This informal CPD article Data-Driven Product Development was provided by The Tesseract Academy, offering consultancy services to help your company become data driven, whether you are an entrepreneur, a start-up or a corporate.
Data-Driven Product Development
The creation of a product is an organic, never-ending cycle. The goal of product teams is to learn about their customers and how their products fit into their daily lives. This could be quantified in terms of monetary value (as in the case of a trading app used to make money for traders) or in terms of time spent being entertained or informed (as in the case of media or game companies).
Product teams are always on the lookout for ways to enhance their product in order to better serve existing use cases or to begin to service new, possibly adjacent use cases as their consumers' needs change and evolve, sometimes in dramatic new directions. Product teams prioritize achieving product-market fit early in the product lifecycle. After that, you can shift your attention to enhancing the user experience and creating additional value for end users and businesses alike.
Here are five processes involved in the introduction of new features:
1. Insights into the importance of data in the innovation process
It's commonly believed that data is something you can only use to analyze the past. In reality, data may play a crucial role in inspiring product teams to plan ahead by revealing untapped areas for innovation. Using quantitative data that describes how consumers actually interact with products can reveal unanticipated uses of the product or workflows that are difficult to navigate. While quantitative data may imply that users are still able to successfully use the product, qualitative feedback can be used to understand how users feel about different components of the product, potentially identifying areas that frustrate users.
2. Data's importance in ranking potential product improvements
Product managers will have to make decisions about the relative importance of various proposed product enhancements as part of the standard sprint planning process. The team's anticipated obvious and quantifiable benefit from the upgrade may guide this choice, and can be stated as part of the larger specification process.
3. Data analysis for determining whether or not to implement a system-wide change
For all new features, a good data-driven product development team will have a formal process for A/B testing with a subset of users first. Keep in mind that the product team will have already specified the desired effect of an update as part of the update's specification. In order to determine whether or not the update has the intended effect, A/B testing can be used formally. Accordingly, fresh A/B tests will be sent out with each product update to evaluate the efficacy of the most recent set of enhancements. The outcomes of the tests will be evaluated when the testing phase has concluded.
Once the results of the A/B test have been analyzed, only then will data-driven product teams release the functionality to the general public. Only by adhering strictly to this formal procedure can product teams assure that, over time, each new release will result in noticeable enhancements to their products.
4. It's important to follow the right procedure
As should be obvious from the previous description, data-driven product teams adhere to a highly structured, detail-oriented approach in which data plays a role in:
- A product's updated features and specs
- Specifications on how the revision should improve the product
- A quantitative analysis of the revised product's effectiveness
- The update rollout choice will determine whether or not to continue with the upgrade.
5. Culture is also an important factor
This rigorous procedure for data management during product development can only be implemented successfully in an organization with the correct culture, which necessitates the presence of:
- Validity to arguments supported by evidence (especially with senior management who often have their own ideas about how a product develops).
- There should be no fear of failure while attempting an experiment.
- A loss is seen as a chance to grow.
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