Product Management [Managers] or Product Owners have started to play an increasingly important role in organizations. They take the overall responsibility of decision making, starting from investing in a new market, proceeding with the product launch, or building the new product capabilities. Certainly, as a PM, the core principles that should be focused on market share, industry trends, product usage, customer-centric design, competitive offerings, etc.
Data analytics helps the PMs to make better decisions to execute the project on schedule and budget. Furthermore, it also helps them to analyze the predictive information to define certain trends and patterns that assist in determining how the resources and projects perform. Based on the predictive information, the team can develop a strategic decision to boost the success rate.
This blog will provide insight on how to be more comfortable with data analysis, how to avoid the common pitfalls, and approaches to move in the right direction.
Product Managers require exceptional decision-making abilities. Most of the successful and product-driven business utilizes data as the base of the feature or product ideation and development. Based on the team environment, culture, and several challenges, the decision may vary. In an agile project environment, important product decisions made by Product Management frequently with every sprint that requires strategic action items. Especially decision-making becomes more complicated in decentralized development teams. Hence, irrespective of the situation, the decisions will tend to affect if it is not a data-driven decision negatively.
The increasing analytical technology will enable the product manager to utilize multiple analytical reports to drill down the charts to break down complex project data and predict their outcomes and behavior in real-time.
Apart from the data tradeoffs, there are other sets of metrics that several product managers are presently using to develop a roadmap, and they are able to mark the success by defining the metrics along with its calculation, such as
Customer Conversion Rate (CCR)
Customer Satisfaction (CSAT)
Customer Lifetime Value (CLV)
Customer Acquisition Cost (CAC)
Daily Active Users (DAU)
User Churn (UC)
Repurchase Rate (RR)
Net Promoter Score (NPS)
Feature Usage (FU, yes that’s the abbreviation)
Using data analytics in the project, executives and managers can watch for the early warning signs of costs, budgets, and timeline. Based on the prediction, they make the appropriate decision. Data analytics can also support managers to watch the progress rate to instantly predict whether the project progression sticks with the estimated timeline.
Furthermore, with an analytics report, the businesses can take a wide view and combine the isolated data streams to provide in-depth insights into the projections and estimates of any early signs in complicated projects.
Measuring, Tracking, and reporting for data-driven decision
In the B2B business, sales and marketing hold the responsibility of closing the deals and expanding the market. However, when it comes to B2C business, it is more about acquiring new users and other factors such as frequency of usage and retention rates. This would not be feasible without appropriate product features that drive sales conversion and adoption.
A PM is responsible for defining metrics and KPI that are measurable and quantifiable. Before launching any new product/feature, it is important to measure the metrics related to the product engagement date to customer outcomes. User Conversion, engagement, and product retention are just a few aspects to make any data-informed decision.
Data and behavioral analysis
They are utilizing the full potential of behavioral data from users in terms of the product usability that can drive a whole lot of new CX flows. Customer experience improvements should completely be based on the data and not instincts. Product managers should collaborate to determine and understand customer needs. They should also work together to come up with solutions and implement the findings in the product. Collaborating with other teams will help you to get more ideas as their expertise will assist in building a data-driven product.
Assisting Strategic Decisions
Real-time analytics will reveal a lot of information that can help businesses align with their strategic objectives. Data Analytics will allow PMs to broaden the understanding of how ongoing/proposed projects will suit the overall organization vision.
Sampling Analysis and Feedback loops
The best example of the sampling analysis is Gmail. The Gmail Beta version was an initial version that was launched in 2004. You could feel the difference well when you compare the initial version with the current one. The transformation involved a lot of analysis, A/B testing, usage analytics, and post-launch surveys. In the long run, Google utilized data to build a solution that satisfies and delight customers and makes their job easier.
Though this strategy is mostly used in B2C companies, this can also be implemented in B2B companies. Whether yours is B2B or B2C Company, you can use all the opportunities to analyze data for better decision-making.
An experienced Product Management knows the importance of avoiding data mistakes. However, for new product managers, it can be a daunting task, so here are some common data mistakes to avoid.
Mistakes do happen everywhere, but the wise-decision lies in the expertise of avoiding the occurrence of those mistakes. Here are some of the common mistakes that can be avoided while making a data-driven decision.
– Collecting irrelevant data
– Do not depend only on trained data analysts for data.
– Not acting on the data.
– Failing to communicate
The opportunity for business intelligence and data analytics is predicted to expand to $22.8 Billion by 2020. With the extensive growth of the discipline, it makes sense to utilize powerful tools combined with appropriate strategy to create a sustainable competitive advantage. This was all about Product Management.