Product Management and Analytics: hand in hand

Why should a product manager worry about product analytics? How does it matter what the past data indicate about the new product features? Is it necessary for product managers to know about the basics of analytics?

Well, to start with, product enhancement is a fundamental reason why analytics are essential to product management. Product teams would not know how effectively their products meet user expectations without analytics, and product development would become a series of blind shots. Product teams can make educated judgements about enhancing product functionality or introducing new capabilities thanks to the measurements obtained by metrics and the insights offered by analytics. They would be operating in the dark if they didn't measure and analyze the results to determine whether the adjustments they performed were valuable or essential.

Whether product managers like it or not, product management is becoming increasingly data-driven. They might not enjoy sorting through enormous amounts of data, yet it is a fact that data are necessary for efficient product management.

Analytics

The word analytics comes from the Greek analytika, which means "science of analysis." It frequently examines massive commercial data using mathematical, statistical, and technological methods. Some claim that the term was created by fusing "analysis of data" and "statistics." The word "analytics" conjures up images of numbers (and crunching them).

It would seem disrespectful to analytics to limit one's covert operations to the "science of analysis." But because it is more than a science, it is today understood to be a process encompassing knowledge, tools, business procedures, and technology.

• Business analytics often has two goals: to analyze past performance to get valuable insights into the current health of the firm and to make decisions based on historical data. Although applied analytics are more precise in product management, the objective is the same.

• Analytics is used in product management to assess the product's present state and how users or customers are using it. Since the analysis cannot be performed on a single measure, analytics must involve several measurements. Let's assume that the company has generated a vast amount of data. However, its analytical tools are subpar. As a result, all that data will be meaningless because management cannot use the wealth of information. Therefore, simply having the data is not enough; what you do with it later makes a difference.

• Analytics is a crucial component of product management for one main reason: improvement. A product team cannot determine whether the product meets the users' needs without the measures. Additionally, the product team won't be able to decide with certainty whether to modify the product or leave it unchanged. If they did make a change, the absence of analytics would also prevent them from knowing whether the adjustment was successful. Analytics and application play a significant role in a product's success or failure.

Functions of analytics in product management

The following five significant functions of analytics in product management and development:

• Product Viability: Various analytics tools can confirm product concepts, assisting developers in testing, learning, adjusting, and retesting to hasten the product design and launch process.

• Informed decisions about products: Analytics have improved the objectivity, accuracy, and speed of decision-making. Although intuition based on knowledge and experience can still be helpful in product creation, it can and should be subordinate to unbiased analytics.

• Quality and performance: Product analytics can notify team members about which functionalities are functioning well and which are not. Analytics play a significant role in creating an accurate product roadmap that can show you where your product is, where you want it to go and how to get there.

• Insights into the customer experience: Product teams can utilize analytics to learn why people purchase their products and how they use them. Inspiration for new product development can be sparked by analytics, which can also help an old product last longer.

• Statistical techniques: Using qualitative approaches, quantitative analytics can provide a more comprehensive view of a product, allowing product management teams to make targeted improvements and changes that will extend the product's value and lifetime.

Product development and management have been revolutionized over the past few decades by the data produced using the analytical tools used to extract insights.

Conclusion

Decision-making is made simpler and more objective due to analytics because data can be relied upon rather than subjective judgements. These measurements can be gathered in various ways, including test- and survey-based methods. The information collected from these actions will subsequently affect opinions, adding a significant amount of objectivity. Decisions on product management would then be more accurate and informed based on facts.

One crucial point that any product team must remember is that analytics provides facts, not reasons. It merely addresses the "what is happening," not "why is it happening." Product managers are aware of the behaviours but not their customers' reasons. Analytics by itself is a potent tool for product management. Product teams should never undervalue the value of focusing more on the qualitative side of analytics rather than only focusing on the quantitative data provided by analytics. If analytics are used with the many qualitative tools available, the product team will have a solid understanding of product management.

Author: Dr. Mayank Mathur, Academic Director, ISB Institute of Data Science (IIDS)