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Overview

Urban Land Institute (ULI) is the world's oldest and largest network of cross-disciplinary real estate and land use experts. Previously, Velir redesigned ULI’s gated membership site, Knowledge Finder to make content more accessible to their members and easier for ULI to manage. We also configured more robust analytics for Knowledge Finder to track member actions, so ULI could better tailor their content to member interests. Since then, ULI asked us to take Knowledge Finder’s member engagement to the next level by helping them deploy advanced personalization.

URL

knowledge.uli.org

Challenge

When Velir redesigned Knowledge Finder, we collaborated with ULI to reduce the site’s scope so they could quickly launch a minimum viable product (MVP). ULI had ideas for expanding the site and increasing member engagement through initiatives like personalization, but those were slated for future iterations.

We launched Knowledge Finder with basic personalization like geotargeting that displayed the correct member support number based on where the user accessed the site. Then after launch, we traveled to the client’s headquarters in Washington, D.C. to discuss what ULI hoped to accomplish with personalization on Knowledge Finder and develop a plan for implementing those ideas with ULI’s technology stack, which included Sitecore, their content management system (CMS) and Coveo, their AI-powered site search provider.

ULI’s challenges included:

  • Expanding on the MVP version of Knowledge Finder with advanced personalization
  • Developing a strategy for personalization and a plan for implementing it
  • Leveraging the personalization capabilities of Sitecore and Coveo to their fullest

Approach

Collaborating with ULI, we developed a personalization plan with a few personalization paths we followed. One of those paths is a feature called “Trending Topics,” a line chart visualization on the Knowledge Finder homepage that shows the most visited topics in the past week. It allows users to compare to the previous week, to see if a topic is trending up or down. While not personalization per se, this feature displays information dynamically based on how members use the site and leverages data to show what members are most interested in.

The second path we explored was “Recommended Content,” a feature driven by user behavior. When a member visits a page, we pass their Sitecore user ID, the page’s ID, and information about the user — like their membership tier — to Coveo. Then Coveo’s machine learning recommendation engine collects that data and looks for patterns. Coveo uses the data to recommend content based on other users who have viewed similar pages. For example, if a user views pages A, B, and C and other users who viewed those same pages also view page D, Knowledge Finder would recommend page D to the user. These recommendations are displayed as a three-up component at the bottom of all pages.

Our third path to personalization was “For You,” a component based on interests that a member selects in their profile. Since the profile isn’t managed in Sitecore, we built a connection from Sitecore to ULI’s account management system (AMS) to sync data for each user. There is a mapping between user browsing interests and the topics they say they’re interested in. The “For You” component displays pages with the most overlap between the two areas and that have been published most recently. We ported this functionality to Knowledge Finder newsletters, which show a “For You” component with similar logic. We created additional rules to ensure we don’t send the same recommendations in two successive newsletters.

Finally, to encourage members to update their interests to receive recommendations tailored to those interests, we added personalization rules on the component to show a banner with a link to the interests page if members don’t have interests selected or haven’t updated them in over a year. There’s also a modal that appears on a member’s first visit to Knowledge Finder, prompting them to set up interests, and a different one in the bottom corner for subsequent visits to remind them if they still haven’t selected interests.

Our approach included:

  • Developing multiple paths to personalization and implementing them
  • Creating the “Trending Topics” visualization to show ULI members what topics other members are interested in to drive engagement with those topics
  • Building the “Recommended Content” component to suggest pages based on the ones other users with similar browsing habits visit
  • Crafting the “For You” component which recommends content based on explicitly defined user interests, and adapting this component for newsletters
  • Using personalization to encourage members to fill out their interests, which power further personalization

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Solution

Our collaboration with ULI helped them develop a strategy for personalization that effectively leverages their technology platforms — Sitecore, Coveo, and their AMS. We developed several advanced personalization features from “Trending Topics” to the “Recommended Content” and “For You” components that have significantly increased member engagement with Knowledge Finder. We also configured tracking to log if a member has updated their interests for personalization. And we receive impressions for personalization. These analytics demonstrate that members who received personalized content and updated their interests have increased engagement with the site. The analytics also indicate that their increased engagement provides a much higher lifetime value to ULI. Seeing the business impact of our personalization, ULI has asked us to finetune and roll out additional personalization efforts that continue today.

Results

  • In 2023, 147k Personalization Impressions were served.
  • Users who see five or more personalizations over their history compared to those who see less than five:
    • +30 seconds average engagement time per session
    • +1 more pages per session
    • +2-3 minutes average session duration
    • +$60 more in lifetime value
  • Users who updated their interests which allows further personalization vs those who don’t:
    • +26 seconds average engagement time per session
    • +32 seconds average session duration
    • +10% more return users
    • +$40 more in lifetime value

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