Pitfalls in Product Decision-Making

By Adam Sewall
August 15, 2016



    Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!

In any given day, your company makes thousands of decisions. Big ones. Small ones. Some have many dependencies, others have none.   Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!      Read Now!

If your company is like most, chances are that it fits one or more of the following conditions:

  • It lacks a process governing how product decisions are made
  • It has a governance model for decisions, but it is too lax
  • It has a governance model, but it is too cumbersome
  • It has a governance model, but it is incoherent or poorly understood

In the absence of a coherent process, a variety of approaches and tools will fill the void. This can happen at the business-unit level, where each unit is organized and makes decisions differently; or it can happen at the team level, where individuals plan and prioritize according to their own homegrown, spreadsheet-based formula.

Once it takes root, this ad hoc decision-making can dramatically impede product success by eroding credibility and trust, introducing bias and obstructing repeatable processes. Here’s what to look out for when assessing your organization’s ability to swiftly make decisions and prioritize work that solves
the right customer problems.

Frequency and Recency

It’s a fact that, as humans, we are more likely to recall things if they have been mentioned frequently or recently. That is why advertisers like to show the same brand message multiple times over a short time period.

This can have obvious consequences when it comes to product planning. When all else is equal, a product team that is asked to produce a plan is more likely to prioritize items they’ve heard recently or frequently.

Now, you shouldn’t avoid paying attention to things you hear frequently. In fact, frequency is a fairly reliable indicator of pervasiveness, which is a fundamentally good dimension to consider in determining the biggest customer problems. The problem, in our experience, is that you usually end up with something that resembles this table.

This is great if you plan to build all five features. But what happens if you only have the resources to build three? How do you weigh these items by relative importance? And what about requested features that didn’t end up on this list? It’s entirely possible that a large, strategically important customer has a need that’s not represented on the list.

Better would be something like this:

And better still would be the following, which includes total revenue attached to the feature requests from all potential customers:

To reduce frequency and recency bias, you must generate a comprehensive and accurate view of all customer needs—not just the ones you heard about last week. In the B2B world, generating this list requires teamwork; you must engage members from sales, customer success and other customer-facing teams.


It’s a bad idea to let the highest-paid person in the office (HIPPO) make product decisions if that person is wrong. The problem is that this isn’t always clear. And, even when it is, no one wants to tell the boss their ideas are bad.

HIPPO-led decision-making can have a number of side effects. First, it can discourage smart people from doing their best work. If everyone knows your CEO will dictate what he wants on the roadmap—regardless of alternate recommendations—they will be less motivated to think long and hard about tough customer problems.

Second, relying on a HIPPO is neither a scalable nor repeatable approach to making effective product decisions. Who calls the shots when the HIPPO leaves the company? What happens when you grow past 100 employees? Startups with strong-minded founders face particularly challenging dynamics in this regard.

Finally—and perhaps most important—a HIPPO is often the farthest removed from customers and has the least insight into what customers actually want. The decision by Kodak CEO Walter Fallon not to pursue digital technologies—despite the fact that Kodak engineers had already developed the first digital camera in 1975—is just one example of C-level decisions that are out of touch with customers and the market.


As anyone with a 2-year-old can attest, it’s hard to ignore someone who’s emphatic and persistent. The loudest voice in the room (LOVR) is not that different; they can have a major influence on the product decision-making process. This fact poses a number of challenges.

First, like 2-year-olds, LOVRs drown out the other voices in the room, making it difficult to identify and listen to the needs of quieter, more introspective individuals. This leads to a narrower, less diverse set of perspectives from which to inform your decision-making process.

Second, and less obvious: If you give LOVRs what they want, others will soon learn that the only way to get your attention is to be loud. When this happens, your team will treat every minor feature request or customer need like the sky is falling.

Page 1 / 2

About the Authors

  • Adam Sewall is the head of marketing and partnerships at Wizeline, a San Francisco-based solutions company offering intelligent product management software and full-stack agile development services. He has more than a decade of experience working at high-growth B2B SaaS companies, with a focus on product management, product marketing and partnership development. Email him at adam@wizeline.com or find him on Twitter: @asewall.

Post a Comment


Allowed HTML: <b>, <i>, <u>