How can you design scalable experiments for different markets and regions?

If you are working on a product that has potential customers in different markets and regions, you might want to run experiments to test your assumptions, validate your value proposition, and optimize your features. However, designing experiments that are scalable, reliable, and relevant across diverse contexts can be challenging. In this article, you will learn some tips and best practices to help you design scalable experiments for different markets and regions.

Key takeaways from this article

Localize user testing:

Interview users in target areas to understand their needs, then create rapid prototypes for feedback. This direct interaction reveals valuable insights and paves the way for more tailored and effective experiments.
Understand regulations:

Knowing legal requirements like data storage, marketing guidelines (e.g., GDPR), and local entity policies is crucial. It ensures your experiments comply with regional laws, preventing legal hurdles that could derail your efforts.

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1 Understand your target segments

Before you design any experiment, you need to understand who your target segments are, what their needs and pain points are, and how they behave and interact with your product. You can use various methods to conduct customer research, such as surveys, interviews, focus groups, user testing, analytics, and feedback tools. You should aim to segment your customers based on their characteristics, behaviors, preferences, and goals, and create personas or profiles that represent each segment. This will help you tailor your experiments to each segment and measure the impact of your product on them.

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Ken Parmelee

Enterprise Software Product and Strategy Executive | Vice President @ Functionize | MIT Advisor, Gartner AR Council, Forrester AR Council
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I have had the great fortune of leading global product teams for many years. In my experience the best methods to scale experimentation in different markets, verticals and regions is through the steps of 1) Interviewing sponsor users in the target area. These interviews thould be as detailed as possible on how the user wants to get the result. 2) Build rapid click-through mock-ups that you can put in front of the user. That interaction should be observed in order to see the reaction, struggles, missing features, poor screen organization... and their verbal feedback should be taken as well. 3) Then look across the feedback for patterns and 4) Start digital A/B testing where you can then get to the stronger variants.

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Joshua Linn

GM, RegTech & SVP, ML Product Management @ Socure | Leading 7 Business Lines | Serving 2300 Customers and 1B End Users Globally | Providing Equitable & Seamless Access to the Products People Love
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Ask yourself - who are the innovators and early adopters? What messaging resonates with them? Can’t underscore this enough - as you find product-market fit and scale, continuously segment users by behavior, not just demographics. Build user personas that capture motivations and objections.

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2 Define your hypotheses and metrics

Once you have a clear picture of your target segments, you need to define your hypotheses and metrics for each experiment. A hypothesis is a statement that expresses your assumption about how your product will solve a problem or create value for a specific segment. A metric is a measurable indicator that shows whether your hypothesis is true or false. You should use the SMART framework to define your hypotheses and metrics, which means they should be Specific, Measurable, Achievable, Relevant, and Time-bound. For example, a hypothesis could be: "Adding a social proof feature will increase the conversion rate of segment A by 10% in one month." A metric could be: "The number of sign-ups divided by the number of visitors for segment A."

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Manali Debroy

Servant Leader | Fintech | Coach | Building Women in Product SG | Keynote Speaker | Ex-Goldman Sach |Author | Fintech 65 Singapore Women Leaders
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This is a very important step. Defining measurable product metrics goes a long way in getting the whole team aligned in terms of product understanding and why are we building this feature. As a product leader, it is very important for him or her to do this exercise with the team and not in silo. Scalable products can be only built: - a clear understanding of the markets (customers, regulations, competitors) - henceforth, identified either a gap in the market or introducing a new habit forming product that complements the market - you have your metrics defined for measuring execution, success and future performance. If you are not ready with any of these, then it’s an indication to go back and do your homework.

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3 Choose your experiment methods and tools

Depending on your hypotheses and metrics, you need to choose the most appropriate methods and tools to run your experiments. There are many types of experiments you can use, such as A/B testing, multivariate testing, usability testing, beta testing, prototyping, and MVP testing. You should consider the pros and cons of each method, such as the cost, time, complexity, validity, and reliability. You should also use the right tools to design, launch, and analyze your experiments, such as web or mobile platforms, analytics software, testing software, and feedback tools. You should make sure that your tools are compatible with your product and can handle different markets and regions.

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4 Adapt your experiments to different contexts

Designing scalable experiments requires customization to different contexts, such as languages, cultures, regulations, and environments. To ensure success, you should avoid a one-size-fits-all approach and instead tailor your experiments to each market and region. Language should be translated and localized to match the language and tone of each market. Additionally, cultural norms, values, preferences, and expectations should be taken into account. Regulation compliance is essential for legal and ethical requirements of each market. Additionally, the technical and physical differences between markets must be accounted for in order to optimize performance and accessibility. By considering these factors when adapting experiments, you can ensure successful outcomes in a variety of contexts.

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Joshua Linn

GM, RegTech & SVP, ML Product Management @ Socure | Leading 7 Business Lines | Serving 2300 Customers and 1B End Users Globally | Providing Equitable & Seamless Access to the Products People Love
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Different customer segments have different needs so we have to go back to basics and question our assumptions about the new context. Develop hypotheses about how results may differ and why. You may find you need to tweak messaging, offerings, or channels to resonate despite similar demographics (fintech and traditional banking is a great example of this). Don't underestimate the cultural nuances of each market and celebrate when your experiments reveal surprises and counterintuitive insights. This means you are asking the right questions to avoid a one-size-fits all approach.

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5 Analyze and compare your results

After you run your experiments, you need to analyze and compare your results across different markets and regions. You should use statistical methods and tools to evaluate your metrics and determine whether your hypotheses are confirmed or rejected. You should also look for patterns, trends, outliers, and anomalies in your data, and try to explain the reasons behind them. You should compare your results with your benchmarks, goals, and expectations, and see how your product and experiment elements performed in different contexts. You should also use qualitative methods and tools, such as interviews, surveys, and feedback tools, to collect additional insights and feedback from your participants.

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6 Learn and iterate

The final step of designing scalable experiments is to learn from your results and iterate on your product and experiment elements. You should use the insights and feedback you gathered to identify the strengths and weaknesses of your product and experiment elements, and to discover new opportunities and challenges. You should also use the lean startup cycle of build-measure-learn to test new hypotheses and metrics, and to improve your product and experiment elements based on your learning. You should repeat this process until you achieve product-market fit for each market and region.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Mohamed Raffi

Chief Architect
(edited)
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Know the Regulations in each market. 1.Storing data within the country or region 2. Marketing guidelines impacted by GDPR 3. Legal entity requirements 4. Policies related to your products. For example: Amazon online entity is not in nordics

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How can you design scalable experiments for different markets and regions?

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1 Understand your target segments

2 Define your hypotheses and metrics

3 Choose your experiment methods and tools

4 Adapt your experiments to different contexts

5 Analyze and compare your results

6 Learn and iterate

7 Here’s what else to consider

Product R&D

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How can you design scalable experiments for different markets and regions?

1

2

3

4

5

6

7

1 Understand your target segments

2 Define your hypotheses and metrics

3 Choose your experiment methods and tools

4 Adapt your experiments to different contexts

5 Analyze and compare your results

6 Learn and iterate

7 Here’s what else to consider

Product R&D

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