Startup Series — Analysing Your MVP Results
In our last article we discussed the different ways you can go about marketing your product in order to get it into the hands of an initial set of customers. This is definitely an ongoing process, and in order to fine-tune your marketing strategy you will need to break down the data you’ve collected so far, in order to determine where to focus your efforts further.
Data analysis is a critical aspect of the Minimum Viable Product development approach. It is what should steer the decisions and directions in the product development strategy. Because it is so crucial, it is very important that the mechanisms for collecting data are put in place thoroughly and appropriately. It is also important that the data is interpreted and evaluated correctly, and in turn used to influence the product direction in the most beneficial way.
Data Collection Methods
The most appropriate data collection methods will depend on the type of product you are offering but there are some metrics such as user registrations, de-registrations, ratings, reviews and direct feedback that remain important across any product type.
Other metrics that might be more relevant depending on the product type are; active users, returning users, installs/uninstalls and downloads. Depending on the platform (web, mobile or desktop), there are different services available to capture this data for you.
Metrics for Web Apps
The primary metrics used to gauge performance of web applications is the number of unique and returning users, signups, as well as the page bounce rate. A bounce occurs when a user lands on a page and then does not navigate to a second page on the same website, they instead leave the website either by navigating out or closing the browser or browser tab. These metrics are arguably the best way to determine if your user base is interested in your product, or perhaps a new feature that you have released.
The most popular data collection and analytics service for websites and web applications is Google Analytics, others include Heap Analytics and Piwik. These services will typically provide you with a code snippet which is to be embedded into your website, then when users browse the website their behaviour will be logged. The data collected can then be conveniently reviewed in the analytics dashboards that the analytics services provide.
Metrics for Mobile Apps
Mobile apps are often subject to stronger influence from ratings and reviews. However, these do not tell the full story of how users are really making use of an app and its features. Metrics such as app usage, and time spent using various app features provides valuable insight and provide an abundance of information for making intelligent business direction decisions.
There are many services out there that provide the ability to track user behaviour inside a mobile app. Some of these include Flurry, AppAnalytics, Facebook, AWS and again Google. Most of these also provide information relating to app crashes which can be helpful in resolving technical issues.
Metrics for Desktop Apps
Tools to analyse usage of desktop apps are also available. These services can be embedded into the software similar to the way they are in mobile applications. The type of metrics which are useful to collect are similar to that of mobile apps. It is also valuable to, if possible, track the number of times that a desktop app is downloaded. This does depends on where the installation file is hosted, if it’s on a website that you own than that is ideal.
As of writing, the two leading services for collecting data on desktop app usage are DeskMetrics and Revulytics.
Interpreting the Data
Having incorporated data collection into the application, it is now time to get acquainted with the dashboard that comes with the analytics service. The tools that these services provide varies greatly, but they all provide you with a way to read and review general patterns and trends in your product’s usage.
These patterns can help identify the success or failure of a given product feature. A high level of ongoing use of a feature might indicate a successful result, whereas an initial spike in usage which then tappers off dramatically might indicate the feature is not popular and has not been a success.
In addition to the usage patterns are ratings, reviews and also direct feedback from customers. If a particular problem or complaint becomes common amongst multiple users, it will be a good indication that it needs to be addressed, particularly if you find that the usage patterns back this up.
At this point your product will be attracting “early adopters”. These are the category of users who will prove invaluable in the development of your product. These users are the ones who strongly correlate to the target audience defined in your Lean Canvas. Developing a strong relationship with these users is a great way to gain valuable feedback on a regular basis.
Originally published at viewport-tech.com.
