What are Some Retention Strategies that Sales Organizations in Various Industries Have Been Using to Retain Sales Reps Aside from Compensation?

[Excerpt] Management of sales organization views sales force retention as a critical objective. The total costs associated with the quit decision of a single salesperson (recruiting, training and opportunity costs of lost sales from unmanned positions) is in the $50,000-$75,000 range. Clearly, the adverse impact of salesforce turnover on both the profitability of the organization as well as the motivation level of the salesforce can be substantial. Therefore, it is urgent as well as crucial for sales organizations to develop retention strategies to incentivize sales representatives to stay with the company. The most crucial reason for voluntary turnover among salesforce is higher wages. Salary growth effects on turnover were greatest for high performers, that is, high salary growth significantly reduced turnover for high performing employees. However, besides salary, there are still a wide range of other strategies that companies can adopt to retain the salesforce

The Dynamic Change in Wage Gap between Urban Residents and Rural Migrants in Chinese Cities

Although a significant wage gap has been found in many previous studies between urban workers and rural migrants in Chinese cities, it is still not clear how such a wage gap may evolve over time. This paper uses both a dynamic wage decomposition method and economic assimilation model with pooled cross-sectional data from the China Household Income Project Survey (CHIPS) of 1999 and 2002 to investigate the change in the wage gap between urban workers and rural migrants over time and its determinants in Chinese cities. The estimation results show that (1) there is a widening on-average wage gap between urban workers and rural migrants across the two surveyed years in Chinese cities, mainly caused by the decline in the return to education for rural migrants; (2) rural migrants can catch up with the wage level of their urban counterparts as the time they reside in the host cities increases, but because of the decline in the speed of catching-up over time, rural migrants cannot obtain wages comparable totheir urban counterparts in their life time, and more importantly well-educated rural migrants do not seem to have a significant advantage in this wage assimilation process than the lowlypoorly-educated ones. Both findings suggest that there might be discrimination against well-educated rural migrants which prevents them from obtaining a fair wage in the Chinese urban labour market.Wage differential, Migration

Impact of image personalization on response rate of direct mail advertising

With widespread adoption of full-color digital presses in the commercial printing industry, personalization has been enabled to create one-to-one marketing communication to enhance the effectiveness of print media advertising. Prior researches showed an increase in response rates with data-driven personalized direct mailings. However, the impact of image personalization on response rate of direct mail advertising is still unknown. This research used an experimental design to test the impact of image personalization on response rate of direct mail advertising. A direct mail postcard campaign was created with three levels of personalization (black-and-white name-only personalization, full-color name-only personalization, full-color name and image personalization) to test the hypothesis that there are no statistically significant differences between the response rates of direct mail advertising with name-only personalization and with image personalization. The Cost Per Response (CPR) for three levels personalization was also observed. The experimental results showed that the response rates for postcard with full-color name and image personalization was 2.6%, and 3.0% for full-color name-only personalization. There were no statistically significant differences between the two levels personalization. Black-and-white name-only personalization postcard turned out to be the most cost effective direct mail advertising with a CPR of $20.50 USD, lower than both full-color name-only personalization ($26.33 USD), and full-color name and image personalization ($30.07 USD) postcard in this case. The reasons for observing these results include consumer behavior towards personalized direct mail, advertising objective, postcard design, product category, timing, and limitations of the experiment. Based on the results, commercial printing companies, especially those provide marketing services need to make a Return-On-Investment (ROI) analysis before recommending the direct mail advertising campaign involving image personalization

硫黄を含む分子間相互作用の気相レーザー分光研究

Tohoku University藤井朱鳥課

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

Probing dynamics of dark energy with latest observations

We examine the validity of the $\Lambda$CDM model, and probe for the dynamics of dark energy using latest astronomical observations. Using the $Om(z)$ diagnosis, we find that different kinds of observational data are in tension within the $\Lambda$CDM framework. We then allow for dynamics of dark energy and investigate the constraint on dark energy parameters. We find that for two different kinds of parametrisations of the equation of state parameter $w$, a combination of current data mildly favours an evolving $w$, although the significance is not sufficient for it to be supported by the Bayesian evidence. A forecast of the DESI survey shows that the dynamics of dark energy could be detected at $7\sigma$ confidence level, and will be decisively supported by the Bayesian evidence, if the best fit model of $w$ derived from current data is the true model.Comment: 4.5 pages, 3 figures, 1 table; references adde