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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.
Stop Worshipping Dashboards
Let’s start with an uncomfortable truth: most product managers don’t use data, they decorate with it.
Your dashboards are probably full of DAUs, bounce rates, and NPS scores that look impressive in a slide deck but rarely change your roadmap. These are vanity metrics, and they give you the illusion of control while masking what really matters.
If you’re building or managing a platform-based product, this addiction to shallow metrics is not just a waste of time; it’s dangerous. Platforms live and die by network effects, and you can’t afford to get fooled by numbers that don’t drive action.
Story: The Checkout That Killed Growth
A retail platform I advised once obsessed over page views and time on site. The team proudly showed charts climbing upward. But churn kept creeping higher. Why?
Because no one noticed the checkout flow was leaking 40% of transactions. Customers abandoned carts because the form was clunky.
The analytics team wasn’t blind; they had the data. But the product manager was so focused on “engagement metrics” that the signal got buried under noise.
When we reoriented around conversion rate, retention cohorts, and LTV/CAC, the real issues surfaced. Fixing checkout drove a 22% revenue lift in a single quarter.
Lesson: Dashboards don’t win. Metrics that force decisions win.
The Framework: 3 Tiers of Metrics
Here’s the lens I now give every PM:
✅ Decision-Driving Metrics
Conversion Rate, Retention Cohorts, LTV/CAC, Feature Adoption, Churn
If they move, your roadmap changes.
⚠ Contextual Metrics
DAUs, MAUs, Bounce Rate, Session Time, NPS
Helpful for hypotheses, but not sufficient for prioritisation.
❌ Vanity Metrics
Downloads, Page Views, Likes, Signups with no engagement
Looks good on slides. Worthless in strategy.
👉 Free Resource: Download the PM Data Decision Matrix (Excel)
Data Without Insight Is Just Noise
Collecting data is easy. Turning it into insight is hard.
Descriptive analytics → what happened.
Diagnostic analytics → why it happened.
Predictive analytics → what’s likely to happen next.
Prescriptive analytics → what you should do about it.
Most PMs never move past “descriptive.” That’s like driving your car by staring only at the rear-view mirror.
Platforms demand more. They live on fragile balances of supply and demand, network growth, and retention loops. That requires predictive and prescriptive thinking, analytics that change decisions today, not just explain yesterday.
The PM vs. The Analytics Manager
Here’s the distinction too many teams blur:
Product Manager → Owns the “why” and the “what.” Defines strategy, trade-offs, and roadmap.
Product Analytics Manager → Owns the “how much” and the “why now.” Extracts insights from data, validates assumptions, and kills bad hypotheses fast.
When these roles clash, products stall. When they collaborate, products soar.
Future-Proofing: AI, Bias, and Data Responsibility
By 2030, you won’t be running SQL queries anymore. AI will surface insights, prioritise features, and even flag anomalies before they happen.
But here’s the catch: AI can amplify bias as easily as it amplifies insight. Just because you can measure everything doesn’t mean you should. Responsible PMs will be judged not just on what they build, but on what they choose not to measure.
Data is not the goal. Decisions are.
If your analytics don’t lead to a roadmap trade-off, they’re just noise. Great product managers are ruthless about this. They don’t chase vanity metrics; they weaponise decision-driving ones.
So here’s the challenge:
Strip your dashboards to the bone.
Keep only the metrics that force action.
Make every number earn its place.
💡 Want a shortcut? Download the free PM Data Decision Matrix (Excel) and start filtering signal from noise today.
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