پیش بینی چشم انداز بنگاه به بنگاه مدل پشتیبانی در مبتنی بر چارچوب کسب مشتری تکراری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23863||2013||8 صفحه PDF||سفارش دهید||7460 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Industrial Marketing Management, Volume 42, Issue 4, May 2013, Pages 544–551
This article discusses a model designed to help sales representatives acquire customers in a business-to-business environment. Sales representatives are often overwhelmed by available information, so they use arbitrary rules to select leads to pursue. The goal of the proposed model is to generate a high-quality list of prospects that are easier to convert into leads and ultimately customers in three phases: Phase 1 occurs when there is only information on the current customer base and uses the nearest neighbor method to obtain predictions. As soon as there is information on companies that did not become customers, phase 2 initiates, triggering a feedback loop to optimize and stabilize the model. This phase uses logistic regression, decision trees, and neural networks. Phase 3 combines phases 1 and 2 into a weighted list of prospects. Preliminary tests indicate the good quality of the model. The study makes two theoretical contributions: First, the authors offer a standardized version of the customer acquisition framework, and second, they point out the iterative aspects of this process.
The phrase customer relationship management (CRM) is often used in contemporary marketing literature. Although it has been in use since the beginning of the 1990s, researchers have reached no consensus with regard to its definition ( Buttle, 2009a, Ngai, 2005 and Richards and Jones, 2008). Most definitions have, however, some core features in common; for example, CRM consistently deals with the acquisition and retention of customers and the maximization of long-term customer value ( Jackson, 2005 and Ngai et al., 2009). Prior literature also distinguishes four types of CRM: strategic, operational, analytical and collaborative ( Buttle, 2009a). This paper focuses on analytical CRM, which involves mining customer-related data for strategic purposes ( Ang and Buttle, 2006, Buttle, 2009a and Ngai et al., 2009), centered on the process of acquiring new customers, and how data mining techniques can facilitate this process. Most CRM literature neglects customer acquisition in favor of other topics, such as retention (Sohnchen & Albers, 2010), because retention strategies are typically cheaper than acquisition strategies (Blattberg et al., 2008a and Wilson, 2006). However, as important as customer retention might have become, customer acquisition is and should be a crucial focus for companies and researchers for several reasons (Ang and Buttle, 2006, Buttle, 2009b and Kamakura et al., 2005). Startups and companies aiming to exploit new markets need new customers, because they lack existing customers. Even existing companies in a mature market will lose some customers and must replace them (Wilson, 2006). Acquiring new customers is a multistage process, in which only certain suspects (for a definition of the terms used herein, see Section 2) become actual customers, also referred to as the “sales funnel” (Cooper and Budd, 2007, Patterson, 2007 and Yu and Cai, 2007). During this process, it is often difficult for sales representatives to cope with all available data (Yu & Cai, 2007). Monat (2011, p. 192) indicates that many companies face this issue: “Sales leads are the lifeblood of industrial companies, yet determining which leads are likely to convert to bookings is often based upon guesswork or intuition. This results in a waste of resources, inaccurate sales forecasts, and potential loss of sales. A quantitative model that may be used to predict which leads will convert, based on information inherent in the leads themselves, would be highly valuable.” In response, this article presents a quantitative model, designed to be used as a tool to assist sales representatives in customer acquisition—that is, a sales force automation tool. Moreover, it is designed to be implemented in a web application, giving it certain specific characteristics and advantages. First, it should be usable regardless of specific company characteristics such as size and industry. Whether for a large company in the automotive sector or a small company in the food sector, the model should render high-quality predictions. Second, it must be fully automated and run without the need for human interference. Third, it must be fast and inexpensive. Because it is a web application, users typically want results immediately.2 When the algorithm is embedded into a web application, the cost to the user is limited. The user (i.e., a business-to-business [B2B] company) only needs to pay a membership fee to obtain access to the application and does not need to pay for the whole database of prospects, which can be expensive. Moreover, the company does not need in-house experts to analyze the data, as the algorithm performs this step and provides intuitive, ready-to-use output. Sales representatives must sometimes make arbitrary decisions in selecting prospects from a list of suspects and further qualifying them into leads. Thus, time is lost pursuing bad prospects and leads, violating the famous “time is money” corporate mantra. A model with high predictive power in forecasting the right prospects to pursue can save a company time and, ipso facto, money. Research indicates that approximately 20% of a sales representative’s time is spent selecting prospects (Trailer, 2006) and depicts prospecting as the most cumbersome part of the selling process (Moncrief & Marshall, 2005). Furthermore, making ineffective decisions in the customer acquisition process decreases the overall value of the company over time (Hansotia & Wang, 1997). The proposed algorithm is designed to make the decision-making process less arbitrary by providing model-based prospects. Although the algorithm should work regardless of the company using it or the industry in which it is situated, note that the proposed sales force automation tool will work best in markets that are highly saturated, in which market penetration is strategically crucial. We expect the highest efficiency in markets in which the pool of potential customers is large. In those markets, the selection process is often costly and arbitrary, due to information overload. In contrast, in industries in which customers are large organizations, well-known, and few in number, the proposed algorithm will not provide a significant advantage, because the selection of prospects is limited (Long, Tellefsen, & Lichtenthal, 2007). The algorithm functions in a B2B environment and uses the current customer base of a company to predict prospects. It also contains a feedback loop that iteratively improves its overall predictive performance. There is a limited amount of research on customer acquisition (Blattberg et al., 2008a). With this research, we aim to fill this void and also stimulate further research. The theoretical contributions are twofold. First, we offer a standardized version of the customer acquisition framework. Second, we point out the iterative aspect of this process, which has been neglected in research. The remainder of this article is structured as follows: We present a literature review on customer acquisition, then describe the different stages of our model. After we elaborate on the data, we report the results of the model and finally discuss the conclusions, implications, limitations, and further research suggestions.
نتیجه گیری انگلیسی
This article presents a procedure to facilitate the customer acquisition process in a B2B environment. The algorithm contains three phases, and the output is a ranked list of prospects. Sales representatives could select a top percentage of these ranked prospects to qualify further as leads to pursue. Because these prospects are higher quality, it is easier for sales representatives to qualify and, in turn, convert them into customers. Real-life and pseudo tests show positive results. The real-life test suggests a conversion rate from prospect to lead that is higher than average. The first pseudo test produced a conversion rate from prospect to customer similar to the average conversion rate by only using the first phase of the algorithm. The second pseudo test needs only four runs to find 24 of 26 companies in a suspect list that contains more than 16 million companies. This study provides several managerial implications. First, the proposed sales force automation tool operates in a fully automated way, but human intervention remains possible, when necessary. As a result, the tool can work in a broad range of situations. It supports sales managers from a starting position, in which there is merely a basic set of current customers and no information on the acquisition process, to a situation in which the customer base is more mature and a vast amount of data is available on the history of this process. However, human intervention might be preferable in some cases. Look-alike models tend to overlook opportunities in other segments (Blattberg et al., 2008a), which is inherent to the method, in that it searches for new prospects similar to the current customers. As a result, it is not always optimal to include the full set of variables. For example, the industry (NAICS code) can be withheld from the algorithm to find prospects in different industries as well. Second, the output of the algorithm can be used straightforwardly without any knowledge of the statistical models running in the background. Thus, its applicability does not rely on any human expertise, such that it lowers the threshold for sales representatives to use this tool. Furthermore, research has shown that the efficiency of sales representatives using sales force automation tools is only augmented when it is accompanied by user training and support (Ahearne, Jelinek, & Rapp, 2005). Because this tool can intuitively be used and no significant training is necessary, the cost and time of such support is marginal, making it more likely that B2B sales managers will implement it and that this implementation is fluent. Third, the tool could help sales managers negotiate with a data vendor to pay for only the prospects indicated by the sales force automation tool and not the whole list of suspects. The tool can also be embedded into a web application, limiting the costs (see Section 1). However, even if a data vendor was already willing to sell a selection of prospects on the basis of some arbitrary rules instead of a list of suspects, sales managers or the vendors themselves could improve the selection using the proposed algorithm. Fourth, this study offers an explicit iterative view of the customer acquisition process. Each iteration provides useful information for the next. Therefore, there is a need for an extensive documentation when sales managers attempt to acquire new customers. Information on decisions made, steps taken, strategies employed, and so on must be recorded and analyzed periodically. This way, new customer acquisition can be improved incrementally. This iterative view is also a theoretical implication. The shift from a static to a dynamic framework is a more accurate conceptualization of reality. When designing models using a customer acquisition framework, modelers should take the iterative aspect into account, which has been neglected to date. A different but related theoretical implication is the need for a standardized customer acquisition framework. We provide a personal, though literature-based, view on the flow between the different acquisition stages and their respective definitions. It is by no means meant as an ultimate framework, but rather as a tool tailored for our purposes.