Ignite Insights
(as a Service)

Meaningful is a continuous intelligence operating system designed for market research and customer intelligence.

Meaningful connects fragmented inputs into a single analytical context, allowing insight to accumulate rather than reset, transforming research from a deliverable into a continuously maintained strategic asset.

Meaningful provides the infrastructure foundation to be used as part of an Insights-as-a-Service (IaaS) operating model.

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Meaningful transforms how research is conducted, managed, interpreted, and applied.

Meaningful enables a shift from research delivery to intelligence stewardship.

With Meaningful, you get continuity across studies, consistent interpretation across data sources, preservation of institutional memory, and faster, more defensible decision-making.

By unifying data, preserving memory, supporting rigorous synthesis, and keeping humans in the loop, Meaningful provides the foundation required to operate Insights as a Service with credibility and trust.

Insight no longer lives in deliverables. It lives in the system.

Integration

Meaningful works across qualitative, social, secondary, and proprietary data in one system

Consistency

Meaningful applies consistent analytical frameworks across disparate sources

Preservation

Meaningful preserves context and institutional memory over time

Continuity

Meaningful moves from episodic analysis to continuous intelligence

Meaningful enables a shift from research delivery to intelligence stewardship.

A Data Orchestration Platform for Market Research and Customer Intelligence

Customer understanding is distributed across many sources: qualitative interviews, focus groups, surveys, social conversations, reviews, competitive intelligence, internal documents, CRM data, analytics platforms, and expert knowledge. Each of these sources requires different collection methods, analytical approaches, tools, and specialist expertise. Most were never designed to work together. The result is structural fragmentation.

Meaningful is a data orchestration tool that connects fragmented inputs into a single analytical context, allowing insight to accumulate rather than reset.

Meaningful enables teams to:

Work across qualitative, social, secondary, and proprietary data in one system
Apply consistent analytical frameworks across disparate sources
Preserve context and institutional memory over time
Move from episodic analysis to continuous intelligence

From market research platform to customer intelligence layer

While Meaningful is purpose-built for market research, its architecture supports a broader role.

For brands, Meaningful functions as a customer intelligence layer—a system that continuously integrates signals from across the organization and the market to build a coherent, evolving understanding of customers.

This includes:

Primary research conducted internally or by partners
Ongoing social and cultural signals
Competitive and market intelligence
Internal documents, historical research, and knowledge assets
Client-owned data ingested via secure connectors

Rather than producing isolated reports, Meaningful enables brands to maintain a living, queryable understanding of customer needs, perceptions, and behaviors that evolves as new data arrives.

Enabling Insights as a Service (IaaS)

Meaningful provides the infrastructure that makes Insights as a Service (IaaS) operational. Agencies retain ownership of the client relationship, strategic framing, interpretation, and recommendations. Meaningful unifies the data, preserves memory, and supports high-quality synthesis at scale.

In this way, Meaningful is not positioned instead of agencies, researchers, or analysts. It is designed to bring out the best in everyone by removing structural friction and enabling deeper, more durable insight.

In an IaaS model:

Primary research conducted internally or by partners
Ongoing social and cultural signals
Competitive and market intelligence
Internal documents, historical research, and knowledge assets
Client-owned data ingested via secure connectors

A Data Orchestration Platform for Market Research and Customer Intelligence

Customer understanding is distributed across many sources: qualitative interviews, focus groups, surveys, social conversations, reviews, competitive intelligence, internal documents, CRM data, analytics platforms, and expert knowledge. Each of these sources requires different collection methods, analytical approaches, tools, and specialist expertise. Most were never designed to work together. The result is structural fragmentation.

Meaningful is a data orchestration tool that connects fragmented inputs into a single analytical context, allowing insight to accumulate rather than reset.

Meaningful enables teams to:

Work across qualitative, social, secondary, and proprietary data in one system
Apply consistent analytical frameworks across disparate sources
Preserve context and institutional memory over time
Move from episodic analysis to continuous intelligence

From market research platform to customer intelligence layer

While Meaningful is purpose-built for market research, its architecture supports a broader role.

For brands, Meaningful functions as a customer intelligence layer—a system that continuously integrates signals from across the organization and the market to build a coherent, evolving understanding of customers.

This includes:

Primary research conducted internally or by partners
Ongoing social and cultural signals
Competitive and market intelligence
Internal documents, historical research, and knowledge assets
Client-owned data ingested via secure connectors

Rather than producing isolated reports, Meaningful enables brands to maintain a living, queryable understanding of customer needs, perceptions, and behaviors that evolves as new data arrives.

Enabling Insights as a Service (IaaS)

Meaningful provides the infrastructure that makes Insights as a Service (IaaS) operational. Agencies retain ownership of the client relationship, strategic framing, interpretation, and recommendations. Meaningful unifies the data, preserves memory, and supports high-quality synthesis at scale.

In this way, Meaningful is not positioned instead of agencies, researchers, or analysts. It is designed to bring out the best in everyone by removing structural friction and enabling deeper, more durable insight.

In an IaaS model:

Primary research conducted internally or by partners
Ongoing social and cultural signals
Competitive and market intelligence
Internal documents, historical research, and knowledge assets
Client-owned data ingested via secure connectors

Data orchestration

A data orchestration platform for research teams to unify and synthesize diverse intelligence sources.

Customer Intelligence Layer

A customer intelligence layer for brands to maintain evolving understanding of their markets.

Insights-as-a-Service (IaaS)

An infrastructure foundation for Insights-as-a-Service (IaaS) partnerships that deepen over time

Insights-as-a-Service

Insights-as-a-Service (IaaS) represents a shift away from episodic research delivery toward continuous intelligence stewardship. Rather than commissioning discrete projects that reset context each time, organizations operate an always-on insight capability that accumulates understanding, preserves memory, and evolves alongside the business.

Meaningful provides the infrastructure that makes this model possible in practice.

Meaningful is designed to bring out the best in everyone by removing structural friction and enabling deeper, more durable insight.

Insights-as-a-Service with Meaningful

Meaningful's data orchestration capability enables a more powerful operating model: Insights as a Service (IaaS).

In an IaaS model, Meaningful acts as the central intelligence layer that sits between data sources and human decision-makers.

In an IaaS model:

Insight is continuous rather than project-based
Learning compounds instead of resetting
Context becomes a strategic asset
Human expertise is amplified rather than replaced

Meaningful provides the infrastructure that makes this model operational. Agencies retain ownership of the client relationship, strategic framing, interpretation, and recommendations. Meaningful unifies the data, preserves memory, and supports high-quality synthesis at scale.

Expert interpretation brought together in a single analytical context

The most powerful insight systems are not built solely on research outputs. They emerge when proprietary data, external signals, and expert interpretation are brought together in a single analytical context.

Meaningful is designed to support this through a flexible system, allowing organizations and agencies to unify all relevant sources of intelligence—without displacing existing tools or compromising governance.

This capability is foundational to advanced Insights-as-a-Service (IaaS) models.

Continuous insight instead of project resets

Because Meaningful retains and connects insight across time, organizations operating IaaS gain several structural advantages:

New research is interpreted in the context of everything that came before

Emerging signals can be detected earlier

Hypotheses can be revisited and tested as new data arrives

Strategic conversations are grounded in accumulated evidence rather than isolated findings

Insight becomes cumulative. Decision quality improves not because any single study is better, but because understanding deepens over time.

A shared intelligence foundation for multiple stakeholders

Another key characteristic of IaaS is that it supports multiple stakeholders simultaneously.

The same Meaningful instance can serve:

Agency teams delivering insight and advisory services

Brand-side research and strategy teams

Product, marketing, and executive stakeholders

Each group interacts with the same underlying intelligence layer, but at different levels of abstraction and responsibility. This reduces translation loss, misalignment, and duplicated effort.

In an IaaS model, Meaningful acts as the central intelligence layer that sits between data sources and human decision-makers.

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Meaningful’s role in the IaaS ecosystem

Meaningful ensures that everyone is working from a shared, continuously updated understanding of reality, even as their roles differ.

In an IaaS model, Meaningful acts as the central intelligence layer that sits between data sources and human decision-makers.

The roles are clearly delineated:

Agencies, researchers, & analysts:

Define strategic questions
Design research approaches
Interpret evidence and ambiguity
Make recommendations and guide action

Meaningful

Orchestrates data ingestion across sources
Applies consistent analytical frameworks
Preserves institutional memory over time
Supports synthesis, interrogation, and validation of insight

Meaningful is designed for human-in-the-loop intelligence. It orchestrates data and analysis so that teams can focus on interpretation, synthesis, and decision-making.

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Services

Core Capabilities

Meaningful unifies all major research and intelligence inputs into one system.

Qualitative Data Execution

AI-moderated qualitative conversations for scalable exploration
Human-moderated interview ingestion with structured analysis and persistence

Quantitative Data Integration

Surveys are designed, fielded, and initially analyzed in existing platforms
Results are ingested to enrich ongoing synthesis
Quant findings are interpreted alongside qualitative and market context

Secondary & Market Intelligence

Continuous ingestion of relevant external information
Structured around strategic questions, not one-off desk research
Preserved with clear source traceability

Meaningful enables a shift from research delivery to intelligence stewardship.

Services

Social & Cultural Signals

AI-moderated qualitative conversations for scalable exploration
Human-moderated interview ingestion with structured analysis and persistence

External Data Connectors

Surveys are designed, fielded, and initially analyzed in existing platforms
Results are ingested to enrich ongoing synthesis
Quant findings are interpreted alongside qualitative and market context

Live Synthesis

Continuous ingestion of relevant external information
Structured around strategic questions, not one-off desk research
Preserved with clear source traceability

Services

Threads (Hypothesis Testing)

AI-moderated qualitative conversations for scalable exploration
Human-moderated interview ingestion with structured analysis and persistence

Observability

Surveys are designed, fielded, and initially analyzed in existing platforms
Results are ingested to enrich ongoing synthesis
Quant findings are interpreted alongside qualitative and market context

Ongoing Support

Continuous ingestion of relevant external information
Structured around strategic questions, not one-off desk research
Preserved with clear source traceability

Meaningful is built on a single architectural principle:

Data should be connected, contextualized, and preserved over time.

Learn more

Rather than producing isolated reports, Meaningful enables brands to maintain a living, queryable understanding of customer needs, perceptions, and behaviors that evolves as new data arrives.

Learn more

Meaningful's data orchestration capability enables a more powerful operating model: Insights as a Service (IaaS).

Insight is continuous rather than project-based

Learning compounds instead of resetting

Context becomes a strategic asset

Human expertise is amplified rather than replaced

Insights as a Service (IaaS) with Meaningful

Meaningful: Quick Video

Journey

MEANINGFUL JOURNEY. EACH STEP OPTIONAL.

THE USER IS IN CONTROL.

OBJECTIVE

RESEARCH CHALLENGE

Identify research challenge / objective. Add as much context as you like to optimize results.

DIRECTION

RESEARCH PLAN

Takes your input and creates a research plan based on an integrated research approach.

SECONDARY RESEARCH

RESEARCH THE MARKET

Gathers deep secondary data such as industry reports and market data to help shape primary research or to add further context to gathered information.

SECONDARY RESEARCH

EXTERNAL DATA CONNECTORS

Integrate your data such as CRM, analytics, support tickets, and internal systems.

SECONDARY RESEARCH

SOCIAL SCRAPING

Track and monitor sentiment across various channels such as Reddit, Twitter, reviews, and forums in real time.

SECONDARY RESEARCH

AI PERCEPTION

Understand how ChatGPT, Claude, and other AI systems perceive your brand.

PRIMARY RESEARCH

QUANTITATIVE SURVEY

Layer in your primary research. You can easily import data from your quantitative survey such as Qualtrics or Forsta.

PRIMARY RESEARCH

MEANINGFUL CONVERSATION

Run a Conversation. It's Meaningful's AI moderated interviews for gathering qualitative data at scale.

PROCESSING

LIVE INTEGRATION & ANALYSIS

Takes 20 minutes what would typically be done in days or weeks. With the click of a button, one complete story emerges from multiple data streams.

OUTPUT

INSIGHTS DASHBOARD

Multiple data sources unified for a deeper understanding of your research goals and delivered through a clear, intuitive dashboard.

UNTIL YOUR NEXT MEANINGFUL JOURNEY!

Journey

MEANINGFUL JOURNEY. EACH STEP OPTIONAL.

THE USER IS IN CONTROL.

DIRECTION

RESEARCH PLAN

Takes your input and creates a research plan based on an integrated research approach.

YEAR

OBJECTIVE

Research Challenge

Identify research challenge / objective. Add as much context as you like to optimize results.

tip: you can upload your Research proposal

OBJECTIVE

Research Challenge

Identify research challenge / objective. Add as much context as you like to optimize results.

tip: you can upload your Research proposal

OBJECTIVE

Research Challenge

Identify research challenge / objective. Add as much context as you like to optimize results.

tip: you can upload your Research proposal

PRIMARY RESEARCH

MEANINGFUL CONVERSATION

Run a Conversation. It's Meaningful's AI moderated interviews for gathering qualitative data at scale.

PROCESSING

LIVE INTEGRATION & ANALYSIS

Takes 20 minutes what would typically be done in days or weeks. With the click of a button, one complete story emerges from multiple data streams.

OUTPUT

INSIGHTS DASHBOARD

Multiple data sources unified for a deeper understanding of your research goals and delivered through a clear, intuitive dashboard.

UNTIL YOUR NEXT MEANINGFUL JOURNEY!

Key Milestones

Explore our Journey

YEAR

Meaningful embarked on a journey of expansion, reaching new horizons and making strides in technological advancements.

Meaningful Case Studies

From complex B2B challenges to fast-turn creative testing, Meaningful has become an essential part of our research workflow. Below are recent projects using Meaningful. Each project demonstrates how smarter workflows, and Meaningful's AI-powered integrated research platform translate directly into successful outcomes (and cost and time savings).

B2B: Emerging Technology

EMERGING TECHNOLOGY

Objective: Understanding messaging and solution requirements for emerging technology in the US.

Audience: Purchase decision-makers (manager level+) in the emerging technology space across multiple industry verticals.

Approach: Online quantitative survey layered with secondary data (market analysis and social scraping) to validate findings and expand recommendations for this niche industry.

Meaningful Application: Meaningful was used to integrate all data sources and create profiles for each of the different verticals. What would have taken weeks took hours. We also made use of Meaningful's "Chat with data" function.

Health: Creative Testing

HEALTH SYSTEM

Objective: Creative testing for a health system in the US. Testing audience reaction before full production.

Audience: Parents of children under the age of 18 in certain geographic regions.

Approach: Online screener + Conversations to get in-depth insight into audience reaction on a few short videos. Respondents were interviewed in a conversational style and asked to give their honest feedback. The conversation also allowed us to get in depth feedback validate the creative direction and provide easy optimizations for the creative team.

Meaningful Application: Meaningful's Conversation was used for the in-depth interviews. Results were delivered to our client via a link that was as easy to read as it was to navigate. The dashboard featured key findings, highlights, sentiment, themes, representative quotes, overall findings, and more, including the transcript of each respondent so the client could view as well.

Rather than producing isolated reports, Meaningful enables brands to maintain a living, queryable understanding of customer needs, perceptions, and behaviors that evolves as new data arrives.

Learn more

Rather than producing isolated reports, Meaningful enables brands to maintain a living, queryable understanding of customer needs, perceptions, and behaviors that evolves as new data arrives.

Consumer: Concept Testing

WELL ESTABLISHED SNACK BRAND

Objective: Test audience reception to two potential campaigns and messages from each campaign for a well established popcorn brand.

Audience: Consumer study of purchasers of salty snacks, ages 18-60 in the US.

Approach: Online quantitative survey.

Meaningful Application: Findings from the quantitative survey showed a very close tie between the two concepts. We uploaded the raw data from our quantitative survey and used Meaningful to gather consumer trends and social insights for the industry. We then ran a synthesized review to recommend a creative decision for the client that was grounded in primary and secondary research.

Retail Media: Visitor Intercept

POP-UP STORE INTERCEPT INTERVIEWS VIA QR CODE

Objective: Collect real-time consumer feedback on brands among visitors of retail pop-up event.

Audience: Visitors of the retail pop-up (invite-only event) which included retailers, buyers, and influencers.

Approach: Meaningful's Conversation was used to create quick conversational style surveys for the different brands represented at the pop-up store. The survey was administered to visitors of the pop-up using a QR code.

Meaningful Application: Each survey was customized per brand and the results were available by individual brand dashboards. Additionally, data was merged across brands into one report and layered with secondary data to generate a unified trend analysis. The dashboard featured core consumer insights, trial behavior dynamics, strategic recommendations, key findings, metrics, trends, insights and more including a detailed breakdown of the individual brands. All the data was consolidated into one clean and easy to read dashboard.

"I think it's fabulous. Everything the client wanted, they got. The dashboards look great, I'm very pleased."

Meaningful client

Meaningful acts as the central intelligence layer that sits between data sources and human decision-makers

Meaningful is designed to operate above individual tools and workflows. It connects fragmented inputs into a single analytical context, allowing insight to accumulate rather than reset.

Meaningful enables teams to:

Work across qualitative, social, secondary, and proprietary data in one system
Apply consistent analytical frameworks across disparate sources
Move from episodic analysis to continuous intelligence

This orchestration layer transforms how research is conducted, interpreted, and applied.

From market research platform to customer intelligence layer

While Meaningful is purpose-built for market research, its architecture supports a broader role.

Research teams use it as a data orchestration platform to unify diverse intelligence sources. Brands leverage it as a customer intelligence layer to maintain evolving market understanding. And agencies deploy it as the infrastructure foundation for Insights-as-a-Service partnerships that become more valuable over time.

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