Visuel.ly's approach

Last updated on 03/04/2024

Visuel.ly's approach

Why it works

There are two key reasons why Visuel.ly's approach is effective

  1. No need for behavioral data: Unlike many platforms, Visuel.ly doesn't depend on behavioral data to personalize experiences. This enables marketers to tailor content even for first-time, anonymous visitors who don't have any prior behavioral data.

  2. Dynamic personalized stories: Visuel.ly can narrate a completely different story on the fly, based on a user's preferences. This deeper level of personalization is something current personalization platforms struggle to achieve.

How it works

Visuel.ly's personalization mirrors real-life interactions. Instead of guessing user preferences, it directly asks users for their interests or personal information. This input is then used to present a relevant story or part of a story. There are three primary methods to gain insights into user's preferences and personalize stories with Visuel.ly:

  1. Branch Action: The simplest method. This action alters the story's flow based on user interaction. Instead of proceeding to the next scene, the user is directed to a different scene based on their choice. For example, you can ask users if they are new or returning visitors, and guide them to different scenes within the story based on their response.

  2. Story Action: This method involves asking users about their interests, such as specific product categories or topics like "leadership profiles" or "technology innovation." Based on their selection, a completely new story tailored to their interests is presented.

  3. Scene Filters: The most advanced method of personalization. This involves labeling scenes into categories (e.g., cosmetics, Invisalign, cavities) and asking users to choose a category. The "filter" branch logic is applied to display only the scenes with labels that match the user's selected category.

These techniques overcome two significant challenges of traditional personalization: the reliance on inferring preferences from visitor behavior and the inability to deliver completely distinct personalized content.