Most technology professionals will probably agree that data-driven business and product decisions deliver better outcomes than simply going with your gut, or worse: with the HiPPO. However, an important challenge remains for product teams trying to become data-driven: where do they start?
Below is a series of steps for navigating the path to data-driven product management. These steps could be applied to an entire product, or to a feature existing within a larger product. Also, while it is focused more on technology products, I believe parts of it could be applied outside the technology industry as well. Here goes:
1. Does your product work?: An awesome app that crashes every single time it’s opened is a worthless app. Therefore, the first set of data and metrics to be collected and analyzed should revolve around product health and operational intelligence. This will help you answer questions like how many hours in a day your product is available, what your crash rate is, and what your product’s performance is like during peak times.
Examples of publicly available product health and operational intelligence tools include Splunk and the elastic stack, or ELK.
2. Is it popular?: Once you get going with analyzing your product’s health, the next step is product and user analytics. This means collecting the data and developing the metrics needed to figure out how many people are using your product, and whether that number is trending up, or down. You also want to analyze your user base to identify your product’s user segments, and to begin understanding which segments are valuable, and what the valuable segments care about so you can tailor your product roadmap and marketing programs accordingly. This is not easy stuff, but getting it right pays huge dividends.
Publicly available tools for enabling product and user analytics include Localytics, Mixpanel, and Amplitude.
3. Are product updates changing key metrics?: Every update made to your product that results in no change in any of the metrics that you care about is probably a worthless update. On the other hand, increases or decreases in your business and product metrics may be as a result of external factors such as the weather or U.S elections. 🙂 One way to improve your certainty around what effects your updates are having on your key metrics is by running experiments. In very simple terms, this involves releasing product updates to a subset of your product’s user base, and then comparing the subsequent behavior of the users who received the update with those who didn’t receive any product updates.
Publicly available tools for enabling experimentation include Optimizely and Unbounce.
4. Is your product Intelligent?: Artificial intelligence (AI) is all the rage today, as it promises to unleash a new wave of tech-enabled productivity on consumers and businesses. However, a critical ingredient for enabling AI scenarios is data. For example, the reason why Amazon can tell you that people who bought item X also bought item Y is because it has huge amounts of transaction data on both items. For your product to be able to support such intelligent scenarios, you need to collect and organize the relevant data sets, and then employ the right algorithms.
The process of enabling AI scenarios within a product usually involves custom development by software engineers. However, publicly available tools that can facilitate this process include Amazon ML and Azure ML.
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