Product-Led Organisations Redefine Research

 

By ANDI MASTROSAVAS

Customer research, performed continuously in product-led organisations, is used to identify pain points, uncover problems worth solving, and validate assumptions.

Undertaken proactively, rather than as a result of an unforeseen consequence arising, mixed methods research has emerged as a dominant practice.

No longer esoteric, product-led organisations have been integrating quantitative and qualitative research methods for some time now, and the benefits of combining both types of data are increasingly understood.

Example - Subscription Video On Demand (SVOD)

During the R&D phase of a new SVOD service, quantitative data of video usage metrics and surveys were analysed to help define the target customer at launch. The analysis uncovered an underserved segment of voracious viewers; teenage Hispanic males.It showed they watched significantly more hours of video content per day than their female counterparts.

If the research had ended there, with purely quantitative data driving decisions,an expensive phase of content creation and acquisition would have centred around this demographic. However, combining this data with rounds of focus groups helped to explain why this disparity in viewership existed.

What emerged from the candid discussions were persistent, familial gender roles that meant teenage Hispanic females attended to more domestic duties, such as caring for younger siblings, cooking family meals, and tackling an uneven share of housework. They weren’t inherently less interested in watching videos, they just didn’t have as much time.

This qualitative understanding forced a re-examination of the quant data which found that teenage Hispanic females, despite their time constraints, were actually more highly engaged viewers. They were more likely to obsess over certain shows and stars, consider themselves fans, and watch an entire series over a random collection of user-generated videos online, as their male counterparts were primarily doing.

Had the service been ad-supported, targeting teenage Hispanic males would have made economic sense; the more time a viewer has to watch, the more ads they can be served. But SVOD has different audience dynamics. Retention is more important than watch time, and part of churn prevention is exclusive content that attracts highly engaged viewers. These findings redirected the content strategy towards a different target customer.

As barriers to entry for building software products continue to decrease, the need for continuous customer discovery increases.

What Is Mixed Methods Research?

The simplest definition of mixed methods is that quantitative data is used to describe what is happening and qualitative data describes why. But this simplification betrays the nuance of its benefits. The combination of data enables the translation of these findings into sophisticated solutions.

Quantitative data can be useful for testing objective theories and quantifying defined variables, such as behaviour. As such, it remains the main currency for analysing and optimising product features and use. Whereas qualitative data exposes subjective meanings, uncovers the underlying relationships between variables, and can strengthen or refute observed patterns.

But the benefits don’t end there. Utilising a single research method or type of data when seeking to influence change can be limiting. This holds true for research aimed at promoting policy reform or used to persuade key decision-makers in organisations. Mixed methods research allows the type of data most highly regarded by the intended audience to resonate more strongly, and enhances the validity of the findings to get buy-in for strategic decisions.

There are three main mixed methods approaches, outlined below. The SVOD example from earlier utilised a sequential explanatory approach, where the qualitative data helped to explain and build upon initial quantitative results.

Mixed methods research allows the type of data most highly regarded by the intended audience to resonate more strongly.

There are three main mixed methods approaches, outlined below. The SVOD example from earlier utilised a sequential explanatory approach, where the qualitative data helped to explain and build upon initial quantitative results.

Type

Characteristic

Purpose

Sequential Explanatory

Quantitative data analysis followed by the collection and analysis of qualitative data.

Qualitative data used to help explain the findings of a quantitative study.

Sequential Exploratory

Qualitative data followed by quantitative data collection and analysis.

To explore a phenomenon, develop and test a new instrument, or identify variables.

Convergent

Quantitative and qualitative data collection and analysis is concurrent and complementary.

To confirm, cross-validate, or corroborate findings, to overcome a weakness in using one method.

As barriers to entry for building software products continue to decrease, the need for continuous customer discovery increases. Mixed methods research provides flexibility, ensures all points of view are considered, and collects comprehensive data to tell a more holistic story.

In product-led organisations, where understanding and serving unmet needs creates more value than other approaches, mixed methods will continue to evolve and be deployed to persuade important product decisions and direct strategic vision.

My two favorite resources on the topic are:

Research Methods in Human-Computer Interaction by Jonathan Lazar, Jinjuan Heidi Feng, & Harry Hochheiser.

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches by Creswell & Creswell.

https://brainmates.com.au/

7 Strategies to Choose the Best Features for Your Product

 

How to prioritize features is always a hot topic for product teams. Even the most seasoned product manager struggles with determining which features and initiatives to put on the roadmap and what prioritization frameworks to employ. With so many opportunities competing for scarce resources, how do you decide?

In this post, I will cover seven popular strategies and prioritization frameworks for prioritizing features.

7 Popular Strategies and Prioritization Frameworks:

1. Value versus Complexity Quadrant
2. Weighted Scoring
3. Kano Model
4. Buy a Feature
5. Opportunity Scoring
6. Affinity Grouping
7. Story Mapping

Whether you’re developing a new product or maintaining an existing product here are seven different prioritization frameworks you can use to prioritize product features. In the end, the technique you choose isn’t as important as the conversation your team has about the priorities. And even if you disagree about the specific prioritization, if you can get agreement on the criteria, you’re ahead of the game.

1. Value versus Complexity Quadrant

In the Value versus Complexity model, you evaluate every opportunity based on its business value and its relative complexity to implement. Based on our conversations with product managers this is a common approach, and many product managers go through this assessment instinctively every day. The prioritization framework of the matrix is simple: The initiatives that have the highest value and the lowest effort will be the low-hanging fruit for your roadmap.

2. Weighted scoring

With weighted scoring, you can use the Value versus Complexity model, but layer in scoring to arrive at an objective result. Based on dozens of interviews with product managers we arrived at this model for our prioritization model in ProductPlan.

By using a scoring method to rank your strategic initiatives and major features, product managers can facilitate a more productive discussion about what to include on the product roadmap. While there are many inputs that ultimately go into a product decision, a scoring model can help the team have an objective conversation.

A clear, objective scoring model can inform the initiatives you decide to include on your roadmap, and lend credibility to your product strategy. In ProductPlan, you can seamlessly drag approved initiatives from the Planning Board onto your roadmap.

3. Kano Model (customer delight versus product function)

With the Kano model product managers can look at potential features through the lens of the delight a feature provides customers versus the potential investment you make to improve the feature.

There are some basic features that your product simply needs to have in order for you to sell your product in the market. You need to have these “threshold” features, but continuing to invest in them won’t improve customer delight dramatically.

There are some features (like performance) that give you a proportionate increase in customer satisfaction as you invest in them.

Finally, there are some excitement features that you can invest in that will yield a disproportionate increase in customer delight. If you don’t have these features, customers might not even miss them; but if you include them, and continue to invest in them you will create dramatic customer delight.

4. Buy a Feature

Buy-a-feature prioritization is an activity you can use with customers or stakeholders to prioritize a set of potential features. The approach is simple but fun. List potential features and assign a “price” to each (based on a relative cost to develop it). Hand out a set amount of cash and then ask participants to buy the features. Some will place all their money on one particular feature they’re passionate about, while others might spread their cash around the room. The result is your prioritized feature list.

5. Opportunity Scoring

Opportunity scoring is a type of Gap Analysis that comes from Outcome-Driven Innovation. Without getting too detailed, the idea is to measure and rank opportunities based on their importance versus customer satisfaction. To conduct opportunity scoring you ask customers to score the importance of each feature and then also score how satisfied they are currently with that feature. Your opportunities are those features that are highly important yet customers gave a low satisfaction score.

6. Affinity Grouping

Affinity grouping can be a fun prioritization framework activity. I’ve conducted affinity grouping sessions with product teams that are trying to understand what to build. The idea is simple: have everyone brainstorm opportunities on sticky notes. Then as a team, begin to group similar items together, and then name the groups. Finally, everyone on the team begins to vote on or rank the groups.

7. Story Mapping

Story mapping is a personal favorite of mine to prioritize features. It’s used in agile organizations. And is a great way to document the Minimum Viable Product by organizing and prioritizing user stories and the development releases.  The idea, in a nutshell, is you can map out the workflow of your product from beginning to end.

Here’s how it works:

1. You create the workflow using cards or a Kanban board, and you arrange the cards in order from the start of the customer experience to the end of the customer experience.
2. Then, you then order the most important things to develop from top to bottom.
3. Finally, you create slices of releases based on that prioritization.

Image Source: ProductPlan

Strategies for Prioritizing Features

Your good product management skills will come into play during the process. I have a few suggestions regardless of the prioritization framework you choose:

• Approach prioritization as a team activity; not only is does it create buy-in on the team, you get different perspectives. It’s also a lot more fun.
• Limit the number of items you are prioritizing – focus on the biggest items rather than the details.
• Categorize and group initiatives together into strategic themes (for example, “improving satisfaction” for a particular persona would be a good way to group).
• Before you begin prioritizing, it’s helpful if you understand the customer value of each initiative. The customer value should be rooted in evidence that you’ve gathered from customers rather than your opinions.
• Before you begin, have a rough estimate of the cost. Even the T-shirt sizing of “small” “medium” and “large” will be helpful during the process.

Product management can often be a difficult balancing act, in which you find yourself constantly trying to satisfy many competing agendas for your product. Your sales team wants a new set of features. Your executives want the product market-ready by a certain date. Development wants to push a few items off until the next release. The investors want to shave costs wherever possible. You want to make sure your product doesn’t fall behind the competition. And your customers want everything.

And because it can be so difficult to know exactly what to prioritize amid all of this noise, product managers can easily fall into several pitfalls — and prioritize the wrong things for their products.

Strategies to Avoid Common Prioritization Pitfalls

• Don’t prioritize based on what your competitors are doing.  Your product’s development should be based on the research, your customer feedback, and innovative ideas that you and your team compile — not on what another product is doing.
• Don’t prioritize based on requests from your sales team. Your sales team will always have a feature-request opinion. But relying on their opinion is the fastest way to lose direction for the product’s strategic purpose.
• Don’t prioritize by what’s easy. Even if your developers tell you that they can get a lot of items checked off of the list quickly. It might sound like a viable option but this isn’t a product strategy. In fact, doing so is a strong indication that you’re not working toward an objective for your product.
• Don’t prioritize based on your gut instinct alone. Driving a product to a successful market launch demands hard evidence and a prioritization framework to support the product manager’s decisions. Think industry research, user surveys, conversations with customers, feedback from the company’s sales or support teams.

https://www.productplan.com/