How to Collect Actionable Feedback with Voice of the Customer Research

 

A popular method used to collect current and potential customer feedback, Voice of the Customer (VoC) research gives businesses data-driven insights to understand what customer’s think about their brand, products, services, and even concepts. Some typical research objectives addressed by VoC studies include investigating what customers want, understanding how customers feel about a product, service, or concept, what drives customer decisions, and how your products compare to those of competitors.

Organizations that effectively use VoC research to drive their business decisions create stronger customer experiences by listening and taking action that inspires brand loyalty, or “brand love.” The most loved companies, the ones with the most loyal customers, use VoC to explore customer expectations and actual customer experiences. The results measure the gap between the two and provides an opportunity to develop strategy to minimize the disparity between experience and expectations.

 

Why is VoC Research Important?

Voice of the Customer research is a critical tool used to build customer-centric, holistic strategies and maintain competitive advantage in your market. VoC helps you monitor your brand for early warning signs of potential brand crisis, screens new concepts, filters down solutions to current workflow challenges, allows you to tweak products and services to meet customer needs, guides budget decisions, and increases customer retention.

Still not convinced? Let’s dig into 5 key reasons you should be doing VoC research:

1- Measure Key Performance Indicators (KPIs)

VoC allows you to measure several KPIs that give you direct insights into how your products and services are performing.  Aided or unaided brand or product awareness, positive or negative perceptions, and purchase intent, including purchase drivers, are all examples of KPIs that you can measure using VoC data.

2- Build Better Customer Relationships

VoC has a direct influence on building customer relationships by revealing opportunities to improve the customer experience. The output allows you to understand a customer’s journey when researching, purchasing, and eventually using a product. Additionally, understanding why customers keep coming back reveals a point of difference (PoD) among competition and helps you understand the key differentiators of your products and services from your customer’s perspective.

3- Learn from Your Mistakes

Mistakes are an inevitable part of doing business. With VoC data, you can better determine the source of a problem, contextualize the issue, and map out your best course of action all based on specific customer feedback. The data can also help you discern which of your customer’s pain points are most significant and prioritize the fixes accordingly.

4- Provide Exceptional Service

Inquiries can improve customer satisfaction levels by providing you with results on the technical training, response times, and interactions with service representatives and/or field engineers. These results can inform where you should allocate training time to appropriately build out the skill set of your customer success teams.

5- Extend Customer Relationships

VoC data helps you to understand what factors are most important to the success of your customers, and which products and services they value most. This helps you to build your product roadmap based on your customer’s needs, making them want to stick around for longer.

 

Research Types and Methodology

The methodology of VoC research has a lot of similarities to the empirical scientific process. A good VoC strategy starts with you defining your objectives. What are you hoping to accomplish with your study? Are you wanting to retain current customers, improve their experience, or gain new customers? Maybe the answer is all of the above. Regardless, you want to go into the study with some ideas, theories, questions, and goals. Once you’ve defined some objectives, it’s time to gather feedback. In the case of VoC research, feedback will be self-report data from customers, potential customers, or other samples you can access. After you’ve collected the feedback, you’ll need to analyze it critically and objectively. Finally, you’ve got to act. The data will tell you the story, it’s up to you to let it guide your insights and your steps forward for implementing what you’ve learned.

There are two types of research methodologies that can be used for collecting actionable customer data: qualitative and quantitative. Neither is superior to the other, they’re just appropriate for different circumstances, take you through different steps of the research process, and, often, get you different conclusions.

Qualitative research includes any non-numerical or observational data. Popular ways to collect qualitative data include interviews, focus groups, and open-ended questionnaires, but you can leverage other non-traditional techniques as well like live chats and social media. One of the main benefits of qualitative research is you’re more likely to get a “real” response from respondents. In these formats, people tend to feel less restricted than they do within the confines of a more structured survey where response options are on a 0 to 10 scale. This results in more naturalistic observations. It also is a great option if you don’t have questions or goals defined, because the broad, open-ended nature of qualitative research can act as a first step for generating ideas.

Contrary to qualitative research, quantitative research always has a numerical data output associated with it. In this format, you are setting up the potential responses that respondents can give you for each question, making it more defined and specific. This is a great option if you want to do statistical analyses like testing a hypothesis for significance or looking at quantitative differences between segments. This methodology allows you to generate common metrics like a Net Promoter Score (NPS) and gives you an opportunity to put real numeric values around more abstract concepts like brand awareness and customer pain points.

 

Actionable Insights that Guide Your Next Steps

VoC data should be taken in consideration at every point in the life cycle of a product or service, from planning and development to implementation and marketing for a holistic product experience. We’ve explained why having this type of data is important, and even given you some insights into effective methodologies for conducting your research, but to set up your study for success, you really need to think about where you’re going.

What do you really want to focus on after you take stock of all the data you’ve collected and all the analyses you’ve done? Think about what you could hone in on to improve your success with customers. What features do they want? What factors are bothering them? What would they like to see? What are they happy with? What are they unhappy with? And make sure that you identify the factors that you think could more successfully help you retain customers and get new ones.

Finally, you need to share the results of your research with the customers themselves. We’re not suggesting you share the spreadsheets of data, but rather communicate to the customers that the improvements they were asking for are being addressed. If customers aren’t told that their ideas are being implemented, they won’t feel that their feedback has had any effect. Customer loyalty grows when a business reports back to the customers with the specific action being taken in response to their comments.

In short, it’s like any relationship: communication is key. The more a customer feels heard by a business, the more they feel understood, the better their needs are addressed in meaningful ways and the more loyal they will be.

To learn more about how VoC research could positively impact your business strategy contact us today. Our team of market research experts look forward to discussing how we can partner with you to get you the insights you need to make data-driven decisions!

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Three Methods for Pricing Research to Drive a Successful Product Launch

The pricing of your products and services has a substantial impact on market perception, your go-to-market success rate, and, ultimately, top line revenue. Whether you’re launching a new product to market, or making a pricing change to an existing product, it’s essential to understand the potential impact that your pricing strategy will have on projected quantity and revenue, and how those findings fit into your overall business strategy.

When pricing a product, the perceived value of the product and its attributes to buyers, customer’s willingness to pay, and price elasticity can all be used to optimize your revenue. Not only do these insights inform your pricing strategy, but they also contribute to your product roadmap, go-to-market planning, segmentation, marketing messaging, and sales strategy.

In today’s post, we’ll explore three different market research methodologies for collecting pricing input. For each methodology, we’ll discuss use cases, pros, cons, and the types of conclusions you can draw from the outputs. Let’s jump in!

1. Van Westendorp Pricing Model

The Van Westendorp Pricing Model is a price sensitivity meter that uses inputs collected through a series of four key questions:

  • What price would you consider this product price so low that you would question its quality or too cheap?
  • At what price would you consider this product to be a bargain? A great value for the money.
  • At what price would you consider this product as starting to get expensive? It’s not out of the question that you would purchase, but you need to give it more thought before purchasing.
  • At what price would you consider this product to be so expensive that you would not consider purchasing it?

The distribution of data collected for each of the four questions is plotted into one consolidated view, and the areas where distribution lines intersect tell you optimal price point, point of marginal cheapness, point of marginal expensiveness, and indifference price point.

The straightforward questions posed by this model leave you with a simple, fast analysis and ample survey room to collect other data. This is an ideal methodology when assessing a new product that most people don’t have experience purchasing or during early production development when testing a simple product concept.

On the contrary, because there are no questions that dig into likelihood to purchase, and the product is being assessed in isolation, this model is not ideal when you’re looking to compare competitive scenarios. It also leaves much to be desired when you’re assessing a common product or service for which respondents generally know what they spend when purchasing because you’ll see the data skew towards the pricing models they’ve experienced with their current or past vendors.

To summarize, if you need quick and easy insights on product pricing, the Van Westendorp Model is a great option, but if you need real data on likeliness to purchase or real-world competitive scenarios, this method will miss the mark.

2. Gabor-Granger Pricing Model 

Like the Van Westendorp Pricing Model, Gabor-Granger can provide simple and quick analysis, but there are some key differences to consider. This research technique is used to gauge optimal price points using a set of predefined price points. For this methodology to be effective, you need to go into the research knowing some ranges of prices that you think the market will accept. You’ll test those specific prices, unlike Van Westendorp where respondents can input whatever price they feel is fair.

To collect data, respondents will simply be asked if they would purchase a product if the price was reasonable. Those who show interest are then asked a series of follow up questions using the pre-determined price points. If, on their first question, the respondent gives a positive response, they’ll be prompted to answer the same question on increasing price points until a maximum price is reached. Alternatively, if the respondent gives a negative response on their first question, they’ll receive varying lower price points until you reach the price that is acceptable for them to purchase. The output of this data would be a chart showing you at what price you could maximize your revenue.

This model allows you to identify maximum price, generates elasticity of demand, and identifies your revenue-maximizing point with low survey efforts and easy analysis. These are key factors when you’re considering a price increase or decrease, but all other components of the product are fixed, and when you’re trying to identify that sweet spot to maximize revenue.

A challenge of this model is that respondents easily understand the exercise and, as a result, are capable of “gaming” their answers to pick lower prices. Additionally, it does not consider real-world competitive scenarios, and, because the price range is pre-determined, respondents could value the product at a dollar amount outside of the tested range, leaving you with an incomplete picture.

Keeping the above in mind, the Gabor-Granger Model is a quick and easy tool for understanding how to maximize your revenue, but, like the Van Westendorp Model, it’s not going to provide more in-depth analysis like competitive comparisons.

3. Conjoint Analysis

Substantially more complex than the two above, Conjoint Analysis is a method for pricing and product research used to measure the value customers place on product attributes and services. To conduct this type of research, you’ll need to create a list of attributes and levels within those attributes. This set up allows you to compare various configurations against each other and gives you a more accurate gauge of tradeoffs customers make when making purchasing decisions in the real world.

Data provided by these tradeoffs is modeled to create several outputs:

  • Attribute Importance Scores: These indicate which attributes within the configuration most impact purchasing.
  • Preference Scores and Utilities: These identify which levels within an individual attribute are most or least preferred.
  • Market stimulators: This is the key output allowing you to test hundreds of different product configurations and price points against the competition.

For this methodology, respondents are presented with multiple options at once and asked to list out which products would or would not meet their needs. As they progress through the exercise, they’ll be asked to pick which option they’d be most likely to purchase from a lineup of products. The results show you how important each attribute is to the purchase decision, or how much impact it’s having on the decision. It also tells you, within each attribute, which levels are most or least preferred. Putting all the data together, and running several analyses allows you to see price sensitivity by segmentation, competitor comparisons, share of preference, and revenue maximization.

Perhaps the biggest benefit of this analysis style is the robust analyses it allows you to conduct. This complexity more closely stimulates real-world purchasing conditions and can be used to predict the acceptance of new products before launch, determine which features drive purchasing decisions, and measure yourself against your competition.

As you might have expected, this methodology does take quite a bit more time than the two previously explained models, but if you have the time, it presents you with richer data insights that can more effectively drive your product strategy.

There are several ways you can collect insights on pricing your product. Whether you utilize one of the three methods above, or a method of your own, this is a pivotal part of your go-to-market planning. To learn more about how pricing research could be a valuable tool for launching your next product, contact us today. Our team of market research experts look forward to discussing the best methodologies to get you the insights you need to optimize your product strategy!

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