Netflix vs Block Buster Comparison, Essay Example

Since the home video market went virtual approximately a decade ago, competitive response by corporations such as Netflix has prompted advancement of online retail IT source solutions. Old guard companies like Blockbuster have been forced to reconfigure not only their model of retail chain operations, but the sheer capacity to supply consumer demand within the internet marketplace has caused retailers in the digital entertainment industry, and especially the ‘view on demand’ segment of products, to rethink how something as seemingly mundane as inventory cataloguing may be turned into a streamlined channel to attraction and retention of consumers. Indeed, the expediency and efficiency of search capabilities within the Blockbuster and Netflix retail websites has much to do with how developed the companies are able to integrate their product offerings into a knowledge sharing interface that is readily accessible, and highly responsive to consumer engagement.

Consistency in interface in regard to structure and physical attributes has been shown to result in higher performance and lower error rates. Companies with inconsistent maintenance of IT network systemization will find converse outcomes. For on-demand entertainment retailers consistency in performance is essential, in that their entire supply to market channel in the virtual domain is linked to publically accessible inventories. Since inception, lexical frameworks within website interfaces have been of interest systems analysts, as they reveal important data on the universal impact of a site: text structure, general text features, information representation, lexical categories, meaning, user knowledge, text content, communicational attributes and physical attributes (Ozok and Salvendy, 2001).

Both Blockbuster and Netflix offer user friendly formats to subscriber members, including brand identity structure built into the aesthetics of the site, and industry specific, popular lexicon in all content communications. As users move through the site for membership, and for entertainment searches, they are guided by way of inventory pages linking the entire repository of product data with simple lexical instructions denoted by ‘categories’ or genres of film. Highly pictorial in design, users do not need to be proficient English language speakers. The simplistic manner by which customers navigate the major functions in the site in fact requires little more than image recognition.

Referential identification is also built into both retailer’s sites, and customers are assisted with the artificial intelligence as the abductive logic of the interface links keywords with natural language processing (NLP) for best match, and closest correlation of commonly searched for items in that product category. The lexical format mirrors the deductive logic of the consumer, yet refines and narrows the search employed by the user interface, records the history of searchers, and prompts users with feedback tools as it constructs a ‘mental map’ of the member (Yang, et al., 2009). Later visits to both Blockbuster and Netflix are interfaced with the prior visit(s), so that subscribers can see their former search categories, with recommendation of other films in the genre.

The sites share an iterative text mining process in this way, as the interface between the natural language text and content reflecting terms generates essential prioritization within each customer’s indexed compendium of products. In traditional interfaces such as thesauri and lexicons had limitations for web content processing which made them prohibitive in terms of meaningful deployment for interfacing large inventories with a significant number of users. Semantic richness has increased at least to the effect that more heuristic search criteria can be picked during text mining (Al-Zaharani 2006). Companies operating inventory dense supply to market channels online require this kind of flexibility designed into their IT systems interface, as mass consumer markets use a great degree of lexical interpretation in identifying product selection.

Making machine languages more coherent and fluent is a core challenge for companies like Blockbuster and Netflix, and lexical refinement stands to benefit from the optimized search capabilities in recognition technology (Yang, 2009). Programmers focused on natural language R+D have often looked to high-level declarative programming languages as a short cut past ambiguities. The desire for syntactic refinement through the use of LISP and Prolog has been transformed in the past several years in the development of ‘lazy functions’ as companies demand better accountability of those unknown terms lost in the consumer-user search (Frost, 2006).

A new type of semantic pattern is also present in the user-interactive question answering (QA) systems. Unique retailers utilize question structure analysis, pattern matching, pattern generation, pattern classification and answer extraction. Of great assistance in market research tracking and financial forecasting, are the high level, customer-centric metrics produced by users, as their searches build predication into the quality of generated patterns as illustrated in Figure 1.

Since 1982, Blockbuster has had a presence in the home entertainment market. The world’s largest video rental chain, “with more than 6,500 company-owned or franchised stores in some 17 countries (about 62% are in the US)” the company sells subscription to 1 billion videos, DVDs, and video games through its locations annually (Hoovers 2010). Blockbuster Online captures a portion of the internet on-demand entertainment market segment – the most lucrative sphere of growth in the industry. In spite of Blockbuster’s trail blazing strategies, in the realm of worldwide online entertainment, competitor Netflix has sustained an edge with its online exclusive operations approach.

As consumer user IT systems interface format advance, competitive distinctions will continue to be carved out in this landscape of undercover language strategies as each company attempts to create better customer-centric models of search capability. With outlier companies entering the arena, such as Apple with its ITunes products, the two home video entertainment retail might be pushed out of their own league as computer corporations push the limits of accessibility through innovations in technology.

Works Cited

Al-Zaharani, S. (2006). From Words to Concepts in Text Mining. Journal of Digital Information Management, 4 (2), 147-149.  

Apple ITune. (2010). Retrieved from: http://www.apple.com/itunes/affiliates/download/

Blockbuster, Inc. (2010). Retrieved from: http://www.blockbuster.com

Blockbuster, Inc. (2010). Hoovers. Retrieved from: www.hoovers.com

Frost, R. A. (2006). Realization of Natural Language Interfaces Using Lazy Functional Programming. ACM Computing Surveys, 38 (4), 1-54.

Netflix.  (2010). Website Retrieved July 30, 2010 from http://www.netflix.com/WiHome

Ozok, A. and Salvendy, G. (2001). How consistent is your web design? Behaviour & Information Technology, 20 (6), 433-447. DOI: 10.1080/01449290110092260

Tianyong, H. et al. (2008). Semantic patterns for user-interactive question answering. Concurrency & Computation: Practice & Experience, 20 (7), 783-799.

Yang, Y. et al. (2009) Investigating readers’ mental maps of references in an online system. Computers & Education, 53 (3), 799-808.

(2009). A Computational Approach of Lexical Refinement. Journal of Advanced Research in Computer Science, 1 (1), 56-76.