Dany

FINAL VERSION - IMPLEMENTATION (DANY)

GOALS + CASES The core goals of implementing a recommender system are to increase efficiency, effectiveness, customer satisfaction, and ultimately profits. Specific business goals that can be achieved through this type of system in order to support a company’s mission are:

- Increase sales and profits by enabling cross-sales and up-sales through recommender systems. - Increase product awareness through recommendation features that show the users products they wouldn’t have seen otherwise (or sought for). - Improve costumer satisfaction, and therefore retention, by reducing search time and providing an intuitive graphic user interface. - Increase website interactivity by drawing upon customer preferences and expertise to provide recommendations. - Rank customer preferences in order to manage inventory more efficiently.

If a business applies the system properly and meet its objectives, according to its tactics and strategies, it will succeed in achieving its business goals. This success is crucial for a company to obtain competitive advantage in its industry. One example of a company that has, so far, effectively implemented a recommender system to support its business goals is Amazon.com.

Amazon’s mission is to “start with the customer and work backwards” (Amazon.com). This is perfectly coherent with goals that involve recommendation IS. The company’s goal is to add value to the customer’s online shopping experience by lowering search costs (Garfinkle et Al. 2008) and personalizing the online shopping experience through recommendation systems.

The collaborative system saves the users time because it shows them other things they are likely interested in, without having to browse through the thousands of products they have available online. The recommendations are presented in a graphic user interface that is simple, clear and standardized throughout the website. Therefore the overall experience of the user with the system, and consequently with the company, is positive; fulfilling the goal of customer satisfaction. Furthermore, a satisfied costumer is more likely to come back to the website and make more purchases at Amazon.com; fulfilling the goal of customer retention. A satisfied, returning costumer, makes more purchases, which means more profits: the bottom line goal for any company.

Another interesting aspect of Amazon’s system is that it helps improve efficiency of inventory control. Cross-sales proposed through recommendations help clear excess inventory (Grafinkle et Al. 2008). Products that wouldn’t be sold because they weren’t sought for are now effortlessly available to consumers who might be interested in them, thus increasing the likelihood of sale. The system accomplished another goal: increasing efficiency in business processes.

Amazon is an example of a company that has, for the most part, successfully implemented a collaborative recommendation system to support its goals. However, when using collaborative systems to increase costumer’s satisfaction, a company needs to be aware that such systems are strictly based in algorithms and is, therefore, limited. There is more to consumer’s choices than what the algorithms of an IS can calculate (Manousellis, 2008). Personal preferences and culture, for example, can’t be assessed through numbers or descriptions in a customer file. This limitation, if not properly addressed, can be a reason for failure of a recommender system application. Thus, managers that decide to make use of this system need to ensure that these other aspects are covered by the business goals as well, in order to guarantee the company’s success.

TACTICS

The main tactic for successful implementation of a recommender system as a marketing strategy is a graphic user interface. It is pointless to adopt this type of system if the end user won’t understand or access the recommendations provided. A study conducted with users of MovieLense recommender system proved that users are more satisfied with the recommendations they receive when they are familiar with how the system works (McNee et al. 2003). The GUI ensures that the recommended items are easily visible to the potential consumer through a well designed website. It should be designed respecting Jakob Nielsen’s principles of usability, requiring minimal effort to operate (Nielsen & Levi, 2004). This ensures not only that the buyer sees the recommendation, but also feels compelled to get more information about it and consequently purchase it. All of which is done intuitively, without struggling with the system.

Another fundamental tactic to support recommender system strategies is an efficient costumer database. Customer files should contain relevant information and be properly catalogued so that pertinent recommendations are made by IS. Furthermore, it is important that the algorithms used in the recommender systems are able to accurately access the information in the costumer files.

With the emergence of M-commerce, scalability of recommendation systems is also an important business tactic. Recommender systemTherefore, in order to fulfill the strategies and goals that predict an increase in costumer base and satisfaction through, it is important to have the system as a standard for all channels consumers use to purchase. - não entendi essa ultima frase, acho que é porque tá run-on algorithms vary according to the device being used (Tveit, 2001). Correct application of tactics ensure the accomplishment of business strategies and consequently goals and objectives; which then contribute to a company’s success and competitiveness.

CONCLUSION

From the research and critical study made in this paper, we conclude that Recommendation systems can be an important asset for decision-making managers. There is even more value added specifically in the e-commerce area. On one hand, costumers benefit from an overall positive shopping experience through personalized services and interactivity of systems. On the other, businesses benefit from the increased sales that result from cross sales and push sales facilitated by recommender systems (which increase the bottom line profits). However, since this type of IS is still relatively new, it should be used with caution. It might have some limitations, which can hamper the company’s progress if implemented inadequately. Therefore, it is important for decision-making managers to consider the business’ fundamental goals and mission before adopting a recommendation strategy to ensure it will appropriately support the company’s operation and increase both efficiency and effectiveness. For this reason, companies can still be successful despite not possessing state-of-the-art technology. IS concerns the circulation and coordination of information, which can be even further improved and accelerated through IT. Nonetheless, a poorly structured IS combined with IT expedites wrong and faulty information to businesses and customers, leading to unwise decisions.

REFERENCES Nielsen, Jakob, and Jonathan Levy. "Measuring Usability: Preference Vs. Performance." //Communications of the ACM//. 4th ed. Vol. 37. New York: ACM, 1994. 66-75. Print.

//Amazon.com: Online Shopping for Electronics, Apparel, Computers, Books, DVDs & More//. Web. 30 Mar. 2010. . Garfinkle, Robert, Ram Gopal, Bhavik Pathak, and Fang Yin. "Shopbot 2.0: Integrating Recommendations and Promotions with Comparison Shopping." //Elsevier - Decision Support Systems// 46 (2008): 61-69. Print.

Manouselis, Nikos. "Deploying and Evaluating Multiattribute Product Recommendation in E-markets." //International Journal of Management and Decision Making // 9.1 (2008): 43-61. Print.

Tveit, Amund. "Peer-to-peer Based Recommendations for Mobile Commerce." //Proceedings of the 1st International Workshop on Mobile Commerce//. International Workshop on Mobile Commerce, Rome, Italy. New York: ACM, 2001. 26-29. Print.

McNee, Sean M., Shyong K. Lang, Catherine Guetzlaff, Joseph A. Konstan, and John Riedl. "Confidence Displays and Training in Recommender Systems." //Human-Computer Interaction: INTERACT '03//. IOS, 2003. 176-83. Print