What does a new user think that your product, or any product, promises to do for them? On a recent multi-state drive I had the opportunity to listen to hours of radio advertisements (thanks to my 6th grader’s insistence on top-40 stations only) and got to hear many “promises.” “We’ll give you cash back on gas purchases,” “We will protect your privacy when you go online,” and “Find the lowest prices on a new car.” However, what the marketing team thinks is the promise is often not what I, the consumer, understand it to be. In buying our mini-van we didn’t need features or lowest prices, we needed reliability, confidence in the safety of the vehicle (precious cargo onboard!), and enough inner capacity to easily pack everything we could need for a weekend trip to the relatives or camping. We ended up paying more for the vehicle that satisfied, and delivered on, the promises that we were looking for.
Measuring the Right Things, pt. 1: What do people click on?
People do not use our products and software in the way that we, the designers and builders, initially expect. In analyzing how web pages are used I wanted a quick way to determine what people are actually clicking on. We have web analytic tools that tell us total page views, link clicks, and form submissions, but is there more we should be looking for? And I had a hunch that users might be clicking on non-interactive elements, expecting something to happen, and experiencing more confusion than joy.
Best Practices in Data Pipelines
The methodology of pulling data out of one software system (via some sort of application programming interface or API) and putting it into a database or other system is commonly referred to as an Extract-Transform-Load (ETL) process. There is also advocacy for ELT patterns (transforming last), but regardless of the location of the T I’ve found that the E and L parts are the most critical. In terms of operational efficiency and the need for constant maintenance, the acquisition of data (E) and storage (L) have to be bug free and running cleanly.
Product-Qualified Lead (PQL) Scoring - Part 2: Delivering Value
Visualizing all of the user actions that go into the score in terms of their predictive importance can be a very helpful way to breakdown the model and communicate its function with other teams. Plotting each action in a 2D field where Necessity and Sufficiency form the axis gives a view of the relative predictive value of each observable element. I used a dashboarding tool to make an interactive chart that allows filtering by product area, and selecting individual bubbles to see metric details.
Product Usage: Discovering Habits, Part 1
Habit loops can be understood as patterns of usage that emerge when users come back to our app over and over again and find value in the solution it provides. I dig into understanding and measuring habits, and explain an analytical model that uncovers “habit signals” in product usage data. Being able to measure the habits from the product perspective (“which parts of the product deliver the most value to users?”) as well as the user perspective (“which users have developed the strongest habits?”) is immensely valuable in growing a SaaS product.
Product-Qualified Lead (PQL) Scoring
This post walks through how to build a “product qualifying” model to identify which users of your product are most likely to buy. For explanations of why this is a great idea, and you should already be doing it, see this excellent article from Madkudu, or the Product-Led Growth book.
If (only) I'd Never Played a Piano
When do we let our perceptions of the limitations of our tools (the apps we use, the vehicles we drive, the mobile devices we carry, the models and frameworks we employ), prevent us from exploring their full range? Of thinking beyond the constraints we impose on ourselves before we even get started?
The First Form on the Internet
We live in a world of HTML and interactive web pages and apps, but how often do we stop and think about how quickly it is changing, and how little we really understand how it impacts how we buy and sell stuff?
Best Practices in Product Experimentation
Experimentation helps us get a clearer understanding of how users find value in our product, what our users’ needs are, and how we can best help them address those needs through our product. We want to understand the good and the bad, the positive and the pain as best we can so that we can keep improving our product and increasing the impact we have through growing our customer base.
The ASP's Sting: What Average Sale Price Won't Tell You
The ASP is a very common performance metric for many types of businesses, where the prevailing notion is that higher ASP is better. If the unit economics stay the same this is born out: selling 10 cars at $20K is better than selling 10 cars at $15K, for the revenue of the business. Finding ways to drive ASP higher and higher seems like an obvious conclusion, if you want to increase your overall revenue.
But is it actually “average”?
Who is Ben Talking to?
Why am I writing this, and who is it for? Is it for you?
After a stimulating conversation with friends or coworkers or family, I often find myself with many additional thoughts and questions about models and frameworks and logical problems. Given the strictures of time and the pressures of practical topics often these paths of greater depth and finer granularity are relegated to ink marked pages of small notebooks on my night stand. These posts will serve as a more visible and accessible rendition, while the self-induced pressure of thinking (nay, daring to hope) that someone else might read them is immensely helpful in instigating a fuller, more coherent formulation.