Meta Wants Better Buyers

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Ready for another day of staying ahead of the competition in the Growth race?

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In Partnership with Levanta

Your Affiliate Roster Should Make You More Money

A roster with hundreds of creators looks healthy until you check the sales.

If fewer than 10% have converted in 90 days and no new partners joined last month, the program is quietly losing revenue every week.

Levanta shows you what is stalling growth and which partners can move products, so more budget reaches relationships producing measurable returns.

• REVO generated $1M+ in affiliate-driven sales, with 96% coming from new-to-brand customers.
• Grace & Stella added $350K+ in affiliate revenue while its Amazon sales rank climbed from #520 to #28.
• Acquco grew monthly affiliate sales from $1,500 to $36,000 in four months after adding 130+ creator partnerships.

You get a growing roster and clear visibility into which relationships drive sales. More converting external traffic can also help your products climb Amazon’s rankings.

Levanta’s self-diagnostic reveals which gaps are holding back your program and where your next growth opportunity may be hiding.

💡 How Meta's pLTV Finds Higher-Value Customers

Not every conversion is equally valuable.

Meta's Predicted Lifetime Value (pLTV) helps advertisers optimize for customers who are likely to generate the most long-term revenue, not just the fastest conversion.

Here's how it works.

1️⃣ Optimize For Customer Value Instead of treating every conversion equally, Meta uses your predicted lifetime value data to prioritize people who are expected to become your most valuable customers.

2️⃣ Your Data Powers The Model You assign predicted values to a conversion event, such as a free trial or registration, and send those values to Meta within seven days. Meta then uses your predictions to improve ad delivery.

3️⃣ Meet The Technical Requirements You'll need a working Conversions API integration, a validated pLTV model, and at least 100 attributed conversion events per week for four consecutive weeks before using the feature.

4️⃣ Use Multiple Value Levels Your model must include at least five different predicted values, with the highest being at least three times greater than the lowest. Simple "good lead" versus "bad lead" scoring won't qualify.

5️⃣ Best For High-Value Businesses pLTV works best for businesses with longer sales cycles, lead generation funnels, subscriptions, or products where customer value varies significantly over time.

Why It Works

Instead of optimizing for immediate conversions alone, Meta learns which prospects are most likely to become high-value customers, helping advertisers improve long-term return on ad spend.

The Takeaway

If your business can accurately predict customer value, Meta's pLTV lets you optimize for profitable relationships rather than just conversions, making every advertising dollar work harder over the long run.

💡 Turn Figma Mockups Into Production-Ready UI With ChatGPT

Building interfaces from Figma no longer has to be a manual process.

With the Figma plugin in ChatGPT Work, you can convert designs into responsive UI while reusing your existing codebase and components.

Here's how to do it.

1️⃣ Connect The Figma Plugin Open ChatGPT, switch to the Work tab, then go to Plugins → Connect Plugins → Browse All Plugins and install the Figma plugin.

2️⃣ Open Your Project Open the project where you want to implement the design.

3️⃣ Select The Figma Frame In Figma, select the exact frame, screen, or component you want to build and copy the link to that specific selection.

Sample Prompt

Implement this Figma design in the current project using the @Figma plugin: [Insert Figma Link]. First, pull the design context, assets, variables, and screenshot for the exact frame. Inspect the existing repository and reuse its components, design tokens, typography, spacing, icons, routing, and styling patterns. Make the interface responsive on desktop and mobile. Once the first version is complete, run the project, open the live page, compare it visually with the original Figma frame, and continue correcting the layout, spacing, typography, sizing, colors, and interactions until they closely match. Do not install Tailwind or introduce a new styling system unless the repository already uses it.

4️⃣ Generate The Interface ChatGPT will analyze the selected design, implement it inside your project, and reuse your existing components, styling, and design system wherever possible.

5️⃣ Review And Refine Test the generated interface, then ask ChatGPT to fine-tune the layout, spacing, responsiveness, typography, or interactions until the implementation closely matches the original design.

Why It Works

Instead of rebuilding every screen manually, ChatGPT understands both your Figma design and your existing codebase, producing interfaces that are consistent with your project's architecture while dramatically reducing development time.

The Takeaway

The Figma plugin transforms ChatGPT into a design implementation assistant, helping you turn mockups into responsive, production-ready UI while preserving your existing design system and development workflow.

As we prepare more "Growthful" content, we'd love to hear your thoughts on today's edition! Feel free to share this with someone who would appreciate it. 🥰