Post on 02-Apr-2018
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B2B Qualitative vs. Quantitative Data
Using data-driven business intelligence to drive sales, acquire customers and improve products as a long-term strategy
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B2B Qualitative vs. Quantitative Data
Qualitative or Quantitative Data—Which is Better?
There’s an ongoing debate over which type of data is better: quantitative (“quant”) or qualitative (“qual”). For researchers who have used and benefited from both, there are distinct advantages and disadvantages from each. There are also instances in which each method is best suited to a specific application.
QUANTITATIVE DATAQuantitative data is all about numbers. When people talk about “big data,” they’re talking about quantitative
data—exact, scientific, precise. Black or white, quantitative data is straightforward, although the
interpretation of quantitative data can be manipulated—think statistics.
But while quantitative data explains the “what,” it doesn’t always explain the “why” or the “how.” Why are the
numbers high, low, or average? How can specific patterns in the results be explained? How can we interpret
the overall trends? For this, we often look to interpretation. And for interpretation, we frequently call upon
qualitative data.
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B2B Qualitative vs. Quantitative Data
QUALITATIVE DATAQualitative data helps explain the “why” and “how” behind the numbers. It gives meaning and context to
the raw data. It provides color. Examples of qualitative data are free form responses to questions asked in
telephone and in-person interviews.
While qualitative data can be rich in insights and can uncover new trends and ideas, it’s free-flowing form
is also a key disadvantage—qualitative data is often voluminous in nature, making extraction and usage of
key insights time consuming and difficult. In market research studies, for example, collecting open-ended
feedback can help identify some very interesting trends—trends that quantitative data alone may not
reveal. However, wading through tens or hundreds of thousands of words is not trivial. It’s a major time and
resource commitment.
TEXT ANALYTICS FOR INTERPRETING QUALITATIVE DATAText analytics is helping to make the analysis, interpretation, and reporting of qualitative data much less
labor and time intensive. Text analytics that includes sentiment analysis is even better.
Text analytics is a set of statistical, linguistic, and machine learning techniques that allow textual data—such
as documents, emails, and speech—to be used for business intelligence, market research investigation,
and outcomes analysis. As text analytics applications have become more widespread since the 1990s, this
technique has found growing usage in competitive intelligence, business intelligence, sentiment analysis,
listening platforms, and social media monitoring, among others.
RECOMMENDATIONS FOR QUANTITATIVE AND QUALITATIVE USAGEHow do you know when and whether you should collect and use quantitative data versus qualitative data?
The sidebar displays general guidelines for both types of data.
1. Straight-forward binary questions (Yes or no, male or female)
2. Ratings questions (Scales of 0-5, 0-7, or 0-10)
3. Check boxes (Lists of possible choices)
4. Sensititive questions (Such as pricing ques-tions)
Collect quantitative data when you have:
1. Want to understand the “why” and the “how” behind the numbers.
2. Want to probe on certain areas of the re-search
3. Have a new product to test
Collect qualitative data when you have:
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B2B Qualitative vs. Quantitative Data
COMBINE QUANTITATIVE AND QUALITATIVE DATA TO GET THE CLEAREST PICTUREWhile some may see quantitative and qualitative information as black and white—an either/or
proposition—many successful researchers use both types of data in combination as complementary tools
in their toolbox. Quantitative data will provide hard numbers against which to benchmark your product,
company and/or competitors over time, while qualitative data will provide invaluable insights behind those
numbers.
A study by Gallup highlights the beneficial outcomes of collecting a combination of qualitative and
quantitative feedback, especially for B2B buyers, where 60 percent of customers are indifferent, 11 percent
are actively disengaged, and only 29 percent are actively engaged. As Gallup points out, and as Primary
Intelligence consistently confirms in our Win Loss and Customer Experience interviews, capturing only
quantitative information misses significant flags in customers’ experiences with their vendors. When vendors
commit to capturing verbal feedback, they typically identify consistent “themes” in customer and buyer
feedback, themes that may be problematic for the long-term relationship if not addressed before they grow
into larger issues and the customer is in danger of defecting to a competitor.
While it’s cheaper, more convenient and more expedient to send out or post to social media a short, online
survey, the use of both qualitative and quantitative information gathering techniques, especially in complex
B2B markets, is essential in understanding important customers, markets, and competitors holistically.
Collecting rich data allows trending over time to see how customer and market needs are changing, as well as how competitor strategies are evolving.
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B2B Qualitative vs. Quantitative Data
Why Collecting Qual and Quant Feedback is Critical for Your Long-term StrategyCan you imagine going into a strategy session not knowing the growth projections for your market, your
product or your industry?
Can you conceive of not knowing your share of the market compared to the market share of your
competitors?
Knowing why the market is growing quickly, knowing why certain competitors are gaining share at your
expense and knowing why key customers may be defecting.
This blending of hard data with softer, descriptive information is the essence of research, analysis and
reporting for many companies. Yet for some organizations, there’s an increasingly strong bias toward data-
only feedback. Driven in large part by big data and data analytics, this trend has led some to mistakenly
believe that qualitative data is superfluous and no longer needed.
5 REASONS WHY USING A COMBINATION OF QUALITATIVE AND QUANTITATIVE DATA IS CRITICAL
1. Collecting a combination of qualitative and quantitative data is important because it provides both the
“what” and the “why” about specific markets, products, competitors, and customers. For example, you
may know that your market share is declining, but you may not know the cause of that decline. Is it due
to product limitations? Pricing? Service and support? Something else?
2. Collecting hard and soft data provides an indication of how serious specific issues are in different facets
of your business. For example, seeing a low rating of your product’s user interface or functionality may
be alarming. However, reading or hearing vivid, passionate, customer testimonials helps you know
exactly how dire the situation is, especially when the feedback is relative to other issues that may need
fixing and prioritization.
Arming yourself with strategic quantitative data is critical to understanding how to grow your market presence, how to sell more products and services and how to win more customers, but…
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B2B Qualitative vs. Quantitative Data
3. Using both quantitative and qualitative methodologies allow respondents to feel they had the
opportunity to provide a comprehensive accounting of their feedback—whether positive or negative. In
contrast, by only capturing quantitative feedback, respondents may feel cheated out of the opportunity
to fully explain the impact of their experiences to researchers, especially if the evaluation took weeks,
months, or even years. At the same time, those providing only qualitative feedback may want to ground
their experiences in a concrete, numeric way.
4. Quantitative feedback, while precise, often isn’t specific in coming up with solutions. In contrast,
organizations at the forefront of using qualitative feedback know that the best ideas for improvement
often come from customers and even competitors. Why not harness these ideas, while at the same time
demonstrating to customers that you’re acting upon their input?
5. Certain types of information, such as competitive information, is frequently surfaced, at least initially, in
the form of rumor or speculation. Piecing together bits and pieces of data is easier when there’s a dual
qualitative and quantitative collection program in place.
For the best long-term organizational strategy, use a combination of quantitative and qualitative feedback.
This approach is typically more reliable, more robust, and more well-rounded over the long term than trying
to rely on any single data collection methodology.
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B2B Qualitative vs. Quantitative Data
16 Best Practices for Collecting Quantitative and Qualitative B2B Research Data
QUANTITATIVE B2B INFORMATIONWhen collecting quantitative B2B information—data that’s numeric, or numbers oriented—the following
techniques are recommended in B2B markets so that respondents have the best experience and researchers
collect the best information:
1. Capture quantitative data online to make the process faster and more efficient for respondents.
2. Include “Unsure” and “Other” categories in “Yes/No” answer options to give respondents more choices if
they don’t fall into predetermined categories.
3. Similarly, when including a “pick-list” of answer choices, include an “Unsure” and “Other” option to allow
respondents to include additional possibilities you may not have considered.
4. Always include a request to “please specify” when respondents choose “Other.” If you see enough “Other”
responses that are similar, you’ve spotted a new trend and can include this choice in your pre-defined
pick list.
5. Ensure ratings scales are appropriate for the questions being asked. Typically, firms collect B2B
information on a 0–5, 0–7, or 0–10-point scale, depending on the degree of detail required.
6. While no survey taker likes long surveys, B2B respondents are generally more willing than B2C
respondents to answer a longer set of questions, due in large part to the longer timeframes, higher
purchase prices, and greater energy they’ve invested during the evaluation process.
7. Consider including text boxes after each survey question so respondents can include additional detail
and context if desired. Make sure text box responses are optional so that respondents aren’t forced to
include additional comments in order to move onto the next question.
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B2B Qualitative vs. Quantitative Data
8. Put pricing questions (or any questions that are especially sensitive) into the online survey, if possible,
and allow the respondent to “refuse” or “decline” these questions. Tucking sensitive questions into an
online survey, especially if the questions relate to competitors, allows the respondent to gracefully
decline to provide input, especially if the individual is under a Non-Disclosure Agreement.
QUALITATIVE B2B INFORMATIONQualitative data seeks to understand the nuances and context around a respondent’s input and is typically
open-ended in format. To get the most from qualitative B2B data collection, researchers should follow the
following guidelines:
9. Collect qualitative data in person or over the telephone to get rich information about the respondent’s
experience.
10. Use probing questions that drive toward greater clarity and understanding of the issues, asking
necessary follow-up questions to ensure actionable intelligence.
11. Follow the respondent’s lead when deciding the order of the questions. While it’s tempting to go in the
exact numeric order as outlined in the interview guide, this can break the flow of the respondent’s input
and can be jarring, especially if the individual touches upon a later question early in the conversation
and the researcher doesn’t take advantage of the opportunity at the appropriate time.
12. Look for themes in the feedback. Respondents typically return to two or three key themes in their
responses—high and low points of their experience that are especially noteworthy in either a positive
or a negative way.
13. Identify trends in feedback between different respondents. What patterns are emerging from the data
that are common across different buyers?
14. Ask individuals to comment on key findings discovered earlier in the research process. Does the current
respondent agree or disagree with the input of previous respondents? What can they add to the earlier
discourse to make it even more relevant and interesting?
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B2B Qualitative vs. Quantitative Data
15. Be mindful of respondents’ time and energy levels. Conversations that last 20-30 minutes are typically
ideal in complex discussions of B2B markets. While technical issues may require more time to thoroughly
vet, make sure to ask the respondent for permission to continue beyond the allotted time or consider
scheduling a follow-on conversation.
16. Record the interviews so that the focus remains one of active listening, not mad scribbling. Ask the
respondent for permission to record the discussion at the beginning of the conversation.
By adopting these tried and true practices, you’ll be on your way to collecting first-rate customer,
competitor, and market intelligence—whether it’s quantitative or qualitative—from B2B respondents.
Buyer Intelligence Programs to Help You Win More!Why not solicit help from data analysis experts who deliver results? Our win loss and customer experience
programs will help you reach your business goals. With over 16 years of experience, we’ve helped more
than 500 companies—including ten of the Fortune 25—improve their win and retention rates with buyer
feedback. Contact us for a demo and get started today.
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B2B Qualitative vs. Quantitative Data
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About Primary IntelligencePrimary Intelligence delivers outcomes for B2B marketing, sales, and product leaders, guiding clients on
ways to win more deals and retain more revenue through Win Loss Analysis and Customer Experience
Analysis. Primary Intelligence speaks directly to buyers and sellers to uncover insights, root causes, and the
path to improvement. Expert consultants initiate transformation in Fortune 500 companies, including 10 of
the Fortune 25.
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