Predictive Content Personalisation the definitive guide — SwiftERM
Predictive content personalisation the definitive guide. The amount of content users now create and interact with has grown exponentially, and the profound effect it has had on the ways we consume information is impossible to ignore.
Attention spans are dwindling and people have become very discerning and impatient online. We have basically trained our brains to scan and immediately reject content that is not relevant to our needs. To combat this issue and keep readers’ attention, we use content personalisation to make users’ website experiences more tailored to their interests.
What is Content Personalisation?
Content personalisation can take many forms, but it is generally intended to deliver better value and more relevant content to users, help them find the content they need, and make them convert quickly. A personalisation strategy can be based on rules or machine-learning algorithms, or both.
Rules-Based Content Personalisation
As a basic personalisation method, rules-based content personalisation uses a number of simple, manually created and easily adjusted rules that, with a number of personally identifiable attributes, divide your audience into smaller segments. The segments can then be individually targeted and sold to.
Think of rules-based personalisation as a series of IF-THEN statements. Enriched with AND / OR operators, they allow us to create a more fitting experience for each user group based on the location, language, and other data collected during users’ previous interactions with the website.
Predictive Content Personalisation
Predictive content personalisation also referred to as machine-learning personalisation, is the more advanced and AI-driven way to dynamically display the most relevant content to each user.
Unlike the rules-based method, it does not target whole segments; instead, users are identified at a more granular level, and a more personalised website experience is created for them. It puts more focus on displaying content and messages to users based on their intent, rather than just on the readily available information about their interests and previous behaviour.
What Data Does Predictive Content Personalisation Use?
Machine-learning personalisation uses a combination of algorithms, filters, and analytics. It either “knows” or “predicts” users’ typical behaviour on the website, their favourite product categories, sorting methods, and more. To do this, machine-learning personalisation utilises:
- Basic algorithms that dynamically recommend different items without using any personally identifiable information about the users. This can include showing them new products, current promotions in the store, trending posts or products, or products currently browsed by other people.
- Advanced algorithms that further customize the content to each user by the available personally identifiable data or demonstrated behaviour.
For example, based on demonstrated behaviour, the algorithms will assign each user to a group of users with similar preferences (think providers of streaming media like Spotify or Netflix). The algorithm dynamically predicts other products or content they might like, saving them the hassle of rummaging through everything that’s not exactly their cup of tea.
Algorithms can be used to create decision trees that are most likely to lead to a conversion, individually for each client.
Filters allow companies to further tweak the results of algorithms and make them exclude or include particular elements.
How Data-Management Platforms Power Predictive Content Personalisation
Predictive content personalisation heavily relies on vast amounts of data about each user’s interactions with the website. To this end, the data has to be aggregated from various sources within a data-management platform (DMP). The content-personalisation platform can then be employed to leverage this data to help a website boost conversion rates and make the personalisation more accurate.
The DMP, once populated with sufficient amounts of data from desktop and mobile devices, can crunch the information to deliver a more detailed image of each user. This involves, among other things, merging and segmenting the data sets using various factors.
Before that happens, however, the DMP has to sync first-party cookies (from the same website) with a number of second-party cookies (from other websites), creating continuously updated segments of data. In this way, the DMP lays the foundation for successful content personalisation.
The email weapon isn’t so secret. It’s common knowledge that email marketing is a powerful customer acquisition tool. First of all, the foot-in-the-door technique applies. Second, you have permission to push marketing messaging to an entire list of people.
There are two main ways to use email marketing in the customer acquisition process: following up and increasing retention.
Capturing an email is an easy way to reach out to a potential customer down the road. Say, when you have a discount or a special offer to follow up with. As long as you don’t abuse the list (leading to unsubscribers), you literally have a list of people who are somewhat interested in your product and are open to being contacted again. But be conscious of the naysayers, as 90% of people do not unsubscribe if they like to shop, products and people, and their customer not abused.
Email also is a great way to improve your customer retention. The most valuable customer is the one you don’t lose. Keeping customers is just as important as acquiring new customers. Reach out to let them know about any outstanding notifications, recent updates, etc. Reignite their interest in your product via email. Remind them of what’s going on, of what they’re missing out on, etc.
Personalised product selection solutions, using predictive analytics technologies, identify consumer’s future behaviour ranking every SKU by greatest likelihood of that individual consumer to purchase from all the SKUs you have listed, in order of greatest likely buying propensity. It presents them 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 etc). Hardly surprising is ranks at the top of all marketing disciplines for ROI.
The development of this has led to predictive personalisation of email, as the retailer doesn’t have to compete against the consumer’s browsing whims, and gets ahead of the curve as far as planting opportunities and invitations to treat and tempt your consumer. leading this fiend is SwiftERM, a plugin SaaS for any size of company, offering a free trial for both verify viability but to self finance the addition to your martech stack.
Challenges of Predictive Content Personalisation
On the flip side, content personalisation may not be for everyone. There are a lot of challenges on the way to success. Implementing a recommendation system that would be valuable for your audience will certainly involve a lot of experience with A/B and multivariate testing methods. Netflix explores these challenges extensively in one of their blog posts on Medium.
Before even considering a foray into predictive content personalisation, it is good to start small with the rules-based method. Why? There is an array of challenges inherent to personalisation like insufficient data to tap into (typical of small companies), inability to properly activate the data at hand, and incompatibility of various sources of data.
Predictive content personalisation allows marketers to harness the power of machine learning and improve the experience for each user visiting their sites. Sure, it dramatically increases the chances of conversion, but there is more than meets the eye. Real-time recommendations also lend a helping hand to the users, streamlining their experience and reducing time to find what they need.
Originally published at https://www.swifterm.com on December 1, 2021.