Bonus Hands On article for the product manager’s guide to digital connectivity series.

tl;dr

Unit Economics, ROI and P&L for Product Leaders

“API-first” is becoming another default for a lot of product teams, perhaps especially with AIs and agents potentially consuming data delivered through such channels. While APIs may have started more as internal plumbing, they’re more product-like than ever.

This has serious impact on product work. Product managers spend a lot of time connecting behavior to product metrics. Conversion. Engagement. Retention. Task completion. Adoption. The next level is connecting these pieces to financial outcomes, both cost and revenue. So we’re going explore that.

This is the idea behind Productify’s P&L guide for product leads. Growth metrics and financial impact should be understood together. A feature that increases engagement but damages margin may not be working economically unless that tradeoff is deliberate and justified elsewhere. A feature that costs more to serve but creates more revenue, retention or risk reduction may be a terrific investment.

APIs can sometimes make this relationship more visible.

The business questions are what it costs to create a successful customer outcome, what that outcome is worth, and whether the economics get better or worse when the product wins.

Stop here if you like. You can skip the rest of the article as this was as the main point. Everything that follows is details. If you want a sample spreadsheet to start building your own model, see this link to a GitHub Repository for a copy: API Product Unit Economics Model

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Question: How do you solve a problem or fix something when you’re not even sure exactly what a problem may be; only that you have a vague sense that there is one? This is something for which AI can supposedly help. By dumping in piles of data, you can find insights and patterns. Sounds Great. Until you run into the problem of missing critically meaningful data. For all the stunning benefits Artificial Intelligence (AI) and Machine Learning (ML) tools offer healthcare, we’re collectively missing something. And that something is comprehensive outcome data.

What is outcome data? Quite simply, it’s asking what happened? Not just after a singular healthcare encounter, but longer term. And what about at a population level. Think about your own experiences in the healthcare system. Ideally you’re being reasonably proactive and preventative with good behavior and checkups. (Because, of course, we all eat perfectly and work out just as we should.) Your other experiences were because something went wrong. You got sick or hurt. What happened? You went in, (physically or virtually), you got diagnosed, (ideally properly), and left with some treatment and perhaps a prescription. Then what? Often nothing. In some cases, you will have follow-ups. And your Electronic Health Record (EHR) will be updated accordingly. But much of the time? Not much happens. Maybe that’s fine for you. Who wants to be bothered with a checkup for nothing or yet another survey. Still, is there just an assumption that you got better? If so, was it quickly or after a months long struggle? Maybe you went to another doctor. Maybe you died! (Well, not YOU obviously.)

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Intro

Understanding how these things work matters.

Not because you’re going to build the next GPT yourself, but because understanding just enough of how LLMs and vector math work can change how you think about products, teams, and strategy. It can help inspire better solutions, make smarter tradeoffs when AI promises start sounding magical, and maybe even help you call BS when needed. Whatever strategic product decisions you may be making, your implementation team could be internal or perhaps a contract shop. In either case, there’s operations impact and costs that will likely impact your roadmap. If you also have P&L responsibility, you’re going to need to look at the costs here with regard to your business case. And if you don’t, chances are you may be the one who still has to justify the spend to others. As usual in product manager land, even if you’re not the one executing the actual work, you likely need to understand enough about the pieces to know what they can do and what this might cost.

tl;dr

• LLMs turn text into numbers using math called vector embeddings. We’re going to look at this below.
• These vectors live in a high-dimensional space, where “distance” equals “semantic similarity.” Again, we’ll look at an example below.
• Transformers (not the ones from the comics/movies) are the model architectures that makes GPT-style LLMs so powerful.
• All this lets us build apps that “understand” language enough to generate answers, categorize, summarize, translate, and more.
• But it’s still math, not magic. And it’s expensive from a lot of perspectives. The question is where do we want to take the expense hit(s) and for what level of benefit.
• If you want to try an interactive demo, I put one here: Search and LLM Math Step Through Here’s another for LLMs that puts mine to shame LLM Visualization

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At a strategic level, Product Analytics isn’t just about the “How.” That’s important, but is more tactical. It’s more about the “What” and the “Why.” Basically, are your outcomes driving toward your goals.

We are overwhelmed by information, not because there is too much, but because we haven’t learned how to tame it. Information lies stagnant in rapidly expanding pools as our ability to collect and warehouse it increases, but our ability to make sense of and communicate it remains inert, largely without notice.

Stephen Few, Data Visualization Expert

The What and the Why

What are you trying to do? Probably running and growing your business; within guardrails including internal financial and external regulatory concerns. Most analytics are historical lagging indicators. You may have items you consider leading indicators; a sales pipeline or scheduling system, possibly equipment failure estimations, etc. And you may be using predictive models including AI tools. But mostly, your performance reports will be historical. And why study history? Typically it’s to try to predict or change the future.

So What are you trying to do?

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SaaS (Software as a Service), and its variants, PaaS (Platform as a Service), IaaS (Infrastructure as a Service), DaaS (Data as a Service or Desktop as a Service), and more… are all on a tear right now. In the classic Buy vs. Build argument, we’ve now got more reasons than ever before to hire out for more and more components of our businesses. Unfortunately, these still nascent services may often lack core analytics capabilities or the ability to provide hooks or tags to insert your own collection capabilities.

Quick Review on Why to Build Your Own Code

There remain some key reasons for continuing to build your own technology, even when there’s some great aaS out there for you. Among them… [Read more…]