Demographics have generally always been the go-to statistic when targeting and segmenting specific audiences. Age, gender, education, working or financial status are very often seen as a key starting point for profiling customer segments. But, there is a fundamental problem with this approach. It makes a considerable number of assumptions which don’t reveal anything of value about actual buyer behavior.
Perhaps this is why so many attempts at personalization fail dismally. It’s foolish for brands to assume that every working mom aged 35 to 45 has the same needs or interests - as an example. There may be a few isolated similarities in what they prioritize being busy women juggling multiple responsibilities, but even that provides little insight into what type of digital experiences will connect with them and get them to engage and buy.
Demographics are too general in nature to make any meaningful contribution to personalizing customer experiences (CX). This is why marketers should not be overly concerned about the phasing out of third-party cookies which are the primary source of demographics. If brands are serious about getting personalization right, they need to be formulating strategies based on the right kind of data. Data that shows how visitors are navigating a website and responding to ideas, and how this changes over time or between visitors. Understanding a visitor’s intent is far more powerful to deliver a personalized experience that leads to success.
Navigating the complexities of personalization
Perhaps one of the reasons marketers love demographics is that it places customer segments in boxes and makes it easier to market to them because it assumes they all want the same thing. It’s exactly this approach that’s making customers demand greater personalization. They are tired of receiving irrelevant marketing messages and having to spend time searching for what they want.
Recognizing the power that they have, customers now expect brands to serve them by delivering better shopping experiences. A major challenge in trying to achieve this is that individuals have different preferences that change over time. This means that the customer experience that will connect with them is almost never the same. It’s like trying to catch a ping pong ball in the surf, as soon as you get close to it, it gets pulled in a different direction. It never stays in the same place and you never quite know where it’s going to go next. It could get dragged below the waves or washed up on the beach due to the constant movement of the water. (If you want to know where we’re going with this analogy read our previous blog: What Does the Ocean and CX have in Common )
AI is the solution in the face of complex problems
As humans we struggle to keep up with the many changing variables, fortunately technologies such as artificial intelligence (AI) and machine learning (ML) thrive on such complexity. The more data, the more variables, and the more possible combinations, the more efficiently AI delivers. It’s this feature that makes it ideally suited to optimizing CX.
AI can also learn from real visitors and start to see patterns which directly translate to intent. These patterns are ideally suited to serve experiences that will better resonate with visitors. With AI you have the opportunity to see a new visitor and identify where they are starting the journey and where they intend to go. That information is far more valuable than demographics as it can be used to predict what that visitor’s journey should look like. The key here is that the journey for that new visitor will be based on what other visitors with similar intent have successfully completed.
How AI-driven optimization delivers greater personalization
Without needing any customer demographics, AI-driven optimization can track visitor behavior on a website and serve up experiences in response to their browsing behavior, even going as far as to flag and respond to possible points of friction.
For example: If a visitor seems to be taking longer than usual to progress through the customer journey, it could prompt a customer service chat window to assist. This just-in-time intervention gives the visitor an opportunity to get questions answered and move forward with their purchase.
Another way in which personalization can be achieved with AI-driven optimization is through post-click optimization. Often a challenge with landing pages is that leads are sourced from a variety of different ads. It’s not really possible to personalize the content unless you’re able to track where the visitor came from. AI-driven optimization can do this.
For example: a travel company may have different ads running, one for all inclusive vacations, another for beach stays, and yet another for family holidays. By knowing which advert the visitor clicked on to get to the landing page, the travel company can serve up information detailing specifics on all-inclusive holidays, which is more personalized for the visitor and therefore more likely to get them to move further in the customer journey.
Personalization is also possible based on an individual's account details and prior purchasing history. If a visitor is an existing customer who is due for a mobile phone upgrade, an optimal experience should include options that they qualify for. Or it could showcase the latest technology if data shows that they commonly upgrade as soon as a new model becomes available. This type of personalization requires no demographic data and can significantly improve the customer lifetime value (CLV) of that customer.
Improving CX is an integral part of personalization because it helps brands connect better with their customers and respond more quickly to changing preferences and needs. This complex process is made much easier with AI-driven optimization that can serve up thousands of unique experiences to website visitors and customers while keeping pace as their buying behavior changes over time.
