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#74: Feature Preference Made Easy

The secret to identifying the features that matter most

Do you know which features your target customer prefers?

Most companies just guess. And, they usually get it wrong. Even the best intentioned product marketers miss the mark.

I’m working on a client project right now, and the goal is to identify the top features needed for a successful product launch.

I started by refining the ideal customer profile (ICP). Once that was nailed down, we sent a survey. Here’s where my process differs from most.

Rather than ask customers to rank features, I used a technique called MaxDiff instead. In today’s edition, I’m going to share why this is my preferred method for identifying feature preference and teach you how you can use this technique yourself.

Let’s get into it.

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The End of Stack Rank

I’ve spoken about the power of MaxDiff before. If you’ve taken our course Ready for Launch, you know I teach an entire section on how to build a Feature Value Matrix.

But, I wanted to break it down to make it even simpler for everyday use.

The Basics of MaxDiff

In a nutshell, MaxDiff (which stands for Maximum Difference Scaling) determines the relative importance or preference of an item. It can be used to calculate the importance of:

  • Product features

  • Value propositions

  • Pricing options

  • And more!

Let’s get back to why it matters.

Most product marketers will ask their customers to rank their features. If you’ve done this, you know you’ll end up with a chart that looks something like this:

Sure, we can see that Feature 1 is most preferred and Feature 3 is least preferred, but we don’t have the full story. The insights are lacklustre.

That’s where MaxDiff comes in. Unlike stack ranking, where respondents are asked to rank items from most to least important, MaxDiff forces them to make trade-offs between items.

MaxDiff forces respondents to choose between the best and worst options from a set, leading to more distinct preferences. This eliminates the tendency of respondents to rate everything similarly. It’s also the preferred methodology when you have a large number of options (ie. more than a handful).

Here’s what the output of a MaxDiff question looks like:

Can you spot the differences? In this chart, we can clearly see that Features 1, 4 and 5 have positive preference, while Features 6, 2 and 3 have negative preference. This means customers do not want those features.

This is real data from my client project. Imagine if I had only ran a stack rank question in my survey. I may have had my client build or prioritize the wrong features. Now we know exactly where to focus.

How to Use MaxDiff

Here’s how you can get these results for yourself.

It’s easiest if your team has access to a survey tool like Qualtrics or SurveyMonkey. But, it’s still possible to run a simple MaxDiff on other tools too.

Follow the steps below:

Define Your Objectives - Clearly outline what you hope to learn with this analysis. Are you testing feature preference or message preference?

Design the Survey - Draft your survey questions and make sure you’ve collected all potential options. If you have more than a handful of features, you may want to consider a more advanced version of MaxDiff that will show different attributes across multiple sets. The software will help you calculate this.

Distribute the Survey - Send the survey to your target customer. Generally speaking, you’ll need around 150 to 200 responses per segment for strong results. If you want to compare answers across segments, it’s OK to send one survey to everyone. Just make sure you have a way to identify one segment from the other (I like to match email addresses or pass through a unique ID).

Analyze the Results - Once the data is collected, it’s fairly easy to calculate the results and build your charts. If you’ve used an advanced software, it will actually do that part for you. But, if you’re like me and you’re using a simpler tool, you’ll need to do this part yourself.

Here’s the formula for Simple Count Analysis.

The higher the score, the higher the preference. A score above 0 means it has positive preference. A score below 0 means it has negative preference.

Create Your Chart - Turn your raw data into a chart, like the one I shared above. Google Sheets will suggest the right type of bar chart based on the data.

That’s it! Simple as that.


🛠️ Tools: Want to run a MaxDiff but don’t have access to fancy software? You can use SurveyKing for only $19/month (not sponsored, just a super useful and affordable tool).

🗓️ Events: Why do some launches flop while others flourish? Join Andy, Jason and I for a 30-minute FREE product launch lesson on June 3 and learn our framework for studying launches and applying their winning strategies.

Until next week,

Tamara Grominsky

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