Numbers alone don’t tell the whole story. In addition to Quantity, we also need to look at Quality such as feedback, interviews, case studies, and narrative analysis.

In a previous article, we dissected the various aspects of calculating quantitative data (you can read it here).

Now let’s talk about quality.

Analyzing Experiences for Decision-Making

Qualitative data has to do with how your business affects people emotionally and experientially. It provides clues about the difficult-to-describe feelings your customers (and employees) have because of your decisions.

With qualitative data, we can look for things that are both subjective (first-hand, potentially biased perspective) and objective (neutral, independent third-party perspective). Typically, this is communicated in a non-linear form using a variety of learning styles and intelligences.

Unlike quantity (where the measurements are standard, discrete, and uniform), quality uses measures that are non-standard, interconnected, intuitive, and spontaneous. By interpreting the complex and rich data from analyzing experiences, these can be turned into useful information to predict future outcomes and make risk intelligent decisions.

Examples of Qualitative Data

Quality can include:

• verbal and written feedback
• narratives (story of how something happened)
  • first-hand (direct experience)
  • second-hand (watching someone else)
  • third-hand (outside story-teller)
• visual images, drawings, or models
• experiential sensations that cannot be measured (by an employee, a customer, manager, owner, etc.)
• descriptions of
  • appearance,
  • beauty,
  • beliefs,
  • capability
  • choices,
  • colors,
  • desirability,
  • distinctiveness,
  • encouragement,
  • feelings,
  • fitness level,
  • inclusion,
  • inspiration,
  • intuition,
  • loyalty,
  • sensations,
  • significance,
  • smells,
  • strength,
  • tastes,
  • textures,
  • values, and
  • well-being.

Forms of Analyzing Qualitative Data

Qualitative data analysis can take a variety of forms, including:

• Ethnography—commentary about people and their cultures
• Social Network Analysis—a visual way to show relationships, links, and themes among members of a group
• Content Analysis— starting with a hypothesis and reviewing themes that emerge after categorizing, summarizing, and tabulating the data
• Grounded Analysis—examining a single case to formulate a theory, and allow the data to reveal new themes that apply to similar situations.
• Narrative Analysis—reformulating stories to understand how people think and they way they organize themselves into groups. Sources include conversations, interviews, journals, field notes, letters, family stories, autobiographies, photos, life experiences, and other artifacts.
  The 4 types of narratives include:

• • Bureaucratic (imposing control, structured and logical)
  • Quest (ambition is to compel and influence others to join something)
  • Chaos (the story is experienced rather than explained)
  • Post-Modern/Metanarrative (the narrator discusses a story along with its historical meaning, experience, and narrator’s awareness and point of view of the situation)

• Discourse Analysis—natural conversations along with the context in which it occurs, previous conversations, power structures, identity, and roles
• Conversation Analysis—evaluation of an audio or transcription including which words are used, in what order, speed and rate, overlapping speech, and emphasis.
• Framework Analysis—organizing data by themes and then coding, charting, mapping, and interpreting it.
• Case Study—an in-depth, detailed description about the experiences of an individual, organization, event, or activity in order to illustrate, explore, aggregate, and/or analyze a problem.
• Autobiography—a self-written account about one’s own experiences.

Tools for Gathering Qualitative Data

In order to collect this type of data, you can use these tools:

• Live and In-Person
  • Interview
  • Narrative story
  • Group discussion
  • Unstructured survey
  • Focus group
  • Observation
  • Description
• Digital methods
  • Online or e-mail survey
  • Web forum
  • Chat
  • Online community

Illustration Using Qualitative Data

In our example of a company asking the question “Why are customers leaving?” we could re-state this as:

“Which factors are causing customers to leave?

The qualitative data to review might include:

• customers’ stated reasons for discontinuing using your services
• ask staff why they think customers have left
• look at past customer complaints and communication prior to leaving
• a survey of current customers to determine their satisfaction level (which is tied to motivation)
• “Employee For a Day” experience to hear first-hand why customers are dissatisfied
• feedback mechanisms to collect ideas and comments from customers, staff, and other stakeholders
• in-depth Vision, Mission, and Values statement analysis
• Company Culture evaluation

 

Discover the many differences between Quantitative and Qualitative Data in these posts:

How to Understand the Quantitative and Qualitative Data in Your Business

Interpreting the Quantitative Data (Numbers) in Your Business

 

In the next installment, I’ll share a method that combines both of these types of data.

 

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Grace LaConte is a business consultant, writer, workplace equity strategist, and the founder of LaConte Consulting. Her risk management tools are used around the globe, and she has successfully reversed toxic work environments for clients in the healthcare and non-profit fields. Grace specializes in lactation law compliance & policy development, reducing staff turnover after maternity leave, and creating a participatory work culture.

Find more at laconteconsulting.com, or connect with her on Instagram and Twitter @lacontestrategy.