This is not a newsletter about how good/bad ChatGPT is at writing OKRs (Spoiler: It’s doing ok in producing by-the-book OKRs) or my ability to design ChatGPT prompts (plenty of room for improvement here as well). It’s more about showcasing the impact that context through strategy can have on OKR design by using a mechanical sparring partner.
I wanted to illustrate a key point often discussed but still overlooked by many teams: The importance of Product Strategy as one, no, THE input for defining actual useful OKRs.
Here are some exemplary results from each iteration:
Prompt No. 1
Suggest some OKRs for an Analytics SaaS Product Team.
Here are two of the OKR sets ChatGPT returned:
That happens when you write OKRs without any Strategic Context: Generic KPIs are re-modeled as KRs, and randomly selected feature ideas are turned into Output Key Results.
Let’s add *some* Strategic Context…
Prompt No. 2
Suggest some OKRs for an Analytics SaaS Product Team that has changed its Product Strategy to focus on upmarket Enterprise clients in EMEA (from SMBs in the US) through a self-service distribution and freemium trial model.
Let’s see how this additional context affected the suggested OKRs:
Our Key Results now reflect the context of our Product Strategy “Playing Field.” We moved from generic activity KPIs to specific growth goals around the company types we want to go after. But they are still vague in terms of the actual value we try to provide, and we don’t give much context about actual buyers or users.
Let’s add data about target audiences beyond geography and how the acquisition strategy has to change due to our other Product Strategy Choices to make it more coherent.
Prompt No. 3
Suggest some OKRs for an Analytics SaaS Product Team that has the following Product Strategy:
- Focusing on Enterprise Clients in EMEA with >100M ARR and >500 Employees
- Moving from a direct sales to a self-service freemium model
- Starts to focus on the new Buyer Persona “AnalyticsOps” Manager who also champions the product inside of companies
- Generating leads through organic search traffic and free partner webinars instead of high-ticket conference booths and paid ads
You can always debate if a self-service strategy works for capturing Enterprise audiences. But this could be an interesting shot since “we” want to sell bottom-up through a hands-on buyer persona (compared to a C-level). For this to work, the patterns of our Product Strategy have to be coherent–Hence the focus on more organic and discoverable acquisition strategies.
Let’s see what OKRs ChatGPT now suggests:
Nice, measuring the success of acquisition efforts is now in line with whom we want to reach. But our “Product Performance“ focus OKR Set still feels random. Like a C-level “shower idea” about “the one metric we definitely have to improve this cycle.” Let’s talk about how we actually plan to differentiate ourselves from competitors.
Prompt No. 4
Suggest some OKRs for an Analytics SaaS Product Team that has the following Product Strategy:
- Focusing on Enterprise Clients in EMEA with >100M ARR and >500 Employees
- Moving from a direct sales to a self-service freemium model
- Starts to focus on the new Buyer Persona “AnalyticsOps” Manager who also champions the product inside of companies
- Generating leads through organic search traffic and free partner webinars instead of high-ticket conference booths and paid ads
- Its key differentiators against competitors like Adobe or Oracle are the customizability done by the professional services team at zero extra cost and the growing library of community plugins on its own marketplace
We now made pretty explicit choices (and thereby trade-offs) in the most relevant aspects of defining a Product Strategy (except for a Product Vision and North Star Metric, maybe). I wouldn’t expect any more random KPIs or off-the-cuff feature suggestions, but let’s see:
You can check out the full conversation here (I started with prompt no. 2 before going back to the basics and slowly rebuilding from there).
To be fair, most of the produced OKR sets are similar to what you can find on generic “OKR Examples” websites, and many fall either on the side of lagging business metrics or Outputs. But, I guess, with more inputs about in-depth user jobs, we could also see more Outcome-ish Key Results emerge.
But the point of this experiment is not to check how good ChatGPT’s OKR writing capabilities are. Instead, it serves as a reliable sparring partner for demonstrating how tightly the quality of OKR and the specificity and coherence of your Product Strategy choices are linked.
Try it by checking how much context your Product Strategy provides along these Product Strategy Patterns.
Speak soon,
Tim
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