Mastering Personalization Strategy With Search

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Mastering Personalization Strategy With Search


The Gist

  • Effective personalization. Personalization strategies, like Amazon’s, leverage customer data and predictive analytics to deliver tailored recommendations, enhancing user engagement and loyalty.
  • Recommendation revolution. Recommendation engines extend beyond product suggestions, including personalized emails, landing pages, and reviews, integral to Amazon’s success in creating a cohesive shopping experience.
  • Composable success. Composable architecture facilitates seamless integration of personalization platforms with decision engines and search solutions, significantly improving user experiences.

When asked to give an example of an organization that gives great personalized experiences, most people will usually talk about Amazon. The ecommerce giant is renowned for its ability to provide users with highly customized recommendations based on their past purchases, browsing history and even real-time behavior through its recommendation engine.

Let’s take a look at how to build an effective personalization strategy.

For instance, if a customer frequently shops for electronics, Amazon’s recommendation engine will suggest new gadgets, accessories, and relevant product launches tailored to their interests. Moreover, Amazon’s recommendation engine extends to other aspects of the shopping journey, such as personalized email notifications, product reviews, and even personalized landing pages, creating a cohesive and tailored shopping experience that keeps users engaged and coming back for more.

Relate Article: 5 Tips for Improving Personalized Customer Experience

Problems Applying Amazon’s Personalization Strategy

To emulate the success of Amazon’s recommendation engine, numerous organizations initiate their personalized marketing endeavors with the aspirations of emulating Amazon’s renowned capacity for enhancing conversions, elevating customer satisfaction, and fostering unwavering customer loyalty, all of which have a direct impact on their bottom line. To realize these objectives, they frequently set out on the path of meticulously analyzing their customer journeys, crafting intricate customer personas, and consolidating their customer data. Nevertheless, the longevity of these personalization programs often poses a substantial challenge.

Many organizations find themselves wrestling with the ongoing pressure of consistently delivering personalized content and extracting valuable insights from the expanding pool of data analytics. Unfortunately, some overlook the need to view their personalization initiatives as a marathon rather than a series of brief sprints, ultimately impeding their ability to unlock the full potential of their efforts.

This common pitfall in personalization programs often overlooks the crux of Amazon’s personalization effectiveness: The utilization of efficient search-based recommendations. In fact, the majority of Amazon’s personalization strategy doesn’t hinge on marketing teams meticulously crafting custom copy for individual visitors or segments. Instead, they harness the power of  analytics with their extensive customer data to execute personalized queries, which, in turn, retrieve the most pertinent products and accessories tailored to each user’s unique preferences and needs.

This approach is often prematurely disregarded in the early stages of a personalization program, particularly when the organization does not operate in the ecommerce sector or lacks a product catalog to offer recommendations. However, the realm of recommendations encompasses far more than just products. Instead, consider the various types of content at your disposal and how it can drive conversions on your site. It might involve suggesting events, local destinations, or even healthcare providers based on factors such as availability and proximity to the user. The possibilities for recommendation strategies are expansive, extending well beyond traditional ecommerce products.

Until recently, the integration of effective search and content recommendations into your personalization efforts posed a considerable challenge. However, with the advent of modern composable architecture, connecting personalization platforms with decisioning engines to search solutions has become notably more accessible through standardized APIs. While these search solutions traditionally support a website’s search feature, empowering visitors to search and filter content to their preferences, linking them to your personalization platform enables the generation of context-specific content recommendations across various aspects of your website experience. This integration can significantly enhance the overall user experience.

A "brass dog tag" necklace is stamped with the name "Susan" on a white cloth background in piece suggesting the need for a personalization strategy.
Until recently, the integration of effective search and content recommendations into your personalization efforts posed a considerable challenge.Matthew on Adobe. Stock Photos

Related Article: Redefining Personalization in Marketing: How AI Is Changing the Game

Composable Personalization and Decisioning Platforms

In one of my earlier articles, “Mastering Personalization in Digital Marketing Strategy,” I outlined an approach for conceptualizing your personalization strategy. By methodically considering the various stages and offers, and defining their specific requirements and limitations, you can establish a robust decisioning framework. This framework aids in determining the next best offer, effectively guiding your visitors toward the next phase of their digital journey.

This approach gains even greater potency when integrated with content recommendations driven by search technology. Instead of laboriously crafting copy to steer user experiences for a chosen offer, the strategy involves creating customized search queries that take into account the visitor’s behavior. These queries are designed to pinpoint the content most likely to resonate with the visitor and, consequently, increase the probability of successful conversion. The synergy between the decisioning framework and search-based content recommendations empowers organizations to deliver personalized and contextually relevant content at every stage of the visitor’s journey.

In the realm of implementing your personalization strategy, there’s a multitude of personalization engines available in the market, each with its own set of philosophies and methodologies regarding data management, decision-making, and content delivery. The more advanced platforms, however, adopt a composable approach. These platforms offer clear and adaptable integration methods, allowing organizations to seamlessly incorporate content recommendations from external search platforms. This harmonious integration empowers these personalization engines to create more sophisticated, data-driven, and contextually relevant user experiences that drive higher engagement and conversions.

I have firsthand experience with Sitecore’s composable personalization platform, Sitecore Personalize. This platform offers a powerful decisioning capability, enabling seamless integration with third-party services. When employed to integrate search solutions that support open APIs, it simplifies the complexities of the underlying integration. Marketers can then easily craft their own targeted queries, review the results, and seamlessly incorporate them into user experiences presented across various channels. This streamlines the process and empowers marketers to harness the full potential of search-driven content recommendations without the burden of intricate technical details.

Going Beyond Basic Search

The crux of making this personalization approach effective lies in the choice of the underlying search platform and its capability to curate and deliver the most fitting content for the specific touchpoint and channel where you engage with your customers. Many organizations will already have a search platform in place, primarily catering to the website’s search function. While this can certainly address basic use cases, it can fall short of harnessing the full potential of what we know about individual visitors.

Advanced search platforms go a step further by harnessing the power of tracking data and machine learning. These platforms have the capacity to generate content recommendations that are exceptionally well-suited to each visitor’s preferences and behavior. Moreover, they can delve into the collective search patterns of multiple visitors and segment them into “look-alike” audiences.



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