Marketing and artificial intelligence

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Artificial intelligence is a field of study that “seeks to explain and emulate intelligent behaviour in terms of computational processes” [1] through performing the tasks of decision making, problem solving and learning.[2] Unlike other fields associated with intelligence, Artificial intelligence is concerned with both understanding and building of intelligent entities, and has the ability to automate intelligent processes.[3] It is evident that Artificial intelligence is impacting on a variety of subfields and wider society. However literature regarding its application to the field of marketing appears to be scarce.

Advancements in Artificial intelligence’s application to a range of disciplines have led to the development of Artificial intelligence systems which have proved useful to marketers. These systems assist in areas such as market forecasting, automation of processes and decision making and increase the efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems which are computer programs that process input and provide valuable output for marketers. In the area of social networking, AI is used to

Artificial intelligence systems stemming from Social computing technology can be applied to understand social networks on the Web. Data mining techniques can be used to analyze different types of social networks. This analysis helps a marketer to identify influential actors or nodes within networks, this information can then be applied to take a Societal marketing approach.

Artificial intelligence has gained significant recognition in the marketing industry. However, ethical issues surrounding these systems and their potential to impact on the need for humans in the workforce, specifically marketing, is a controversial topic.

Artificial Neural Networks[edit]

An artificial neural network is a form of computer program modelled on the brain and nervous system of humans.[4] Neural networks are composed of a series of interconnected processing neurons functioning in unison to achieve certain outcomes. Using “human-like trial and error learning methods neural networks detect patterns existing within a data set ignoring data that is not significant, while emphasising the data which is most influential”.[5]

From a marketing perspective, neural networks are a form of software tool used to assist in decision making. Neural networks are effective in gathering and extracting information from large data sources [5] and have the ability to identify the cause and effect within data.[6] These neural nets through the process of learning, identify relationships and connections between data bases. Once knowledge has been accumulated, neural networks can be relied on to provide generalisations and can apply past knowledge and learning to a variety of situations.[6]

Neural networks help fulfil the role of marketing companies through effectively aiding in market segmentation and measurement of performance while reducing costs and improving accuracy. Due to their learning ability, flexibility, adaption and knowledge discovery, neural networks offer many advantages over traditional models.[7] Neural networks can be used to assist in pattern classification, forecasting and marketing analysis.

Pattern Classification[edit]

Classification of customers can be facilitated through the neural network approach allowing companies to make informed marketing decisions. An example of this was employed by Spiegel Inc., a firm dealing in direct-mail operations who used neural networks to improve efficiencies. Using software developed by NeuralWare Inc., Spiegel identified the demographics of customers who had made a single purchase and those customers who had made repeat purchases. Neural networks where then able to identify the key patterns and consequently identify the customers that were most likely to repeat purchase. Understanding this information allowed Speigel to streamline marketing efforts, and reduced costs.[8]

Forecasting[edit]

Sales forecasting “is the process of estimating future events with the goal of providing benchmarks for monitoring actual performance and reducing uncertainty".[9] Artificial intelligence techniques have emerged to facilitate the process of forecasting through increasing accuracy in the areas of demand for products, distribution, employee turnover, performance measurement and inventory control.[9] An example of forecasting using neural networks is the Airline Marketing Assistant/Tactician; an application developed by BehabHeuristics which allows for the forecasting of passenger demand and consequent seat allocation through neural networks. This system has been used by Nationalair Canada and USAir.[10]

Marketing Analysis[edit]

Neural networks provide a useful alternative to traditional statistical models due to their relibility, time saving characteristics and ability to recognise patterns from incomplete or noisy data.[6][11] Examples of marketing analysis systems include the Target Marketing System developed by Churchull Systems for Veratex Corporation. This support system scans a market database to identify dormant customers allowing management to make decisions regarding which key customers to target.[10]

When performing marketing analysis, neural networks can assist in the gathering and processing of information ranging from consumer demographics and credit history to the purchase patterns of consumers.[12]

Application of Artificial Intelligence to Marketing Decision Making[edit]

Marketing is a complex field of decision making which involves a large degree of both judgment and intuition on behalf of the marketer.[13] The enormous increase in complexity that the individual decision maker faces renders the decision making process almost an impossible task. Marketing decision engine can help distill the noise. StrategyVP.com is the world's first decision engine that matches marketing strategies with business goals.[14] The generation of more efficient management procedures have been recognized as a necessity.[15] The application of Artificial intelligence to decision making through a Decision Support System has the ability to aid the decision maker in dealing with uncertainty in decision problems. Artificial intelligence techniques are increasingly extending decision support through analyzing trends; providing forecasts; reducing information overload; enabling communication required for collaborative decisions, and allowing for up-to-date information.[16]

The Structure of Marketing Decision[edit]

Organizations’ strive to satisfy the needs of the customers, paying specific attention to their desires. A consumer-orientated approach requires the production of goods and services that align with these needs. Understanding consumer behaviour aids the marketer in making appropriate decisions. Thus, the decision making is dependent on the marketing problem, the decision maker, and the decision environment.[15]

Expert System[edit]

An Expert System is a software program that combines the knowledge of experts in an attempt to solve problems through emulating the knowledge and reasoning procedures of the experts. Each expert system has the ability to process data, and then through reasoning, transform it into evaluations, judgments and opinions, thus providing advises to specialized problems.[17]

The use of an expert system that applies to the field of marketing is MARKEX (Market Expert). These Intelligent decision support systems act as consultants for marketers, supporting the decision maker in different stages, specifically in the new product development process. The software provides a systematic analysis that uses various methods of forecasting, data analysis and multi-criteria decision making to select the most appropriate penetration strategy.[15] BRANDFRAME is another example of a system developed to assist marketers in the decision-making process. The system supports a brand manager in terms of identifying the brand’s attributes, retail channels, competing brands, targets and budgets. New marketing input is fed into the system where BRANDFRAME analyses the data. Recommendations are made by the system in regard to marketing mix instruments, such as lowering the price or starting a sales promotional campaign.

Artificial Intelligence and Automation Efficiency[edit]

Application to Marketing Automation[edit]

In terms of marketing, automation uses software to computerize marketing processes that would have otherwise been performed manually. It assists in effectively allowing processes such as customer segmentation, campaign management and products promotion, to be undertaken at a more efficient rate.[18] Marketing automation is a key component of Customer Relationship Management (CRM). Companies are using systems that employ data-mining algorithms that analyses the customer database, giving further insight into the customer. This information may refer to socio-economic characteristics, earlier interactions with the customer, and information about the purchase history of the customer.[19] Varinos Systems have been designed to give organizations control over their data. Automation tools allow the system to monitor the performance of campaigns, making regular adjustments to the campaigns to improve response rates and to provide campaign performance tracking.[20]

Automation of Distribution[edit]

Distribution of products requires companies to access accurate data so they are able to respond to fluctuating trends in product demand. Automation processes are able to provide a comprehensive system that improves real-time monitoring and intelligent control. Amazon acquired Kiva Systems, the makers of the warehouse robot for $775 million in 2012. Prior to the purchase of the automated system, human employees would have to walk the enormous warehouse, tracking and retrieving books. The Kiva robots are able to undertake order fulfillment, product replenishment, as well as heavy lifting, thus increasing efficiency for the company.[21]

Use of Artificial Intelligence to Analyze Social Networks on the Web[edit]

A social network is a social arrangement of actors who make up a group, within a network; there can be an array of ties and nodes that exemplifies common occurrences within a network and common relationships. Lui (2011),[22] describes a social network as, “the study of social entities (people in organization, called actors), and their interactions and relationships. The interactions and relationships can be represented with a network or graph, where each vertex (or node) represents an actor and each link represents a relationship.” At the present time there is a growth in virtual social networking with the common emergence of social networks being replicated online, for example social networking sites such as Twitter, Facebook and LinkedIn. From a marketing perspective, analysis and simulation of these networks can help to understand consumer behavior and opinion. The use of Agent-based social simulation techniques and data/opinion mining to collect social knowledge of networks can help a marketer to understand their market and segments within it.

Social Computing[edit]

Social computing is the branch of technology that can be used by marketers to analyze social behaviors within networks and also allows for creation of artificial social agents.[23] Social computing provides the platform to create social based software; some earlier examples of social computing are such systems that allow a user to extract social information such as contact information from email accounts e.g. addresses and companies titles from ones email using Conditional Random Field (CRFs) technology.[24]

Data Mining[edit]

Data mining involves searching the Web for existing information namely opinions and feelings that are posted online among social networks. “ This area of study is called opinion mining or sentiment analysis. It analyzes peoples opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics, and their attributes”.[22] Data mining eliminates the need for costly market research for example surveys and opinion polls in the quest for primary data. However searching for this information and analysis of it can be a sizeable task, manually analyzing this information also presents the potential for researcher bias. Therefore objective opinion analysis systems are suggested as a solution to this in the form of automated opinion mining and summarization systems. Marketers using this type of intelligence to make inferences about consumer opinion should be wary of what is called opinion spam, where fake opinions or reviews are posted in the web in order to influence potential consumers for or against a product or service.[22]

Search engines are a common type of intelligence that seeks to learn what the user is interested in to present appropriate information. PageRank and HITS are examples of algorithms that search for information via hyperlinks; Google uses PageRank to control its search engine. Hyperlink based intelligence can be used to seek out web communities, which is described as ‘ a cluster of densely linked pages representing a group of people with a common interest’.[22]

Centrality and prestige are types of measurement terms used to describe the level of common occurrences among a group of actors; the terms help to describe the level of influence and actor holds within a social network. Someone who has many ties within a network would be described as a ‘central’ or ‘prestige’ actor. Identifying these nodes within a social network is helpful for marketers to find out who are the trendsetters within social networks.[22]

References[edit]

  1. ^ Scholkoff, R.J. (1990) Artificial Intelligence: An engineering Approach, Mc Graw Hill, New York.
  2. ^ Bellman. (1978). An introduction to artificial intelligence: can computers think? Boyd & Fraser Pub. Co.
  3. ^ Russell, S., & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. New Jersey: Prentice Hall.
  4. ^ Whitby, B. (2003). A beginner's guide: Artificial Intelligence. Oxford, England: Oneworld Publications.
  5. ^ a b Tedesco, B. G. (1992), Neural Analysis: Artificial Intelligence Neural Networks Applied to Single Source and Geodemographic Data. Chicage, IL: Grey Associates.
  6. ^ a b c Tedesco, B. G. (1992). Neural Marketing: Artificial Intelligence Neural Networks In Measuring Consumer Expectations. Chicago, IL: Grey Associates.
  7. ^ Bloom, J. (2005). Market Segmentation: A Neural Network Application. Annals of Tourism Research, 32(1), 93-111.
  8. ^ Schwartz, E. I. (1992, March 2). Smart Programs Go To Work. Retrieved from Business Week: http://www.businessweek.com/archives/1992/b325470.arc.htm
  9. ^ a b Hall, O. P. (2002). Artificial Intelligence Techniques Enhance Business Forecasts: Computer-Based Analysis Increases Accuracy. Graziado Business Review, 5(2). Retrieved from http://gbr.pepperdine.edu/2010/08/artificial-intelligence-techniques-enhance-business-forecasts/
  10. ^ a b Hall, C. (1992). Neural Net Technology- Ready for Prime-Time. IEEE Expert, 7(6), 2-4.
  11. ^ Woelfel, J. (1992). Artificial Neural Networks for Advertising and Marketing Research: A Current Assessment. University at Buffalo.
  12. ^ Lin, B. (1995). Applications of Neural Network in Marketing Decision Making. Shrevport: Louisiana State University.
  13. ^ Wierenga, B. (2010). Marketing & Artificial Intelligence: Great Opportunities, Reluctant Partners. Marketing Intelligent Systems Using Soft Computing: Managerial and Research Applications , 258, 1-8.
  14. ^ StrategyVP (2012, Dec 21). StrategyVP.com Launched: Reach Your 2013 Business Goals. Retrieved from Marketing PR: http://marketingpr.eu/news,url,strategyvpcom-launched-reach-your-2013-business-goals.html
  15. ^ a b c Matsatsinis, N. F., & Siskos, Y. (2002). Intelligent Support Systems for Marketing Decisions. Norwell, MA, USA: Kulwer Academic Publishers.
  16. ^ Phillips-Wren, G., Jain, L. C., & Ichalkaranje, N. (2008). Intelligent Decision Making: An AI Approach. Spring Publishing Company.
  17. ^ Crunk, J., & North, M. M. (2007). Decision Support System and AI Technologies in Aid of Information Based Marketing. International Management Review , 3 (2), 61-86.
  18. ^ TechTarget. (2004, February). Marketing Automation. Retrieved April 20, 2012 from Search CRM: http://searchcrm.techtarget.com/definition/marketing-automation
  19. ^ Sharma, S., Goval, R. K., & Mittal, R. K. (2010). Imperative relationship between data quality & performance of data mining tools for CRM. International Journal of Business Competition & Growth , 1 (1), 45-61.
  20. ^ Gaffney, A. (2008). DemandGen Honors Top 10 Firms Using Automation Toolds to Fuel Business Growth. Retrieved April 20, 2012 from Demand GenReport: The Score Card for Sales & Marketing Automation: http://www.amberroad.com/pdf/DemandGen%20Honors%20Top%2010%20Firms.pdf
  21. ^ Murray, P. (2012, March 21). Amazon Goes Robotic, Acquires Kiva Systems, Makers of Warehouse Robot. Retrieved April 18, 2012 from Singularity Hub: http://singularityhub.com/2012/03/21/amazon-goes-robotic-acquires-kiva-systems-makers-of-the-warehouse-robot/ -
  22. ^ a b c d e Liu, B. (2011). Web Data Mining: Opinion Mining and Sentiment Analysis (2nd ed.). New York: Springer. Retrieved April 19, 2012
  23. ^ Fei-Yue, W., Kathleen, C., Zeng, D., & Wengi, M. (2007). Social Computing: From Social Informatics to Social Intelligence. Intellegent Systems, 22(2), 79-83. Retrieved April 20, 2012
  24. ^ Culotta, A., Bekkerman, R., & McCallum, A. (2004). Extracting social networks and contact information from email and the Web. University of Massachusetts- Amherst. Amherst: Computer Science Department Faculty Publication Series.