An abundance of choices isn’t always a good thing. How many times have you stood in the cereal aisle and spent 10+ minutes picking out your breakfast? And movie night? Figuring out what to watch when thousands of movies and shows are at your fingertips can be exhausting. Luckily, technological advancements allow us to make refined decisions without taking away freedom of choice.
This article explores personalization and its importance for capturing modern audiences. It also highlights cutting-edge technology’s pivotal role in presenting us with smarter, more relevant choices.
Personalization – The Key to Acquiring Satisfied and Loyal Customers
The biggest marketing mistake companies can make today is to try to appeal to everyone. Modern audiences have heard it all before, and generic messaging is bound for poor reception. People want to be treated as special. They expect a tailor-made approach, whether shopping for clothes or picking their next read. That’s why personalization is crucial for nurturing a happy customer base.
Quality and fair pricing remain important. Still, four-fifths of shoppers claim customer experience plays a major role in their purchasing decisions. A personalized approach might make marketing efforts costlier in the short term. However, the long-term benefits are worth it.
Personalization fosters engagement. An engaged customer feels like the company reaching out understands their unique preferences. They are more likely to act accordingly. Engagement improves the chances of a positive purchasing experience. This, in turn, boosts the chances of turning one-offs into repeat customers. That means lower losses, better sustainability, and more people who’ll expand your influence through word of mouth.
What Role Do AI and Machine Learning Play?
Delivering personalization at scale was unthinkable before the advent of the digital age. Two problems persisted even then. One was the lack of available information to source personalized recommendations. As people spend much time online and share ever more info about themselves, this has become a non-issue.
More importantly, we lacked the means to interpret this information in a way that would yield meaningful insights. Humans aren’t good at analyzing ceaseless streams of data. Even if we try, the results are incomplete and prone to errors. Artificial intelligence, and more specifically machine learning, is the breakthrough that brings us a step closer to genuine personalization for everyone.
Machine learning is a subset of AI that simulates humans’ ability to learn and adapt. Large amounts of data substitute our experiences. Armed with such data, machine learning algorithms can create predictive models and offer personalized recommendations. The longer they operate, the more actionable their recommendations become.
Feeding the machine
Machine learning algorithms need vast amounts of data to train on first. After all, how can you recommend something with no prior info? Providing information on your company’s top-selling products, customers’ purchase histories, and previous market research is a good start. Having only your data to go on might not lead to the most accurate predictions, though.
Scraping the data from publically available sources is a popular option. Sophisticated tools exist that can collect pricing info, customer reviews and profiles, top lists, and other information that forms a picture of people’s interests and preferences. Sometimes, websites will restrict the amount of data you can scrape or won’t allow access from certain geographical locations. Pairing trustworthy data scrapers with reliable datacenter proxies is the way to go in such cases.
How Do AI and Machine Learning Shape Personalized Recommendations?
All the data gathering, training, and refinement culminate in so-called recommender engines. These sophisticated ML-based systems can deliver real-time recommendations for any individual who accesses a site or service.
Better yet, they integrate with websites to create a unique layout filled with offers based on your past behavior. Have you ever seen the “people also bought” section of an Amazon page? That’s a recommender engine in action.
Types of recommender engines
We can split recommender engines based on purpose or filtering principles. Currently, the two main purposes of these engines are to recommend products and content. You’ll encounter the former when visiting online retailers. The latter integrates into video and music streaming services.
Regardless of purpose, the most accurate recommender engines derive their picks based on a combination of content-based and collaborative filtering.
Content-based filtering delivers recommendations similar to things you already enjoyed. For example, if you bought an RPG from an online game store, its algorithm could recommend other RPGs, games with a medieval fantasy setting, or other products by the same publisher.
With proper categorization, this method gives new products a better shot at recognition. Conversely, users may never discover more obscure games even though they might like them.
Collaborative filtering leverages the power of the crowd. Traditional methods recommend highly-rated comparable items to ones you bought before or ones people with similar tastes liked.
Introducing machine learning models uses all legally available data, uncovering patterns and leaving more room for unexpected recommendations. New users can’t reap the full benefits right away since there’s no information on them for the algorithm to use. That’s why a hybrid approach has proven the most successful so far.
Conclusion
We’re only beginning to experience the impact of personalized recommendations on the way we shop, consume media, and form opinions. It’s unclear which challenges we’ll face down the line. Even so, artificial intelligence is bound to have a hand in creating as well as solving them, and it already does in many cases.
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