Personalization Algorithms: Why it matters and how it impacts people.
Personalization algorithms are sets of code that observe your digital habits and predicts your next choices.
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.
What are personalization algorithms?
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.
How do they work?
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:
- What you usually search for.
- How often you visit a website that interests you.
- How long you spend on a particular website.
- What and how often you purchase something.
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.
What is their purpose?
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).
Why do companies rely on them?
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:
- Improved customer retention.
- Improved conversion rates.
1. Improved customer retention
A personalized experience helps nearly 44% of new customers convert into repeat buyers. And just a 5% increase in customer retention can exponentially grow profits anywhere between 25%
to 95%.
2. Improved conversion rates
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.
Are personalization algorithms used for monetary
gains?
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.
What is the impact on customers: Positive or
Negative?
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.
Positive Side
Data Ethics
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.
User Privacy
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.
Taking consent from people
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.
Negative Side
Data Laws
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.
Advantages and disadvantages of personalization algorithms
Advantages
For businesses:
- Higher revenue.
- Enhanced customer engagement.
- Predictive digital behavior.
- Ability to build sales personality models.
For You:
1. Relevant content suggestions.
2. Product recommendations.
3. Appropriate targeted ads.
4. Customized responses.
5. Disadvantages
For businesses:
- Costly data acquisition needed for A/B testing.
- Difficulty in balancing profits with privacy concerns.
- Audience irritation by pushing outdated relevant ads
- Resentment by digital rights activists.
- Risk of violating data laws such as General Data Protection Regulations (GDPR).
For You:
- Sense of being observed.
- Risk of data used by other businesses without your
consent.
Types of personalization algorithm
Personalization algorithms are varying in dimensions and strategies. It depends on the data scientists to adopt a modus operandi that works best for them. While there are many, primarily we will only be touching on the three most relevant ones, segmentation-based, 1 to 1, and product recommendations.
Segmentation based algorithms
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.
2.1 to 1 algorithms
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.
Product recommendation 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.
Examples of companies using predictive personalization.
Netflix uses a recommendation algorithm 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.
Future of personalization algorithms
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.
Should you get paid for it?
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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