What are the most effective ways to optimize your product lifecycle with data and analytics?

Agreed. Defining metrics up-front is critical. I want to highlight two key components: 1) Competitive Analysis & 2) Consumer & Market Trends. In my view, product life cycle is heavily influenced by how competitors react to a launch / re-launch / any changes in product mix. This reaction will be based on trends. These trends, in turn, will be impacted by product offerings. Consider this: You launch an organic shampoo in a market thus far promoting (chemical) products with natural, herbal ingredients. Your primary competitor not only launches an organic shampoo as well, but offers shampoo refills in consumers' own containers. What was expected to be a shift to organic products & ingredients, has become a discussion on eco-friendly packaging.

I totally agree with this article. Actually, my colleagues and I in the product development and innovation department compile a product document from scratch. In this document, we define the following: vision missions Goals strategies and key performance indicators. These indicators should be defined in such a way that they can be measured. Finally, comparing these indicators with the goals shows where we are in the product plan.

The key focus has to be on translating the data into actionable insights. Unfortunately, people get so excited with the process that application and outcomes fall off the radar!

In a business world where data is crucial, choosing and analyzing the right metrics becomes the foundation for successful growth. These simple metrics serve as a starting point, allowing a business to get an overview of its standing. However, as the company grows and develops, the importance of detailed analysis increases. Tracking flows, analyzing customer churn and understanding retention rates become key. These metrics will help identify weaknesses, identify opportunities for growth and improve customer engagement.

While defining the metrics make sure you define your North Star metric and also the guardrails that will make sure that your product/service/feature is sustainable through time and there’s no side effect in other parts of the business. As an example, if you have an e-commerce you might define ‘orders’ as your North Star and ‘AOV’ and revenue’ as guardrails: you probably don’t want your orders to increase if clients are spending less in each, with an impact on your revenue.

Defining the period of analysis is also important to understand deviations and avoid misinterpretation due to specific events in time, seasonality and others. Make sure you clean your data and remove outliers accordingly and keep your audience well informed of those events and deviations. Data visualization is key in this process of communicating insights to your audience: they need to understand with a simple glance what are the impacts of the product/service/feature independently and in the whole business. In this process you will probably need to combine data from different sources to share valuable insights and empower people to make decisions, so make use of tolls that will automate and leverage your work such as Tableau and Power BI.

Agreed. We would have real time data presented on our factory floor so the whole team could see when any step or test was not meeting the desired results. This made it easier to identify issues as they started, like a bad batch of parts, that could lower the yield of the product.

Dashboard development: Effective data visualization In the era of big data, dashboards are becoming a key tool for businesses by providing a clear view of information. To create an effective dashboard, start by defining your goals and audience. Then interview users to understand their needs. Choose appropriate visualizations: tables, bar charts, or maps. Once developed, test and provide support. This approach will help you create a truly useful tool for your business.

I completely agree with these steps. One caution is to prevent yourself from analysis paralyses. Since product lifecycle is complex, organizations tend to face two real-world problems: 1. Reporting on too many product health KPIs, losing sight of key problems. 2. Not having a single source of data.

One thing I have found helpful is reviewing metrics on a recurring basis (weekly or monthly). You can do it yourself, but will prefer doing it with your team to get diverse perspectives. That way you will both keep a track of your key goals, as well as find interesting insights and patterns that might fuel new innovation to help your customers.