition period experienced in Turkish grocery retail market. A multi-channel usage behavior or higher adoption to modern channel might me the result of this transition. (d) Research Limitations/Implications: Channel level analysis limits to define channel-related variables, such as price, place, product-variety specific to each channel format. Excluding these variables might explain partially the low explanatory power of the model. (e) Practical Implications: Analyzing channel-switching behavior, its progression and explaining determinants is important for retailers and producers in predicting the future of market structure, modern retail customer base and in designing the growth strategies. The variables influencing channel switching help to explain some barriers of modern retail development

Empirical validation and comparison of Pareto/NBD and BG/NBD models using a grocery retailer customer database [Working Paper]

Understanding the benefits of retaining customers, companies have started to search for means to profile their customers individually and track their retention and defection behaviors. Capability to predict changes in individual customer purchase patterns is an indispensable advantage in identifying and targeting the right customers for retention investments. The major issues addressed in customer base analysis literature are identifying and counting the customers, who are active at any time, and predicting their future purchase levels and remaining lifetimes. Fader et al, 2004 have recently proposed a BG/NBD model as an easier alternative to the highly regarded Pareto/NBD model proposed by Schmittlein et al., 1987 for customer base analysis. In this study, we validate and compare the empirical performance of these two models at both aggregate and individual customer levels, using grocery retail transaction data. We measure the predicting performance of the models on repeat purchase levels, active status and lifetime of customers. In the individual level evaluations, both models capture the major characteristics of the purchase and dropout process and produce similar forecasts of the future purchase patterns. At the aggregate level, there are differences in forecasts of two models, which may be due to the way seasonality in the actual data is handled

Empirical validation and comparison of models for customer base analysis

The benefits of retaining customers lead companies to search for means to profile their customers individually and track their retention and defection behaviors. To this end, the main issues addressed in customer base analysis are identification of customer active/inactive status and prediction of future purchase levels. We compare the predictive performance of Pareto/NBD and BG/NBD models from the customer base analysis literature — in terms of repeat purchase levels and active status — using grocery retail transaction data. We also modify the BG/NBD model to incorporate zero repeat purchasers. All models capture the main characteristics of the purchase and dropout process of individual customers and produce similar forecasts. There are some deviations in the cumulative purchase estimates of the models, which may be due to the characteristics of grocery purchasing

Comparison of Effects of Dexmedetomidine-ketamine and Dexmedetomidine-midazolam Combinations in Transurethral Procedures

Batislam, Ertan/0000-0002-7493-4573WOS: 000304720600012PubMed: 22656405OBJECTIVE To compare the effects of dexmedetomidine-ketamine and dexmedetomidine-midazolam combinations on the recovery time, hemodynamic and respiratory variables, and side effects in patients undergoing transurethral procedures. METHODS Sixty patients scheduled for elective outpatient transurethral procedure were randomized into 2 groups. In the group K, a ketamine-dexmedetomidine combination was administered, and in the group M, midazolam-dexmedetomidine was administered, to provide sedation/analgesia. Pain and sedation levels were assessed using visual analog score (VAS) and Ramsey Sedation Scale, respectively. The recovery time was assessed with the scale of Aldrete. Time was measured and recorded to the moment at which patient responses brought the Aldrete score to 10 points. Time to eye opening and length of stay in the recovery room were recorded. RESULTS Group M showed significantly lower mean arterial pressure (MAP) values at 5 and 10 minutes during the procedure when compared with group K (P = .02 and P = .01, respectively). Visual analogue scale scores were greater in group M than in group K at 5 and 10 minutes for the transurethral procedure (P = .039 and P = .028, respectively). Sedation scores were similar between groups during the procedure. Time to eye opening and length of recovery room stay were shorter (P < .001 and P < .001, respectively), and Aldrete scores were greater in group K than group M. CONCLUSION Both combinations provided satisfactory sedation levels, but the dexmedetomidine-ketamine combination provided better analgesia and hemodynamic stability, with less nausea and vomiting and shorter recovery time, than the dexmedetomidine-midazolam combination. UROLOGY 79: 1214-1219, 2012. (c) 2012 Elsevier Inc