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In the current competitive world, advertising has become an essential strategy for many businesses in winning over market share and gaining a competitive advantage over peers. This has resulted in rapid growth in online advertising, consequently leading to the generation of large data volumes on targeted customers. Data mining applications such as customer behavior analysis, a recommendation system for e-commerce, market trend analysis, and link prediction for social networks have widely been used to foster advertising. Primarily, much of online advertising for businesses today focus on what the advertisers know about the customers, otherwise known as ‘audience intelligence’ (Li & Shen, 2007). Audience intelligence entails understanding the target audience in online advertising.
In online advertising, businesses use data mining to foster three main objectives. The first is to reach the customers who browse on web pages, search for items via search queries with the intent of purchasing goods online. The second objective involves targeting the media channels to convey information to consumers through ads. These channels include social media websites, search engine portals, and Internet marketing tools. The third objective is identifying the appropriate advertisers who develop the actual ad content (Wang, 2012).
The overwhelming use of data mining in the development of online advertising has led to businesses holding huge volumes. As much as this is a good thing for businesses in understanding the target audience, it has brought an equal share of data mining problems both to the business and the customers. One of the most prominent challenges faced by advertisers using data mining is delivering the right advertising message to the target audience at the right time. According to Li and Shen (2007), in an information-rich business environment, competition for the customers’ attention is inevitable. Businesses with a very vast pool of end-users’ data face a problem of deciding what to use in capturing their attention. In some cases, the algorithms that the advertisers use may not be the attention getters for their intended audience hence loss of market share.
Another profound problem facing data mining in online advertising is issues of data safety and security, especially with increased cases of cybercrime. Readily available data has enabled many businesses to use consumer data to conduct profiling and targeted marketing. It is estimated that 80% of end-users are aware that companies track their consumption behaviors across different e-commerce and social media websites (Singh & Swaroop, 2013). However, what they do not know is that rules in marketplaces permit businesses to share or sell customer data in regards to what they purchase. Data insecurity and lack of transparency make customers suspicious and angry towards online marketers, the media as well as authorities responsible for protecting them from the advertisers who misuse their data.
The most probable solution to the problem of delivering the right advertising message to the target audience at the right time is using a language-independent machine learning system in establishing attention getters of the end-users. Accurately matching the needs and wants of customers to the content of an advertisement is not an easy task. However, due to the modern machine learning system technology, businesses can take advantage of the relevant end-user data in devising attention-getting ads and eventually improve revenues. Machine learning systems collect several vital data that help in matching the end-user and an ad. These data include demographic data such as age, economic endearment, and geographic location. Additionally, the technology establishes an individual’s consumption behavior, and through these insights, a business can target the audience with precision.
In regards to the security of customer data, data miners need to be more responsible while using the data to avoid it falling on the wrong hands of fraudsters and hackers. Advertisers should be transparent to the customers regarding the kind of data they have on them and, more importantly, seek their approval to use the data in developing advertisements. On their side, end-users need to limit the amount of data that advertisers have regarding them. One way of escaping data miners is constantly changing passwords of common websites one visits. With these, data miners have no access to information regarding the end-users’ consumption behavior. Another secure way of being online is using high-quality malware and VPN. This software assists in avoiding individual tracking by data miners as they are not able to establish details such as the geographic location of the end-user.
Using machine learning systems in matching a customer to the right advertisement content is very workable for modern-day businesses. Machine learning systems provide an advertiser with audience intelligence, which involves the cognition of customer’s desires and passion, character, personality, as well as media habits (Wang, 2012). Audience intelligence advertising, developed through machine learning systems and artificial intelligence, creates customers’ value in that the advertiser understands customers’ thinking and behaviors as opposed to conventional advertising.
Similarly, it is vital for data miners and upholds customer’s data privacy to avoid exposing them to cybercriminals. However, the performance of this recommendation in the current world has proved difficult as hackers and fraudsters have sharpened their data mining skills. Additionally, the government has not implemented laws prohibiting businesses from selling or exposing their customers’ data to third parties. Even when individuals opt to use VPNs to hide their identity from data miners, most high-quality VPNs are too costly for them to afford.
In conclusion, I think it is appropriate for businesses to use sophisticated artificial intelligence such as machine learning systems in learning more about their target customers and hence devise relevant adverts and eventually growing their revenue. However, this process of collecting data from consumers should be in a responsible manner such that customer data is secured at all costs, less it falls to the wrong hands. Consumers, too, should make individual efforts in securing their data from data miners through VPNs and other Internet security features.
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