It’s time for online businesses to accept that personalization is outdated. Yeah, we said it. You might have impressed your customers by putting their first name in the welcome line of an email a few years ago, but now, everyone’s doing it. Basic personalization at that level is now an expectation, not an innovation. For businesses who want to be offering customers what they want, rather than just what they expect, personalization based on basic data simply isn’t enough anymore. It’s time to start participating in the personalization vs individualization discourse that’s changing the face of online marketing and setting new standards for the way marketers communicate with prospects.
Big Words with Bigger Implications
Let’s start by getting on the same page and understanding what the differences between personalization and individualization are, in terms of how they relate to online marketing. Personalization was the first step away from untargeted mass marketing messages. As big data and personalization AI became commonplace, there was no reason not to use basic user data to personalize basic mass communications like emails. It also became easy to offer a personalized greeting to website visitors, or to drive product recommendation engines off basic data like past purchases or items in abandoned carts.
Segmentation based on basic user data also allows designers to create different designs and layouts for different segments, or to scale interfaces to suit different devices. In essence, personalization has allowed us to segment our audiences into groups, in order to create better content or massaging for each group.
Individualization, however, attempts to speak directly to each individual within those groups, and to utilize a much more specific data set. Rather than communicating with an individual lead based on the group or segment that they best fit into, we try to communicate with them based on their personal preferences, and on data that is unique to them.
Artificial Intelligence in Individualized Marketing
With definitions out of the way, let’s take a closer look at the type of data that drive individualized marketing. Rather than looking for common ground between groups of customers, which can be used to target those groups more effectively, here we are looking for hints and cues as to what each customer’s goals and needs are.
Let’s take a classic machine learning personalization feature, like an article recommendation engine, as an example. In the past, such an engine may be driven by internal metrics like post popularity, or engagement. It could also be driven by basic user data metrics, such as favorited categories or time on page. The same types of data drive most shopping personalization engines.
If we were to individualize that same recommendation engine, however, our focus would be on more intimate, user-specific data. Almost every activity that people undertake at work or at home has a digital input these days. If messaging is individualized based on user behavior on social media, mobile apps and connected devices, rather than only data collected on site, the relevance and value of machine learning-driven recommendations increase exponentially.
Where personalization actually requires relatively little data, individualization is only possible by combining data of various types, collected from a much wider range of sources. Fortunately, we’re doing business in an age of advanced machine learning and AI systems, which brings these marketing pipe dreams firmly into the realm of reality.
Privacy and its Place in the Personalization vs Individualization Debate
Individualization and its implications may seem like an overstep to some marketers. After all, we are talking about using increasingly personal data to guide marketing messages. Consider this though; consumers have come to expect personalization as fair-trade for the collection of their behavioral data. Indeed, Cloud-IQ’s latest consumer report shows that 64% of consumers acknowledge this as a reality of shopping in the online world.
So, if consumers will accept you tracking them around the web and gathering personal behavioral data, so long as you use that data to create better experiences for them, then you best make sure that your business is fulfilling its end of the bargain. You can consider creating a truly individualized shopping or content experience repayment for privilege of access to all that juicy user data.
Talk to Me and Not to Us
Individualization truly is redefining the global marketing landscape. Marketers now have access to user data of unprecedented detail, scale and quality, and failing to use that data to drive marketing strategy virtually guarantees your inability to compete in a crowded marketplace.
As the ‘internet of things‘ becomes even more ubiquitous, this data will only become more valuable and more detailed. Marketers who have already made individualization a foundational aspect of their strategies will be in a position to create ever more efficient, valuable and engaging experiences for their audiences.
Join the innovators who are using artificial intelligence in individualized marketing and add powerful and versatile machine learning and AI features to your marketing toolset. Liftigniter offers a range of solutions that can help you make individualization accessible to your marketing team.