How to Use VOC Patterns in B2B Product Innovation- 3 Proven Strategies
For B2B services organizations that are innovating, effective voice of customer research (VoC) is critical to de-risk the up-front investment in new product development.
But high-quality VoC interviews and surveys are only part of the process - how you sense patterns and apply judgment to draw conclusions from that data and feedback is just as important, and often that judgment is more art than science. It’s not something most professionals are used to doing, and requires an interesting, and complex combination of skills.
Synthesizing VoC is best done by someone with a special balance of:
- Expertise to contextualize answers, identify compelling outlier opinions, and
- Open-mindedness to recognize and entertain the validity of emerging paradigms; to be able to “spot patterns” and think creatively about different perspectives that may be in opposition to their own experience.
Most organizations have the expertise. What’s harder is for expertise to not crowd out an open mind. Expertise and open-mindedness feel in opposition; the longer a professional has spent gaining expertise in a certain field, the more entrenched the heuristics and assumptions about customers, the industry, solutions, and challenges become. Luckily, an open mindset can be cultivated even in the most experienced individuals; there are several techniques and approaches that can help ensure that expertise and open-mindedness can co-exist to synthesize VoC for accurate, productive, and insightful findings that ultimately help the business make better product decisions.
Hypotheses Required to Establish Patterns
For starters, all research - VoC or other - should be anchored in validating a set of hypotheses. Without hypotheses, you will be hard-pressed to establish patterns - and therefore unable to draw any confident conclusions as responses will likely go every which way; think of your feedback without hypotheses as scattershot, and with hypotheses, a sniper. Therefore all questions you ask should be in service to discovering if your specific assumptions are correct… or incorrect. Either way, you’ve de-risked your product investment if you have faithfully challenged your own expertise via testing hypotheses (for more on how to generate product hypotheses, see this article).
Patterns (or non-patterns) Worth Your Notice
Presuming you have exercised the discipline of hypothesis-based VoC research, there are a number of techniques for synthesizing findings into high-confidence recommendations. And they are largely based in identifying relevant patterns, identifying relevant outliers, and closely examining “scattershot” answers.
1. Relevant Patterns - when most of your interviewees answer in pretty much the same way to a question, you can be confident that’s the majority opinion and draw conclusions accordingly (e.g. “13 out of 15 customers said that X was one of their top 3 problems to solve this year).
2. Relevant Outliers - when most interviewees respond one way but a small portion have a different answer, that could be a “fringe” opinion… or it could be critical insight. How can you tell? This is where your expertise in your customers, their problems, and the existing solutions comes into play.
When reviewing call notes and transcripts, if you have industry and client expertise you are well equipped to apply judgment to determining relevant outliers. You should flag outliers as relevant when:
1. The interviewee is particularly thoughtful and rational in their replies.
For example, they can clearly articulate the “why” behind their beliefs and perceptions, they are demonstrating synthesis of complex inputs, or are proactively anticipating the evolution of their business to account for trends in the industry, talent, macroeconomic environment etc.)
2. The interviewee expresses progressive or innovative perspectives on the problem (as opposed to conventional wisdom).
Interviews that contain these thoughtful and prescient perspectives are extremely relevant and often the most valuable of feedback you can gather, as they can lead you to new and fresh ways of thinking about the customer problem and solution - as long as you are open to hearing it.
3. “Scattershot Answers” - sometimes feedback and answers to even your most focused questions fail to yield a clear preference in one direction or another. Wide ranges in responses to urgent & expensive problems, how much a customer would be willing to pay for a product, or the expected buyer journey are all areas where you could conceivably hear many different, and thereby confusing answers. When there’s no majority, or even plurality answer, it makes it hard to determine what learning to take away.
Dispersion of answers is typically a function of one of two things: a poorly designed question (unclear in how it’s asked, or unattached to a hypothesis) or - and here is where the value might lie - because there’s additional segmentation that you’ve just discovered.
When you have no clear answer, look at the people you interviewed and ask yourself, what do those who did answer in similar ways have in common? For example, if your interviewees answer the question “What would you expect to pay” with either a very high dollar amount or a very low dollar amount that’s a signal for you to step back and determine: what are the commonalities and differences between these groups that might explain the variability in willingness to pay? Is it because for one group the problem is urgent & expensive and for the other it is not? Is it because one group tends to have the budget for this kind of thing and the other does not? Think through what might explain the dispersion. Examine trends in:
- Industry
- Size of Company
- Geography
- Organizational maturity
- Organizational culture or goals
And sometimes the difference is not demographic, but psychographic (e.g. personal beliefs, motivations etc). You should consider different buyer personas (personas are the combination of demographics and psychographics that help define a common buyer archetype) which may help you ultimately make savvy decisions about the product’s feature prioritization, positioning, marketing etc.
An example of persona discovery occurred when we at Vecteris were helping one of our customers test a product for new-to-role CEOs. There was a wide dispersion of the perceived urgency & expense of the problem we were looking to solve, and therefore significant disagreement across interviewees over the likelihood to purchase. When we examined our interviewees we found that there were two different personas at play - we called them the “Open Learners” - CEOs who came to their new role with vulnerability and a hunger to learn everything they did not know - and the “Wunderkind” - new CEOs who projected total confidence and were not at all vulnerable, i.e. they either did not believe or felt they could not admit that external support in the new role would help them succeed. Understanding these personas helped our customers better frame the value proposition of their product and focus first on building for and selling to the most likely buyers, the “Open Learners.” And planning further down the line, their product roadmap accounted for a marketing strategy to eventually sell to the tougher group, the “Wunderkind.”
Ultimately, pattern sensing across your voice of customer interviews is at the heart of savvy synthesis. It requires expertise, an open mindset, and the ability to apply judgment to the wealth of data and information gathered. As you look back at the transcripts and notes from your VoC interviews, look for how answers fall into these three categories - majority responses, relevant outliers, and scattershot answers. These categories should help you start to build that judgment and draw incisive conclusions to help you build stronger, more profitable products.
To talk with a Vecteris Productization Coach about incorporating best practices of VoC research, get in touch today!