In today’s fast-moving and fragmented market, businesses need more than numbers to understand their customers – they need context. That’s why mixed-method research is on the rise. By combining quantitative survey data with qualitative feedback and behavioral observations, insights professionals get a more complete, accurate picture of consumer attitudes and actions.

Each data type brings something different to the table. Surveys provide structured, scalable opinions. Interviews and open-ends offer depth and emotion. Behavioral data – often collected passively – shows how people truly behave, not just what they say they do. Bringing them together bridges the gap between "what" and "why." That’s the power of mixed-method analysis.

When Diverse Data Types Work Together

Using these approaches in tandem can answer business questions more confidently:

• Combining survey results with in-the-moment feedback can validate customer satisfaction metrics with actual user comments.
• Layering behavioral data on top of interview insights can test hypotheses about motivation and decision-making.
• Merging purchase history with qualitative feedback helps uncover unmet needs hidden behind behavior patterns.

In practice, most projects already gather multiple data sources. The challenge is how to bring them together in a meaningful way – which is where tools like Looker come in.

Looker: An Opportunity to Unlock Unified Insights

Looker’s modeling layer (LookML) empowers teams to build customized, reusable analytics frameworks. It’s flexible enough to handle survey scores, behavioral metrics, and even qualitative tags – but only if the model is designed with mixed-method analysis in mind. When structured correctly, Looker dashboards can surface connected insights across methodologies, speeding up discovery and helping teams act intelligently.

For brands who run lean insights teams or use DIY tools to move faster, this matters more than ever. But with DIY also comes complexity, especially when stitching data together from disparate sources. And if teams don’t have deep expertise in both research methodologies and tools like Looker, valuable details can be lost in the shuffle.

That’s where SIVO’s On Demand Talent shines – bridging strategy, data, and tooling with the precision that only experienced consumer insights experts can offer.

Looker is a powerful analytics platform, but when it comes to mixed-method research, blending qualitative and quantitative data can quickly become a technical maze. LookML – the language used to create data models in Looker – is incredibly customizable. However, that flexibility also means there are more ways things can go wrong if the structure isn’t thought through carefully.

Here are some of the most common problems teams run into when using Looker for consumer insights across different data types:

1. Disconnected Schema Design

Looker models rely on linking datasets through schemas. But qualitative data (like interview tags or sentiment coding) rarely fits neatly with tabular metrics. Without a clear plan for how these connections should work, dashboards can become fragmented or misleading. For example, if survey data is structured by respondent ID but qualitative insights are tagged at the theme level, joining the datasets properly becomes a challenge.

2. Loss of Qualitative Context

Looker excels at aggregations and filtering – ideal for numbers, but not always for narratives. When qualitative data gets squeezed into quantitative formats (e.g., sentiment “score” buckets), teams risk losing the nuance that made the data valuable in the first place. This can lead to oversimplified insights or dashboards that miss the depth qualitative input provides.

3. Inconsistent Data Preparation Across Teams

Mixed-method research often involves multiple data sources, collected by different teams or tools. When internal processes for prepping survey, qual, and behavioral data aren’t aligned, integration becomes messy in LookML. The lack of standardized naming conventions, timeframes, or coding practices can result in unreliable datasets and hours spent troubleshooting errors.

4. Overwhelmed DIY Teams

DIY platforms like Looker are tempting for insights teams with limited budgets – but without experienced talent guiding the build, things can stall or misfire. Market research teams often understand the business questions but may lack the data modeling skills to implement them effectively in LookML.

This is a key area where bringing in On Demand Talent can save time while enhancing output. These professionals understand research methodologies and LookML – bridging the technical and strategic divide. Unlike freelancers or consultants who may need time to ramp up, SIVO’s experts hit the ground running to structure models that serve both data integrity and research goals.

By solving these common issues early in the process, teams can create Looker models that truly support mixed-method analysis. This leads to better insights – faster, smarter, and more aligned with consumer reality.

Designing LookML models that support mixed-method research – where qualitative insights, quantitative survey data, and behavioral metrics come together – requires a thoughtful and scalable approach. A well-structured Looker model enables researchers and business stakeholders to explore insights from multiple data types in a unified framework, without overwhelming the user or compromising data quality.

Here are some best practices to guide your LookML schema design:

Use Clear and Consistent Naming Conventions

When combining multiple data types, consistency is critical. Use intuitive names for dimensions and measures that clearly reflect the underlying data source, such as "survey_score_overall" or "interview_theme_customer_loyalty". This helps avoid confusion as the model grows.

Separate Logical Views for Qual, Quant, and Behavioral Data

Design your LookML model with discrete views for each data type – qualitative, quantitative, and behavioral. This lets you handle relationships and join keys appropriately for each domain, while maintaining flexibility to integrate data during exploration. For example:

• survey_responses view for structured survey data
• interview_findings view for coded qual themes
• digital_behavior view for web or app events

Design for Ease of Use

Market researchers using Looker may not be data engineers, so model usability matters. Group related fields into logical categories using dimension_groups, and set intuitive field descriptions that clarify each metric’s purpose and format. Consider hiding or restricting advanced joins unless necessary for power users.

Incorporate Metadata and Flags for Mixed-Method Data

Adding fields to tag data (e.g., channel_source, response_type, methodology_flag) can help users filter by method or identify the source of insight. This is especially important in mixed-method analysis where questions like “Where did this insight come from?” can impact decision-making confidence.

Anticipate Cross-Join and Granularity Issues

Combining qual and quant data can lead to mismatched granularity – for instance, joining aggregated themes with individual-level behavioral events. Prevent performance issues by pre-aggregating qualitative codes or building separate Explores for different analysis levels.

Ultimately, your LookML model should empower consumer insights teams to analyze, synthesize, and act on data from multiple sources. By thoughtful schema design and aligning with how researchers actually use the data, you reduce friction and guide users toward confident decision-making across methods.

As DIY analytics platforms like Looker become more central to consumer insights workflows, many teams face the challenge of combining powerful tools with the right expertise. A robust analytics stack is only as valuable as your ability to use it effectively – especially in mixed-method contexts that blend qualitative, survey, and behavioral data.

That’s where SIVO’s On Demand Talent can step in and make a significant difference. These seasoned consumer insights and analytics experts help bridge the gap between technical tools and strategic research needs – without the commitment or cost burdens of hiring long-term staff or relying solely on consultants.

Bridging the Research-Tech Divide

Most DIY tools are optimized for structured data, yet mixed-method research often requires interpretation of open-ended responses, coded interviews, and behavioral nuance. On Demand Talent professionals bring cross-functional know-how – pairing analytical skills with research thinking – to ensure your Looker models actually tell the full story.

Rather than forcing standard dashboards to fit complex questions, these experts help you shape Looker’s Explorers to reflect how your team thinks, creates hypotheses, and draws conclusions.

Accelerating Tool Adoption and Team Learning

For smaller or leaner research teams, there’s often a knowledge gap between acquiring the tool and making full use of it. On Demand Talent can help with:

• Modeling mixed data into Looker using best-practice schema
• Training your staff on data interpretation and design thinking workflows
• Auditing current LookML builds to identify performance and structural improvements
• Creating processes for regular updates as new data types or studies are added

In one fictional brand case, an insights team was struggling to make survey metrics visible alongside customer verbatims from interviews. An On Demand Talent specialist restructured their LookML views, allowing category managers to filter themes by NPS segment – unlocking new levels of storytelling from previously siloed data.

Flexibility Without Sacrificing Quality

Many teams are exploring AI tools, automations, and shortened research cycles. But even the best tools can't compensate for lack of experience or strategic alignment. On Demand Talent offers flexibility to scale up capacity or bring in specialty skills – from data modeling to qual-quant integration – exactly when you need it, without sacrificing rigor or speed.

As consumer insights evolves with AI, real-time data, and flexible tooling, your analytics stack needs to do more than handle current demands – it needs to adapt to what’s next. Structuring your LookML architecture for mixed-method data is a strong foundation, but future-proofing your ecosystem requires collaboration, continuous learning, and scalable design. Expert guidance can be the difference between quick wins and long-term sustainability.

Build for Flexibility, Not Just Function

Platforms like Looker are evolving fast, with new integrations, AI features, and user-friendly interfaces. That’s good news – but only if your data model can keep pace. With expert help, your Looker environment can be structured to scale both technically and strategically:

• Modular LookML views that support new data sources without breaking old queries
• Schema designs that support both dashboarding and advanced exploration
• Metadata tracking for methodology or project attribution
• Governance frameworks to support accurate, secure decision-making

An On Demand Talent professional can help transform your stack from reactive reporting to a proactive insight engine that evolves with your business questions.

Enable Cross-Team Collaboration

When different teams – from brand managers to UX researchers – use the same platform but speak different “data languages,” models can easily become fragmented. Seasoned experts ensure your LookML models reflect a shared structure and terminology that promotes transparency and trust across functions.

Plan for Human + AI Partnership

As generative AI tools integrate into consumer insights processes, teams need to think beyond just machine efficiency. Human interpretation, context, and ethical analysis remain central – especially with qualitative data. Looker models that are built to surface nuance alongside numbers will enable smarter AI augmentation down the line.

By partnering with On Demand Talent, you gain access to professionals who not only understand current best practices, but also anticipate where research and analytics are moving. They can help your team experiment, implement, and refine new tools without breaking rhythm – or budget.

The future of data analytics in research is hybrid: cross-functional, cross-method, and increasingly self-service. With expert help guiding the way, your team can stay confident, creative, and ready for what’s next.