How product analytics can eliminate blind spots and improve key planning activities like product roadmaps, end-of-life and more.
You know that making product decisions (e.g. product roadmaps, end-of-life timelines) based on anecdotal information poses real challenges. It’s like a boat without a rudder – you can’t steer your products in the right direction with certainty which can lead to missed market opportunities, lost revenue and angry customers.
Product analytics eliminate guesswork from product decision making by providing demographic information such as which operating system your products are being installed on, which versions and features have been adopted, event data and more. Visibility into this relevant information enables support and product teams to have a deeper insight into specific end user devices, which simplifies remote diagnostics.
Further, you can gain detailed insights into problem areas by collecting and retrieving anonymous application data including application logs and crash reports from end-user machines. This instrumentation data helps producers analyze problems in the running application which results in lower maintenance costs and less downtime.
Product roadmap planning is more accurate as you’ll have visibility into which products and features are actually being used by your customers. Further, demographic data about the user base can be invaluable in planning as you’ll see things like which operating systems your products are being deployed on. In the example below, the software producer (Flexera Software) can see how many customers are still using Windows® XP so they can be confident that customer impact will be minimized.
In the accompanying chart, the software producer can answer the question – “can we drop support for the Windows XP operating system” – with actual data:
End-of-life (EOL) planning is improved with demographic data that can provide visibility into which versions are being adopted and what rates. The example below reveals which versions of a product are being adopted and at what rate over time, so that when an older version is end-of-life’d, you can be confident that customer impact – and potential aggravation - will be minimized.
In the graph below, the producer can see the adoption count (y-axis) over time (x-axis) by product (legend):
The key take-away here is that by leveraging product analytics you can rest assured that you have a boat that you can steer with confidence. Learn more here!
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