How to prioritize product development

Published on — Written by Wonderflow

Wonderflow Image
For most product managers, the process of new ideas and directions for product development is a relatively straightforward one. It gets more complicated when it comes to the real-world problem of deciding what to do next. There’s never enough resources or time to do all the new product development (NPD) ideas required, so how do you decide what features or experiences stay and what gets cut or pushed back? Should you listen to your customers? Or should you listen to the CFO? Especially within a larger product team, how do we reach consensus? Is consensus even a good idea? If you are a product, experience, or NPD manager, it’s likely to be an issue that has caused stress at some point. 

In this blog, we’ll look at effective methods for building prioritization into your NPD system. In particular, we will look at how to find out what consumers find most important in specific product improvements through Wonderflow’s customer feedback analysis tool.

The challenges of NPD

In a survey from Mind the Product managers and leaders discussed their biggest challenges. The results made clear that prioritizing product road mapping decisions without meaningful market research was by far the biggest issue. An incredible 49% of product managers said their biggest day to day issue is being able to conduct effective market research to validate whether the market truly needs what they’re building. When we add the responses from enterprise software PMs, this figure jumps up to 62%. 

The implications are incredible- many product managers see it as their role to develop the right product, but at the same time, many don’t think they have the tools to do this. Nearly a quarter of respondents in the survey indicate they have no time to do any market validation at all.  

The reasons for this are many- an important one is that traditional market research can be expensive and time-consuming. Qualitative or quantitative fieldwork is often complicated to brief, may take weeks or even months to complete, and the answers that come back may be ambiguous. Add into this that for a global product launch customer opinions may be needed from around the world. It’s easy to see how product managers will do everything they can to avoid market research if they have to, even if it’s what they need.

Below we outline three vital steps to help you prioritize product and experience development, which allow the process to work without having to source traditional and slow market research.

1. Your company strategy must guide your roadmap

Mina Radhakrishnan, the first Head of Product at Uber, says, “a big part of product leadership is thinking about why are we doing this-and-that to set the basis for saying no, we shouldn’t do that.” 

As Richard Banfield goes on to say, “At the most basic level, choosing one thing over another means saying ‘no’ to someone. That someone might be a customer, a salesperson or a board member. It doesn’t matter whom you’re saying ‘no’ to, it’s still hard to do.” 

Your priorities don’t exist in a vacuum- just as it is for your peers, your priorities can only really be judged by how they align with the overall company vision and strategy. The vision also serves as a filter to de-prioritize the things that won’t make a meaningful difference to the value of the product or experience.

Linking the product vision to the practical work can feel like a distraction when you’re trying to meet deadlines or quarterly objectives. However, in reality, making the connection makes your product work easier to understand, and can save time in the long run. For leaders and managers, reminding the team why you’re building that feature or making that improvement gives the work meaning. It gives you context as well as direction.

If you don’t have a clear strategy and high-level roadmap that was crafted based on customer development, then there’s no point in trying to prioritize features yet. You will always need a long term direction first, which states what your long term plan is, and you intend to get there. The same will be true in all other departments, from marketing to engineering, as the long term plan must connect to everyone.

Your company’s strategy will drive your high-level product roadmap. This tells you what type of functionality you need to build in the short- and mid-term to satisfy your customer’s needs as well as other internal company needs. Once you have gone through this exercise, you can start to consider product features.

2. The three primary NPD factors

A strategy gives us the overall framework and roadmap- it gives our development work direction and purpose. With this as a starting point, we can begin to look at what we need to do objectively to meet these goals. There are, in essence, only three major product considerations at this stage: feasibility, desirability, and viability.

Feasibility: This is primarily a technical issue. In many cases, all things are feasible given enough money. Still, we should lead with understanding what’s needed by product engineers, UI and UX experts, front end developers, packaging designers, and so on. Ask in terms of effort for development, operation and implementation. Frame questions as to what should and can be done rather than what’s impossible, improbable, or unlikely. This allows you to understand the technical constraints you may work under.  

Desirability: this is a fundamental question- do customers actually want it? This means talking to UX designers, researchers, analysts, marketing, and sales experts. In most instances, this will involve understanding what customers vocalize as well.

This is the first instance where we need to understand how potential products match up against market needs. The Wonderboard is the first port of call as it allows a thorough scan of existing data. This data can be customer feedback or review data from sites like Amazon, social media, or call center logs. Wonderflow’s AI engine extracts the sentiment behind customer messages to give an understanding of what customers feel about product features, relative pricing, or product faults. The same could even be done on competitor data to provide a 360 view. You can see more about how Wonderflow do this here.  

Viability: How does this feature relate to or support your overall strategy and the requirements of the market? This is understanding how the product or feature fits into the bigger universe and how it slots into cost envelopes, strategy, and goals as well as regulatory or legal requirements. In a large organization, this may mean understanding how your product fits with all the other products in development at the same time.


 3. Prioritizing with RICE

By this stage, you will likely have a good understanding of the internal and external environment as well as a list of proposed features for product development. You’re now ready to start prioritizing! 

In some cases, a simple grid mapping urgent versus important will be enough. Here, projects with no time priority (not urgent) and no overall importance to strategy are relegated, and features that need to be done immediately or which are critical for strategy are done first. This process of understanding may be tricky, but the system itself is simple and may be useful for small feature developments.

The RICE method is a great way to prioritize more complex projects or one with a large set of options- the idea allows the prioritization to be done in a way that is systematic and consistent.

With RICE, you examine each of these in turn for your product or feature:

Reach: How many people will this feature affect in a given period? Reach is measured using real product metrics like “customers per quarter” or “transactions per month” to help avoid the bias of picking products or features that you personally want to build. It also allows the same metric to be used for all your list of options. Ideally, this should be a metric already used in your organization.

Impact: How much will this project impact your goals and strategy? To make this more uniform, a sliding scale from 3 to zero can be used where 3 means “massive impact”, 2 is “high,” 1 for “medium,” all the way down to 0.25 for “minimal.” Again use the same period you used in “R” above for consistency. Note that a feature with high upfront costs and very long term payback could potentially have negative Impact.

Confidence: Based on what you know, how confident are you that this feature will be a success? How much data do you have? Again, this can be rated on a sliding scale, which is consistent across your company: 100% is “high confidence,” 80% is “medium,” 50% is “low.” (And anything below that means total guesswork).

Effort: How much time (or other resources) will the project need from engineering, product, or design teams? You can measure this in “person-months” and stick to whole numbers.

Each product can then be assigned a single RICE score:

(R x I x C)/E
This simple calculation gives you the total impact of your feature per unit of time worked. This means that in a list of potential features, it will be possible to immediately rank them and see why some features may be more desirable than others: is a high score because E is low? Or perhaps because I is very high? The results can tell you about deficiencies in your data collection or areas which may be poorly understood. It can be useful to put a range of responses in to check for sensitivity in your results. For example if C went from 75% to 90% for one feature, what would the impact on overall ranking be?

Balancing act

With the methodologies outlined above, it’s absolutely clear that overall corporate strategy and the product roadmap that sits next to it need to be central to all product development planning. Any feature that does not contribute to the long term plan shouldn’t be done. 

The second thing to bear in mind is that with any decision, more data is better. The more information you have, the better the ideas will be: it’s a virtuous circle of product development. Within the RICE score the better defined your strategy is in “I”, both qualitatively and quantitatively, the more likely your development ideas will meet it. “E” requires a thorough understanding for how the technical aspects of product feature design and production work. And of course, with both “R” and “C” the more you know about your customers the better the ideas will be.

Customer knowledge, therefore, has an enormous impact on product optimisation. In most consumer-focused organizations it also has an influence on strategy as well and may well be the guiding principle behind the whole product roadmap.

How Wonderflow support NPD

The Wonderflow solution analyses customer data in large volumes from multiple sources. We collect any type of textual content such as online reviews, client and agent NPS, surveys, emails, customer service reports, and even from the app stores. The aim is to get a 360 view of the customer, what they value and what they think of your product versus your competitors. Our AI analyzes the data to extract sentiment analysis and generate automatic insights. 

Analyzing multi-language customer feedback in large volume from different sources is a complex process. With an AI-based technology and years of experience, Wonderflow is helping global brands to become customer-centric. Find out more about our solution.


The process of product optimization and development is difficult and requires negotiating between the outside world as well as inside your organization. Understanding the customer is a critical element, and it’s clear that whichever process you use to prioritize, the more customer knowledge you have early on, the better the prioritization process will be.

You can read more about how Wonderflow’s analysis and insights helped a consumer electronics company to save its product launch by identifying the products’ problems and optimizing its promotion in this case study here. If you’re ready to talk to us about how to integrate customer feedback at scale into your product development process, contact us.

About Wonderflow

Wonderflow empowers businesses with quick and impactful decision-making because it helps automate and deliver in-depth consumer and competitor insights. All within one place, results are simplified for professionals across any high-UGC organization, and department to access, understand, and share easily. Compared to hiring more analysts, Wonderflow’s AI eliminates the need for human-led setup and analysis, resulting in thousands of structured and unstructured reviews analyzed within a matter of weeks and with up to 50% or more accurate data. The system sources relevant private and public consumer feedback from over 200 channels, including emails, forums, call center logs, chat rooms, social media, and e-commerce. What’s most unique is that its AI is the first ever to help recommend personalized business actions and predict the impact of those actions on key outcomes. Wonderflow is leveraged by high-grade customers like Philips, DHL, Beko, Lavazza, Colgate-Palmolive, GSK, Delonghi, and more.

Start making winning decisions based on customer feedback todayGet a free demo

Business · Dec 12, 2018

Review of the week: Polar watch Vantage V

You’ve probably heard the term sentiment analysis before but never really took much notice of how important it can be for your company. Wonderflow offers many tools that help brands discover the “why” behind customer feedback (ex. Why do customer dislike X feature?). This is because the language people are using can be mined to discover their true emotions and the intent in their sentences. With this information, you can gain a real understanding of…

wonderflow image

Press · Apr 22, 2020

Wonderflow supports companies with free employee feedback analysis tool WonderWork

Wonderflow supports companies with free employee feedback analysis tool WonderWork WonderWork extracts actionable insights from employee feedback using artificial intelligence, aiming to help companies support new ways of working, as well as monitor and improve employee well-being.   Amsterdam (NL) and Trento (IT), April 23, 2020 – Wonderflow, a leader in artificial…

wonderflow image

Business · Mar 02, 2020

Video: What is the biggest limit of data science teams in an enterprise?

If we think about innovation today, the first thing that comes to our mind is probably related to information technology, complex software, and hardware. If we think about the word “innovation” inside a company, we are therefore pushed to think about the IT department, and we sometimes believe that all the new good…