Is Your Product As Great As Your Idea? A Guide To Product Research

What actually makes your product great? your idea? the problem it solves? the fact people use it? which features you decide to implement?

The first thing early entrepreneurs must focus on is of course the problem their product is going to solve, rather than the way it will achieve that. Simply because building a product that no one use is a waste of time.

But this reasoning is often pushed to extreme, and this is paying disregard to the importance of product decisions that come after this step. Going for features that don’t really solve the problem, or mistaking about how future customers can integrate your technology in their processes, can just kill all your effort.

Defining the right features for your product is at least as important than knowing perfectly the problem you want to solve.

Although features originally are a means to achieve a goal -solving the problem-, themselves will intrinsically and extensively participate to the success -or not- of your product.

Feature Usage as a metric for Execution

While the problem your product solve will be the focal point of your communication effort to drag prospects in, what will decide afterwards of its adoption, and retention, will lie in its good execution.

If strategy is deciding what to do, execution is all about making it happen.

Feature Usage scores good or bad execution. And Utility, Usability and Ease of Use .. are the metrics.

Smart choices about features, and good execution have a positive effect on:

Adoption and Appropriation: comprehensive and intuitive features help novice customers to get on board
Product Differentiation, by making your product unique and specific among your market
User Experience, and by this way, Customer Retention

Think features as conversion funnels

Funnels are used for every conversion and convergence point: sign-up processes, multi-steps forms, subscriptions, etc.. They allow to visualize and understand what is going on at every step of those processes.

The usage made of your features is not that different: it comes in several steps, with a specific objective, and through several smaller goals – actions to perform, buttons to click on.. Everything here could (and should) be tracked through analytics with conversion funnels.

Which analysis may funnels provide for your features?

Data points are numerous and rich:

Feature Usage (or not): which part of your traffic use, or not, this specific feature
Drop Points: where, in which step, are your visitors exiting the feature without completing the process
Feature Comprehension and Usability: how much time your visitors do spend on each step
Dispersion: which step does generate reloads, new tab openings or exits

These metrics must be monitored continuously, and more specifically when you perform features updates, in order to measure the impact of the changes you performed.

The problem with anonymous data

Do you have any idea about the usage your users really have of your service ? Who, among your users, is doing what?

The answer is simple for most of you: you don’t.

Here are some examples that illustrate why it is crucial to work on nominative analytics:

to define priorities for your roadmap: which are the features your 10 best customers use?
to optimize your retention: is there any reason in your features that make your users leave the service?
to empower your marketing actions: who is this user who can’t use this feature, and why?
to make g reat p roduct moves: who should you talk with to get feedbacks?
to defend your project in front of investors: can you give today any figure about user engagement?

This article is an introduction to Onbrowse.io, the UX Analytics Software we build at Onvey. With Onbrowse, we solve the problem of anonymous data by making your session data working jointly with your user data. This means, you will now be able to understand who in your users exactly do what with your service: which features they use or not, and how they use them. And much more!

As we recommend you to monitor feature usage, we obviously apply this principle to ourselves first, which means .. :

Analyzing the usage of features that analyse the usage of features ..