Tag: Artificial Intelligence

Meta Ads Gets New AI Tools to Improve Conversions

June 5, 2025 Content Marketing disabled comments

Meta New Tools for Conversions

Image from Meta’s Media Briefing

Meta’s advertising platform just got an intelligence upgrade. In a closed-door APAC briefing, Shefali Srinivas, Head of Business Communications, and Damian Kim, Managing Director and Head of Product, explained how Meta is rebuilding its entire ads ecosystem to make AI do more than just automatic placements.

This isn’t just a change in tools, but a change in how Meta’s ads system learns. Meta now adapts to what each advertiser defines as success — whether that’s a bigger cart size, a higher profit margin, or a new customer instead of a repeat one.

But to get that performance, advertisers need to feed it the right signals. The more value you define, the smarter it becomes.

How AI Will Run the Show in Future

Image from Meta’s Media Briefing

Meta’s new ads setup runs on four interlinked systems, each handling a part of the learning and optimisation process. (You can read their official blog for more.)

#1 Meta GEM

This engine processes advertiser-provided data. It looks at value signals like purchase amounts, add-to-cart events, or customer profitability. It then uses that to make better predictions about which users are most likely to take meaningful actions. This makes campaign recommendations more aligned with your bottom line.

#2 Meta Lattice

This is the link layer. It connects models across different parts of the platform — Reels, Feed, Stories, Shops — and allows them to share information. That way, if a user adds a product to cart on Facebook but finishes the purchase on Instagram, the system treats it as one customer journey, not separate actions. This helps Meta allocate budget where it actually drives value, not where it merely captures attention.

#3 Meta Andromeda

This model predicts user preferences based on behaviour. It learns what users actually like, not just what they’ve clicked on before. That means it can predict future intent, and deliver ads that anticipate what the user may want to see — especially important for mid-funnel discovery on formats like Reels.

#4 Sequence Learning

This AI system builds temporal context. It doesn’t just look at what users do, but when and in what order. If a customer tends to browse before bed, add to cart the next morning, and buy in the afternoon, Meta will learn to surface your ads in sync with that rhythm. It also compares sequences between exposed and unexposed groups to calculate incremental value.

Each of these models feeds the next, forming a closed feedback loop where the system gets smarter with every campaign — as long as advertisers provide clear goals and rich data.

Why This Is Bigger Than Conversions

Apparently, the old model of Meta Ads rewarded basic outcomes. Clicks. Impressions. Add-to-carts. Or even leads.

That worked for simple campaigns, but it often missed the bigger picture of conversions and (yes), sales!

Meta’s new approach is more responsive. Rather than treat all conversions the same way, it now listens to what your business truly values.

Image from Meta’s Media Briefing

With its new AI capabilities, Meta’s Ads Manager system is able to prioritise the right outcomes — whether that’s higher-margin sales, larger baskets, or customer retention.

Three new tools are driving this shift: Value Rules, Value Optimisation, and Incremental Attribution. Let’s look at what they do here.

Value Rules Lets You Control Who Matters Most

Advertisers can now guide Meta’s optimisation with Value Rules. This feature lets you assign higher value to specific customer groups, geographies, or devices. If a customer segment generates higher revenue or is more likely to repeat purchases, you can instruct Meta to prioritise them in the bidding process.

Quoting from their website: “With value rules, you can:

  • Create rules to tell us how much more certain audiences are worth to your business. Our system will optimise for outcomes based on these rules.
  • Maximise performance by focusing on the audiences that matter most.
  • Drive towards lower-funnel goals such as lifetime value by prioritising bidding in high LTV segments.”

Previously, advertisers in China and Southeast Asia hacked this manually by duplicating ad sets with multipliers. Now, the system does it natively — and more efficiently.

Advertisers using this have reported up to 5 percent improvements in performance, especially when rules are tied to margin tiers or new-customer acquisition goals.

Value Optimisation Focuses on What You Actually Earn

Image from Meta’s Media Briefing

Traditional optimisation targets things like “clicks” or “purchases,” but not all conversions are equally valuable.

Value Optimisation changes that. Advertisers can now input purchase values, product tiers, or even profit margins. Meta’s AI then uses that data to maximise total value created rather than just count conversion volume.

For instance, if a $200 sale yields more profit than three $20 sales, the system will learn to prefer the former — even if the latter converts more often. This is especially powerful for ecommerce brands with wide SKU ranges or tiered pricing.

Early results show a 37 percent uplift in ROAS when advertisers switched from standard conversion optimisation to value-driven models.

Incremental Attribution Gives Credit Where It’s Due

Image from Meta’s Media Briefing

Advertisers have long struggled to answer this question: Did the ad really cause the sale, or would it have happened anyway?

Incremental Attribution now answers that using a test-vs-control structure. Meta creates a statistically sound control group that sees no ads, and compares it to an exposed group that follows the normal campaign flow. The difference in conversion outcomes between these groups is your incremental lift.

This changes how performance is measured. The goal is to find a way to fairly attribute the role that social media advertising plays in conversions, rather than depend on tools like Google Analytics.

In a recent case study (see below), Laura Geller Beauty used this feature and found that 27 percent of sales driven by Meta were wrongly being credited to paid search, due to last-click models. By adjusting based on true lift, they halved their search budget and improved overall return by 46 percent.

Image from Meta’s Media Briefing

Conversion Lift Adds New Signal to Reports

Image from Meta’s Media Briefing

Building on Incremental Attribution, Conversion Lift lets advertisers measure impact more accurately, especially in top-funnel and mid-funnel campaigns. This is critical for Reels and discovery formats, which often assist rather than close conversions.

Conversion Lift tests whether users who saw your ad were more likely to convert later — even if the conversion happened via search or direct site visits. It helps validate spend on channels that are part of the customer journey but don’t get last-click credit.

Meta is rolling this out more widely to smaller advertisers, not just the large enterprise accounts who’ve had access through Meta’s Measurement Partner ecosystem.

In Short, Small Signals Now Drive Big Shifts

The underlying principle behind Meta’s new system? Your ad outcomes are no longer driven only by the platform’s algorithms. They are shaped by your signals, your definitions of success, and your internal logic.

Feed in richer data — not just sales, but average order value, profit margin per SKU, or customer lifetime value — and Meta’s AI will reflect those in how it optimises delivery, timing, audience, and spend.

In short, they want you to treat Meta like an employee who wants to do a good job. You just need to define what that job looks like.

What’s Coming Next

Image from Meta’s Media Briefing

Beyond the above optimisations, Meta is also building towards the following directions:

• Wider rollout of Advantage+ lead and app campaigns
• Expanded formats across Threads and in-app Shops
• Attribution that passes data between platforms
• Third-party measurement and incrementality integrations
• Support for omnichannel optimisation using online and offline signals

Through these actions, it seeks to turn the ad platform into a feedback system that works for each business. As an agency owner and digital marketing trainer, I’m hopeful that these developments can help improve the accuracy of Meta ads — particularly in conversions.

What to Do Now if You Want Better Results

So what should you do if you are an advertiser on Meta platforms like Facebook, Instagram and (yes) WhatsApp?

Stop optimising based on what you think you know. Instead, start feeding Meta’s AI-fueled algorithms the signals that reflect your real business goals.

This means reviewing your current conversion events, implementing server-side tracking if needed, and testing incrementality whenever possible. Do also work with your finance or product teams to surface the values that drive real outcomes — not just leads or clicks.

To find out more about Meta’s insights on what works (and what doesn’t) in Meta ads, read this article here.


How AI-Enhanced Prototyping Can Accelerate the UX Design Process

March 26, 2025 Content Marketing disabled comments

Artificial intelligence (AI) has significantly transformed how industries work across the board. Amongst these, user experience (UX) design has been one of the most affected. Thanks to AI-enhanced prototyping, the way digital products are conceptualized, tested and refined is rapidly changing.

By taking advantage of AI, businesses and designers can organize workflows better, improve collaboration and create more user-focused designs faster. Understanding what AI-enhanced prototyping is, its benefits to the UX design process and reasons for adopting AI-driven tools is important to stay ahead in a competitive digital context.

Understanding AI-Enhanced Prototyping

For small and medium-sized business owners, designers and marketers, AI-enhanced prototyping presents a unique opportunity to accelerate development cycles while improving general user satisfaction. The artificial intelligence market grew beyond $184 billion in 2024, and it is expected to reach around $1.85 trillion by 2030.

AI-enhanced prototyping integrates artificial intelligence into the process of creating interactive digital product models. For starters, understanding the difference between UX and UI is key. The UI stands for user interface, meaning menus, interaction animations and all the general engagements that users can interact with. UX stands for the user experience, meaning the entire experience that users have, from opening the app or website until leaving.

Through AI-enhanced prototyping, designers can visualize, test and refine functionalities before getting to full-scale development. AI-driven tools automate repetitive tasks and create design recommendations while offering real-time insights based on the user’s behavior.

In traditional UX design, prototyping requires manual wireframing, user testing and frequent refinements. The AI accelerates this process by automatically generating wireframes, predicting user movements and even suggesting possible UI improvements.

Benefits of AI-Enhanced Prototyping

Explore the key advantages of AI-enhanced prototyping for boosting UX design below.

1. Time Efficiency: Faster Design Iterations

AI-enhanced prototyping significantly reduces the time required to create and refine digital products. Traditional prototyping can be labor-intensive, often requiring multiple iterations before reaching an optimal design.

AI automates fundamental steps in this process. For instance, the wireframing tools can create draft layouts within seconds based on minimal input. Instead of manually designing every screen, designers can use AI to generate UI components, refine layouts and quickly test different versions. This allows teams to focus on high-level creative decision-making rather than spending hours on repetitive tasks.

2. Improves Collaboration and Workflow Efficiency

UX design is a collaborative effort that involves designers, developers, marketers and stakeholders. Applying AI-enhanced prototyping tools benefits smooth collaboration by allowing real-time feedback, automated documentation and shared design libraries. Teams can work on the same project simultaneously, identifying changes reflected instantly across all devices.

AI-driven platforms ensure consistency in element design, reducing the risk of miscommunication or inconsistent branding. Moreover, these tools can lead to interactive prototypes that stakeholders can test without requiring extensive technical knowledge, speeding up the approval process.

By adopting AI-enhanced prototyping, SMBs can launch digital products with confidence, knowing they have been thoroughly tested and optimized before going live. This agility allows businesses to adapt to market demands faster while staying ahead of competitors.

3. Improved Usability Testing and Data-Driven Insights

One of the most valuable aspects of AI in UX design is its ability to analyze user behavior and come up with actionable insights. These prototyping tools are able to simulate user interactions, identify engagement patterns and recognize pain points before launching a product.

AI-driven usability testing allows designers to assess user experience more efficiently. Instead of waiting for post-launch feedback, teams can leverage AI analytics to detect if there are any navigation issues, accessibility concerns and user preferences at the early stages of the process. This proactive approach leads to fewer costly revisions down the line and higher user satisfaction.

In this phase, marketers can test different layouts, calls to action and visual elements to determine what is more connected with their target audience. This leads to higher engagement rates and better conversion outcomes, making AI a powerful tool for optimizing digital campaigns.

4. Personalization: Crafting Tailored User Experiences

When it comes to digital products, personalization is one of the most important features to focus on. AI algorithms can analyze user data to tailor content, layout and functionality based on individual preferences. This results in engaging, relevant and user-friendly experiences.

For example, AI can predict user needs by analyzing previous interactions, suggesting placing frequently used features in the more accessible areas or adjusting UI elements based on the user’s behavior. By including these tools early in the design process, the marketing team can refine messaging, content placement and conversion strategies while creating interfaces that resonate more deeply with their audience.

5. Automation of Repetitive Tasks: Freeing Designers for Creativity

UX designers often spend a significant amount of time on repetitive tasks such as resizing buttons, aligning text or converting low-fidelity sketches into high-fidelity mockups. By automating technical and repetitive aspects of UX design, AI frees up time for designers to focus on storytelling, aesthetics and user engagement.

For example, AI-powered design assistants can create multiple variations of a layout, suggest improvements based on best practices and even fill wireframes with realistic placeholder content. Taking advantage of new technologies helps designers decrease the number of tedious tasks while increasing the time dedicated to conceptualizing unique experiences that make a product truly stand out.

6. Addressing the Challenge of Meaningful Work in UX Design

A significant concern in the workplace is the lack of meaningful work, which leads to high employee turnover rates. In fact, 31% of employees quit their jobs in 2022 due to a lack of meaningful work.

AI can help handle this issue by automating low-impact or unengaging tasks, allowing UX professionals to take care of more fulfilling and high-impact work. Instead of getting stuck in routine design adjustments, designers and marketers can focus on tasks such as problem-solving, innovation and user engagement. This ultimately leads to greater job satisfaction and career growth.

Implementing AI-Enhanced Prototyping in Your Workflow

These are some steps to consider when integrating AI-enhanced prototyping into your UX design process:

  1. Choose the right tools: Invest in an AI-powered design software that aligns with your team’s needs. Some of the most well-known are Figma and Adobe XD.
  2. Take advantage of AI for data-driven decisions: Use AI analytics to identify the user’s behavior patterns and to make informed design choices.
  3. Encourage mutual collaboration: Create a team culture that approaches AI as a supportive tool instead of a replacement for human creativity.
  4. Continuously test and refine: Analyzing AI-generated insights on a regular basis helps to improve UX design and optimize user experience.

With 73% of U.S. executives assuring that they will work with generative AI companies, it’s clear AI is here to stay.

Conclusions on AI-Enhanced Prototyping

Increasing efficiency, improving collaboration and offering deeper user insights are just some of the ways AI-enhanced prototyping is reshaping the UX design process. For designers, marketers and small business owners, capitalizing on AI-driven tools accelerates product development and encourages creativity and innovation.

By integrating AI into the team’s workflows, professionals can focus on what truly matters — creating meaningful, engaging and user-friendly digital experiences.

Eleanor Hecks

Eleanor Hecks is a design and marketing writer and researcher with a particular passion for CX topics. You can find her work as Editor in Chief of Designerly Magazine and as a writer for publications such as Clutch.co, Fast Company and Webdesigner Depot. Connect with her on LinkedIn or X to view her latest work.


How to Tap on Powerful AI Tools for Marketing

February 26, 2025 Content Marketing disabled comments

Artificial Intelligence (AI) is now an integral part of your marketing strategy. You can now find marketing AI tools to help your business improve virtually everything—from customer service to social media management.

However, it can be mind boggling to navigate the different AI tools that you can use to automate and streamline your marketing.


The Role of Human Emotion in Enhancing AI-Powered UX Design

February 25, 2025 Content Marketing disabled comments

Artificial intelligence (AI) has quickly gained traction as a design tool. As helpful as it can be in ensuring functionality and accessibility, though, these are not the only factors to consider in user experience (UX).

A good UX must also incorporate human emotion. This requires the uncanny ability to study, dissect and interpret what users feel in a way that is machine-learning friendly.


Optimize Conversion Rates with AI-Powered A/B Testing and Data Analytics

January 22, 2025 Content Marketing disabled comments

A/B testing is an industry go-to for discovering your customers’ preferences. The usual way to do this is to isolate a specific variable (eg copy, visual, audience, channel, pages) and to see which performs better with everything else being equal.

If you have an underperforming email marketing campaign for instance, you could create two templates to send to customers to see which yields click-throughs.


How Predictive AI Is Revolutionizing Content Strategy for Marketers

November 25, 2024 Content Marketing disabled comments

Content is at the core of every successful marketing strategy. Yet, as brands generate more content across diverse channels, understanding what truly resonates with audiences has become a complex challenge.

Enter predictive artificial intelligence (AI) — a transformative tool shaping the future of content strategy by helping marketers predict trends, refine messaging and make more informed decisions.