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The relevance of context aware recommender system for one to one marketing in E-commerce Name: Joyce Rose ID: 1433345 Email: jr25depaul@gmail.com Class: ECT 584  

Abstract: The movement of the physical store to the virtual space, that is the world of e-commerce has not only resulted in the explosion of information but has also created the possibility of converting standardized products into personalized products for customers. The importance of having a robust recommender system in an environment laden with information, for a virtual store, from a marketing perspective is to convert browsers into sellers, increase cross sell between products and build loyalty by creating a positive interaction between the site and customer which in turn increases profitability for the company. Traditionally the adopted method for implementing one to one marketing through recommendation systems is by the use of collaborative filtering or content-based recommendation. Though both methods rely on user profiles and items. The crux of the recommendation system fails to capture the impact of contexts or situations - the context in which a customer purchases a product - as an important factor in understanding customer shopping behavior. Therefore, the purpose of this paper is to explore the relevance of context in improving the quality of a recommender system and its importance to a one to one e – commerce marketing strategy.

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1. Introduction and Motivation In the world of e-commerce where the Internet serves as the primary medium to “facilitate, execute and process business transactions” (DeLone & McLean, 2004, p. 31), the birth of the ‘virtual store,’ an alternate reality to physical stores, is a paradigm shift from ‘tradition.’ A shift in that e-commerce not only experienced a decrease in operational costs (Vafopoulos & Oikonomou, 2011), but also encountered the possibility of converting standardized products into personalized products for customers. A most noted move away from marketing to the masses to one to one marketing. With the advent of personalization Schaefr, Konstan and Riedi write, “At a minimum, companies need to be able to develop multiple products that meet the multiple needs of multiple consumers. While E-commerce hasn’t necessarily allowed businesses to produce more products, it has allowed them to provide consumers with more choices. Instead of tens of thousands of books in a superstore, consumers may choose among millions of books in an online store. Increasing choice, however, has also increased the amount of information that consumers must process before they are able to select which items meet their needs” (para. 1). The virtual store, therefore, is an ocean of information one in which consumers can feel overwhelmed and lost. In order to combat the negative repercussions of information overload - which is less foot traffic and poor sales performance - recommender systems (RS) have been used as a “virtual salesperson” to “propose products for purchase,” (Vafopoulos & Oikonomou, 2011, p.5) each personalized to the needs of the customer. The goal of recommender systems is to create a positive discourse between the recommender and the recommendee by exploiting “user’s characteristics (e.g. demographics) and preferences (e.g. views and purchase) to form recommendations” (Vafopoulos & Oikonomou, 2011, p.6). Hence, the robustness of RS for a virtual store, from a marketing perspective, has been defined based upon the systems’ ability to convert browsers into buyers, to increase cross sell between products and to build loyalty by creating a positive interaction between the site and customer. This in turn increases profitability for the company. Traditionally, the adopted method for implementing one to one marketing through recommendation systems is by the use of collaborative filtering or content-based recommendation. Both methods rely on user profiles and past transactions/items to make recommendations. The crux of such a recommendation system is limited in that it fails to capture the impact of contexts or situations - the context in which a customer purchases a product - as an important factor in increasing sales. The failure of recommendation systems to capture contextual information is not a poor reflection on the algorithm itself. However, it is a reflection of marketers’ inability to understand the importance of context in consumers’ online shopping behavior. The primary goal of a marketer is to identify, anticipate and satisfy customer requirements profitably. “In particular, one to one marketing is defined by four principles, namely: (1) identify customers, (2) differentiate each customer, (3) interact with each customer and (4) customize products for each customer” (Vafopoulos & Oikonomou, 2011, p.10). Though there are no arguments against the principles themselves, it is important to ask the two following questions to systematically understand the limitations of the traditional recommender systems: “how do customers behave in electronically enhanced buying environments? And how can marketers

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design e-services that deliver quality and value to customers?” (Bolton, 2003, para. 44). Bolton argues that marketers often fail to realize that the online shopping environment is vastly different than shopping in store in that customers are no longer able to feel the product but instead are offered information to decipher the quality of it. Therefore, the basis of customers’ purchasing a product or not is primarily rested on perception – perception of value [be it need or relevance] in the product being recommended and perception of the website. And based on perception trust and customer loyalty is built. The marketing challenges that Bolton has pointed out calls attention to the fact that recommendation systems are built from a ‘seller centric’ point of view irrespective of the ‘personalized’ item recommendations offered to consumers. The personalization remains seller centric because considerations such as customer perception on trust, lack of interpersonal interaction or customer psychographics in general is not accounted for by the algorithms. Therefore, the purpose of this paper is to explore and emphasize the importance of “embodied knowledge” or contexts when building a recommendation system and the impact of such a context aware recommender to a one to one e-commerce marketing strategy. The premise of the paper is that a positive customer behavior is elicited by a recommender system only when the product being recommended is placed within a context without which even a quality recommender system with robust filtering mechanisms that match items and users will fail to reap profits. As DeLone and McLean (2004) write, “the law of economics has not been rewritten. The long terms success or failure of companies is determined by their ability to generate positive net revenues” (p. 31). Much research on recommender systems has been conducted with detailed descriptions of the algorithms themselves. The goal of this paper is not dwell in technicalities but to understand and achieve a balance between recommender applications and business relevance/importance. In this regard, the rest of the paper will be divided as follows: Section 2: an overview of recommender systems and its use in e-commerce, Section 3: the notion of embodied knowledge/context in terms of customer online shopping behavior and its implications for one to one marketing strategy in e-commerce, Section 4: finally concludes the paper with suggestions for future research. 2. An overview of Recommender Systems and its Use in E-Commerce The purpose of a recommender system is two fold in nature. One, to collect information on user preference and two to filter information in a manner that provides accurate recommendations based on the needs of the customer. Information on consumer preference is collected explicitly [example: user’s ratings, questionnaire] or implicitly [example: by “monitoring user’s behavior, such as songs heard, applications downloaded, websites visited and books read”, past transactions and items in the shopping cart] (Bobadilla, Ortega, hernando, Gutierrez, 2013, 109). It is important to note that the information collected by recommender systems is not limited to ratings and web usage. In fact, demographic information such as age, gender and nationality combined with social information such as tweets, tags and GPS locations might be collected depending on the type and purpose of the recommender system. The collection of varied information about a particular user is crucial to the quality of the recommender system in that the more a recommender system learns about a particular user the better the recommendation.

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The methodology adopted to filter the data of a particular user is situational. So the question at hand is: how do we filter data and what is the purpose [cross-sell, up-sell, etc.]? One must keep in mind the process by which the data is filtered is indeed the method in which a recommendation is made. In other words, there are three ways of making recommendations: (1) content based filtering, (2) collaborative filtering and (3) hybrid. Content-based recommenders use key words that describe items that were liked by a particular user to recommend similar items. “For example, if a user likes a web page with the words “car”, “engine” and “gasoline”, the CBF [content based filtering] will recommend pages related to the automotive world” (Bobadilla, Ortega, hernando, Gutierrez, 2013, 119). Collaborative based filtering makes recommendations based on the similarity between the user and other users with similar items. Bobadilla, Ortega, hernando, Gutierrez, 2013, write “CF is based on the way in which humans have made decisions throughout history: besides on our own experience, we also base our decisions on the experiences and knowledge that reach each of us from a relatively large group of acquaintances” Hybrid recommendation systems are used in conjunction. For example, Netflix might recommend movies sharing similar characteristics to that which user A has rated highly in the past (content based filtering) or might recommend movies that other users similar to user A have watched (collaborative filtering). Research on recommender systems generally indicate that the hybrid algorithm is the better method to use as hybrids can handle issues such as cold start and sparsity adequately. Also, hybrid algorithms can offset the disadvantage of one algorithm over another. For instance, the disadvantage of a content based recommender is the problem of ‘overspecialization’ in that the algorithm recommends only similar items in light of the items he/she has preferred in the past but fails to recommend items that they might like but are not known (Bobadilla, Ortega, hernando, Gutierrez, 2013). The use of content based along with collaborative filtering will mitigate this issue. Selecting the type of filtering method for a recommender system is not the only decision to be made. A recommendation system can be memory based or model based. A memory based method for a collaborative filtering approach would mean that the algorithm computes the correlations between all the users in the dataset and the target user. Then based on the number assigned for the k nearest neighbor selects the closest neighbors, then computes the respective weights and finally predicts the ratings. It is easy to see that the usage of the entire dataset will render the algorithm computationally slow and expensive though the quality of predictions might be good. Model based algorithms on the other hand is faster and scalable in that only a subset of the data is selected and a model is built on a portion of the data to make recommendations. The quality of the prediction is comparatively less than the memory based algorithm. However, model based algorithms favor real time recommendations due to its speed and scalability. Though recommendation systems are built to accurately predict ratings there are three factors [sparsity, cold start and scalability] that challenge the quality of the recommendations. “The data sparseness issues arise irrespective of the type of recommendation systems. For example, the data in MovieLens is represented as a user – item matrix.” (Abbas, Zhang & Khan, para.14). Sparseness occurs when the number of items increases however since not all users have watched all movies not all movies are ranked. Abbas, Zhan & Khan write that sparsity is often addressed

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by applying dimensionality reduction techniques such as SVD, matrix factorization and latent semantic index. Cold start is a term that refers to the problem of not having a rating for the following three reasons: new community, new item and new user (Bobadilla, Ortega, hernando, Gutierrez, 2013). All three cold start problems have the same issue, insufficient data to make recommendations. The cold start problem, however, can be addressed by implementing a hybrid algorithm approach where users can be asked to rate random movies. The recommender system can learn from content similarities and once enough data is collected a collaborative method of recommending products can be implemented. Scalability is a problem that must be addressed before building a recommender system. The pivotal question is: can the algorithm handle large volumes of data to process data and provide recommendations as quickly as possible? A notion discussed in the context of memory and model based algorithms. Having an overview of how recommender systems function in general offers a better foundation upon which the applications of recommender systems in e-commerce can be examined. Driskill and Riedi write within the context of e-commerce recommender systems have indeed helped customers “sort through large information spaces to find items of most value to them” (p.73). In fact, e – commerce has been successfully applied in the following three areas: direct product recommendations, gift centers and cross – sell recommendations. In agreement with Driskill and Reidi’s statement, a study conducted by Dias, Locher et al (2008) on the value of personalized recommender systems to e-businesses indicate that the introduction of a recommender system on LeShop, the leading online grocery store in Switzerland, with the goal of (1) recommending products from new categories to customers and (2) reminding customers of “forgotten items,” that is, items that customers usually buy but have forgotten resulted in an increase in sales by at least 66%. Though the argument for the successful implementation of recommender systems is currently lauded by the very existence of Amazon, Netflix, MovieLens and Pandora the problem of shopping cart abandonment brings our attention to the fact that though recommendation systems are recommending products to consumers marketers do struggle with the inability to convert browsers to buyers. According to Simpson (2012), “U.S. consumers spent $194.3 billion online in 2011” (para. 1). Nevertheless, looking at consumer expenditure alone can be detrimental in the light of the number of sales lost due to shopping cart abandonment. The abandonment of shopping cart refers to online shoppers initiating a purchase but failing to complete the check out by abandoning the cart instead. The following table shows the rate of abandonment over specific years.

Year Cart Abandonment Rate Source (Author of Article) 2015 68.53% Baymard Institute 2012 65% Simpson 2004 57% Moore & Mathews 2003 61% Morris

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The consistency that is found in the rates over the given years are red flags to marketers in that the high cart abandonment rates equals the inability to capture sales that would potentially generate profitable net income for companies. According to research conducted by Forrester, abandoned carts result in “$18 billion of lost revenue annually” (Gordon, 2014, para. 4). Hence, much research has been conducted to understand why customers are reluctant to complete orders online. The common theme that runs through the current studies on the factors that instigate abandonment is the notion of perceived risk. That is the need to avoid “buyer’s remorse” or the “possibility of a new product introduced after purchase” (Coppola, & Sousa, 2008, p.387). According to Cho, Kang, Cheon (2006) marketers fail to realize that the online shopping experience is quite different from a physical store in that the inability of the customer to examine the product in person heightens consumers perceived value for the product. Therefore, this increased perception of risk normally translates to customer’s abandoning their carts. It must be noted that there are a number of other factors that also contribute to shopping cart abandonment such as the cost of the product, shipping cost, security issues, lack of information on the product etc. These issues are not a concern for this research paper as these factors are not tied to the robustness of recommender systems but are a matter of web design, features and functions. However, the notion of perceived risk is a matter that pertains to recommender systems. Eddy writes, “personalization is seen as the next big thing in online shopping, however most of the personalization we see today is designed to trick shoppers into buying more. True useful personalization is something that helps the web visitor have a better shopping experience, and we just aren’t seeing many multichannel retailers do a great job of that yet” (para.5). In other words, the current application of recommender systems in ecommerce, that is the 2D or traditional recommender systems 𝑅:  𝑈𝑠𝑒𝑟 ∗ 𝐼𝑡𝑒𝑚 → 𝑅𝑎𝑡𝑖𝑛𝑔  𝑜𝑟  𝑅:  𝐼𝑡𝑒𝑚 ∗ 𝐼𝑡𝑒𝑚 → 𝑅𝑎𝑡𝑖𝑛𝑔 , may in fact contribute to shoppers’ online hesitation, the antithesis of a recommender’s purpose. 3. The notion of embodied knowledge/context in terms of customer online shopping behavior and its relevance to one to one marketing The notion of embodied knowledge or context cannot be entirely understood without examining what factors contribute to the execution of recommender systems in an antagonist manner. Recommender systems are tools that are often an accurate reflection of how marketers perceive customers. Marketing research aims to understand customers not only from a demographic perspective but also more importantly from a monetary point of view. In other words, what is the value that a particular customer brings to the company? The abbreviations RFM (recency, frequency, monetary) are the pillars of marketing upon which recommender systems are executed. “RFM data is how recently the customer has purchased from the website, how often the customer has purchased from the website and how much the web site has earned from the customer’s purchases in the past” (Driskill and Riedi, 1999, p.74). It’s not rocket science to see how a recommendation system built to make recommendations based on the data collected against these measures would function. For instance, a content-based recommender system would recommend to customers who are high spenders only those similar items that are of high value. Likewise, a collaborative filtering algorithm would have clustered

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all the high spenders according to their similarities with other users and items. This strategy of analyzing customers according to RFM is business savvy. However, this marketing strategy assumes that the virtual and the physical world share similar customer shopping behavior. Such an assumption often misleads marketers to overlook an important link between the user and the items recommended that is the human mind. The human mind is a simulator in that consumers in general call on a frame of reference or point of contact, which may be knowledge of a product or past experience to make a decision. A consumer in a physical store is offered the opportunity to ‘experience’ the product sold by both body and mind. The quality of the product can be felt, its appeal can be seen in terms of dimensions, height, width, texture etc. And based on the combination of prior knowledge and by experiencing the product first hand a consumer is able to make a purchase decision. Even when prior knowledge is unavailable the experience of handling the product has a better chance of a buyer walking away from the store with a product. For instance, in an apparel store customers can wear an outfit to gauge quality, fit and feel of the product that eventually leads to a decision. The draw back of the virtual store is that the new product being recommended is removed by space and time. Herein lies the pivotal difference between physical and virtual stores. The physical store relies much on the creation of an experience to sell products and the virtual store relies heavily on relevance or contexts to sell new products.

Rosa and Malter write (2003), “it is significantly more challenging to market products for which consumption involves high levels of somatic and sensorimotor inputs (e.g., touch, body movement) through constrained two – dimensional interfaces, as can be seen from the limited success that internet retailers have had with sensory products, and the high return rates in many product categories” (p.63). Retail stores experience customer returns of about 8.7% of retail sales. However, the number is higher for [18% to 35%] e-commerce retailers depending on the product category. “In all, it is estimated that managing product returns costs U.S. companies well over $200 billion annually” (Ofek, Katona, Sarvary, 2010, p. 2).

Therefore, marketers must spend time on understanding how the notion of embodied knowledge and pre – existing knowledge plays an important role when recommending items to customers. Embodied knowledge is instinctual. For example, the feel of driving a car at a auto showroom where the mind is stimulated to think of all the possibilities of having the product in their possession versus the details about a specific product which would be pre – knowledge. Though the importance of embodied knowledge and pre-knowledge is an important customer behavior in the physical and virtual space. The need to elicit a simulation using embodied knowledge and pre-knowledge is pivotal to improving conversion rates. The key factor that creates a simulation is the word ‘context’ which also can be referred as relevance. In the absence of a direct experience generated within the walls of a physical store, relevance or context calls on experience indirectly to instigate the buyer to purchase a product. The CEO of a Santa Barbara Commission Junction, a company that provides marketing services to merchants writes, “contextual commerce is about human nature” (Pack, 2011, p. 24). The lack of context that is in essence inclusive of embodied and pre knowledge in recommender systems can be attributed to certain customers abandoning their shopping carts. Many researchers indicate that most often customers fail to complete their purchase because of the perceived risks

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of buying a product online. Perceived risks can be broken into two categories: (1) perceived need and (2) perceived duress or the fear that consumer might have to go to great lengths to return a product if it does not meet their expectations. The traditional recommender systems that do not cater to creating a contextual recommendation have a high risk of running up against the shopping cart abandonment syndrome. After all, without a point of reference or relevance consumers waver between the ‘want’ to buy verses a ‘need’ which most often leads to not only an abandonment of the cart but also a missed opportunity to convert a browser to a loyal customer. The notion of context in terms of time, season, location, and company is currently a topic of ardent discussion among artificial intelligence specialists, computer scientists and data miners. For instance, Stormer (2007) argues that by utilizing the context of ‘season’ recommender systems can alleviate discomfort among consumers who are recommended untimely seasonal items. For instance, recommending winter coats during summer. Similarly, time can be used as a filter by recommender systems to recommend the right vacation packages given temporal factors. For instance, a consumer from the Midwest during the month of December might look for a warm weather winter vacation package. A mobile app will consider GPS location as a context to provide real time recommendations. The point to note here is that context is varied in meaning and the implications of its diverse meaning calls the attention of marketers to move away from a seller centric point of view to that of a buyer. Marketers must now understand the context or the stimulant that would elicit a favorable response from the perspective of the buyer. This is important because in order to truly understand the needs of the customer the customer’s intent to purchase becomes the center upon which other factors such as RFM are later built. As Champiri, Shahamiri and Salim (2014) write, “in order to build relevant, useful, and effective recommender systems, the validity of contextual information through the eyes of users ought to be evaluated” (p. 1755). Building recommender systems to make recommendations contextually does not necessarily change the nature of the functionality of the content based filtering or the collaborative based filtering algorithm. Instead, the contextual filtering process truly becomes specialized to the purchase intent or the needs of the customer. Therefore, the traditional recommender has a re – birth by filling in the missing link between the user and item, that is, the customer which equals context - 𝑅:  𝑈𝑠𝑒𝑟 ∗ 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 ∗𝐼𝑡𝑒𝑚 → 𝑅𝑎𝑡𝑖𝑛𝑔  𝑜𝑟  𝑅:  𝐼𝑡𝑒𝑚 ∗ 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 ∗ 𝐼𝑡𝑒𝑚 → 𝑅𝑎𝑡𝑖𝑛𝑔. Given that this paper has established a case for the importance of using context as a factor for filtering data. And has emphasized the importance of marketers understanding context from a behavioral perspective in order to abate perceived risks and consequently increase perceived site value exhibited by not abandoning the cart. The next logical question to ask is how does a contextual ‘customerization’ of products change the nature of one to one marketing or does the execution methodology remain the same? If we look purely at recommender systems as tools being a part of a one to one marketing strategy. Then it is clear that customers have played the role of a passive participant taking directions from the recommender based on their past orders. Customerization of products creates a sense of urgency in that one to one marketing is based on customer intent. And the pivotal point about the notion of decision making and intent is that the decision made by consumers changes according to situations based on whether the purchase is made for family, self or as a gift. Therefore, marketers have to be more dynamic in deciphering

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when, how and where to deliver their marketing campaigns. This can only be done by generating a rich study on what specific contexts are more applicable than others to the products sold on the respective website. Implementing contextual factors can result in an increase in profits for organizations but the immediate challenge is to ensure that the contexts used to filter data are not too granular that its not relatable to all nor too broad that its usage is futile. It is not in the scope of the paper to discuss how context(s) can be gathered and implemented in a 2D recommender system. However, research conducted by Adomavicius & Tuzhilin on context – aware recommender systems offer more insights on possible implementation strategies. 4. Conclusion The main purpose of this paper was to emphasize that marketers have failed to understand the relevance of the human mind, the context in which a customer shops as pivotal in converting browsers into buyers. The very existence of high shopping cart abandonment rates over the years is indicative that marketers have not truly understood both the psychological and commercial value of including contexts in the 2D recommender systems. The paper also argued that marketers’ myopic vision, that is a seller centric perspective, of personalization has rendered the use of recommender systems futile in that the lack of contextual recommendations based only on user and item similarity might lead to heightened perceived risk of the website and the recommendation(s) made. Much research has been conducted on the different ways to use context aware recommender systems. However, it is surprising that very few have discussed the psychological implications of context to a customer. Understanding the notion of context must be done in a systematic manner in that context not only refers to situations such as location, time, season etc. To understand context only as a situation is to undermine the fact that ‘context’ acts as trigger that creates a point of reference, the frame within which consumers engage in a positive dialogue with the recommender. Therefore, marketers must cognize that a customer centric recommender system must be executed in a manner that truly captures all the relevant contexts that act as embodied knowledge in the stimulation creation process. Doing so could potentially lead to consumers completing their purchase orders as context mitigates perceived risks such as the need for the product and the quality of the recommendation. The nature of this paper is qualitative and hence the argument: 2D recommender systems functioning without a context can have an antithesis effect on the consumer might serve as a hypothesis for a quantitative research project. The value of a recommender system is only ascertained by its utility. Within the world of business this would be analyzed in terms of ROI. The argument that context aware recommender systems have the power to mitigate perceived risk calls for the need to do an implementation project where upon implementing a context aware recommender marketers can study its impact on customer shopping cart abandonment rates. A semi qualitative and quantitative study can be conducted to ascertain the importance of contextual stimulants to consumers as they make their purchase. This would offer marketers the opportunity to understand what customers expect when a recommendation is made to them from a contextual perspective.

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