How Product Managers Can Effectively Quantify Business Impact

Ways product managers can calculate and communicate business impact internally to deliver on real business growth.
Headshot of Brooke Carter.
Brooke Carter
Expert Contributor
January 7, 2021
Updated: January 8, 2021
Headshot of Brooke Carter.
Brooke Carter
Expert Contributor
January 7, 2021
Updated: January 8, 2021

Delivering business growth is what product managers strive to achieve — that’s likely why you’re reading this article. And while measurement is key to quantifying success, communicating results in terms of revenue impact should be the ultimate goal. This ensures that growth teams can demonstrate wins, gain internal support, and increase team resources.

Communicating results clearly starts when planning experiments and requires a critical eye on data throughout the process. Beginning with definitions of experiment metrics helps you understand what you need to achieve, recognize when results may not tell the whole story, and clearly tie data to business impact.

 

Choosing Your Growth Metrics

Understanding the impact of growth experiments is critical for organizations, but can be challenging to align on internally. In some cases, this leads to retroactively defining experiment goals to fit the desired narrative. To ensure team cohesion in growth strategies and achieve impactful experiments, planning must begin with choosing the right growth metrics.

It’s important first to understand the nuanced differences between common measurement terms. These are some terms that are too often used interchangeably — and how to better distinguish between them.

  • North Star: What do we need to achieve as an organization or team? An example: increasing lifetime value (LTV).
     
  • OKR: What is the objective and what is the key result that you want to achieve this quarter or year? For instance, increasing paid conversion by 10 percent.
     
  • Goal: What do you want your users to do? Perhaps it’s to convert users from a free to a paid product.
     
  • Signal: What are the user actions that indicate that your goal has been met? This could be visits to the pricing page.
     
  • Primary Metric: How can you capture data about those signals? Such as transaction data (payments received).
     
  • Hypothesis: What is your expected change? Like a new feature that will improve the value proposition and increase upgrade rates.
     
  • Support Data: What data supports your belief that you will achieve the desired change? Consider beta user feedback on a new feature.

It can be easy to confuse signals, primary metrics, and north star metrics — I know I did early in my career.

Having one — at most two — primary metrics helps to stay laser focused on desired results and make decisions faster. Primary metrics are how an experiment’s success is ultimately judged, such as upgrade rates and paid product retention. In comparison, a signal can tell you something about your end users’ behavior, but it may not change your decision on whether to implement a new feature. Signals may include click-through rates, the aggregate number of users, or visitors to a page. These tell you something about how your audience engages with the brand but don’t necessarily mean there is actual business impact.

Meanwhile, north star metrics are critical for team-wide alignment, pointing everyone in the same direction for maximum impact and resource prioritization. Consider north stars as team-wide success goals, while primary metrics are specific to features or experiments.

For example, imagine Company A has one product with several plan types. When Company A digs into data, they notice their monthly plan had higher upgrade rates, but the annual plan had a higher LTV. What this tells them is that not all plan types are the same. Company A could easily decide whether they care more about prioritizing monthly or annual plans with a north star metric.

 

Validate Metrics Through Measurement

Data is abundant these days, and there are many ways to retrieve it. You might consider A/B tests, transactional data, or performance data. By setting growth metrics first, you should have an understanding of what you need to test. Choose the best measurement approach based on your defined performance indicators.

Unfortunately, there are times when measurement won’t work — at least not immediately. For example, if your goal is to increase annual subscriptions, you need to wait an entire year to determine whether the experiment was successful. In these situations, consider leveraging qualitative data gathered through customer interviews or surveys.

 

Clearly Communicating Impact

Once you’ve set growth metrics and executed the experiments, it’s time to quantify the impact. After I finish an experiment, I quantify the impact of the results to best communicate them to my team.

Let’s look at a simple example: a product that is a one-time purchase with no recurring payments (not a subscription product). Let’s say you just ran an A/B test and achieved a statistically significant 20 percent increase in day-one upgrade rates for users on desktop. What does that actually mean in terms of the actual revenue impact?

The important factors to consider are the percentage of revenue from day-one upgrades on desktop, last year’s annual revenue, and the annual growth rate. Once you have these numbers, you can calculate the relative impact of this experiment and its overall impact on revenue. In this example, it would be a simple calculation using this formula:

= 20% (experimental lift) X % of revenue from day-one desktop upgrades X last year’s annual revenue X annual growth rate.

When measuring your impact, it’s important to watch out for these common oversights:

  1. Ignoring Side Effects: My product usage increased, but I’m cannibalizing another product.
     
  2. Short-Term vs. Long-Term: Upgrade rates improved but then went back to the same/lower levels.
     
  3. Vanity Metrics: More users are clicking, but there is no change in upgrade rates.
     
  4. Attribution: This change drove X increase in retention, but we also made three other changes at the same time.

 

Moving From Experiment to Productization

One rarely sees the expected impact match the actual impact — with growth experiments, two plus two often equals three, not four. There are several reasons why this is the case.

Take the novelty effect. Data will show a short-term lift with a new feature upon rollout, but consumer novelty will shrink. This will degrade experiment results over time.

Additionally, you are not trying to optimize a static world. You are creating a new experience for a customer that is changing their behavior. This means that your experiments and how you measure the same impacts will need to evolve to keep up.

Another possibility is that a user’s behavior may have only been shifted earlier in the life cycle. For example, an experiment may have gotten a user to upgrade in their first week instead of their first month. This means that the impact may be a smaller lift than originally anticipated. It is important to track cohorts over time to understand if this might be happening.

All of this may seem like an awful lot of planning and caveats, but in the end, establishing your metrics and being mindful when interpreting your data can make all the difference in delivering real business growth. In my experience as a product manager, following these steps helps you ensure that you’re not just able to hit your goals — but to communicate what they indicate for your organization’s roadmap in a meaningful way.

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