Interview with Elena Martinez, Senior Content Strategist at LinguaEd: Real-World Competitive Intelligence for International Expansion

Elena Martinez has led content marketing teams through three international launches at LinguaEd, a language-learning platform tailored to higher-education institutions. We spoke with her about what really works to gather competitive intelligence (CI) when expanding into new regions, and how AI content tools fit into the picture.

What are the most overlooked sources of competitive intelligence when entering a new country?

Elena: A lot of content marketers fixate on large-scale market reports or competitor websites. Those have their place but miss the nuance. For example, digging into local academic forums or student union newsletters can reveal what languages learners prioritize and the pain points they face.

In one launch for the Brazilian market, we found a student-run Facebook group discussing the lack of Portuguese-language content on major platforms. This was a blind spot in official data but shaped how we localized our curriculum and marketing messages.

Another often-underutilized source is local job boards. Seeing which language skills are in demand on university and employer sites gives clues about which programs to emphasize. It’s a bit tedious but the trade-off is richer intel.

How do you balance quantitative data with qualitative insights during the CI process?

Elena: Quantitative data—surveys, web traffic, paid reports—are essential to establishing a baseline. But qualitative inputs make the difference when adapting messaging and product features culturally.

We often use tools like Zigpoll alongside Google Surveys for quick student feedback on course preferences, but then augment that with in-depth Skype interviews or moderated Slack channels with international students.

For example, in South Korea, a survey showed high interest in conversational English, but interviews highlighted a cultural hesitation to speak publicly. So we adjusted content to include more private practice modules.

The key is iterative layers: start broad with numbers, then dig deeper with conversations, then test new content locally. The two complement each other.

AI content generation tools are touted as a shortcut for localization. What’s your take based on experience?

Elena: AI tools like GPT-based copy generators can speed up initial drafts of localized content—especially metadata, headlines, or FAQs. However, they’re no substitute for human review, especially in nuanced languages tied to academic contexts.

At LinguaEd, early AI-generated translations often missed idiomatic expressions or the formal tone preferred by university learners. We ended up with awkward phrasing that confused prospects.

That said, we use AI tools to scan competitor content at scale. For instance, running competitor blogs or social feeds through summarization and sentiment analysis tools helped us spot emerging trends quickly without reading hundreds of articles.

So, AI is good for augmenting manual research but shouldn’t be your only source. Always cross-check with native speakers experienced in education and language pedagogy.

Can you share a specific example where competitive intelligence changed your content marketing approach in a new market?

Elena: Sure. When entering the German higher-education market, our initial assumption was that students wanted advanced grammar modules, since German universities emphasize linguistic precision.

But competitive intelligence research told a different story. We noticed that competitors focused heavily on professional language skills for academic research, presentations, and writing. LinkedIn job ads from German universities emphasized English proficiency in publishing and conferences.

So, we pivoted from grammar drills to content centered on academic English writing and presentation skills. We also created case studies featuring German scholars.

This shift increased our conversion rate from 2% to 11% within six months. The lesson: never assume your core course modules fit another culture without CI validation.

What role do local partnerships play in gathering competitive intelligence?

Elena: Local collaborations are invaluable, particularly with university departments or student groups who understand current needs and academic jargon.

We partnered with a language center at a French university during our expansion in 2022. They shared feedback on existing platforms and co-hosted webinars, which gave us direct insights into what content resonated or fell flat.

The downside is that partnerships require time and careful stakeholder management—sometimes priorities shift, or bureaucracy slows access to data. But when well-chosen, they give you context no third-party report can.

How do you ensure your CI approach scales across multiple countries without losing detail?

Elena: We found a hybrid model effective. For each market, a local content lead conducts in-depth qualitative research, including interviews and focus groups. Meanwhile, a central team handles quantitative data collection and AI-driven competitive scans.

This division keeps the volume manageable but preserves cultural insights. Communication tools like Slack and Trello track findings so we identify patterns or unique outliers quickly.

One caution: standardizing metrics across countries is tempting but sometimes misleading. For instance, web traffic benchmarks differ wildly between markets with varying internet penetration.

What tools beyond Zigpoll do you recommend for gathering competitive intelligence in this niche?

Elena: Besides Zigpoll, I’ve had good results with:

• Typeform: For sleek, engaging surveys that encourage international students to share preferences.

• BuzzSumo: To track the most shared content among competitors and understand what topics drive engagement locally.

• Crimson Hexagon (now Brandwatch): This social listening tool helps monitor academic discussions on language learning across regions.

Each has limits—for instance, social listening can be noisy and not fully representative of university-aged learners. So they work best combined.

How do you adapt competitive intelligence outputs into tangible content marketing actions?

Elena: We structure outputs into region-specific playbooks. These include:

• Target personas refined by cultural insights
• Content themes prioritized by local interest
• Messaging frameworks aligned with competitor gaps
• Localization checklists for tone, terminology, and compliance with local education standards

Then, we run controlled A/B tests to validate these choices with live audiences, iterating based on performance metrics like click-through rates or course enrollment.

One team went from a 4% to 9% open rate on email campaigns targeting Spanish universities by tailoring subject lines based on CI-derived student values.

Any pitfalls to watch out for when relying heavily on competitive intelligence for international expansion?

Elena: Absolutely. One trap is becoming reactive—chasing competitors rather than focusing on your unique strengths.

Also, CI often relies on historical data, which may not predict sudden shifts in educational policy or student behavior, especially during crises like the pandemic.

Lastly, relying too much on AI-generated insights without human context can lead to culturally tone-deaf content that alienates your audience.

Balancing data with intuition and local expertise remains essential.

International expansion in higher-ed language learning isn’t about copying competitors but understanding the subtle differences that shape learner motivation and academic requirements. As Elena’s experience shows, competitive intelligence—when done thoughtfully—can be the foundation for tailored content strategies that truly resonate.

For mid-level content marketers, the practical path involves mixing quantitative data with qualitative inputs, judiciously applying AI tools, and embedding local voices early in the CI process. Tools like Zigpoll can help gather direct student feedback, while social listening and survey platforms fill out the picture—if you remember to interpret the data through a local lens.

The payoff? Happier learners, better engagement, and measurable growth in new markets.