Guest Post by Ann Chan, www.annchan.com

One of the key jobs PMs perform is goal setting. This important task shapes how the company judges the product and team behind it, and is almost always driven by the Product Manager, in conjunction with his/her data scientist.

Work Scenario

Goal setting usually happens at the start of a quarter or half-year, and the team “signs up” for the topline goals the PM sets for the product. For example, if you’re a PM on Instagram Stories, you may set a goal on increasing the number of stories shared each week, and the team’s success gets evaluated against these goals by the end of that time period.

This exercise is also frequently done on the feature level. A high-performing product team should always be crystal clear whether a feature is doing well or not, since this enables individual team members to course-correct, double down, or pivot quickly.

“A good PM defines success and measures progress against goals.”
Two sales people working in a meeting with laptops
Photo by LinkedIn Sales Navigator / Unsplash

3 Tips on Process

  1. Consider the product life cycle before setting goals. Is this a new, growth stage, or mature product? The goals for a growth stage product are likely to be the wrong goals for a product pre-product market fit.
  2. Be able to communicate goals in ‘english’, not just in terms of metrics and numbers. We need alignment from many stakeholders and not all of them speak metrics.
  3. Work with your data scientist/analyst to select the right metrics e.g. Monthly Active Users first, then project a numerical goal e.g. 10K MAU.

Selecting Metrics

When it comes to selecting metrics for goal setting, less is more. Remember metrics are only useful when the team can take action upon it. To help ourselves prioritize ruthlessly, imagine your job is to pick metrics to display on a large dashboard so that everyone can read it from 10 ft away. Limited screen real estate + large font size = not a lot of metrics.

Hypothetical product: An online platform where people can buy and sell plants.

Replanting small plants
Photo by Daniel Öberg / Unsplash

Main Dash

Success Metrics – What’s this for: are you proving the hypothesis you set out to prove?

Examples:

  • Feature participation rate (PR)
  • Monthly Active Users (MAU)
  • Annual Recurring Revenue (ARR)

Counter Metrics – What’s this for: are you hurting any part of the experience?

Examples:

  • Customer Satisfaction
  • Performance ie. site speed
  • Ecosystem effects ie. site wide usage

Tracking Metrics – What’s this for: are there any other secondary learnings you’d like to glean?

Examples:

  • Secondary effect on weekly retention or conversion rates

Secondary Dash

  • Funnel Metrics - people’s conversion rates as they move across each page/step of a given flow ie. a registration flow
  • Key Drivers - behavior that influences people’s likeness to take desired actions ie. number of page impressions, CTR on Google ads, number of items added to the cart
  • Quality Indicators - page load times, number of page crashes, number of bugs from broken flows etc

As a Product Manager, you don’t need to be the expert on numbers, metrics and data. However, you do need to have a genuine willingness, if not excitement, to learn more about them, understand them and utilize them as a way to strategically monitor your products’ success.

Tip: Find a data mentor at your organization or online who is patient and will answer all your “dumbest” questions. The more you ask, the more you will learn.

Like anything else, becoming a data-savvy PM takes time, so don’t give up before you get started!

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