Essay on AI for Marketers: Unlocking New Market Fundamentals & Predicting Fluctuations

Artificial Intelligence is an important technique that has enabled market designers to unleash new market fundamentals and accurately predict market fluctuations (Paul Milgrom and Steven Tadelis, 2018). Machine learning can help marketers to predict lifetime value and the likelihood of conversion. Training data is usually a continuous process used to teach the machine learning system how to identify the correct output for a given random input. There are three powerful algorithms used in Machine Learning: supervised, unsupervised, and semi-supervised learning. Machine learning is transforming the retailer's marketing strategies, customer behaviors, and business models. According to Siau and Wang (2018), customer consumption has changed from brick-and-mortar stores to online shopping. Artificial intelligence in marketing is on the rise due to increased computing capabilities in the data processing.

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Social media takes a greater share of consumers` time spent online, leading to a shift of the firm's advertising budgets (Lee, D., Hosanagar, K., & Nair, H.,2018). This has increased the need for content engineering to derive valuable market information from social media platforms like Facebook. Facebook is the largest social media platform in the world creates a communication link between brands and consumers. Small businesses have utilized free social media advertising channels of advertising to connect with their existing and potential customers.

The Facebook social network's dynamic and ad creative solution has delivered a personalized version of an ad to everyone who comes across it. Businesses also can use auto-translation for a single-media ad to enable them to reach international markets in their most preferred language of communication. Machine learning technology can help small businesses in the selection of formats, creative, and text to use in an ad with appropriate impression-level based on target customers' tastes and preferences (Lee, D., Hosanagar, K., & Nair, H.,2018).

Machine learning enhances marketers’ capability to comprehensively understand their target customers and create and distribute customized content that is personalized, relevant, and impressive (Siau and Wang,2018). Social media marketing lets the brand earn customers` business at the right moment, and customers gain by saving money on their purchase. Social media business- customer interaction builds a relationship that generates revenue for the brand and also customers` value.

The digital age creates a dynamic marketer - customers' relationship (Bailey, 2010). The social media interaction allows marketers to co-create their content through viral content, influencers, user-generated content, and word of mouth. Analysis and conceptualization of the collected information in brand development consequently improve the brand market performance and in tracking customers` engagement rates.

The Facebook platform features such as Pages enable businesses to create profile pages, and to make posts status updates, advertise and offer customers experience. Insights feature is an analytical tool that allows businesses to monitor the performance of their Facebook posts, which consists of `likes` and `comments` consumers` engagement metrics. These metrics help businesses in measuring the level of engagement. They offer more detailed information than other metrics, such as several impressions of each post per day. The businesses can easily analyze their market using daily recorded data of their Facebook pages consumer engagement metrics.

Marketers should be creative on post content creation to be able to impress their target customers. Brands that can delight their target customers with meaningful stories immerse experience, and impressive designs become market leaders. Social media provides insights to enhance creativity in strategy and brand development to improve product and services differentiation (Inskin Media, 2017). This enables a small business to deliver its niche with quality products and services in the right way and at the right time. It also helps businesses in localization of market campaigns to fit local market needs by adhering to norms and customs of the society in a given geographical location

Small businesses can track customers’ behaviors by use of image recognition and computer vision. Facial recognition offers opportunities for seamless, intelligent marketing and numerous channels of customers' interaction than before. Consumers usually reveal their product and services' taste and preferences through the photos they post, tag, and comments (Arslan & Telang, 2015). Marketers can also be able to track information about the brands the consumers are posting about their experience on the use of these brands and preferences. Facebook image recognition gives marketers the ability to see when a brand's logo appears in an image in real-time (Shah, 2016).

According to Lee, D., Hosanagar, K., & Nair, H. (2018), firms gain from sharing persuasive content such as their brand emotional and philanthropic content while the informative coverage of the product has a negative impact. Businesses also consider other measures, such as sharing posts with friends by customers. Users who sign-up as followers of businesses` Facebook pages are more likely to recommend and buy products advertised on the page than before (Bertrand Karlan, Mullianathan, et al. 2010).

Ad-ins marketing has enhanced personalization in the digital era, as its efficacy is evident. They help small businesses in tracking information about individual consumer's online behavior and browsing history. The collected data helps the business make informed market segmentation decisions and develops increasingly personalized ad contents (Arslan & Telang, 2015). Targeting customers based on their online behaviors such as their history with the brand's Facebook page (comments, likes, and post sharing), activity with previous marketing efforts, and geographical location, can help make a detailed market segment. Consumers get pleased by businesses' use of their information in customizing and improving their experience. However, privacy rights have been affected by ad advertisements over the years as they have been increasingly being regarded intrusive.

Conclusion

Despite ads having proven successful, retargeting ads leads to distrust and annoyance. Poor timing in ad frequency, exposure, and ad extent of personalization are significant considerations in developing ads marketing content (Inskin Media, 2017). Ads are not generalized to all customers; the content creativity, messaging, and timing should be done in consideration of the customer's relationship. Machine learning algorithms can help in controlling ads that consumers receive while avoiding the levels of annoyance at the same time.

References

Arslan, A., & Telang, R. (2015, March 31). What is a Cookie Worth? (Rep.). Retrieved March 26, 2018, from Heinz College, Carnegie Mellon University website: http://www.law.northwestern.edu/research-faculty/searlecenter/events/internet/documents /Telang_What is Cookie Worth_III.pdf

Bertrand, M., D. Karlan, S. Mullainathan, E. Shafir, and J. Zinman: 2010, 'What's Advertising Content Worth? Evidence from a Consumer Credit Marketing Field Experiment'. Pp. 263–306.

Inskin Media. (2014, October 23). RESEARCH – Consumers 37% more likely to click on an ad on a site they trust [Press release]. Retrieved April 4, 2018, from http://www.inskinmedia.com/blog/retargeted-ads-put-half-people-buying/

Lee, D., Hosanagar, K., & Nair, H. (2018). Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook. Management Science, http://dx.doi.org/10.1287/mnsc.2017.2902

Paul Milgrom and Steven Tadelis (2018) How artificial intelligence and machine learning can impact market design. Retrieved at: Working Paper 24282 http://www.nber.org/papers/w24282.ack

Shah, S. (2016, August 25). Facebook opens up its image-recognition AI software to everyone. Retrieved February 20, 2018, retrieved at https://www.digitaltrends.com/computing/facebook-open-source-image-ai/

Siau, K., Wang, W. (2018). Building Trust in Artificial Intelligence, Machine Learning, and Robotics. Cutter Business Technology Journal, 31(2), 47-53.