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

Despite the promise that personalization brings, marketers are still having trouble cracking it’s code and the lack of personalization is starting to affect the brand experience. Nearly three out of four consumers are frustrated when content is not personalized to fit their needs.

Now, it’s possible to meet your customers’  needs based on the data from the context of their search behaviour and of other customers like them. 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, on average, a 19 percent increase in sales.

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.

How do marketers overcome this challenge? Site search and machine learning.

The Role of Site Search in Personalized Customer Experiences

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

If you cannot answer this, you are not listening to your visitors. You’re missing out on crucial insights on how to personalize content to the needs of key personas, as well as opportunities to deliver that targeted messaging.

Creating personalized customer experiences with site search should be one of the first steps on your personalization roadmap because it solves the major challenge with getting started: not enough data.

Without site search personalization, tagging content to personas is initially an estimate. Conversion rates on landing pages, personalized emails and retargeting advertisements provide some clues, but it is a mistake to consider the insights from those initiatives to automatically translate to your website visitor personas.

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 site search personalization, 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

Site search delivers personalized content, but it still requires the intervention of the IT department to manually analyze and automate the relevance of your search engine. With traditional site search, the issue of scale still remains.

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, a new limited-edition keyboard becomes the most popular one month. The IT department will have to go in and manually adjust the results.

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 in many cases. By synthesizing your users’ context, online behavior and query with your usage analytics, machine learning automatically personalizes your search 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.

Find out more about how Coveo personalizes your site search with machine learning by downloading our checklist “Mastering Micro-moments That Build Customer Journeys.”


About Rachel Schultz

Rachel Schultz is the Content Marketing Manager at Coveo. She blends her background in journalism seamlessly with her B2B marketing expertise and obsession with data to create compelling content for the Coveo community. When she’s not working, you’ll find her reading (strictly non-fiction), hanging out with her puppy or taking in all NYC has to offer with her husband.

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