دانلود رایگان مقاله لاتین مقایسه گزینه ها برای عدم همبستگی در مدل بازاریابی مستقیم از سایت الزویر
عنوان فارسی مقاله:
مقایسه جایگزین ها برای عدم همبستگی نامتعارف در مدل های بازاریابی مستقیم
عنوان انگلیسی مقاله:
The Effects of Channel Experiences and Direct Marketing on Customer Retention in Multichannel Settings
سال انتشار : 2016
بخشی از مقاله انگلیسی:
Applying HMM to Multi-channel Customer Retention
Retention in many works of CRM literature refers to a single and constant ratio used to represent the portion of retained customers and to calculate lifetime value, which means that the estimated retention probabilities do not vary over a customer's lifetime (Blattberg and Deighton 1996). Many of the research studies assert that small increases in retention drive large increases in profits (Gupta and Lehmann 2003; Pfeifer and Farris 2004; Reichheld and Sasser 1990), thus inaccurate estimates of retention rates would lead to large biases for the value of a customer base. Most previous research in contractual settings uses a family of hazard models to address retention duration, predict the customer's lifetime, and examine the impact of predictors on relationship length (Boehm 2008; Fader and Hardie 2010; Schweidel, Fader, and Bradlow 2008). While these approaches are appropriate in contractual settings with clearly stated drop-out times, they could not be readily applied in non-contractual settings where customers' retention tendencies are unobserved and where relationships can change over time, thereby making the retention rate time varying. In non-contractual settings, issues around how to estimate customer lifetime value based on accurate retention and how to count retained customers persisted until the Pareto/NBD (Reinartz and Kumar 2000; Schmittlein, Morrison, and Colombo 1987) and BG/NBD (Fader, Hardie, and Lee 2005) models were proposed. In the context of CRM, the Pareto/NBD and BG/NBD models explore such issues as predicting future demand, customer churn, and retention rate by assuming that customers may transition from an “active” state to an “inactive” state at different rates. The two models attempt to estimate customer retention and dropout rate with slightly different assumptions and provide good answers to questions about how many customers will be active or “alive” in the future given their past behavior. One major property of the Pareto/NBD and BG/NBD models is that both assume that customers start off as active and, based on observed activities, will experience a discreet jump to an inactive or “dead” state at some point and do not switch back to an active state. Thus, they imply that there is no spectrum between active and inactive and that customers who are identified as inactive remain inactive permanently. This is a strong restriction on estimating retention and neglects important factors such that relationships are inherently gradual and dynamic in nature and precludes the possibility that even inactive relationships might be revived through marketing interventions. In our modeling framework, we model latent relationships as a gradual process instead of discreet jumps, and flexibly allows for the possibility that the customer could be revived. Beyond just the retention rate number, managers are interested in understanding the factors underlying retention rates; for example, under which circumstances would a customer come back after being inactive and under which circumstances retention would increase. Also, as different channels have different value propositions, customers who are used to purchasing through one channel may have a different relationship and hence a different retention rate than those who purchase through an alternative channel. Therefore, the effect of channel experiences on retention should be considered. Our HMM framework not only allows for customers to evolve flexibly among relationship states, but also explicitly models the impact of channel experiences and marketing on retention by incorporating those factors into customer state transitions. Table 1 summarizes the relevant extant research along the above important dimensions and highlights our study's comprehensiveness and contributions.
Handbook of Marketing Decision Models https://books.google.com/books?isbn=3319569414 Berend Wierenga, Ralf van der Lans - 2017 - Business & Economics Measuring the lifetime value of customers acquired from Google search advertising. Marketing Science 30 (September–October): 837–850. Chang, C.W., and J.Z. Zhang. 2016. The effects of channel experiences and direct marketing on customer retention in multichannel settings. Journal of Interactive Marketing 36 ... Customer Experience (CX) news, Ernan Roman Direct Marketing News www.erdm.com/news_articles.php Opinion piece in Direct Marketing News titled Break from the Acquisition Cult. The best way to make increased loyalty and retention a reality is to create Reciprocity of Value-based relationships with customers. Top Mistakes Direct Marketers Make An article posted by BtoBonline.com, titled: Top Mistakes Direct marketers ... Loyalty & Retention Marketing FAQs - Canadian Marketing Association the-cma.org/disciplines/customer/archive/loyalty-retention-marketing-faqs CMA's Direct Marketing Council sought-out four top-level marketers to delve into some frequently asked questions about loyalty and retention. ... With that in mind, achieving true customer loyalty and retention is a challenge and is only deemed successful if brands can deliver superior experiences in the following areas:.