Best Practices for Driving Youtube Sales With Trueview for Action Ads
The TrueView for Action advertising format on YouTube is one of the most effective for developing lower-funnel strategies on this popular online video platform which already has 2 billion active users worldwide, according to figures from Statista . Unlike YouTube advertising which aims to generate awareness and encourage brand memories, TrueView for Action Honduras WhatsApp Number List campaigns aim to elicit a response from the user , take them to action and take action. to advance in the customer journey . TrueView for Action ads can be configured for two types of purposes: driving traffic to the website or capturing sales opportunities.
These can take the form of requests for information via a form, subscriptions to newsletters, downloads of gated content, inclusion of an item in the basket or, directly, the purchase of a product. . However, to get the most out of TrueView for Action, you need to do the right job of optimizing video campaigns . At Labelium, we have condensed in this best practices guide the most appropriate tactics to achieve the best results with the TRV4A format on YouTube . It focuses on three elements: message, audience selection and auction strategy.
Effective Messages for Trueview for Action Ads Trueview for Action
ads are in-stream videos that play before, in the middle, or after viewing content. Static clickable elements are superimposed on the video: a title, a call to action, a final screen and, optionally, links to sites. The secret to good creativity for TRV4A can be summed up in two words: keep it simple. And by simple, we don’t mean “without substance”, but short and to the point. By combining the available resources, advertising must be able to connect with the receiver from the first seconds , arousing in him surprise, intrigue or curiosity. On the other hand, the message must be clear : we must tell the user what we expect from them, whether it is a purchase, request or registration.
Audience segmentation on YouTube’s TrueView for Action The second pillar of TrueView for Action ad optimization on YouTube is audience segmentation that takes advantage of the vast amount of added data that Google accumulates. Defining audiences based on demographic information alone is out of the question: Google research has shown that YouTube campaigns using advanced audiences achieve exceptional KPIs. For example, in the sample analyzed in this research, in campaigns in the financial sector, the memory left by advertising was multiplied by 1.5, while in the retail sector, purchase intention increased. been multiplied by 1.4. The advanced audiences that tend to provide the best results for the TRV4A format are: Custom Intent.
People Who Use Certain Keywords in Their Google Searches
The crucial point is to include the keywords that denote an intention to purchase the product or service offered by the brand. Custom Affinity. Users who are interested in specific subjects, such as gastronomy, sport, crafts … This selection can be further refined by choosing specific groups (for example: vegetarians, runners, scrapbooking enthusiasts) according to the channels they follow and content they watch. Remarketing. People who have already interacted with the brand, but not yet driving conversion. Con la opción Custom Affinity, el anuncio TrueView for Action se puede orientar a audiencias interesadas en temas de belleza o moda
Bid Optimization in TrueView for Action Ads In TrueView for Action, the bidding strategy can be defined based on two criteria: maximizing conversions with the given daily budget or sticking to a target CPA (cost per acquisition) . Normally, the best approach, especially if the optimal CPA for the chosen target (tCPA) is not known, is to start with a conversion maximization approach . Thus, as the Hagakure method for Google Ads advocates , smart bidding can work and the advantages of YouTube’s machine learning algorithm are exploited. The campaign will gain in volume and strength and will have a history that will serve as the basis for the first optimization decisions.