Personalization algorithms are sets of code that observe your digital habits and predicts your next choices.
Companies are investing heavily to improve their personalization algorithms every day. In this article, we dive into the details of personalization algorithms and how their use affects all internet users.
Have you noticed that the internet is getting a bit too personalized?
You order a frying pan online and the images of spatula and spoons follow you around. Ever noticed how Google keeps reminding you of the ETA to reach home at the right time, everyday? This isn’t a coincidence. A blend of science and engineering work behind the scenes to create what is known as a personalization algorithm – the digital fortune-teller.
From data mining to data crunching, every phase is processed through a complex set of code. This code or algorithm, monitors digital activities and builds up your personality model to serve you with a customized experience. You then receive messages, advertisements, and promotions based on your activity.
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Personalization algorithm is basically a well-written set of code that observes your digital habits and predicts your next choices. Here are a few data points it considers:
The relevant and specific information is processed through constant match-making and habits’ analysis. The data is then sold out to advertisers, mostly for marketing purposes.
The purpose is to serve you with contextual ads and customized experience without looking like a desperate attempt to sell you random stuff. This is basically how your social media news feed is curated.
Back in 2019, Facebook changed its Status feed from “Most Recent” to “Most Discussed”, the focus shifted to see what interests you. And amazon does this flawlessly. Whenever you search for something, you get a recommendation on what people with the same product search are also buying. It looks something like this:
Mass marketing ads on radio and TV are more like a hit and miss strategy. This one-way of advertising targets a broad, and yet irrelevant audience. By utilizing personalization algorithms using Machine Learning and Artificial Intelligence, marketers can build segments based around user activity and narrowly target them for more efficient ROIs (Return on Investments).
The ability to target the right audience and yield highly efficient results at a lower cost than traditional marketing is what makes personalization algorithms so effective.
Here are 2 reasons why companies rely on personalized algorithms:
You are more likely to see an ad for dog food, if you have been searching for articles and videos that an algorithm is using to identify you as a pet owner. When pet companies market products to pet owners, there is a higher chance of conversion than promoting the same product to a car enthusiast.
Social media companies use these algorithms to provide more efficient campaigns, and in return they make billions each year by providing better advertising results to their customers.
Setting aside a few privacy cautions, the overall impact of personalization algorithms is facilitating. It’s a sort of “you-say-it and you-have-it” mantra. Advertisers get to reach a targeted audience, and you get to see ads concerning your interests. But sometimes, algorithms display offensive content and can cause harm. Personalized algorithms are man-made sets of codes and can include bias in their structure.
Data Ethics defines the moral code of how data can be stored, processed and managed by any business. Amazingly, the core of data ethics supports the utilization of personalization algorithms for the good at large.
As a matter of principle, user privacy is and should be respected. The mined data should be dumped in a disfigured raw form, so none can exploit it for personal benefits.
Consent is the prerequisite, deployment of personalization algorithms without knowledge of the user is a criminal practice. Moreover, the consent should be clear-cut, concise, and not loquacious. The font, placement, and time must also be conspicuous and well-placed.
As in the United States, there is no comprehensive data privacy federal law in place. A complex patchwork of medium and sector-specific laws is used on a make-shift basis. Most of the data laws in the US revolve around data protection and usage rights.
Refers to the method of grouping users based on geolocation, demographics, and other notable characteristics. Segmentation is an age-old technique that still has some relevance. It is similar to the targeting settings advertisers set in Facebook and Google to target users based on their location, age, and demographics. This method has shown good results for the Big Tech.
Refers to the practice of rolling out an exclusive experience for each customer taking leverage right throughout their buyer journey. For referrals, recurring sales, and client retention; it is important to emphasize 1-to-1 personalization algorithms.
Display products based on customer data metrics such as style, size, color, or gender. Brands are now able to offer focused, and highly relevant suggested products to their customers. An example given earlier is that of Amazon, which uses a combination of machine learning to predict the best recommended products.
Netflix with over 210 million global users, can’t provide its users with the same homepage and generate an individualized experience, maintaining a 74% retention rate. Every time you log into Netflix, the system optimizes your homepage to recommend you titles based on your latest watch history.
Amazon does the same. As the world’s e-commerce store, you will always get personalized product recommendations based on your search history and predictive behavior.
There is no doubt that given their current level, personalization algorithms are only going to get better. AI is coming up with predictive shopping algorithms that can even deliver gifts on your behalf, without your action.
Personalization algorithms will keep dominating in the coming future. They will continue to learn more about you to deliver more accurate and personalized experiences.
Absolutely. This is what Invisibly is all about. We are one of the first tech companies to compensate you for helping us create your personalized algorithms. Rewarding users for sharing their data is one of the core propositions of our company, which makes us truly unique, and unlike Big Tech. Sign up for Invisibly and start monetizing your data.
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