Generative AI has the highest likelihood of any technology in our lifetime to unlock marketing’s holy grail - true personalization at scale.

Generative AI will allow marketers to go beyond coarse group-level personalization (e.g. trying different landing pages for men vs. women) - these will be customer journeys personalized 

Ad images with a high likelihood of conversion will be generated on the fly to resonate with a specific potential customer.

Landing pages and copy will be altered in real-time to meet the customer where they are in their journey.

Customers will be shown the right second product for them at the right time for them with messaging specifically crafted to nurture them.

It is going to give marketers and designers superpowers.

Simply put, the use of generative AI tooling is going to become table stakes for ecommerce brands in 2023 and beyond.

Two opposing forces are colliding: the second order effects of the ATT/iOS14.5 headwind with the tailwind of two technological breakthroughs, large language models (LLMs) and image generation models (also called diffusion models).

LLMs (like ChatGPT) generate text and Diffusion models (like DALL-E or Stable Diffusion) generate images.

Everyone in the industry knows that ATT made it harder to sell online. The policy released as part of the iOS14.5 update limits the amount of data that advertisers and retailers can collect about their users, making it more difficult to deliver targeted ads and personalized product recommendations.

In the wake of the roll-out, CAC has spiked, conversion rates have been hit and measuring the effectiveness of marketing efforts became sorcery instead of science.

The response of the two major ad networks to ATT has been to try to move to a model where the networks themselves try to control as much of the targeting and campaign management as possible. Google’s and Meta’s push towards PMax and Advantage+ respectively is part of this strategy. In order to try and compensate for the conversion data loss caused by ATT, the platforms are trying to automate most of the levers that marketers currently use to optimize acquisition. Meta’s and Google’s reasoning is that they both have more capacity on their side to build complex machine learning models to compensate for iOS14.5 than any individual marketer or brand could.

As we see this trend continue to play out, the highest leverage a brand or marketer will have to drive performance will be in the creative (images) and copy they use - 

creative and copy will be the new targeting

Generative AI, a subset of artificial intelligence, is a technology that can create infinite new and original content based on a user prompt and other input data. Depending on the use case and the technology used, this original content can be generated in real-time or on the fly.

Here are three ways that this technology can be used today to improve the customer experience and counteract the iOS14.5 headwinds:

Generative AI can be used to create “lookalike creative” - by analyzing ad creative performance history, 1st party data, and demographics, generative AI algorithms can create unlimited personalized images in seconds that are likely to resonate with specific groups or hit specific goals.

For example, suppose I give a generative AI system the prompt “create 20 different creative concepts for my moisturizer cream that will lead to higher CTR among women 18-25”, the tool can understand what visual elements lead to higher performance according to those parameters across your brand’s ad library (and across the industry) to help you automatically produce more of them.

So, if the system detects that images with a beach setting perform better among women 18-25, you will be able to generate and refine as many of those kinds of images as you like until you find concepts you want to test.

These images can be: pushed directly into Advantage+ or PMax to run rapid tests as to which creative angles drive uplift, used as part of the creative brief for the design or the video team, or be used to give guidance to influencer or UGC teams as to what to prioritize (e.g. ‘target influencers who can shoot content at the beach’).

, the company I co-founded, is building out tooling.

Generative AI tools are already being used in e-commerce to create original content such as product descriptions, social media posts, and blog articles. By analyzing existing content and customer data, generative AI algorithms can generate new content that is tailored to the brand's voice and style. This saves time and resources for the brand while ensuring a consistent and high-quality content output.

(btw, all of that paragraph was written by ChatGPT, a product from OpenAI).

What will a brand need to truly leverage the power of Generative AI

1. An enriched understand of who their customers are

As the cost of rapidly creating content (images and copy) drops to close to zero, the value of creating ‘good content’ will increase dramatically. Brands will need as much 1st party, behavioral, and demographic data about their customers as they can get to be able to really personalize the images and copy that people see. Brands should strongly consider partnering with vendors who either have these capabilities pre-built or can help acquire this data to drive the most value from these technologies.

These tools are cutting-edge and will still need human supervision. However, most underlying models should get better with usage and produce results that are more specific to a particular brand overtime. Brands looking to get a head start should jump in and start trying things to calibrate the most valuable use cases of this technology.

The end state for these tools is that AI will be used to generate personalized high converting shopping experiences for every customer as an individual. Two different customers will see different ads that bring them onto a site, different landing page copy optimized to resonate with them specifically, they will be taken on personalized up-sell and cross-sell journeys, and will be sent different marketing messaging that anticipates their needs and wants. For brands that invest early in testing these products, this should increase customer satisfaction, customer loyalty, and sales.

As brands start experimenting this year, I think we’ll see several finally start to crack the personalization code and pull ahead of the pack. The race is on.

As brands start experimenting this year, I think we’ll see several finally start to crack the personalization code and pull ahead of the pack. The race is on...

AI Arms Race in eCommerce

ROAS stands for return on ad spend. It is a measure of the effectiveness of an advertising campaign.

ROAS is calculated by dividing the total revenue generated from an advertising campaign by the total cost of the ad spend.

For example, if a company spent $100 on an advertising campaign and generated $200 in revenue, the ROAS would be 200/100 = 2. This means that for every dollar spent on advertising, the company generated two dollars in revenue.

The problem with ROAS as a metric for performance marketers is that it over-incentivizes first orders, and under-values more costly channels.

The best and most resilient e-commerce brands have a high returning customer rate, but optimizing for first order value alone could mean you’re sacrificing channels that bring in your BEST customers - those who come back again and again.

This is exacerbated by the fact that most first orders are discounted, which means they’re not as profitable for the company. Some companies even take a loss on first orders…

We believe brands should optimize their acquisition efforts towards high-LTV customers and the channels that they come through.

We do this by leveraging 1st party or owned data, 3rd party demographic data, and machine learning to create an enriched customer profile that we use to predict LTV.

Now, marketers can use a real-time LTV:CAC ratio by channel or campaign to get the most out of their ad budget. This is marketing efficiency powered by customer context.

ROAS as a metric for performance marketers is that it over-incentivizes first orders, and under-values more costly channels.

The Problem With ROAS

Customer Lifetime Value (CLV) and Lifetime Value (LTV) are some of the most important metrics for eCommerce and D2C brands, as they provide a measure of how well the business is doing in terms of customer engagement and revenue.

CLV and LTV can help businesses make changes to their marketing strategies that will ultimately result in increased revenue. In this blog post, we'll explore what CLV and LTV are, how to calculate them, and how they can be used to improve an eCommerce or D2C company's bottom line.

Customer Lifetime Value (CLV) is a metric used to measure the value of a customer over their lifetime with a company.

It measures how much money a customer brings into the business through purchases, subscriptions, or other activities over time. This number can be used as a prediction of future revenue earned from that customer.

Companies use this information to inform decisions on pricing, product development, marketing campaigns, and more.

Calculating CLV requires looking at past customer behavior and data related to it.

The simplest way to do this is by taking the average amount spent per purchase multiplied by the average number of purchases made over time by that customer.

However, there are more detailed calculations that can be done for more sophisticated analysis.

For instance, companies might consider factors such as churn rate (the percentage of customers who leave within a certain period), customer loyalty (how likely customers are to return), or LTV-to-CAC ratio (an indication of how profitable each new customer relationship is).

By integrating these various pieces of information into a single equation, an accurate estimate of CLV can be created.

Lifetime Value (LTV) measures the net profit that a business earns from its customers over their lifetime with them.

This metric encompasses not only any direct sales made but also recurring fees like subscription fees or membership costs which add up over time.

Additionally, intangible benefits like brand loyalty or referrals may also contribute to LTV if they can be quantified in some way as part of an overall calculation. Companies use this information to assess the relative profitability of different types of customers or strategies in order to allocate resources appropriately across channels or products lines.

The basic formula for calculating LTV involves subtracting total costs associated with acquiring and serving the customer from total revenue generated from that same customer over the course of their lifetime with you.

To do this accurately requires incorporating all relevant transactional information such as purchase frequency, average purchase size and discounting rates when appropriate since these variables impact both costs and revenues differently depending on whether or not they're taken into consideration when making calculations about long-term profitability from customers..

Additionally incorporating factors like intangibles mentioned above will give you even better insight into your true long-term value from each individual customer relationship .

Understanding both Customer Lifetime Value (CLV) and Lifetime Value (LTV) is essential for any eCommerce or D2C enterprise looking to maximize its profits while still providing value for its customers over time.

By incorporating these metrics into your decision-making process you can make sure you’re always taking into account both short-term gains as well as long-term sustainability when evaluating different approaches or strategies for growing your business successfully.

With the right calculations performed consistently throughout your organization, CLV & LTV should become indispensable tools in helping you optimize your offerings & improving financial performance going forward.

eCommerce CLV vs LTV: What's the difference?

The Treat team experienced firsthand the energy and excitement that came along with our inaugural eComm dinner held in NYC back in early January.

A group of talented eCommerce marketers, consultants, CMOs, and investors under one roof led to a thoughtful and spontaneous night of networking and fun.

But, there was no reason New York should have all the fun, so we decided to do it again— this time on the opposite coast, in L.A.

On February 28th a group of talented eComm folks gathered together at Birdie G's, one of LAs most unique resturaunts, for a night of delicious food and drinks.

🎉 🥂 🍔 Here are some highlights from the event!

Treat is hosting another eComm dinner in NYC on 

If you're interested in our next event click here

CMOs, eComm pros, and marketers gathered in Santa Monica for a fun night of drinks, networking, and shop talk.

Treat's L.A. Dinner Recap 

We are thrilled to announce that Treat, our AI platform built for e-commerce marketers and designers, has successfully raised $8.5 million in a seed round led by 

. This exciting milestone marks the beginning of a new chapter for Treat as we embark on our mission to help brands use generative AI to turbocharge creative and individually personalize every online shopping experience.

eCommerce/DTC brands are operating at the intersection of the aftermath of recent privacy regulations (iOS14.5/ATT) and the proliferation new AI tools like chatGPT and Stable Diffusion. 

As third-party cookies and other tracking technologies face increasing scrutiny, the focus has 

shifted toward creative as the new frontier for reaching audiences.  

This mass shift and reliance on creative paves the way for new generative AI tools to power the ad creative and copy needs of D2C marketers.

We are moving towards a world where 99% of pixels will be generated not rendered.

Treat is at the forefront of this transformation, enabling marketers to harness the power of AI-driven creative insights to generate highly personalized content without compromising user privacy.

“For marketers, now more than ever, creative is THE determinant of profitable acquisition. Matt and Hugh come at this problem with uniquely deep customer empathy; had Treat been in my life while running Growth at Stitch Fix, I not only would have saved countless hours in creative processes, but the surface area of experimentation would have been so much broader & ultimately more profitable.”

Thanks to generative AI tooling, the cost of  rapidly creating content drops close to zero. 

The value of knowing what content to create and why increases proportionally in the opposite direction. 

Brands will need as much 1st party, behavioral, and demographic data about their customers as they can get to be able to really personalize the images and copy that people see.

Treat empowers D2C marketers and brands to unlock the power of their data to fuel creative strategies, effortlessly generate on-brand high quality visuals, and achieve true personalization at scale.

Treat’s AI intelligently uncovers the creative concepts that resonate with your target audience and combines them with customer behavior, preferences, and demographic data to generate highly personalized content that drives results.

Treat's image generation capabilities simplify the content creation process, enabling marketers and designers to produce a wealth of on-brand, high-quality visuals in no time. Say goodbye to resource-intensive manual tasks and hello to a streamlined workflow that keeps you focused on what matters most: engaging your customers.

In addition to Greylock we’d like to thank Vinyl VC, Eniac Ventures, SV Angel, Homebrew, VMG Partners as well as our amazing group of angels: Brian Long (Attentive), Ed Hallen (Klaviyo), Adam Turner (Postscript), Ben Jabbawy (Privy), Nik Sharma (Sharma Brands), Taylor Sicard (Win Brands).

Stay tuned for more exciting updates as we continue to develop and expand the capabilities of Treat. Together, we'll revolutionize the world of e-commerce marketing and design.

Greylock Partners backs Treat, a data driven generative AI platform for eCommerce marketers and designers.

Treat Raises $8.5M in Seed Funding from Greylock Partners

Apple's App Tracking Transparency (ATT) has undoubtedly impacted the DTC and eCommerce marketing landscape. But in the midst of these changes, 

forward-thinking startups are harnessing AI and generative technologies to reshape performance marketing

. As marketers, it's crucial to adapt to these innovative tools, optimize campaigns, and stay competitive in this privacy-centric environment.

To explore the effects of ATT and the revolution brought on by AI-driven solutions in marketing., Treat’s CEO and Co-Founder, 

 on Greylock’s Greymatter Podcast, hosted by 

In this post, we’ll highlight key moments from the conversation focused on how generative AI tools are helping DTC brands transition their performance marketing in 2023. 

We can view the Post-ATT world as the result of a transition from a deterministic state, where marketers had come to rely on repeatable and efficient results from ad networks, to a more probabilistic one, where attribution has become increasingly unreliable and ad networks have shifted to machine learning models to target audiences.

There’s been this kind of game of whack-a-mole leading up to this, we’ve moved to a probabilistic world where honestly, I think the ad networks have made the call that the only people who can really measure correctly or solve attribution or even attempt to are those with very, very large machine learning staffs who understand this space exceptionally well.

In the deterministic era, platforms like Facebook (now Meta) allowed marketers to achieve near-perfect mapping of consumer data by utilizing post-back links. This approach facilitated a convergence of data on the brand side and performance on the ad network side, creating a highly efficient advertising engine.

With the implementation of ATT and other privacy-focused measures, the deterministic advertising approach has been disrupted. Today, we find ourselves in a probabilistic world where ad networks must rely on large machine learning teams to tackle attribution challenges.

The big thing that all of adtech recognized at that moment – and it was sort of a strange gentleman’s agreement that if we admit that there’s no solution to this for the next six months, our stock price is going to tank. That’s just true. And I wish there was another sort of collegial way of saying this, but this is actually what was true.

In response to this shift, major players like Meta and Google are moving marketing activities in-house, leveraging tools such as 

Advantage Plus (Meta's rebranded automation suite)

 These moves are an attempt to regain control over the advertising landscape, driven by the need to address economic pressures and maintain high stock prices.

What This Means for CMOs and Growth Marketers:

Marketers must stay informed about the changing landscape and adapt their strategies accordingly. The shift to probabilistic advertising calls for 

a deeper understanding of data attribution and a greater reliance on machine learning capabilities.

So when Apple said, “Hey, you have to get a consumer to opt in,” by definition, a certain set of those consumers are opting out. And so you no longer have that signal. So you now need to like Matt has been saying, you need to move to probabilistic models.

Small and medium-sized merchants, in particular, are grappling with two significant problems:

Attribution becoming more difficult due to signal loss 

Scaling issues on individual channels like Facebook.

The way to fully prevent this behavior – which Apple calls tracking, which is kind of mixing the third-party and the first-party context, it’s commingling that data – is to just starve these (ad tech) companies of the data. There’s no way to sort of ask them to abide by this policy unless you remove the data from that sort of transactional frame where it will be used.

With the shift to probabilistic models and a growing need for multi-channel marketing, merchants are turning to multiple tools for attribution. Brands are now triangulating between Google Analytics, third-party attribution tools (such as Rockerbox, Northbeam, or Triple Whale), and in-platform data to navigate these challenges.

I think there is a begrudging acceptance now (at least amongst the brands that we speak to) that there’s no one source of truth anymore, and now the kind of big topic is triangulation and using like a multitude of different techniques to try and get to the answer.


The complexity of attribution has increased, and there is a noticeable gap in the market for tools and education tailored to small and medium-sized businesses. Merchants are looking for more effective ways to measure the success of their marketing campaigns and better understand the best practices for attribution across multiple channels.

The need for improved tools remains, leaving room for innovation and growth in the performance marketing space.

 into traffic generation by developing their Audiences product.

 As the platform evolves into a marketplace-like model, it presents new opportunities and challenges for both Shopify and its merchants.

Shopify Audiences has shown early promising results, striking a balance between compliance with ATT and addressing merchants' needs. By leveraging first-party data, Shopify offers a high-margin solution without violating the privacy of brands on their platform.

 importance of brands better leveraging their first-party data and exploring novel approaches to improve their marketing strategies

. As Shopify continues to grow and adapt, businesses should become more strategic in how they use the available data and tools to drive success in the ever-changing e-commerce landscape.

As the advertising landscape evolves, retail media networks are becoming increasingly popular for businesses in search of targeted and direct response ads. This trend is driven by companies like Amazon and Walmart, who are leveraging their vast audience data to launch their own ad networks. 

The technology required to create these networks is becoming more accessible, allowing even smaller businesses with sizable audience profiles to monetize their data and explore new advertising opportunities.

But I think that’s behind the dynamic of what I call the “Everything is an ad network phenomenon.” Everything is becoming an ad network because well, Facebook’s not the everything store now. And hey, that’s an opportunity for me to spin up pretty meaningful super high-margin revenue.


The Shift Towards Creative and Generative AI

As third-party cookies and other tracking technologies face increasing scrutiny, the focus has shifted toward 

creative as the new frontier for reaching audiences.

The recent rise of generative AI is revolutionizing the marketing landscape, offering new ways to streamline the creative process and enhance ideation.

I think most can agree that image models are going to give designers superpowers. There’s a bunch of new apps being marketed to marketers, given it’s such an obvious use case with a quantifiable ROI of all this stuff.

As a variety of powerful creative tools emerge, the focus should shift towards developing better filters and upstream tools to determine what to create. The rapid advancements in AI technology offer promising opportunities for marketers to harness its potential in the creative process.

and Figma are incorporating generative AI into their offerings, making the production aspect of creative work easier and more efficient. Adobe's recently launched Firefly suite and 

Figma's upcoming Stable Diffusion integration are examples of this trend.

The true potential of generative AI lies in its ability to enhance the ideation phase, where the bottleneck in creating high-performing creative content often occurs.

And as the cost of content creation (both images and copy) kind of tends to zero, which is basically what we’re riding right now, the value of knowing what to create and why increases proportionally in the opposite direction. And it gets super, super important.

Generative AI can facilitate rapid creative variant testing and help marketers pinpoint the most effective creative concepts and visual elements.

Content Abundance and the Future of Marketing Tools:

As we enter a world of content abundance, marketers should focus on tools that help them understand which creative concepts to feed into generative AI systems. The production process will likely be integrated into existing tools like Photoshop and Figma, 

so the real value lies in finding tools that aid in the ideation and decision-making stages.

Generative AI is a powerful tool for ad creative, but it is crucial to ensure that third-party tools used for generating creative are not anchored into the actual ad network, as their incentives may not align with yours. 

The rise of generative AI highlights the competitive advantage of skilled creative directors and heads of ad creative, showcasing their value in the marketing process.

Discovering the Potential of AI in Marketing: Treat's Lookalike Creative

Treat's Lookalike Creative is an AI-based tool 

that analyzes a brand's past campaigns, including images, videos, and copy, to uncover visual patterns and correlations with specific goals or target demographics. This capability can help brands optimize their creative assets to better connect with their audience.

In one case study, Treat's Lookalike Creative identified a specific type of marble bathroom that correlated highly with click-through rates for a beauty brand. This pattern was challenging to perceive across 15,000 creatives, demonstrating the tool's effectiveness in handling large data sets and pattern matching, tasks where machines excel.

It’s just very hard for a human eye to perceive, which was that we were working at a beauty brand. And there was one specific kind of marble bathroom. And it was a specific kind of marble, and it just happened to be really, really eye-catching for the machine that was highly correlated with CTR, which was the thing that they were trying to optimize for. Armed with that, the creative team can kind of do its magic and focus on the stuff that they’re good at. But the pattern matching across, I think this was across like 15,000 creatives. That’s just not something a human really should be either manually tagging or reviewing themselves. That should be outsourced.

Leveraging AI for Specialization and Division of Tasks:

By incorporating AI-powered tools like Treat's Lookalike Creative, creative teams can focus on their core strengths, while machines handle areas where they outshine humans. This symbiotic relationship between human ingenuity and machine precision is the future of marketing.

The Future: Shared Intelligence Database:

As we look ahead, there is potential for creating a shared intelligence database, where brands can opt-in to share effective visual elements with others. This collaborative approach fosters collective growth and innovation, further leveraging the power of AI in marketing.

The integration of AI in marketing represents a crossroads where growth marketers and design teams can specialize in what they excel at while allowing machines to handle tasks where they clearly outperform humans. In the future, it is possible to create a shared intelligence database where brands can opt-in to share effective visual elements with others, fostering collaboration and collective growth. 

The value of AI in marketing lies in the specialization and division of tasks between humans and machines, allowing each to contribute their unique strengths to the marketing process.

Treat CEO, Matt Osman, joins the Greymatter podcast to discuss the Post-ATT world and what it means for commerce brands and the companies building tooling to support them

The Post-ATT World: What DTC Brands Need to Know

If you work in the eCommerce/D2C space, then you’ve probably heard of the App Tracking Transparency (ATT) policy by Apple.

This policy has impacted many brands in terms of how they acquire customers, as well as their return on ad spend (ROAS).

In this blog post, we will discuss the impact of the ATT policy and how brands can take back control by leveraging 1st and 3rd party data to create a powerful customer context.

What is the App Tracking Transparency Policy?

The App Tracking Transparency (ATT) policy was introduced by Apple in 2021.

The goal of this policy is to give users more control over their data.

It requires all apps to ask for user opt-in when tracking their data across other companies’ apps and websites for advertising purposes. With this policy, users have more control over their data privacy and can choose whether or not they want to be tracked.

Since the introduction of the ATT policy, many brands have seen an increase in their customer acquisition costs (CAC).

This is because they are no longer able to track users across other companies’ apps and websites for advertising purposes.

This means that brands are no longer able to retarget those users who have been exposed to their ads but didn’t complete a purchase or sign up for a service.

As such, their return on ad spend (ROAS) has also decreased due to not being able to track these users and target them with relevant ads at a later date.

Although the ATT policy has made it more difficult for brands to acquire customers, there are still ways that they can take back control of their marketing efforts.

One way is by developing an enriched customer context – which is a combination of both first-party and third-party data – that can help them better understand who their customers are, what products/services they are interested in, etc.

This will allow them to create highly targeted campaigns that are tailored specifically towards those customers that are most likely to convert into high CLV customers.

Additionally, this will enable them to optimize campaigns in real time based on insights from the customer context so that they can get better results from each campaign at a lower cost per acquisition (CPA).

Apple’s ATT policy has had an impact on many eCommerce and D2C brands by increasing their customer acquisition costs and decreasing their return on ad spend.

However, there are still ways that these brands can take back control by leveraging 1st and 3rd party data to create a powerful customer context that helps them accurately target potential customers while improving ROAS at the same time.

By developing a strong customer context, brands should be able to see improved performance from their digital marketing campaigns despite any changes made by Apple or other industry players in regards to consumer privacy rights.

Since the introduction of the ATT policy, many brands have seen an increase in their customer acquisition costs (CAC). This is because they are no longer able to track users across other companies’ apps and websites for advertising purposes.

How Apple Broke Marketing

The emergence of AI image generation is transforming the way e-commerce marketers and designers work, granting them powerful tools to enhance their productivity and creativity. This groundbreaking technology doesn't intend to replace jobs, but instead aims to augment and remove tedious tasks, empowering professionals to focus on the bigger picture.

The first wave of AI image generation tools has unlocked the potential for marketers and designers to create and edit images using natural language. 

By simply inputting a phrase like “Show me my product in a rainforest” or "Show me Elvis, alive and well, hiding out on the moon," the system can generate a corresponding image. The limiting factor is the human imagination. This remarkable feature allows users to produce various iterations with ease, making it an essential tool for creative projects.

AI image generation models provide an element of excitement and unpredictability, much like a slot machine. With each "pull of the handle" or re-run query, the system generates different results, which can be both entertaining and useful for brainstorming ideas. However, to truly harness the power of these tools, marketers and designers must have a clear vision of their objectives and desired outcomes.

The Importance of “Knowing What You Want”

As creating images gets more affordable, it's crucial to know which ones to make and why. Combining AI-generated images with tools that evaluate image performance helps eComm professionals identify the best visuals. 

DTC marketers can use this new technology to develop ad creatives that perform better in metrics like CTR and CPA, making their campaigns more effective.

Treat is at the forefront of utilizing AI image generation to revolutionize marketing and design.

"Based on the data we have available to us, what ad should we make next that has the highest probability of achieving our goals?"

To achieve this, we employ AI to analyze all images and videos a brand has ever used, extracting visual and emotional elements and correlating them with performance and demographic data.

This data-driven approach allows Treat's AI tool to make recommendations on creative angles and themes to explore next. It even generates initial concepts, empowering marketers and designers to prioritize creative strategies and allocate resources and ad spend more effectively.

The Future of AI Image Generation in Marketing and Design

As AI image generation continues to develop and improve, it will become an indispensable tool for e-commerce marketers and designers. By enabling professionals to focus on strategy and execution rather than busywork, this technology will ultimately drive increased efficiency and better results.

To make the most of these powerful tools, it's essential to maintain a clear vision of the desired outcome and work in tandem with AI-driven analytics. Pairing AI image generation with performance data analysis is the key to unlocking the full potential of this innovative technology. By embracing these tools and combining them with data-driven insights, professionals can stay ahead of the curve and elevate their marketing and design strategies to new heights.

AI image generation is like a "slot machine" for marketers and designers, providing exciting and unpredictable results for brainstorming. Pairing with performance data analysis is key to maximizing its impact.

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