Interpreting the Quantitative Data (Numbers) in Your Business

Do you hate reviewing your business financials? If so, you’re not alone.

In this article, I’ll explain:

  • the difference between quantitative and qualitative data,
  • where to collect the data,
  • an illustration that demonstrates which questions to ask, and
  • how to use it to make risk intelligent decisions.

For many owners, the term data analysis can be confusing. You might choose to avoid looking at your financials because… “as long as customers are happy and the bills are paid, that should be enough. Right?”

While it can feel uncomfortable to look at what’s going on “behind the scenes” in your business, it’s a vital step to understanding what is going wrong. Not just because doing a deep-dive helps you see customers’ needs and employees’ biggest frustrations, but also because it provides the data you need to make decisions with a high degree of risk intelligence.

The good news is, you can easily learn which numbers are important and how to calculate them.

Let’s start with a review of the difference between the two types of data in your business (Quality and Quantity).

Why Measuring Everything Matters

Quantitative data has to do with measuring numbers: the things you can count, arrange, align, sort, and organize.

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We can learn a lot by measuring things that can be counted discretely (such as a sales amount), and by measuring continuous events (such as temperature or distance).

To be most effective, quantitative measures should be objective and fact-based.

Examples of Quantitative Data

There are quantities we can evaluate, including:

  • money,
  • time,
  • speed,
  • frequency,
  • movement,
  • height,
  • length,
  • area,
  • volume,
  • weight,
  • temperature,
  • humidity,
  • pressure,
  • sound level,
  • quality,
  • degree,
  • intensity,
  • event,
  • item,
  • categories (age, gender, occupation),
  • positioning, and
  • status.

 

Ways to Analyze Quantitative Data

There are two main ways to collect data about quantities. These include:

  • Direct
    • Structured survey
    • Phone call
    • Meeting
    • Face-to-face discussion
    • Online form
    • Direct mail or package
  • Indirect
    • Spreadsheet
    • Statistical evaluation
    • Analytics and reports
    • Comparison of past, current, and projected data

In order to turn discrete data into useful information, we can use a variety of methods, such as:

  • Descriptive Statistical tools
    • mean
    • median
    • mode
    • frequency distribution
    • standard deviation
    • variance analysis
  • Inferential Analysis tools
    • surveys
    • experiments
    • hypothesis testing
    • T-Test
    • ANOVA
    • chi-square
    • z-score

An Illustration Using Quantitative Data

Let’s say that a company has been losing customers at a faster rate than normal, and we want to find out “Why are we losing customers?”

Instead of focusing on all the possible reasons, we could re-state this question as:

How effectively are we retaining our best customers?”

By changing the question into a retrospective look at what is working, we can review data and see patterns of what has and has not been generating results.

Using quantitative numbers, we can look at:

  • how long the average customer has been an active purchaser
  • the longest and shortest duration as a customer (e.g. 1 day vs. 5 years)
  • how often each customer purchases our services
  • what our Ideal Customers buy
  • how much they spend on each purchase
  • how often they buy
  • what were the customers’ buying habits prior to their departure
  • correlation between a decrease in purchases and the loss of a customer (are there any signals that they are about to leave?)
  • which customers are purchasing the largest volume of services or products
  • which customers purchase services with the highest profit margins (Ideal Customers)
  • analyzing trends, such as why customers leave
    • Is it after an event or announcement?
    • During a specific time of year?
    • After a competitor’s new ad campaign?

For a comparison between quantitative and qualitative data, read How to Understand the Quantitative and Qualitative Data in Your Business

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Decision-Making Using Numbers

Once you have data that answers your specific questions, you can use this to make decisions about how the business will reach its goals — by increasing customer purchases, seeing higher engagement, lower staff turnover, or improving public perception. All of these are also a wonderful sources of input for decision-making.

The Decision-Making Matrix is a tool that helps us to categorize the value of a possible course of action.

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Grace LaConte’s Decision Making Matrix

Options with a high amount of benefit and a high degree of confidence in a positive outcome are considered High-Value. These options should have immediate priority (top-right corner).

A course of action that has a high degree of benefit but for which there is a lower chance of success should be considered Secondary (top-left corner).

For options that have the potential to provide benefit but with low confidence of success,  these are Insignificant and should be postponed (lower-right corner). And possible options that have low benefit and low likelihood of success should be completely eliminated, since they will Waste time and resources (bottom-left corner).

 

Learn how to interpret emotions and experiences (i.e. qualitative data) in a way that can benefit your business in these posts:

How to Understand the Quantitative and Qualitative Data in Your Businessquantitative data, qualitative data, business data, business analysis, data analysis, evaluating data, strategic risk, strategic analysis

Interpreting the Qualitative Data (Experiences and Emotions) in Your Businessqualitative data, business experiences, customer experience, customer emotions, strategic risk, risk analysis, business data, business analysis, data analysis, evaluating data, strategic analysis

Interested in hearing how you can reverse a toxic workplace? Find out more here.

 


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.

Grace is a business management consulting with experience in healthcare strategy, IT, and marketing. She is the founder of LaConte Consulting and is passionate about helping business owners to identify profit leakage and improve their long-term value.

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