Setting KPIs can often feel arbitrary, especially when entering new markets. How do you get past this uncertainty to set realistic goals?

Such a great question! When you first set a KPI especially if you are in a new market and/or in a new product/customer space, it can feel uneasy. The best way I have learned is by setting something and tracking it over time, seeing if there is any measurable change. If not start by marking out the customer's journey (no matter who they are) and see if you can collect data on their interactions along the way. This may reveal some hidden trends you weren't yet measuring.

I would start by understanding what is the company being graded on by its investors and how is this new product going to deliver/contribute to that KPI. Let's say your investors are keen on seeing revenue growth this year. You can begin by benchmarking the lifetime revenue growth of the various product offerings of your company and then estimate (based on your user research/data) what will be the adoption rate for this new product area/market. You can then begin to model out some first year numbers. Of course, this will seem super arbitrary but truth be told once you launch, your predictions for subsequent quarters/years will only improve and that's when it's critical that the KPIs are accurate. Focus on product market fit for your launch and then focus on modeling according to comperative product growth.

+1 to arbitrary! I think setting goals for both new and existing markets may feel like excel magic, some numbers that are based on mainly assumptions and the product manager's gut. Its uncomfortable and may feel unscientific but I think all forward planning is like that. 

I would address some uncertainity in the following way

1. Make sure I am tracking the right metrics, New markets could mean new metrics, the business is starting from scratch and may be different from the metrics that a mature market may have. Example in a mature market I would focus on expansions vs in a new market I'd focus on acquisition
2. Small experiments to inform goals. I cant stress this enough and often see product managers trying to solve for too much at once. Its important to setup small experiements, learn from them, adjust goals and then rinse and repeat

Its not possible to remove uncertainity completely but its definitely possible to reduce it and get better at making assumptions. 

This is a great question. I am a fan of actually being ambitious and setting unrealistic targets or stretch goals and seeing where we end up in the first couple of reporting periods. To get a dose of realism, you can always reference analyst reports and right-size your total available market vs. serviceable market and current penetration into the new market. I don't advise setting realistic targets when entering new markets because that can lead to complacency instead of innovation. You can also leverage a range, and use a moderate case and best case target achievement. For example, if you are offering an existing product with 1M marketing analyst users to a new persona like project managers, you would want to evaluate what is the total presence of project managers in your customer base or industry. If you see that you only have 2K project managers in the current user base, but there are over 3M project managers in the industries you are serving, you can set an incremental goal somewhere between 2K and 3M, but you wouldn't want to set a 3M project manager user target right out of the gate since you have no idea how many of those users are actually servicable. 

Setting KPIs should not feel arbitrary. That's a smell. It means that the people choosing those metrics or setting their targets don't clearly understand how they influence the business or the outcomes desired. Perhaps good modeling has never been done to demonstrate either correlation or causation.

When it comes to entering new markets, my opinions change. My approach to leadership is to measure and model things that are known or knowable. Entirely new products or markets will, at best, be understandable through competition and alternatives. Collecting this type of data is imperfect: noisy, sparse, highly filtered, and coarse. It's dominated by qualitative information and not quantitative behavioural data. Where you do get quantitative data, you have to be skeptical of how it was gathered and analyzed by the 3rd party vendors (e.g. Gartner, Forrester, McKenzie, etc.) In short, you're flying with minimal visibility and a malfunctioning instrument panel.

You need to gain clarity through experimentation. You can absolutely ask yourself some question:

1. What metrics do I think would indicate we're achieving our outcomes?
2. What targets would suggest our outcomes are being achieved quickly enough to be worthwhile our investment?
3. What is a reasonable amount of time to run experiments before we'd expect to see some results?

But the KPIs and targets must be motivational and directional at best. They need to be malleable as you ship, research, and learn. Modeling would be pointless, filled mostly with fake confidence and lies. Instead, spend your energies being rigorous with your experimentation methodologies (qualitative and quantitative), think big and start small, and move at pace. Some experiments may take 6+ months to launch so your leadership has to have good intuition based on the sparse data whether this is worthwhile and trust the process. So set goals around how well you run this process until you have enough information to then form an actual longer term strategy where KPI targets go beyond hopes and prayers.

KPIs can help identify that in a new market whether we have got the right product fit. This alone will allow to define the right metrics.

The key goals could be

1. Acquisition – how well are you getting customers to your site or app?

2. Activation – are your customers having a great ‘first run’ experience?

3. Retention – how often are your customers coming back?

4. Referral – are they telling others about your product?

5. Revenue – are they paying for your service?

Identify the right KPIs that can give data to answer whether above goals are met or not.

See question: “What are good OKRs for product management?” for a general introduction to the topic.

Specifically when it comes to high level of uncertainty, it becomes particularly important to adopt strict scientific methodologies. Observe, formulate questions, make hypotheses, experiment and analyze data, formulates and communicate conclusions, and finally iterate.

See question: “How do you approach setting crisp KPIs and targets for Engine features and linking them to your topline metrics?” for my step by step process for realistic OKRs.