Targeted, precise messaging for every single website visitor – is it really possible?

Despite the promise that personalization brings, marketers are still having trouble cracking its code and the lack of personalization is starting to affect the brand experience. 42% of consumers get frustrated when content is not personalized to fit their needs.

Now, it’s possible to meet your customers’ needs based on the data of every one of their digital interactions, as well as the interactions of other customers like them, and personalize each experience accordingly. Once a customer has been identified as a specific persona by their online behavior, a series of targeted CTAs, dynamic landing pages and targeted messaging will ensure your content resonates with their pain points. Offering a relevant and personalized experience generates, anywhere from 6-10% additional revenue.

If you’re wondering why, with solid proof, marketers still aren’t delivering personalized customer experiences and benefiting from increased sales, the answer is: it is a lot of manual work. Even just identifying which persona to start with for personalizing content can be a daunting task. Personalization is not a one-and-done effort; as your visitor base grows and your personas evolve, providing the same degree of personalization for every visitor becomes a nearly impossible resource challenge to overcome. But yet we as consumers have become accustomed to intuitive and customized experiences, so we know it’s possible.

So, how are marketers delivering relevance at scale? By ditching much of the manual work and embracing AI-powered search and recommendations.

The Guide to Delivering Intelligent Self-Service

The Role of AI-Powered Search and Recommendations in Personalized Customer Experiences

What is the most common search query on your site this year for each of your key personas?

If you can’t answer this, you’re not listening to your visitors intently enough which means you’re missing out on crucial insights that would enable you to personalize your experience by delivering content that matches both the context and intent of your key personas. This also means that you’re missing opportunities to delight and convert your customers.

One of the biggest hurdles of personalization at scale is the ability to understand data and extract actionable insights. At a certain point, it becomes impossible to manage manually. Fortunately, with AI and machine learning, the days of manual data-crunching are long behind us. AI-powered search and recommendations are revolutionizing the way customers engage with brands and are yielding unprecedented results for marketers industry-wide.

Without AI-powered search in place, tagging your content to key personas is essentially a hunch, which is not a growth strategy I’d recommend. Conversion rates on landing pages, personalized emails and retargeting advertisements provide some clues, but you would be doing your organization a huge disservice if those were the only signals you were using to personalize your customer experience.

A travel website, for example, may discover there is more nuance to their family vacation planner persona. Some parents may view the vacation as the opportunity for children to have new and exciting experiences; others are more motivated by the opportunity to spend quality time together relaxing. Both could respond to an email for a family rate on a suite at a hotel with a rental car, but their searches will reveal the difference. The ‘experience planner’ will search for nearby activities outside of the hotel. The ‘relaxing planner’ would search for the amenities inside the hotel that fit into their desired vacation: which rooms have a jacuzzi and if there is a complimentary happy hour.

With AI-powered search and recommendations, powerful usage analytics gives the insight into what content gaps exist and what content leads to successful outcomes. Content authors build their content plan based on what the prospects are actually trying to find. Taking the time to fill in those gaps leads to more engagement from prospects and higher time-on-page. Running targeted A/B tests on what messages are helping to convert these search users into seeing a demo or downloading more gated content can also enable your sales team to make a more tailored pitch.

View every search query as your key persona telling you the content they need, and a vital opportunity to personalize that journey.

How Machine Learning Delivers Personalized Customer Experiences at Scale

Tagging content for appropriate personas can help enable personalization, but it still requires the intervention of the IT department to manually analyze and automate the relevance of your search engine.

Your content team will take the insights they receive from usage analytics to understand what content is still needed but there’s still a gap for improving the relevance of the results of the content that does exist. For example, if users search “Keyboard” on your technology manufacturer site, the first result should be the most popular keyboard. But, for example, if a new limited-edition keyboard becomes available for one month only, your IT department will have to go in and manually adjust the results to promote that keyboard.

Now imagine this happening with multiple product lines. Tuning for relevance becomes very time-consuming and a hard cost for website support. It is a myth that site search optimization is not worth this cost; site search users are more than 200 percent more likely to convert, according to research from WebLinc.

Machine learning is the solution to minimizing this cost without affecting the conversion rate, and even improving it. By synthesizing your users’ context, online behavior and query with your usage analytics, machine learning automatically personalizes your website experience.

In the travel website example, the IT team would not need to analyze and invest in tagging content for the different family vacation planner personas to deliver personalized customer experiences. By understanding their context, the machine learning will boost the results that more closely match their needs, and anticipate their needs with predictive recommendations. Once the database recognizes a family vacation planner that wants to relax, it can provide a personalized recommendation, for example, of a package for a spa or hot springs nearby. The ‘experience” planner’’ will receive recommendations for exciting family excursions, like whitewater rafting or a water park.

The Guide to Delivering Intelligent Self-Service