The History of Test and Learn in Retail: From Direct Mail to Digital

Reading time: ~10 min

Table of Contents

• The Origins: Direct Mail and the First Retail Experiments
• The 1970s and 1980s: Scanner Data and the Birth of Retail Analytics
• The 1990s: Loyalty Programs and the Customer Data Revolution
• The 2000s: Digital Commerce and the Democratization of Testing
• The 2010s: In-Store Testing Comes of Age
• The 2020s: AI, Automation, and the Next Frontier
• What the History Tells Us
• The Bottom Line

Every major methodology in business has an origin story. Lean manufacturing traces back to Toyota’s production floors in postwar Japan. Modern financial modeling grew out of the academic work of the 1950s and 60s. And test and learn — the structured, evidence-based approach to retail decision-making that the industry now treats as best practice — has roots that go back further than most people realize.

Understanding where test and learn came from isn’t just an academic exercise. It explains why the methodology is built the way it is, why certain principles matter so much, and how the capability has evolved from a slow, expensive research process into something that modern retailers can run continuously, across hundreds of variables, in near real time.

This is the story of how retailers learned to test.

The Origins: Direct Mail and the First Retail Experiments

Long before the phrase “test and learn” existed, direct mail marketers were running experiments. In the early twentieth century, catalog retailers and direct response companies discovered something important: if you sent two versions of a mailer to two different groups of customers and tracked which one generated more orders, you could systematically improve your results over time.

This was primitive by modern standards. Tests took months to design, execute, and measure. Sample sizes were determined more by intuition than statistics. And the variables being tested — a different headline, a different price point, a different product featured on the cover — were limited by the cost and lead time of print production.

But the core logic was sound, and it worked. Companies that tested their direct mail consistently outperformed those that relied on instinct alone. By the mid-twentieth century, direct response advertising had developed a rigorous culture of experimentation that would later become the intellectual foundation for much of what we now call test and learn.

The key insight from this era was simple but powerful: customers tell you what they want through their behavior, not their opinions. What someone says they prefer in a survey and what they actually do when presented with two real options are often very different things. Testing behavior, not preferences, became a foundational principle that still holds today.

The 1970s and 1980s: Scanner Data and the Birth of Retail Analytics

The next major leap came with the introduction of point-of-sale scanner technology in the 1970s. When retailers began scanning barcodes at checkout, something transformative happened: for the first time, they had a precise, real-time record of what every customer bought, when they bought it, and at what price.

This data was crude by today’s standards, but it was revolutionary at the time. Retailers and consumer packaged goods companies quickly realized that scanner data could be used to measure the impact of promotions, pricing changes, and in-store merchandising decisions with a precision that had never been possible before.

Through the late 1970s and into the 1980s, a cottage industry of retail analytics emerged around scanner data. Academic researchers, market research firms, and the analytics teams of large CPG companies began developing methodologies for using this data to run controlled experiments in stores. You could designate a group of stores to receive a promotion, designate a matched group to serve as a control, and measure the difference in sales over a defined period. The basic architecture of the modern retail experiment was taking shape.

This era also produced some of the earliest thinking about what made a good retail experiment. Researchers began grappling with questions that are still relevant today: How do you select a control group that is truly comparable to your test group? How long does a test need to run to produce reliable results? How do you account for external factors — weather, competitive activity, local events — that might distort your findings? The methodological foundations being built in this period would underpin the field for decades.

The 1990s: Loyalty Programs and the Customer Data Revolution

If scanner data gave retailers a record of transactions, loyalty programs gave them something even more valuable: a record of individual customers. When retailers began rolling out loyalty card programs in the late 1980s and early 1990s — Kroger, Safeway, and Tesco were among the early adopters — they gained the ability to track not just what was selling, but who was buying it, how often, and in combination with what else.

This was a qualitative shift in what was possible. Instead of measuring the impact of a promotion on aggregate store sales, retailers could now measure its impact on specific customer segments. Did the promotion bring in new customers or just reward existing ones? Did it drive incremental trips or just shift spend that would have happened anyway? Did it increase basket size among high-value customers or mostly attract cherry-pickers?

These questions had always existed. For the first time, retailers had the data to answer them.

Tesco’s Clubcard program, launched in 1995, became perhaps the most famous example of loyalty-driven retail analytics in this era. Working with the data analytics firm dunnhumby, Tesco used Clubcard data to develop a granular understanding of its customers that was genuinely unprecedented in grocery retail at the time. The insights that came out of that program — about customer segmentation, price sensitivity, promotional effectiveness, and product affinity — shaped Tesco’s strategy for years and demonstrated what was possible when a retailer committed seriously to data-driven decision making.

The 1990s also saw the rise of dedicated market research firms specializing in retail experimentation. Companies began offering retailers the ability to run controlled in-store tests with rigorous statistical methodology, using matched store panels and increasingly sophisticated analytical techniques. Test and learn was becoming a professional discipline, not just an informal practice.

The 2000s: Digital Commerce and the Democratization of Testing

The arrival of e-commerce changed everything about the pace and scale of retail experimentation. When customers interact with a website rather than a physical store, the barriers to testing drop dramatically. You don’t need to brief store managers, ship new signage, or wait for a monthly sales report. You can make a change to a digital experience, expose half your traffic to the new version and half to the old one, and have statistically significant results within days or even hours.

Amazon was an early and aggressive adopter of this approach. By the early 2000s, Amazon was running hundreds of simultaneous experiments on its website — testing everything from button colors and product page layouts to pricing algorithms and recommendation engines. The company famously built a culture in which almost every product decision was validated through experimentation before being fully deployed. That culture became one of Amazon’s most durable competitive advantages, and it set a standard for digital experimentation that the broader retail industry spent the next decade trying to match.

The tools followed the need. A generation of A/B testing and experimentation platforms emerged in the mid-2000s and 2010s — Optimizely, VWO, Adobe Target, and others — making it possible for retailers of all sizes to run sophisticated digital experiments without building the infrastructure from scratch. Testing, once the exclusive domain of companies with large analytics teams and significant technology budgets, became accessible to almost anyone with a website.

This democratization had a profound effect on how the retail industry thought about experimentation. As digital testing became routine, the logic of the methodology started migrating back into physical retail. If you could test a digital promotion so easily and so quickly, why wouldn’t you apply the same rigor to an in-store promotion? If you could measure the impact of a website redesign at the customer level, why wouldn’t you try to measure the impact of a store layout change the same way?

The answer, for most retailers, was that the tools and infrastructure for rigorous in-store experimentation still didn’t exist in an accessible form. That was about to change.

The 2010s: In-Store Testing Comes of Age

The 2010s saw a wave of investment in purpose-built platforms for in-store retail experimentation. Companies like MarketDial, 84.51°, and others developed software specifically designed to help brick-and-mortar retailers run controlled experiments with the same rigor that digital teams had been applying online for years.

These platforms solved several problems that had made in-store testing difficult at scale. Store matching — selecting control stores that are truly comparable to test stores — was automated using sophisticated algorithms that accounted for dozens of variables simultaneously. Statistical significance calculations were built into the reporting interface, so analysts didn’t need to run manual calculations. And results dashboards made it possible for non-technical stakeholders to understand and act on findings without needing a statistics degree.

At the same time, the explosion of data available to retailers made experimentation more powerful than ever. POS data, loyalty data, foot traffic data, digital interaction data, supply chain data — the modern retailer sitting on all of this information had the raw material to design and measure experiments with a precision that would have been unimaginable to the direct mail marketers of seventy years earlier.

The 2010s also brought a broader cultural shift in how leading retailers thought about decision-making. The influence of data-driven cultures from the tech industry — companies like Google, Netflix, and Facebook, which had built experimentation into the fabric of how they operated — began to seep into retail strategy. Books like “Thinking, Fast and Slow” and “The Signal and the Noise” popularized ideas about cognitive bias and the unreliability of intuition that made the case for systematic testing accessible to a general business audience. CEOs and boards started asking for evidence. Merchants and operators started being asked to show their work.

Test and learn stopped being an analytics team initiative and started becoming a strategic priority.

The 2020s: AI, Automation, and the Next Frontier

The most recent chapter in the history of retail experimentation is still being written, but the direction is clear. Artificial intelligence and machine learning are transforming both the design and analysis of retail experiments in ways that are making the methodology faster, more precise, and more accessible than ever before.

On the design side, AI is making it possible to identify which experiments are most likely to produce meaningful results before they are even run. By analyzing historical data, competitive signals, and patterns from previous tests, AI-powered platforms can help retailers prioritize their test pipeline more intelligently — focusing time and resources on the experiments with the highest expected value rather than working through a list based on intuition or organizational politics.

On the analysis side, machine learning techniques are enabling retailers to extract more signal from their experiment data. Traditional A/B testing compares average outcomes across test and control groups. Modern analytical approaches can identify which customer segments, store formats, or geographies responded differently to a change — producing richer, more actionable insights from the same experiment.

Personalization at scale is another frontier. Rather than testing one version against another and rolling out the winner to everyone, retailers are beginning to use experimentation infrastructure to serve different experiences to different customer segments simultaneously — a form of continuous, dynamic optimization that blurs the line between testing and deployment.

And the speed of iteration is increasing. What once took months can now take weeks. What took weeks can now take days. The gap between asking a question and getting a reliable answer is narrowing continuously, which means the pace at which retailers can learn and adapt is accelerating.

What the History Tells Us

Looking back across a century of retail experimentation, a few consistent themes emerge.

The methodology has always been ahead of the tools. The core logic of test and learn — form a hypothesis, run a controlled experiment, measure the result — was understood by direct mail marketers in the 1920s. What changed over the decades was not the logic but the speed, scale, and precision with which it could be applied. The tools kept catching up to the idea.

The retailers who tested consistently won. From Tesco’s Clubcard insights in the 1990s to Amazon’s relentless digital experimentation in the 2000s, the competitive advantages created by serious investment in experimentation have been documented repeatedly. This is not a coincidence. Retailers who know what works — because they tested it — make better decisions than those who guess.

Culture matters as much as capability. The history of retail experimentation is also a history of organizational change. The companies that benefited most from test and learn were not just the ones with the best tools or the most data. They were the ones that built cultures where testing was normal, where negative results were valued, and where decisions were expected to be backed by evidence. Capability without culture produces reports that sit in inboxes. Culture with capability produces competitive advantage.

The pace of change is accelerating. Each decade has seen a significant expansion in what is possible — more data, better tools, faster cycles, deeper insights. The retailers who are building experimentation capabilities today are doing so in an environment of unprecedented analytical power. The question is not whether the tools are good enough. The question is whether the organization is ready to use them.

The Bottom Line

Test and learn did not emerge fully formed from a conference room or a business school. It evolved over a century of practice, driven by retailers who were trying to solve a fundamental problem: how do you make better decisions in a complex, fast-moving, customer-driven business?

The answer they arrived at — test before you scale, measure what actually happens, and build on what you learn — is as relevant today as it was when direct mail marketers were tracking response rates on split-run catalogs in the 1930s. The tools are unrecognizably more powerful. The principle is exactly the same.

Understanding this history matters because it tells you something important about the retailers who are winning right now. They are not doing something new. They are doing something old, very well, with modern tools. And that is an advantage that compounds over time.