With the rapid evolution of digital marketing, providing personalized content to customers has become an everyday reality. There’s no way back. Gone are the days of guessing customers’ needs, promoting your top products or services and hoping for the best. Your site visitors expect to see the content that resonates with their unique requirements and preferences.
That’s why today’s digital marketing has marked a significant shift in approach, looking further ahead and trying to see what will come next. The road to this is called predictive personalization. And by predict, we don’t mean that there’s some fairy tale fortune-telling, rather pure science named machine learning.
Machine learning is one of many methods for personalizing web content. It relies heavily on statistics, computers science and engineering. The method involves searching through data to find patterns of user behaviors, then analyzing results to produce personalized content.
Then, tailored offers are implemented via software programs that grind loads of data to “learn” user tastes and desires. We’re not going dive into details, but we present the basics behind this powerful customization process. Its clout has been already acknowledged by giants like Amazon or Netflix for their product recommendations.
This way of personalization combines algorithms and predictive analytics; an approach that ensures a more scalable way to create unique experiences for individual users rather than for large audience segments. New and historical data from your website analytics drive the predictive algorithms.
Finally, with the data patterns and statistical inference, you can create self-learning algorithms which foster your decision-making and help you design experiences relevant to a specific user.
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With the myriads of data up for grabs, you can configure the algorithms that serve best your website personalization. They determine what content and product recommendations you provide. You can begin with creating simple recommendations.
You can base these on general trends like the most frequently viewed page within a specific period of time or recommend items based on the purchase histories.
If you want to have an audience stay on your site longer, view the specific offer and comes back for more, you need complex algorithms. Take for example Netflix’s favorite collaborative filtering technique, it allows you to foresee users’ interests by combining preferences from many users and comparing them.
Simply put, based on the visitor’s opinion of one item, they’re grouped with other users with similar tastes. The algorithm then recommends items by comparing the visitors’ tastes to others in a given group.
These are just examples to give you the gist of how these algorithms work. You may configure algorithms to improve the performance of your website and meet your specific business goals. The only limit is your imagination.
Every time a user interacts with your site, you learn something about them. Customers leave traces of their preferences by using search engines, communicating on social media platforms and making online and offline purchases. You can get a good grasp of their implicit behavior with data such as:
- site navigation path
- view duration
- search refinement
- back-and-forth navigation
Then you may analyze more explicit actions when users participate in a poll, search a specific keyword or fill out a form. With machine learning, acquiring all this information is just a part of the game, the key is to efficiently transform this knowledge.
The more data you gather, the better you can anticipate customer behavior, likes or wishes. In the end you are able to precisely tailor your offers. When you display the most relevant content, you will make the user feel at home and that guides them to conversion or any other desired action.
Tailored algorithms allow you to provide individual (one-to-one) experiences, meaning your content or product recommendation. It can be as simple as a student bank account offer at the beginning of a term.
Or it could be more specific like a minivan with an extra child safety seat displayed for a new parent that is looking for a car. You, or rather the algorithms, present the relevant content that is based on both user’s past and present actions rather than on assumptions.
As a principle, look at the audience and break it down into individuals. We’ve already mentioned that predictive personalization is a way to present a relevant experience for each and every visitor. But how can you know what suits your customer? How can you know what data to feed your hungry machine learning algorithm?
Any affinity of your customer with your organization, their behavior across your platforms shed light on the marketing tactics you should consider. Getting a detailed picture of your customer by integrating data from multiple channels is crucial.
If you would like to see more details, checkout our post on rule-based personalization.
Once you know the who, that is the user profiles you’re targeting, you can design your content and deliver contextual offers. For example, say the visitor has browsed a few sites from a single category.
Then you can display a pop-up that recommends related offers from the same category inspired by the user profile.
However, the customized offer is not enough. In order to boost your marketing campaign, don’t underestimate the where, the placement of your callouts, notifications and other experiences. Choose the most suitable elements for optimization. Consider these key areas where it could be implemented:
- list sorting
- category page
- search bar
It will come as no surprise that there’s no perfect solution for your website optimization. Predictive personalization has some notable advantages. Most importantly, it’s fully automated, in contrast to e.g. rule-based alternative.
The algorithms monitor and learn about visitors, create micro-segments, and enable you to provide optimization to your site or app automatically. Once implemented, these automations significantly speed up the whole process. Predictive algorithms can anticipate and identify the content strictly relevant to a particular user.
This marks a significant increase in marketing accuracy and proficiency. Tons of data can be analyzed and divided in real time, delivering the right content instantly across all your marketing channels.
We should also mention the drawbacks of machine learning personalization. To begin with, it entails more complex setup issues which might be daunting at first. It also requires more expertise and advanced preparation.
Also, the faster pace of testing might involve potentially more work for the marketing and creative departments. One of the downsides is also a fact that there is a fine line between adroitly managed personalization and manipulation. The latter makes user feel eerily watched so they turn their backs to you.
Finally, it demands a more strategic approach and a multi-featured platform that helps choose, configure and tests algorithms.
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Predictive personalization falls under the category of dynamic website optimization with good reason. As trends, favorites, requirements, tastes change continuously, the same applies to your customer profiles. Engaging your site visitors with unique content, determining product fit, and anticipating customer’s actions calls for a method that embraces ongoing tweaks and improvements.
If you ready to put some effort to keep up with the rapidly changing demands within marketing field then you can give a thought to machine learning. It encourages marketers to leverage the power of data and foster their marketing strategies that translates into increased conversion and revenue growth.
We have presented you with only the basics this complex subject so expect more in the near future. In case you already have some urgent questions, don’t hesitate to contact us.