The Future of Marketing: Anticipating Consumer Behavior with Big Data and Artificial Intelligence

Rationale and context

Over the years, business organizations implementing the effective marketing strategy suggest monitoring social media to reach the consumers and find the best way to meet their needs (Arli and Pekerti, 2016). Now a huge amount of data has been gathered from the sources, and there arises an immediate need to implement it for predicting consumer behaviour days. Anticipating the needs a consumer is made possible and easy through the use of artificial intelligence and big data. The term big data is used to denote data that is to be used in the size and complexity analysis process efficiently with the use of traditional methods (Wu et al. 2017). The data in the mentioned context is seen to grow exponentially with the passage of time. The data gathered from the consumer through the use of social media is termed as big data and is characterized by variability, volume velocity, and variety.

Big data and AI

Forecasting of consumer behavior is usually done through the amalgamation of artificial intelligence with big data. As per the prediction from Pappas (2016), there is an increase in the adoption of big data and artificial intelligence among 80% of businesses on global platforms. The advancement in analytics machine learning and artificial intelligence helps businesses to forecast the needs of the consumers and the possible market trends in the next 24 hours to one year from now. The event of decision-making is no longer dependent on accurate service or guessing and study conducted on focus groups (Bhuian et al. 2018). The progress made using artificial intelligence with big data analytics simplifies the process of decision making within real-time. Big data can be structured unstructured, semi-structured, and in this form, it may be difficult to ensure effective interpretation by the human mind. artificial intelligence does make use of an algorithm to generate sales of the data collected and produce actionable insight contributing to measure decision-making process

Social media is seen to grow as a significant tool for monitoring the needs of the customer's overall behavior satisfaction and satisfaction (Liu et al. 2017). The progress made by the domain of big data analytics through the implementation of artificial intelligence simplifies the process of decision making. Artificial intelligence provides a better customer experience by predicting the demands of the consumers. Companies in easily in their existing line of services and products are introduced new ones, all based on the information gathered through the analytics of artificial intelligence. Social media tools work on understanding the concern of the consumer. The information can be deemed useful in bringing the consumer closer to the company. However, the next big thing remains for the company to anticipate the needs of the consumers (Chovanová, Korshunov, and Babčanová, 2015). Advances in the domain of artificial intelligence help in the organization to produced better forecast with high levels of accuracy providing greater value to advertiser and marketer

Aim

Use of artificial intelligence in predicting consumer behaviour through a social media platform

The use of machine learning gained immense importance in recent times. The neural network, for instance, is compared with the biological aspects of the human brain and thus rightly used for tracking the psychology of consumer behaviour. AI is a broad term used

Research questions

How big data and AI evolved in recent years?

In what ways can AI be implemented to enhance marketing related opportunities?

What are the possible ways that can be used for enhancing the use of AI?

Proposed research method

Philosophy

The philosophy in research can be instituted as a term that can be expressed as a conviction about the information identified with the specific effect of computerized advertising and the manners in which it has developed in the previous decade and change the manner in which shoppers and showcasing recognitions are influenced. As expressed by Jin, Cheng, and Xu (2018), the term epistemology can be utilized for understanding the way that is now valid. According to the present contextual investigation, the epistemology incorporates how computerized showcasing has advanced in the previous decade and changed the manner in which customers and promoting recognition are influenced. The examination theory of ebb and flow exploration depends on constructivism. The term constructivism is known to coordinate subjects of human enthusiasm inside the examination (Attia and Edge, 2017). Thus, through the course of referenced research reasoning, the examination would pick up the pace and right heading to accumulate information for additional investigations.

Secondary research

The examination in the ebb and flow situation doesn't utilize essential research members as their perspectives are exposed to change, and likely a bunch of them have restricted memory to give applicable data on how advanced showcasing has changed in the course of the most recent couple of decades. It is for a similar explanation the examination utilizes optional wellsprings of information. As brought up by Fletcher (2016), optional wellsprings of information are frequently gotten from scholastic sources or recently framed research writing that has been completely dissected with enough proof. The auxiliary research consequently takes a shot at understanding the manners in which computerized promoting has advanced as contrasted and the most recent couple of years.

Data collection and analysis

The information is the referenced research is gathered from the auxiliary sources, for example, scholastic diaries and even sites. The examination moreover utilizes a subjective strategy that supposedly describes things. The subjective information supposedly provides an unmistakable comprehension on the manners in which the examination discoveries can be improved. Notwithstanding that an avoidance and incorporation criteria can be incorporated to streamline the information gathered further

Other than depending on web sources, the examination utilizes a content investigation method. According to the examination led by Han (2015), the strategy referenced above supposedly makes replicable, however legitimate references by coding and deciphering the accessible printed materials. Through an orderly examination of the content, subjective information can be changed over into quantitative information (Fletcher, 2016).

Ethical and access issue

The examination utilizes online sources that are effectively accessible over the web. The consent for additional examination is required, and the responsibility for real online assets is recognized. In optional subjective research, the social documents of information are frequently absent. Notwithstanding, Attia and Edge (2017) called attention to the reality that filing of individual information frequently falls under moral confinement and can be maintained a strategic distance from using enmity. Notwithstanding that, it very well may be expressed that the prime impediment of the exploration study can be credited to time. In near enough time, the exploration could embrace better investigations. The exploration utilizes an optional method of information assortment instead of essential because of the impediment of both time and cash.

Value of the research

The research in the mentioned context will provide great value to the marketers. Hence, it is evident to say that the domain of marketing is continuously evolving. The evolution of consumer behaviour makes marketing a robust field, and the use of artificial intelligence can be done to enhance the competitive advantage (Kim, Kandampully and Bilgihan, 2018). there is no doubt that the use of social media is on the rise, and companies have a clear idea about the same. The use of modern technology can be made in enhancing ways to track consumer behaviour. Business assets are seen to make use of technology and can harden a wide range of profit through to the newly invented technology.

The utilization of internet-based life has improved the protection identified with gushing worry for the purchaser (Ferreira and Ribeiro, 2016). The usage of robotization inside the calculation can be actualized to comprehend sway on decadent. The consistent clash among accommodation and fulfillment from the utilization is clearly significant for the structure of the item. As opened by Kaklauskas et al. (2019), being worked based on information, the decision of the buyer is made simpler to be speculated. Anyway, the restriction concerning the investigation requests consideration. For example, the absence of concern is, for the most part, accused of a constrained degree of comprehension. The utilization selection of man-made consciousness and nonattendance of the equivalent in specific zones as an issue of concern reflecting constrained handling of the clients. On the view of reasonably showcasing the execution of man-made brainpower for working and promoting exercises is seen to make clear comprehension for the morals identified with advertising (Gupta and Arora, 2017). The utilization of calculation rehearsals without the correct sort of information in the field can show up as a troublesome obstruction to be crossed. For instance, in present-day situations, the banks are believed to utilize man-made reasoning to distinguish the potential in any case, there exist some burnable areas of the calculation which can be explained through the course of experience and acquire benefit to the business. In any case, it turns out to be exceptionally hard to give a goal to man-made brainpower and comprehend whether the training followed is savage or biased.

Research plan (timeline)

Gantt chart

Literature review

Introduction

Understanding the emotions of consumers while predicting behavior is a vital importance. However, Martínez-Ruiz and Izquierdo-Yusta (2017), stated the fact that understanding the exact behaviour of the consumer is never termed as an easy task. Artificial intelligence is often termed as a precursor for enhancing and motivating the customer to make the purchase. Making online posts about experiences with the product or company e gives rise to the actual issue. The use of emoticons is efficiently thrown as a mixed story of struggle together with the actual inside and creates a strong strategy. The current section aims to and understands the social and artificial intelligent analytics in the purchase of a product or service (Kaklauskas et al. 2017). the market is simultaneously analyzed for understanding the correct use of artificial intelligence and tracking out the trends in the market for influencing the behaviour of the customers

Artificial Intelligence and Social Media Analytics

Making use of spreadsheets to track consumer behavior is a redundant approach. As opined by De Pelsmacker, van Tilburg and Holthof (2018), audience gain through social media platforms are complex in nature, and similarly, there inside related to online activities are difficult to comprehend. Identifying the possible market trends that consumers connect with is vital and needs sophisticated mechanisms. Brands are often left with data that are not of much importance. The use of smart social media analytics can help the brand to choose important data and form the required action. As stated by Gerrikagoitia et al. (2015), intelligent social media analytics can help the brand to meet the demands of the consumer while promoting engaging content that works on increasing awareness of the brand among the consumers. For instance, contextualized data is the core of brand promotion over social media. A study by Järvinen and Karjaluoto (2017) suggests that even if a brand is mentioned several times over social media platforms, it may hold little or no value at all until the customers are aware of the actual set of services being provided by the business.

The next-generation levels of AI are group and customize data that is gained from both structured and unstructured data. The data gathered is consolidating covered media in real-time while sharing is done through the use of a dashboard that is known to offer an actionable insight (Gerrikagoitia et al. 2015). The accuracy of the data in the mentioned technology is unfiltered and raw, coming directly from the source that is from the potential customers, which can bring profits to the business. Hence it can be seen that businesses are unlikely to make use of artificial intelligence in order to automate the choices of consumers. While analyzing the market, the development of competition can be termed as an engendering factor for triggering consumer behaviour. An evidence-based study by Liu et al. (2016), suggest the fact that nearly 72% of marketing activities are based on the use of artificial intelligence.

On the basis of the figure drawn above, it can be clearly seen how artificial intelligence can be implemented for tracking the behaviour of the consumer. In the mentioned case, the consumer is seen to make use of social media platforms for giving a review of a product that he or she is using. The use of the hashtag is rightly made to amalgamate trends with technology (Han and Stoel, 2016). In addition to the brand, the behaviour of the person is traffic through the review, and based on the same similar products can be provided to the consumer in the future. The neural network is often termed as a well-known fact that can work on understanding the behaviour of the consumer. On the basis of a research study, it can be stated the neural network is implemented for sales prospecting. Research by Ismail (2017), a 40% time salesperson, fails to identify the good potential customer and to pitch the right kind of sales or to build the needed pipeline.

Hence the following ways can be implemented for understanding the behaviour of the consumer while creating a strong pipeline of potential customers. Firstly, it is evident that for identifying potential customers, it is important to work on creating a persona. The mentioned intervention works on identifying the customer's needs. Hence it can be conclusively stated that the classification of the consumer is important, coupled with the extraction of data that helps in understanding the person of the consumer and proceeding with the business plan (De Pelsmacker, van Tilburg and Holthof, 2018). Second, it is important to label the data and work on finding the consumer who takes the least time to convert into a strong sales prospect. The use of neural networks to implement algorithms is done for tracking the behaviour of the consumers (Järvinen and Karjaluoto, 2015). Once rightly trained, the mentioned neural network based on an algorithm of artificial intelligence. The data can be classified as good or bad output. Hence, it can be stated that companies are seen to adopt artificial intelligence for enhancing performance in business over social media platforms. With help from the tools from social media powered by machine learning and artificial intelligence, consumer behaviour can be easily tracked. Thus, it can be clearly stated that the behaviour of the consumer can be modulated through the use of artificial technology.

Advanced technologies and market analysis

A large amount of attention is devoted to the content curated through the help of algorithms on the domains of customer behaviour. As stated by Liu et al. (2016), a limited focus on fast literature recommendation opens through the use of AI to give rise to a predictable pattern of consumption, depriving the customers of their ability to evolve over time, causing a change in the medical choices.

The use of social media has enhanced privacy related to streaming concern for the consumer. The implementation of automation within the algorithm can be implemented to understand the impact on hedonic and non-hedonic well being. The constant conflict between convenience and satisfaction from the use is evidently important for the design of the product. As opened by De Pelsmacker, van Tilburg and Holthof (2018), being operated on the basis of data, the choice of the consumer is made easier to be guessed. However, the limitation in regards to the study demands attention. For instance, a lack of concern is mostly blamed on limited levels of understanding. In the scenery of practical marketing, the implementation of artificial intelligence for operating and marketing activities are seen to create an evident understanding of the ethics related to marketing. The use of algorithm practices without the right kind of knowledge in the field can appear like a difficult obstacle to be crossed. For example, in a modern scenario, the banks are seen to make use of artificial intelligence to identify the potential. However, there exist some burnable sections of the algorithm, which can be solved through the course of experience and bring in profit to the business (Gerrikagoitia et al. 2015). Nevertheless, it becomes highly difficult to provide intent to artificial intelligence and understand whether the practice followed is predatory or discriminatory

A nuanced understanding of the social audience

Social audiences are often seen to have a greater level of importance, and they clearly know it. There is a visible shift in online power, coupled with the rise in the number of influencers based on whose recommendation, the customers buy a specific product or avail services. As observed by Gerrikagoitia et al. (2015), consumers operating on Facebook or other social media platforms are aware of the post they have made. Hence it can be clearly stated that an amalgamation of understanding the consumer demands coupled with deep learning of artificial intelligence can help in analyzing the data used for tracking consumer behaviour. If the analytics used on social media platforms is successful in gathering the nuances of the targeted customers, then it can be leveraged to on the sun the choices of the customer in making the final purchase. In similar regards, Jin, Cheng, and Xu (2018); thus, it can be conclusively stated that social media is successful in gathering the demands made by the target customers. Brands are often interested in the task of forming a personal connection with the consumers, and social media can be termed as a good way of doing so. The increasing level of automation in relation to artificial intelligence is used for the recommendation of algorithms at personalized content. The mentioned intervention is implemented for helping and even empowering the customers. On the basis of the previous literature, it can be clearly stated intelligence off machine an algorithm can save time and cost. Finally, it can be concluded the social media amalgamated with machine learning and artificial intelligence 38 greater implications on the lives of the business and consumer alike (Liu et al. 2018).

The literature in the mansions provides a huge range of ideas about artificial intelligence and the ways it can be used over the customers to gain profit.

Literature gap

However, it should be noted that there remains a lack of literature that promotes the use of artificial intelligence as a holistic approach to marketing. The limitation is due to the lack of funds that need to be implemented in research related work. In addition to that, the literature suggests the evident impact mostly positive on predicting consumer behaviour through the use of artificial intelligence. However, the extent of the impact is not evaluated through the current research work.

Reference list

Arli, D. and Pekerti, A. 2016. Investigating the influence of religion, ethical ideologies and generational cohorts toward consumer ethics: which one matters?. Social Responsibility Journal, 12(4), pp.770-785

Bhuian, S., Sharma, S., Butt, I. and Ahmed, Z. 2018. Antecedents and pro-environmental consumer behavior (PECB): the moderating role of religiosity. Journal of Consumer Marketing, 35(3), pp.287-299.

Chovanová, H., Korshunov, A. and Babčanová, D. 2015. Impact of Brand on Consumer Behavior. Procedia Economics and Finance, 34, pp.615-621.

Ferreira, A. and Ribeiro, I. 2016. Are you willing to pay the price? The impact of corporate social (ir)responsibility on consumer behavior towards national and foreign brands. Journal of Consumer Behaviour, 16(1), pp.63-71.

Gupta, A. and Arora, N. 2017. Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory. Journal of Retailing and Consumer Services, 36, pp.1-7.

Han, T. and Stoel, L. 2016. Explaining Socially Responsible Consumer Behavior: A Meta-Analytic Review of Theory of Planned Behavior. Journal of International Consumer Marketing, 29(2), pp.91-103.

Ismail, A. 2017 The influence of perceived social media marketing activities on brand loyalty. Asia Pacific Journal of Marketing and Logistics, 29(1), pp.129-144. Jiménez-Zarco, A., Rospigliosi, A., Martínez-Ruiz, M. and Izquierdo-Yusta, A. 2017. Marketing 4.0. Web Services, pp.2172-2195.

Kaklauskas, A., Jokubauskas, D., Cerkauskas, J., Dzemyda, G., Ubarte, I., Skirmantas, D., Podviezko, A. and Simkute, I. 2019. Affective analytics of demonstration sites. Engineering Applications of Artificial Intelligence, 81, pp.346-372.

De Pelsmacker, P., van Tilburg, S. and Holthof, C. 2018 Digital marketing strategies, online reviews and hotel performance. International Journal of Hospitality Management, 72, pp.47-55.

Fletcher, A. 2016. Applying critical realism in qualitative research: methodology meets method. International Journal of Social Research Methodology, 20(2), pp.181-194

Gerrikagoitia, J.K., Castander, I., Rebón, F. and Alzua-Sorzabal, A., 2015. New trends of Intelligent E-Marketing based on Web Mining for e-shops. Procedia-Social and Behavioral Sciences, 175, pp.75-83.

Kim, S., Kandampully, J. and Bilgihan, A. 2018. The influence of eWOM communications: An application of online social network framework. Computers in Human Behavior, 80, pp.243-254.

Liu, C., Hsieh, A., Lo, S. and Hwang, Y. 2017. What consumers see when time is running out: Consumers’ browsing behaviors on online shopping websites when under time pressure. Computers in Human Behavior, 70, pp.391-397.

Järvinen, J. and Karjaluoto, H., 2015. The use of Web analytics for digital marketing performance measurement. Industrial Marketing Management, 50, pp.117-127.

Han, C., 2015. How to do critical discourse analysis: A multimodal introduction.

Jin, C., Cheng, J. and Xu, J., 2018.Using user-generated content to explore the temporal heterogeneity in tourist mobility. Journal of Travel Research, 57(6) pp.779-791.

Liu, Y., Li, H., Xu, X., Kostakos, V. and Heikkilä, J. 2016. Modeling consumer switching behavior in social network games by exploring consumer cognitive dissonance and change experience. Industrial Management & Data Systems, 116(4), pp.801-820.

Méndez-Aparicio, M., Izquierdo-Yusta, A. and Jiménez-Zarco, A. 2017 Consumer Expectations of Online Services in the Insurance Industry: An Exploratory Study of Drivers and Outcomes. Frontiers in Psychology, 8.

Mou, J., Shin, D. and Cohen, J. 2015. Trust and risk in consumer acceptance of e-services. Electronic Commerce Research, 17(2), pp.255-288.

Pappas, N. 2016. Marketing strategies, perceived risks, and consumer trust in online buying behaviour. Journal of Retailing and Consumer Services, 29, pp.92-103.

Wu, H., Cheng, C. and Ai, C. 2017. A Study of Experiential Quality, Equity, Happiness, Rural Image, Experiential Satisfaction, and Behavioral Intentions for the Rural Tourism Industry in China. International Journal of Hospitality & Tourism Administration, 18(4), pp.393-428.

Dig deeper into The Falklands War and its Impact on Margaret with our selection of articles.