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How Jennifer Aniston’s LolaVie brand grew sales 40% with CTV ads
For its first CTV campaign, Jennifer Aniston’s DTC haircare brand LolaVie had a few non-negotiables. The campaign had to be simple. It had to demonstrate measurable impact. And it had to be full-funnel.
LolaVie used Roku Ads Manager to test and optimize creatives — reaching millions of potential customers at all stages of their purchase journeys. Roku Ads Manager helped the brand convey LolaVie’s playful voice while helping drive omnichannel sales across both ecommerce and retail touchpoints.
The campaign included an Action Ad overlay that let viewers shop directly from their TVs by clicking OK on their Roku remote. This guided them to the website to buy LolaVie products.
Discover how Roku Ads Manager helped LolaVie drive big sales and customer growth with self-serve TV ads.
The DTC beauty category is crowded. To break through, Jennifer Aniston’s brand LolaVie, worked with Roku Ads Manager to easily set up, test, and optimize CTV ad creatives. The campaign helped drive a big lift in sales and customer growth, helping LolaVie break through in the crowded beauty category.
Meta is offering lower prices for users of its Muse Spark AI model when they agree to share prompts and model outputs for future model development. The model is designed for coding agents and other task-based AI systems.
Under Meta’s standard pricing, one million input tokens cost $1.25 and one million output tokens cost $4.25. Under its contributor pricing tier, input tokens cost $0.10 per million and output tokens cost $0.20 per million. The reduction averages around 95%.
The pricing structure makes the tradeoff direct. Users receive cheaper access to the model. Meta receives data showing how people use agents in real tasks, including prompts, outputs, corrections, and repeated task patterns.
For AI agents, this information has value beyond ordinary training data. Agent systems must learn how users describe work, which tools they need, where outputs fail, and when a person must step in. Public benchmarks do not fully capture those conditions.
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This also explains why data collection has become a difficult issue for AI companies. Coding workflows, internal documents, customer records, financial data, and operational plans can contain sensitive material. Companies may accept lower prices for low-risk experiments, but the same decision becomes harder when prompts include confidential information.
Meta’s pricing guidance says its contributor tier is intended for prototyping, testing integrations, and scaling experiments where training on user data is acceptable. This suggests that the model may be better suited to public, synthetic, or carefully screened data than internal business workflows.
The split between contributor plans and enterprise plans may become more common. Some users may choose lower costs and allow selected data to be used for training. Others may pay more for stronger data retention controls and internal governance.
The issue is not only the size of the discount. A company needs to know what enters an agent workflow, who can access that data, how long it is retained, and whether the data can later influence future models.
Meta’s approach treats workflow data as something with a measurable price. It may reduce access costs for some users, while making data classification and internal review more important for everyone else.
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