Why AI is the new engine for retail marketing

Retailers run on razor-thin margins — a reality every retail marketer knows. It’s a challenge faced by the owners of your corner bodega and by the CEOs of the largest, most successful brick-and-mortar retailers in the world.

But retailers are also among the most nimble marketers: According to GrowthLoop’s 2025 AI and Marketing Performance Index, 76% report marketing campaign cycles of fewer than 30 days, and 13% have compressed those cycles to under a week, outpacing their counterparts in every other industry by as much as 225%. 

Marketing isn’t a race to the finish line, however, and faster alone doesn’t necessarily mean more profitable. Retail marketers have at least one thing in common with their slower-but-steady colleagues in tech, media, manufacturing, and finserv: They all list bottlenecks in data collection, reporting, and analysis as one of the biggest obstacles to turning rapid marketing cycles into growth marketing cycles. 

Marketers in retail, e-commerce, and consumer goods are also the most likely to single out artificial intelligence as the best way to uncork those bottlenecks and turn data into the real-time insights and predictions needed for faster, more fruitful campaigns.

Faster cycles and new revenue streams

AI-driven marketing technology is more than an accelerant; it’s a way to fine-tune every marketing cycle and continuously improve their performance over time — a tactic we call compound marketing. By ingesting data on the results of each campaign back into your customer database, AI can combine it with existing customer data to hone audience-building or journey orchestration. And it can do so much faster and more successfully than even the savviest marketers could manually. 

The results? Each marketing cycle performs better than the last, making it easier to reach (or exceed) your growth-related KPIs — in far less time. Iterations involving multiple channels, manual data pulls, SQL coding, and duct-taped attempts at optimization that might have taken weeks (or even months at large organizations) can now happen in seconds. And growth rates grow — or compound — exponentially.

AI is also an instrument for building entirely new revenue streams by leveraging the value in the customer data. By selling access to AI-sharpened audiences to other brands, you provide them with data they would not otherwise have about consumers whose profiles match their target audience. Formally organized into a retail media network compliant with data privacy laws and policies, this gives brands the ability to reach a whole new set of potential customers. 

And while a retailer’s margin on selling those products might be 1 or 2% at most, the margin on providing access to high-quality data about the consumers who purchase them can be 50% or even 75%, because the overhead is so much lower: just the cost of the hardware and software that runs the agentic AI building those audiences.

Without the right strategy, data is noise

AI is a tool your competitors already wield. But your first-party data is a differentiator. While AI best-practices are key to an AI-supported marketing strategy, AI is only as good as the data underlying it. So having the right data strategy is key — especially for retailers. 

The best data strategies begin with an enterprise data warehouse containing a 360-degree view of your customers that goes beyond demographic information and includes data unique to your relationship with each one of them. Purchase and channel-specific engagement histories are examples of customer data that are discrete to every organization. 

Centralizing your data in a single cloud-based data platform yields many other benefits:

• It makes it easier for AI agents to access, analyze, and update in real-time with performance data from all your marketing channels. 

• It makes it simpler to leverage marketing automation tools that can activate data-driven campaigns created by agentic AI. 

• It enables AI to generate predictive analytics for KPIs such as customer lifetime value, churn potential, and whether a first-time purchaser is likely to become a regular shopper. 

• It permits AI agents to fine-tune campaigns for particular KPIs such as increasing per-item purchase value or, for e-tailers, average shopping cart size.

• It keeps data safer by avoiding clumsy data transfers from platforms siloed in different departments. 

Adopt it now or regret it later

C-suites in every industry are engaged in a frantic scramble to find the right business applications for artificial intelligence; its powerful applications for marketing make it clear that retailers who don’t adopt AI-driven tactics will fall behind. But integrating any new technology can be a heavy lift, and AI can be especially intimidating. 

And as with most new technologies, it’s probably begun to spring up organically and haphazardly at your organization. 

Perhaps your colleagues have use ChatGPT or Gemini to translate emails from colleagues in another country or summarize a 300-page transcript of the post-mortem videoconference for your last big campaign. Your software engineers may even be using Cursor and Windsurf to help them write code faster.

But the kind of growth-compounding AI applications I’ve discussed require not only an openness to new technology but the skills to use it effectively, as well as a sound plan for its implementation. And AI champions at every retail organization are finding themselves staring up at a steep learning curve. But an even bigger barrier, especially at large enterprises, is inertia, and its gravitational pull originates in two areas: existing technology and human resources. 

DIY AI rollouts: More chaos, less ROI

If your company is like most, your tech stack and business processes are a result of both planned and organic growth. Finding a way to integrate AI without pulling everything out by the roots can seem daunting. Finding people with the right skills to implement AI — especially if yours is a DIY culture — and then building the skills for using it effectively in your workforce — can seem overwhelming.

But the risk of inaction would be difficult to overstate, especially because the evolution of AI and its capabilities is gaining momentum. In fact, the rate of AI-powered acceleration toward your growth-related KPIs will soon be a good indicator of your competitive position in your market.

As with any change management project, working with a partner who’s actually overcome this inertia and successfully integrated AI at a company similar to yours is essential: An AI solution with a yearlong implementation window or eighteen-month time-to-value is just as bad as no AI solution at all.

But just as important is your organization’s affinity for the technology itself. AI can’t be something that operates separately from your existing technology constellation, and it has to work alongside your existing processes. Just as importantly, it can’t be opaque: Your team needs to understand how it works, what it can (and cannot) do, and how to interact with it effectively, whether that’s writing an accurate generative AI prompt, editing the output from that prompt, applying the analyses AI agents provide, or overriding those suggestions. 

Right now, a 100% programmatic, black-box AI solution is as much of a hindrance as a (perceived) help. As enterprises build familiarity with AI agents and systems, organizations — and marketers — need visibility into how AI is making decisions to build trust and gain knowledge. 

AI implementations bring with them important security issues as well. When data is your differentiator, you don’t want it leaking into training data sets for publicly available large language models (LLMs). All commercially available LLMs offer customers their own instance of the model. But if this is your first foray into AI, make sure your RFIs and RFPs include security-related questions vetted by reputable partners with proven industry expertise in data governance and compliance. While custom model-building from scratch is an option, commercial foundational models such as Snowflake’s Cortex and Google’s Gemini perform beautifully out of the box for most company’s use cases.

The human/AI partnership

Marketers know that defining goals is based on both data and intuition, and the ability to coach a team to reach those goals is intrinsically human. AI is not a replacement for marketing expertise or institutional memory. Your marketing team knows much more about your business than any AI model. 

But AI models do know a lot about business — and marketing — in general. They can access publicly available information about your business. And they can be securely taught the nuances about confidential aspects of your business such as quarterly objectives and key results and apply that knowledge contextually. 

A human/AI partnership can achieve outstanding business results, and the most successful Compound Marketing Engines are those that are fueled by AI and driven by flesh-and-blood marketers. The time to implement one is now.