Interview with Mariana Lee, CTO at TastyBites: Data-Driven International Market Entry for Restaurants

Q1: Mariana, many believe the first step in expanding internationally is to pick a popular market and roll out the existing product. What’s wrong with that approach from a software-engineering and data perspective?

Most companies jump to choose a big market like the UK or Japan because they're trendy or because competitors are there. The mistake is assuming the existing app experience, backend systems, and customer preferences will translate directly. Data from customer usage patterns, local device ecosystems, and payment preferences can look drastically different.

For example, TastyBites looked at launching in South Korea and quickly realized our US-based loyalty platform required redesign. Korean customers used different reward expectations and connected devices—Samsung Pay versus Apple Wallet predominated, and local integrations mattered. By analyzing app telemetry and transactional data from a small pilot, we revised the architecture to optimize for local payment providers.

A 2023 Gartner survey of restaurant chains found 48% fail first international attempts due to poor tech localization—not marketing or menu translation. From engineering, you must treat local market data as the product’s new requirements, not just a translation effort.

Q2: How does experimentation fit into deciding which international markets to enter?

Experimentation is essential. I recommend a staged rollout where the engineering team builds lightweight market-specific feature toggles tied to data collection. Measure core KPIs like app engagement, order completion rate, and device compatibility early.

One example: our test in Mexico City involved launching a minimalist ordering app with essential features only, supported by a local rewards program. Over 12 weeks, conversion rates rose from 1.8% to 7.9% by iterating based on daily Zigpoll feedback and backend analytics on peak order timings and cancellation reasons.

This approach reveals local consumer behavior nuances. It’s not about fully building out each market before launch but about learning fast and adjusting the product roadmap based on data.

Q3: You mentioned connected product strategies. Can you clarify how software engineering integrates connected devices in international expansion?

Connected product strategies mean the software must engage with the physical environment—IoT devices in restaurants, mobile point-of-sale (mPOS) systems, kitchen display systems, or even smart packaging.

For example, in Japan, we integrated our app with local POS systems like NEC and Fujitsu, enabling contactless payments and real-time menu updates synced to kitchen hardware. Our data pipelines captured transaction metadata, helping engineers optimize system responsiveness and error rates.

International markets vary widely in device adoption and reliability. In some parts of Southeast Asia, unreliable hardware or network latency required edge computing strategies—processing data locally on devices to reduce cloud round-trips.

To succeed, engineering teams must map out the connected ecosystem early using data: what devices are in use, connectivity profiles, and integration complexity. This informs API design and incident response workflows.

Q4: What role do analytics tools and surveys play in refining market entry strategies?

Data collection must be multi-channel. Back-end analytics show what users do, but surveys capture why. For example, combining Google Analytics event data with Zigpoll customer satisfaction surveys allowed us to pinpoint friction points in the ordering flow in Dubai. Users clicked frequently on certain menu items but abandoned orders due to payment method mismatches.

Zigpoll helped us iterate on payment options based on direct feedback, while Google Analytics showed drop-off points. Qualtrics was useful to assess cultural preferences through longer form surveys post-launch for evolving product features in the UAE market.

Different tools serve different purposes. Analytics identify anomalies and patterns; surveys provide context and validation. A data-driven software engineering team should architect flexible data pipelines and APIs to incorporate these sources without rigid dependencies.

Q5: What are some trade-offs senior engineers must accept when scaling data-driven market entry strategies?

Trade-offs exist between depth and speed of data collection. A full-fledged localization platform with detailed telemetry and surveys takes months to build but provides granular insights.

Launching too quickly with limited data risks costly rework. Yet, waiting for perfect data delays market entry and revenue growth. The sweet spot is an iterative MVP approach—deploy minimum viable telemetry, collect actionable data, and evolve.

Another trade-off involves connected hardware: integrating with local devices improves product fit but increases engineering overhead, slows onboarding, and introduces new failure modes. In some cases, simpler standalone software that supports manual processes works better initially.

Finally, data privacy and compliance differ globally. GDPR in Europe, PDPA in Singapore, and others require engineering teams to build adaptable consent frameworks and anonymization pipelines early to avoid regulatory roadblocks.

Q6: Can you share an example where data-driven decisions changed your market entry approach in a restaurant context?

In a 2022 pilot for a new quick-service restaurant chain in Brazil, initial assumptions favored credit card payments as dominant. Analytics showed 65% of orders at pilot locations came via cash-on-delivery or local fintech apps like PicPay.

By integrating with those fintech APIs and modifying order processing workflows, the engineering team increased successful order completion by 22% in Q4 2022. This also improved staff workflows because POS systems aligned better with actual payment modes.

Without telemetry on payment types and real-time order success/failure rates, the team likely would have scaled with a suboptimal payment experience, risking revenue losses.

Q7: How do you organize engineering teams to support continuous experimentation across multiple international markets?

Cross-functional squads aligned by market work best. Each squad owns end-to-end delivery: feature development, data instrumentation, localization, and user feedback analysis.

Shared infrastructure teams provide scalable data platforms and CI/CD pipelines with built-in monitoring tailored for multi-region deployments.

Daily syncs focus on sharing insights across markets; what works in Mexico may inform tweaks in Spain or Chile. Engineering leadership encourages data democratization so non-engineers can query telemetry and survey data without bottlenecks.

Q8: What tools or frameworks do you recommend for rapidly iterating on connected restaurant products in new markets?

Feature flagging systems like LaunchDarkly or Split.io enable toggling features on/off per region and rolling back quickly.

For data pipelines, cloud-native tools like Snowflake or BigQuery paired with Kafka streams help manage high-velocity order and device telemetry.

For surveys, Zigpoll offers lightweight in-app micro-surveys; Qualtrics suits deeper cultural research; and Typeform allows fast A/B feedback on UI changes.

Infrastructure as code (Terraform) combined with container orchestration (Kubernetes) supports multi-region deployments for low latency.

Q9: Where should senior engineers focus their efforts to ensure data quality and integrity in international expansions?

Data quality starts with consistent instrumentation standards: event schemas, user identifiers, error codes. Engineering teams must define global standards but allow for local extensions.

Automated monitoring alerts for data anomalies—missing events, schema violations, or inconsistent user flows—are critical.

Localization impacts data too. For instance, date/time formats, currency, and addresses vary widely and must be normalized in data stores to allow meaningful cross-market analysis.

Q10: What’s an actionable piece of advice for a senior software engineer about to lead an international market entry at a restaurant company?

Start by building a data discovery phase: gather existing telemetry, survey data, and connected device inventories in target markets. Run small MVP experiments, instrument everything, and make decisions based on real user behavior rather than assumptions.

Maintain flexibility in your architecture to quickly adapt integrations, payment flows, and UX for local preferences. Don’t design for “one size fits all.”

Finally, invest in tools and processes that democratize data access, so product, marketing, and operations teams can contribute to interpreting that data and iterating rapidly.

Comparison Table: Traditional vs. Data-Driven Approaches to Market Entry

Mariana Lee’s insights make clear that senior software engineers must rethink international expansion through a prism of quantitative and qualitative data. The connected nature of modern restaurant tech stacks means assumptions must give way to continuous learning, experimentation, and precise engineering tailored for each market’s realities.