Data and Personalization
Marketing personalization is becoming not just a competitive advantage, but something that users expect at each stage of the buyer’s journey. It ensures that your prospects’ experience is useful, relevant, and enjoyable.
With an abundance of personalization possibilities, however, things start to get a little complex: how do we figure out what each customer needs and how do we tailor our campaigns around them? That’s where AI-powered data analytics and real-time testing come in.
As users and search engines become more sensitive to data privacy, first-party data is arguably the most valuable asset brands can leverage in order to build great personalized experiences.
Intellimize is a company in that niche. Their machine learning solution runs multiple experiments at the same time, while also automatically selecting the best ones for each individual customer throughout their buyer’s journey. Chris Newton, the Vice President of marketing and business development at Intellimize, explains:
“This is about exponentially more experimentation and personalization, at an unprecedented scale. You’ve handed so much of the work and control to the machine, but you’re still coming up with the ideas that you want to test and personalize on your site. As it learns from each individual visitor on the website, the machine gets better and better at serving up the right personalized experience for that individual.”
While leveraging website personalization is no guarantee that you will engage every single prospect, using customer data the right way will help you gain valuable insights into what your audience wants and the most effective ways to reach them.
Going Beyond A/B Testing: Optimizing Your Customer Journeys
A/B testing is a powerful tool. Fundamentally, however, traditional A/B testing has two major flaws – selecting a winner is a time-consuming task, and half of your visitors will see the worse variant until the winner has been selected.
Performing multivariant A/B tests with AI and machine learning, on the other hand, allows you to test new variations, as well as optimize the ones you show to users based on past performance. This offers more room for experimentation and personalization, simplifies work, and requires less manual tuning.
Using machine learning personalization solutions allows marketers to kick start the analysis from the first visitor that comes to the website, see what has been presented and how the user interacted. For most SaaS brands, 30K unique monthly visitors is a good starting point for this approach.
As the number of visitors grows, the algorithm gets better and better, allowing you to see exactly what works best for a certain type of user and, ultimately, use this data to optimize their journey.
How to Get Started With Personalizing the User Journey
In essence, the journey personalization should construct the path to conversion with the least amount of friction.
To get started, you could look at different types of behavioral and contextual data, such as what pages visitors have been to and what kind of platform they are using. Newton shares:
“Sometimes there’s demographic or firmographic information that’s available to you. We see this a lot with the B2B customers that we have – you may know the intent signal or a type of technology that’s in use on their site.
Those signals will clue the machine in and it’s going to present certain types of experiences on the website that are going to work for that person to drive the conversion.”
Exploring the most likely flows through the website – from the point where visitors enter the site to the point they leave – can be eminently useful for showing you the steps that people who converted took along their journey.
Finding commonalities can also give you insights into where customers tend to drop off, so you can focus on improving your content.
To illustrate this with an example: Let’s say you have an e-commerce site and you’ve got people coming in to the homepage and getting to the right category page, but not getting to the product page or adding items to the cart.
So, what can you do to optimize those category pages? How to nudge people to either move items directly to the cart or get them to a more detailed product page that will then get a cart conversion?
According to Chris from Intellimize, “it’s the view of that overall user journey and where the gaps and the drop-offs are that really help us identify what sort of experimentation and personalization we ought to be considering for that particular site.”
User Journey Personalization and Advertising Touchpoints
When a page is intercepted at the time that it’s being presented to the visitor, the machine learning algorithm may determine that one or more customized personalized experiences should be run on the page based on the data that’s available in the browser.
The data can come from a number of different sources, including ads that have the right URL or UTM parameters. This data helps shed light on what type of message people saw and on what channel before they landed on your page. The machine can then utilize this information to ensure consistent messaging on the website or landing page. Chris from Intellimize adds:“If you’re on a particular Marketo list, you’ve been put on that list because of past engagement and other advertising campaigns (e.g. prior downloads on the site) – that’s information that we can make available to drive some of the personalization on the site.”
Personalization and Marketing Automation
Personalization and automation enable you to efficiently reach leads across your audience at the right time and with the right content.
That said, they are only effective when they are well balanced. Too much automation can make your business seem inhuman, driving the customer to lose interest in your campaigns. On the other hand, too much personalization can feel invasive and overwhelming.
A way to balance personalization and automation is to feed leads’ information into a system like Intellimize and adapt the content that they see in the journey based on how “warm” the lead is. Vial solutions may include chatbots that respond to simple queries and FAQs, as well as more nuanced live chat features that enable human communication.
Essentially, providing the machine learning tool with your hot lead list will enable it to decide whether prospects behave differently or whether there’s something else that they should be seeing.
This way, when the next visitor from that list comes in, you’ll be ready to go with a better answer.
Leveraging Personalization in Account-Based Marketing
In simplest terms, account-based marketing (ABM) is a focused growth strategy in which the marketing and sales teams join forces to create personalized buying experiences for high-value accounts.
ABM requires marketers to personalize everything for each targeted account, from content and product information to communications and campaigns. Chris Newton from Intellimize shares how personalization with AI-powered A/B testing can benefit ABM:
“ABM campaigns are traditionally very integrated and multi-element, so there might be emails, direct mail, and targeted advertising. They [the leads] may be getting emails from BDRs and SDRs on your team. The website [personalization] offers one more opportunity, one more channel to integrate with those messages that they’re seeing in these other locations and maintain that brand consistency and messaging for that customer.”
Because ABM relies on individually targeted marketing, efficiently utilizing structured and unstructured data is crucial. AI-driven tools offer a way to move from simple reporting and descriptive analytics to predictive analytics, as well as interpret unstructured data in an effective manner.
Start Creating Seamless User Journeys
Next-generation analytics powered by machine learning and data science can give marketers remarkable insights into the visual, textual, and behavioral cues that are driving prospects to convert.
At the end of the day, using these insights to create smoother, more personalized user journeys can enrich the customer experience, leading to increased revenue, greater customer satisfaction, and long-term loyalty. And that’s not all – see more thoughts on the future of digital marketers in the era of AI automation.
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