There are few things on the internet more obnoxious than spam. Generic emails trying to lure everyone and anyone into buying things. Blanket statements that may or may not apply to you. Companies you never heard of before talking to you like you were their best friend. It’s faceless. It’s impersonal. A generation that has practically grown online needs a bit more than that to engage. A bit? A lot more. Cue personalized user experience.
If you haven’t heard this term before, you can probably still guess what it’s about. In essence, it’s the process of tailoring a product or service to match the unique expectations, wants, and needs of each user. It makes sense — practically no one wants to be addressed as a part of a multitude. A unique individual demands more respect.
So, if you’re launching a product or offering a service, you’re bound to wonder – how can you achieve that level of personalization? Artificial intelligence and machine learning can give you the upper hand.
AI and the personalized user experience
AI can create everything from surprisingly articulate chatbots to drastically improved cancer diagnostics. In the hands of a skilled developer, it can also create a more personalized user experience.
Artificial intelligence relies on users’ past behavior (browsing and purchase history) to generate recommendation algorithms and suggest products. It’s able to offer tailor-made solutions to a number of problems in real time.
Let’s take a big sports equipment retailer as an example. The company will need a drastically different approach to address a 20-year-old gymnast and a 55-year-old rugby enthusiast. AI can be tweaked to deliver personalized messaging — including AI-powered virtual assistants.
Machine learning and personalization engines
One branch of AI that becomes crucial in this process is machine learning (ML). In layperson’s terms, it’s a way to take sample information and build a digital model around it. The ML algorithm uses this info to “teach” itself how certain patterns lead to different results. It’s then able to extrapolate what’s most probable to happen — and make decisions without being directly instructed to do so by the developer. This is a way to leverage data and improve performance.
Examples include email filtering, speech recognition, mathematical optimization, and unsupervised learning.
Machine learning also shows great promise in digital marketing and in creating a better user experience. In this context, ML can become a personalization engine able to “understand” digital behavioral context and deliver desired content. The ways to do this are:
- Regression analysis detects pages most likely to lead to conversion (i.e., purchase). It can also suggest the best follow-up actions, e.g. for an abandoned cart.
- Association is the base building block for recommendation engines as used by Netflix or eBay.
- Clustering groups customers into categories or segments based on age, location, background, interests, and other relevant criteria.
- Markov chains analyze real-time behavior and make navigation predictions.
- Deep learning is the most advanced option. It includes natural language processing (NLP), the sort you get from Siri or Alexa, and the closest option to human-like intelligence.
Examples of AI-powered personalization
Practical examples are perhaps the best way to understand how all of this works as a part of the digital acceleration trend. You can then decide which ones you’d want to embed into your existing business model.
- AI-enabled avatars are digital characters appearing within games or social networks. These human-like bots can interact, answer questions, train employees, and even maintain relationships with the end user.
- Personalized AI-powered chatbots are similar to the above yet mostly text-based. Natural language processing (NLP) lies at the core of their code. In the world of digital marketing, they can act as the first point of contact for customer care. Chatbots can also increase sales, analyze complaints, moderate reviews, and a long etcetera.
- Personalized content includes interactive quizzes, retargeted ads, location-based adverts, gamification, and custom-made emails.
- Personalized messaging uses parameters such as age, location, gender, etc., to correctly address the audience in a more customized way.
- Personalized ad targeting and product recommendations rely on cookies and browsing history to offer better suggestions.
- Customer sentiment analysis is the most recent addition to the digital arsenal. It includes techniques that let you determine your customers’ emotions. These methods can be fine-grained, aspect-based, and reliant on emotion detection or intent analysis. Knowing this information can help you better identify the best moments to engage with your target audience.
How to implement machine learning
All this sounds like an endless plethora of possibilities, but how do you actually make it work? In order to implement any new method, it’s good to follow a few simple steps.
Begin by clearly defining who your users are and what their needs and preferences may be. The better you understand them, the stronger the connection you can make. Next, identify key areas for personalization through machine learning.
Take your time in collecting and analyzing relevant data. You can find out that using a chatbot in your website reduces a lot of friction for new customers or you can learn that your email marketing reaches a wider audience through automated scheduling. Whatever you find, don’t just settle on one use. Try a combination of them for maximum effectiveness.
Finally, test and iterate until you find the best formula for staying competitive in your niche. Pay attention to what your competitors are doing and adjust accordingly to keep at the forefront of your industry.
Don’t forget to consider the cons
After mentioning all the potential benefits, we can’t conclude this without taking a look at some of the challenges. Large-scale data mining and analysis inherent to personalization does, after all, come with privacy concerns. The users may find it intrusive and be put off by it.
Furthermore, AI, being an artificial creation, can be prone to bias and discrimination. No matter how complex the algorithms are, stereotyping based on gender or race does occur. Last but not least, we should also mention custom software development costs, which can be steep if you don’t have a trusted development partner or your own team.
That isn’t to say that you should abandon the idea of using AI altogether. Quite the contrary! The benefits far outweigh the (perfectly overcomable) challenges. These systems practically “teach” themselves how to reach a wider audience, boost engagement, and augment user retention. They have proven their worth. Personalized messaging and AI-powered assistants are here to stay.
