Applying Traditional Research Methods to Product Management by@markschindler
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Applying Traditional Research Methods to Product Management

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“Product Science” best practices derived from the principles of social science research


Product management is ultimately about people — users, business stakeholders, team members (developers, etc.), investors, et al — and the interactions, relationships, and systems between them. There are countless articles about what skills are necessary for good product management, how best to set and achieve goals as a product manager, and the skill-sets needed to succeed in the role. But what I find most interesting is the growing (but still scant) explicitly defined connection between product management and traditional social sciences — particularly the research side of social sciences.

This post is intended to boil down the important must-knows of social science, product management, and the overlap — aka: Product Science — that they share, so that entrepreneurs can enhance their product development cycle as well as avoid common pitfalls and fallacies by leveraging tried and true research methods.


This post is organized into the following sections:

  • Introduction to social science
  • Introduction to product management
  • Introduction to product science
  • Takeaways

The “Introduction to…” sections each include the following sub-sections:

  • What is it?
  • Example disciplines
  • Research strategies
  • Why it’s like…


What is it?

  1. A branch of science that deals with the institutions and functioning of human society and with the interpersonal relationships of individuals as members of society
  2. A science dealing with a particular phase or aspect of human society


Example disciplines:

  • Anthropology
  • Communication studies
  • Economics
  • Education
  • Geography
  • History
  • Law
  • Linguistics
  • Political science
  • Psychology
  • Sociology


Research strategies:

Social research methods are often divided into two broad design categories:

  • Quantitative designs approach social phenomena through quantifiable evidence, and often rely on statistical analysis of many cases (or across intentionally designed treatments in an experiment) to create valid and reliable general claims.
  • Qualitative designs emphasize understanding of social phenomena through direct observation, communication with participants, or analysis of texts, and may stress contextual and subjective accuracy over generality.

There are entire textbooks written about this topic, so we’ll pause here and highlight key points later in the post. For more information about the foundations of social research, here is a great resource.

Why it’s like product management:

Like product management, social science often includes both technical and non-technical skillsets

Like product management, social science involves evaluating (and often improving) the functioning of systems

Like product management, social science research warrants analyzing both quantitative and qualitative data


What is it?

  1. An organizational lifecycle function within a company dealing with the planning, forecasting, and production or marketing of a product or products at all stages of the product lifecycle.
  2. Similarly, product lifecycle management (PLM) integrates people, data, processes and business systems.


Example disciplines:

  • Technology
  • Dev ops
  • Engineering
  • Users
  • User research
  • Industry expertise
  • Business
  • Office administration
  • Finance


Research strategies:

Product research often focuses on product usability and includes simple research methods like:

  • Surveys
  • Interviews
  • Observation
  • A/B Testing

Here’s a great graphic that shows when to use which user-experience research method:



Why it’s like the social sciences:

Like social science, product management involves understanding the thinking (e.g. motivations, preferences, etc.) of different people in different settings (or under different circumstances)

Like social science, product management is best executed when combining both micro and macro viewpoints

Like social science, product management research requires both quantitative and qualitative data


What is it? Here’s what I believe it is…

  1. The multidisciplinary framework by which companies can develop a flywheel of product innovation using valid and reliable data
  2. Drilling down at the crossroads of data science, psychology, and product management / design for enhanced product development and easier
  3. Visually:


Example disciplines:

  • Data Science:

Statistical research / Data processing

  • Product Management:

Technology — engineering and development / Fulfilling business needs

  • Psychology:

Cognitive psychology / Behavioral psychology / Social psychology

Research strategies:

Using traditional social science research methods and applying them to product management and research, we get the tenets of product science best practices. You may have already noticed the similarities and overlaps in the previous sections, but the main areas are:

  • Using both quantitative and qualitative data
  • Attitudinal vs. behavioral insights
  • The context of what is being researched

See the next section for the key takeaways of product science and its inherent benefits.

Why it’s like the combination of a social science and product management:

Like the social sciences and product management, product science improves decision making through the use of data

Like the social sciences and product management, product science is multidisciplinary

Like the social sciences and product management, product science requires a leader (or leaders) who possess a wide range of skills and traits


Research flow:

The key sequence of social science research and product management, aka: product science, can be summarized by the following graphic:



Primary benefits of product science:

  • User or product (general) research design and methodology

Contribute to a PRD with data-specific requirements — including primary data fields associated with a feature, but also secondary fields needed for deeper analysis

Improve the efficacy of how data is being used in the feedback loop of product development

  • Reporting

Presenting data/results in clear and digestible ways for all stakeholders

  • Data management

QA’ing the structure of the database and tables before/during product development

Monitor naming conventions, etc.

Designing robust and sustainable dashboards

  • Pre- and Post-mortem product (innovation) analysis


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