Sometimes you need to make decisions based on more than just intuition. Most of the time we do that. Especially in a world where data is everywhere. Grow exponentially. They are diligently collected, collected, stored, organized, and analyzed. We are used to tracking and measuring almost everything you can think of.
Data is everywhere, and if you don’t use it to drive your product development process, you may be unable to achieve competitive success. That being said, it’s not always smooth sailing. Leveraging data and, importantly, asking the right questions about it, is the key to a successful product development strategy.
So where exactly teeth data?
There are several ways to review the data available for product development. Data to learn about the platform on which the product is installed. Everything from performance, error rates, traffic volumes and patterns. All the information you need to ensure a platform that’s scalable, performant, and meets your users’ needs.
The other side is product-related data. How users are using it. What kind of journey are they on? Where they fall off. Where and how much time do they spend? In other words, this tells you whether your product is providing the desired experience. If not, where are they getting stuck (more on “why” later)? Of course, both of these data sets are related, and both impact the success (or failure) of your product.
It can also be viewed as qualitative and quantitative data. Therefore, while data during the research phase of the product development process is important and informs the product being developed by understanding what users want, it is only part of the story. You should check in after deployment to make sure it’s working. Please refine it. Make it better and more relevant. There are many ways to collect and use data throughout the product lifecycle. One way to test new features is A/B testing or multivariate testing. These allow you to deploy different ways to achieve your results. Therefore, we split the test. Direct some users to the old features and some users to the new features. Then wait and see which one gives you the best results. It doesn’t matter whether it improves conversion rate or speed. Or which one is more easily adopted. Or any other benchmark you use is fine.
Practicing data-driven product development
Can data really help your product delivery process? The answer is yes. And at every stage. This is critical, from the beginning of the process of deciding which products and features to develop, to checking whether everything is working as expected a month or year later.
Not only can data help validate decisions and build a business case in front of investors and stakeholders, it can also create a culture of experimentation. This helps make a significant shift from predicting what users want (guessing) to using data to know what users want (informed decision-making). The data indicates whether the feature is working or not. Should I stick to a certain path or abandon the idea altogether? It is very important to continue this cycle of experimentation and testing throughout the life of the product to maintain its freshness.
This is the secret to the success of the large entertainment organizations we work with. We’re continually looking for ways to more effectively engage with our users, and we do this by understanding which experiences hit the mark. The split testing methodology I mentioned earlier is employed, and all decisions about which features to roll out, update, or retire are made based on this data. Essentially, become adept at pivoting quickly to give your customers what they want based on how they interact with your platform.
Democratize data and leverage experience
A major challenge in adopting a data-driven product development approach is fundamentally access to data. Often, the teams that need the data simply can’t get it because their organizational structure doesn’t allow for it, or because it’s siled across the business.
You also need to know what questions to ask to get the right answers. This is made easier with clear, user-friendly dashboards that allow you to explore your data in a variety of ways. This is especially true if you need to raise a ticket with the data team. It may take several days or even weeks to receive a response. At this stage, it is very likely that you will realize that you asked the wrong question and will have to start the whole process over again, but it will still be a few weeks before you can take action.
Knowing what questions to ask also influences what kind of tests you run. The retailer we consulted was using her A/B testing, which focuses on statistical significance. However, it was not taken into account that the low amount of traffic generated by the platform meant that the platform had been running for many months before reaching its statistical significance. So the platform might be performing poorly for several months, but we couldn’t make a decision about what to change to improve it. Although the intentions for using the data were good, the process did not allow for quick decision-making. It accomplished exactly the opposite.
Related to this is the issue of using data appropriately and at appropriate levels. Just because it’s plentiful (and often too much) doesn’t mean you need to use it all. Most organizations do not have mature systems and decision-making processes in place to effectively use every unit of data. Nor is it necessary. In most cases, checking every data point will delay delivery and hinder product development. The lesson is to be clear about what decisions need to be made and how often they need to be made.
A little advice at the end. While data is invaluable and can infinitely improve product development, it is not foolproof. It is only as good as the people who lead its testing and interpretation. Human judgment and experience are essential, so make sure you leverage both to plan how to measure the right metrics and execute a data-driven product development strategy.
If you would like to further discuss anything on this blog, please feel free to contact me.
