In a time of economic uncertainty and rapidly evolving trade dynamics, bold ideas alone aren’t enough. To drive growth and stay competitive, businesses need precision, agility and evidence-based decision-making.

Pursuing innovation means asking the right questions:

• Will this initiative generate true incremental growth or simply shift existing demand?
• What pricing strategy will optimize both profitability and customer loyalty?
• Which markets are most likely to respond positively?

Business experimentation provides a powerful framework to answer these questions with confidence. By testing new ideas in controlled environments, organizations can reduce risk, validate assumptions and scale only what works.

Across industries, experimentation helps companies adapt to shifting consumer behavior and market conditions. For example, businesses might test:

• Store or branch operating hours
• Pricing strategies
• SKU rationalization
• Loyalty programs and incentives
• New product launches
• Multimedia campaigns

Staffing and training models

In today’s unpredictable landscape, companies are searching for clarity. And while AI can offer predictions, it’s not enough. AI models are trained on the past — but the future is breaking from historical patterns. In uncertain times, leaders don’t just need forecasts — they need confidence. Business experimentation delivers it. For a more in-depth read on this topic, check out our white paper, Commerce in flux: Navigating uncertainty through experimentation.

Business experimentation refers to the process of testing a new initiative in some locations or with some customers and comparing performance in the “test group” to a well-matched “control group” that does not receive the new initiative.

Performance metrics like sales and profit can be extremely volatile due to factors like weather, changes in socioeconomic conditions and competitor actions. Isolating the impact of a program amidst all of this “noise” is tough, and you might ask yourself: Did my initiative really work, or was it just due to external factors? 

When businesses experiment with test and control groups that are similar in their characteristics or behaviors, they can minimize the influence of factors that they can’t control. Any difference in the experiment’s results is from true cause-and-effect between what they did (for example, a restaurant makeover) and what they measured (for example, a 3% increase in sales). 

Businesses need to experiment because well-designed business experiments fuel innovation in a cost-effective way. Experiments empower businesses to try new ideas on a small scale before investing in a more resource-intensive rollout. Check out our guide, Bold moves, smart bets: De-risking innovation in the age of accelerated AI, to unlock best practices for accelerating innovation with clarity, purpose and impact.

The stakeholders involved in the business experimentation process vary depending on the type of initiative. For instance, the marketing department should be heavily involved when testing a new promotional offer, while finance and real estate teams would weigh in more on large-scale store remodels.

Organizations can use business experimentation to understand how new initiatives will perform. Experimentation platforms like Mastercard Test & Learn® can help isolate the true incremental impact of an initiative. Remember: Big data can provide insights on historical behaviors, but data alone isn’t enough to fuel innovation

Business experiments can measure a variety of KPIs, depending on the initiative and the type of business running the experiment. For example, a restaurant that is adding a new drive-thru may focus on measuring the impact on transactions and sales. A loyalty program manager, on the other hand, may be more interested in customer lifetime value.

• Bias - External factors can lead to distorted results and inaccurate conclusions.
• Control group - Locations or customers that were intentionally excluded from the test treatment. This will serve as a baseline to determine the efficacy of the test.

There are five steps in the business experimentation process:

Business hypotheses can be centered around solving a problem or capitalizing on an emerging opportunity. To create the most effective hypotheses, organizations should develop a deep understanding of the latest industry trends and customer purchasing patterns. With over 159 billion anonymized and aggregated transactions, Mastercard’s business and market intelligence solutions help organizations fuel bold ideas with deeper insights.

To accurately measure the impact of any business action, organizations must first minimize bias in the target population and then isolate the test segment from outside influences. This can be achieved with rigorous test and control sizing and selection using advanced simulations.

Organizations should then measure the performance before and after an initiative across all channels for both the test and control groups. With a well-designed experiment, any differences in performance between the “test” and “control” groups can be directly attributed to the initiative.

By drilling down further and scanning hundreds of attributes, organizations can identify the strongest drivers of performance. These drivers can be combined into a predictive model, which can show how to target the roll out to maximize the benefit.

Organizations should use learnings from current business experiments to inspire new ideas and hypotheses for future tests. Leading organizations are taking it a step further, creating a testing pipeline that prioritizes potential tests against the following: feasibility, economic impact, decision-focused and strategic fit.

There are a few challenges that companies face in the business experimentation process:

• Analyzing NFL game day ads
• Measuring loyalty program effectiveness
• Improving operational efficiencies
• Understanding if retention offers work
• Calculating ROI of a restaurant promotion
• Evaluating the impact of remodeling stores

• Consumer packaged goods
• Convenience retailers
• Banking
• Telecommunications
• Quick service restaurants (QSRs)
• Apparel retailers

A beverage brand in North America worked with Mastercard to analyze the effectiveness of its NFL game day television ads. Although the most accurate way to measure an initiative’s impact is with a designed test and control group, the NFL game day ads were rolled out to the entire market and there was no control for comparison.

By using Test & Learn® with SpendingPulse™ Modeling, the CPG brand was able to analyze the effectiveness of their ads. Rather than a test vs. control approach, this methodology predicted the brand’s expected performance in the absence of the ads by using machine learning, proprietary Mastercard insights and correlated datasets. The analysis showed which products benefited the most from the ads and what timeframes worked best. With these insights, the brand developed more effective marketing investments.