The Objective of the Landing Page Personalization for Ads
Improve the relevance of a landing page to a display or social ad campaign.
How to Personalize Landing Pages for Ads
Landing pages are tailored to their campaign audience and use message matching to reassure customers that they've arrived at the right place.
Personalization can be used to push more relevant content after the initial engagement. This will pique the interest of visitors who have been referred by display ads. Change the text on your key pages, for example, to emphasize why a specific influencer recommends your product.
Effects of Landing Page Personalization
Personalizing the landing pages with ads message and images increases engagement and conversion rate.
Personalizing the Landing Page of SumUp Based on Display Ads
SumUp is a leading financial technology company with 32 markets across three continents. SumUp card readers are used by over 2 million merchants worldwide, enabling business owners to accept card payments in-store, in-app, and online in a simple, secure, and cost-effective manner.
In websites with a large number of components, such as Sumup, it is critical to start with simple cases and iterate. Through continuous learning and experimentation, this strategy enables various teams (for example, content creators, growth, product management, or development) to create meaningful personalization experiences.
The Sumup product team can implement the Ninetailed Next.js SDK in just one sprint (coding time of a few minutes) and become familiarized with the SDK and capabilities of the Ninetailed Personalization API in a first personalization sprint.
The growth team can create variants with Ninetailed and Contentful that match the content to social campaigns during the same sprint.
The growth team can analyze the personalization uplifts without the need for new tools or processes integrating Google Analytics. Ninetailed uses a holdout groups approach in which 90% of the audience visitors see the personalization variant and 10% see the original (control).
The outcome is that all teams can launch the first personalization in a single sprint and iterate based on the results and learnings.