How to take advantage of predictive personalisation. With the rapid evolution of digital marketing, providing personalised 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 personalisation. And by predict, we don’t mean that there’s some fairy tale fortune-telling, rather pure science named machine learning.

Decoding the notion of machine learning

Machine learning is one of many methods for personalising 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 personalised 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 customisation process. Its clout has been already acknowledged by giants like Amazon or Netflix for their product recommendations.

This way of personalisation 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.

It’s all about algorithms

With the myriads of data up for grabs, you can configure the algorithms that serve best your consumer personalisation. 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, as per SwiftERM, impressions, frequency, patterns, repetition, recurrence, sequence visited – the list is enormous – all captured.

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. Predictive analytics technologies like SwiftERM, identifies consumer’s future behaviour ranking every SKU by greatest likelihood of that individual consumer to purchase, and presents it to that individual at exactly the right moment, thereby maximising that individual’s customer lifetime value CLV potential. (i.e. Likelihood to Purchase, Discount Affinity, Likelihood to Churn).

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.

Highlights and challenges

It will come as no surprise that there’s no perfect solution for your website optimisation. Predictive personalisation 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 individual profiles of each consumer – not micro-segments, and enable you to provide optimisation 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 analysed and divided in real time, delivering the right content instantly across all your marketing channels.

We should also mention the drawbacks of machine learning personalisation. To begin with, it entails more complex setup issues which might be daunting at first. It also requires more expertise and advanced preparation. Adopting plugin that do it for you has therefore has become to obvious solution, and highly cost-effective. A lot of money can and will be wasted and then will abandon privatisation.

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 personalisation 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.

Conclusion

Predictive personalisation falls under the category of dynamic website optimisation 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.