Although the veil of encryption and privacy regulations shroud personal data, brands may struggle to keep up with the increased demands for personalized ads. Transcending over the barriers of sensitive data is getting more difficult with each passing day as IoT devices such as smart speakers, beacon and edge technologies continue to pervade homes, workplaces, and public areas, while organizations try to use AI-enabled technology to automate and simplify aspects of the customer experience.
Factoring in such limitations, a question emerges about obtaining consumer data to aid in the development of the finely precise tailored behavioral and interest models for AI advertising engines. The best solution to such a crisis while aligning with privacy rules can be found using edge AI.
Typically when data is gathered from a computer or device it is uploaded to the cloud, where it is stored and analyzed, but with edge computing, the same processing takes place at or near the source of the data rather than transmitting it over the internet or edge. Here, the edge refers to the location where edge devices such as computers, mobile phones, smart robots, sensors, and actuators connect to the internet and communicate with one another. Unlike cloud IoT implementation where everything is centralized, edge computing focuses on decentralizing the architecture.
Artificial Intelligence has long been a catch-all phrase for a variety of technologies such as computer vision, machine learning, neural networks, deep learning, and many more. Edge AI is specially designed to execute AI algorithms locally on data embedded in Internet of Things (IoT) endpoints, gateways, and other data-processing devices. In short, edge AI can be thought of as a merger of two technologies, AI and edge computing.
Marketers may create automated systems that react to consumer inputs quickly as data is processed at the source. Edge AI will empower brands to respond instantaneously to consumer interaction, resulting in a hyper-personalized experience that the end-user has control over — all at a fractional cost. By utilizing real-time data processing, brands can quickly offer customized yet personal experiences to customers before they leave a store or close a tab. This data can be based upon information like location and time of day or past interactions with a web page.
Advertising has always been concerned with understanding and exploiting human behavior as a medium to boost market revenue and attract loyal customers. Today, the ability of AI algorithms to convert huge volumes of complicated, confusing data into insight is backing the need to carry the analysis of customer behavior. Brands can now evaluate an individual’s complete social activity, including every phrase, image, like, review, and emoji.
Yes, this sure sounds like stalking users to sell services and products by playing a marketing gimmick via ads. However, edge computing can tackle these intrinsic concerns.
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People are more interested in products that are relevant to them, therefore an increasing number of businesses are taking advantage of personalized advertisements. Personalization is crucial because it improves the buying experience by making it more enjoyable, efficient, and most importantly, it boosts sales. As a result, lifeless mannequins and static billboards will soon be a thing of the past.
Consumers enjoy a strong sense of agency and control when they interact with brand offerings. They also wish to have better control over their engagement data to protect their privacy and prevent data misuse. This is why most of the data sources are asked to adhere to data encryption policies.
But how to mine user behavior data when everything is end-to-end encrypted? Simple, by sifting through data before it is encrypted and after it is decrypted, without the need for storing the data and processing data locally. The information collected can be classified as per various labels and used to identify the interests of the users. By moving recommendation engines on the user devices, brands will save tremendous amounts of processing power, increase user privacy, and would not require their own hardware resources to store the data. As the inbuilt edge AI capabilities of smartphones and other devices improve, advertising companies can construct even more nuanced classification models.
Advertisers are already skilled at precisely targeting various audiences depending on their preferences. Yet earlier, the advertisement campaigns were based on average customer interactions and interests i.e., they were basically a hit and trial stunts to attract the public to the brand offerings and turn them from casual visitors to long-term customers. Now, equipped with edge AI and better data encryption, the brands can offer specially curated ads that capture the user interests with higher accuracy while building trust.
There are some challenges too that need to be attended to when deploying or switching to using edge AI on IoT devices. To enforce edge AI for personalized ads, capabilities in the AI space (especially client-side behavior modeling), hardware, software, networking, as well as maintenance of these must be improved. The existing IoT architecture must have programmable gateways before upgrading to edge.
In addition, with new variants of cyber attacks and spyware, encryption alone may not be sufficient to enforce user data privacy. Therefore, brands must be careful while prioritizing what data or metadata they need to train their edge ai models for producing relevant and engaging personalized ads.