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.
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.