how analytics and data are used in product strategy

How Analytics and Data Are Used in Product Strategy

  • By Himanshu Goyal
  • 09-03-2023
  • Technology

Data and information are increasing quickly; because of how quickly information is being created, the information that will be accessible to us in the coming years will be unexpected. A huge number of consumers, businesses, and industries collectively create data. If this data—not big data, however, the data you received from your company—is squandered, try to calculate what you would lose.

We frequently cite data-driven product managers as being among the finest. But just what does that mean? No digital product can be built, maintained, or disrupted without data, so surely every PM understands how to utilize it correctly, right?

You would assume! Yet, data is frequently a critically underutilized resource and is also the source of several errors. Product teams may make informed judgments about the jobs to enhance a product's function or add features using metrics measures and analytics insights. Without measuring and examining the outcomes, they wouldn't know whether the modifications made were worthwhile or required. Let's first discuss why data analytics is genuinely important for product managers and how it aids in creating better product strategies.

Product Strategy: What Is It?

The broad plan outlining your company's objectives for a feature or item is known as a product strategy. Jobs to be done while building a product strategy includes the product's design, how consumers will be affected, and how it advances your company's objectives. It directs how your product is conceived, developed, and launched. Different teams can stay on track by referring to a unified plan and using it as a guide when they are unsure or must make judgments. The development of a roadmap and the actual product may be done when a sound strategy has been established.

Additionally, it describes how the product will help the firm by outlining the issue it resolves and its effects on consumers. This approach provides a clear product specification and may be used to describe what is being created and when. It is a benchmark to assess success before, throughout and following product development. Designing a product strategy, i.e. the Jobs to be Done in product strategy, must be done properly.

What is a product strategy guided by data?

A product strategy outlines your product's goals at a high level. Generally, it outlines how you'll fulfill client wants, accomplish corporate objectives, and offer distinctive value. Your product strategy will advance thanks to a data-informed approach. A data-informed product strategy, as the name implies, is built on qualitative and quantitative data. As a result, you use information from your clients' behavior, comments, etc., rather than making educated guesses about what they need.

A product that is backed up by information is said to be data-driven. The decisions taken to develop it are supported by research and insights rather than intuition and guesswork. The same is true for data-driven Product Managers who use the information to guide their decisions. This can come off as overly simple. All goods and product managers, after all, rely on data. In a perfect world, they'd be! Yet to be data-driven is to be data-obsessed. Any chance to go further into the data and utilize it to inform decision-making, innovation & development is taken. When a data-driven PM comes up with a fresh feature proposal, they don't immediately start presenting it to leadership. They investigate the data and reveal how useful the truth might be.

What is Product Analysis?

Analyzing how people interact with your product is referred to as product analytics. It entails gathering information, monitoring user behavior and product metrics, and gaining insights that will guide your product choices.

Product analytics is the process used to understand how customers engage with digital products. It is a framework for putting customers at the core of a business by analyzing behavioral data, identifying opportunities for conversion, and creating impactful digital experiences that bring about high customer lifetime value.

Product managers are enthusiastic about product analytics. After all, you want to have a thorough understanding of your clients. Also, you want to evaluate how successful your product addresses the issues it was intended to address. Product analytics allows your team to measure, evaluate, and analyze real-time interaction and behavioral data to optimize the whole customer journey. By moving past vanity metrics and connecting every stage of the customer lifecycle to a specific data point, you can enable your team to enhance the digital user experience, win over customers, and link digital bets to business effects.

Product analytics emphasizes numerical measurements. For instance, you may examine the use of statistics for a group of features or pinpoint friction areas in the registration for a trial process. By providing you with data-driven reference points to inform your decisions, analytics help removes the guesswork. Naturally, you'll also need to collect qualitative feedback via client calls, support requests, and an ideas portal. When combined, qualitative and quantitative data provide a complete picture of your consumers' experiences. Jobs to be Done must be done properly to enhance the work.

What Sorts of Data Is a Product Manager Supposed to Pay Attention To?

We could get specific and discuss every kind of data piece that a Product Manager may want.

Yet there are much too many of them. Let's examine the primary types of data that product managers utilize daily instead. Various kinds of data are used to guide the creation of new products. We may classify them approximately into the following groups for data-driven product management:

1. Product Data

You must understand both the internal workings of your product and your users' thoughts. You must know the normal User Flow, how many users complete onboarding successfully, which features are utilized more frequently than the others, and where users tend to lose interest. Having a website, a software program, or an app doesn't change this. As it will provide you with a real-time perspective of how well your product performs, tracking what occurs inside it is vital. Product data may also offer unexpected innovation opportunities, including a pivot you might not have thought of otherwise, as well as fresh insights and insights.

Product data types:

  • User Flows
  • Adoption and abandonment rates
  • Bounce rates
  • Meta-data
  • Heatmaps

2. Market Research

Market research is also crucial to introduce a product and positioning it for success. Even the greatest snowsuit in the world wouldn't help much in a desert. Before you can create anything that meets the wants of your clients, it is essential to comprehend the environment in which your item would be expected to function. Some businesses make the error of doing market research just at the beginning of their venture. However, the competitive landscape changes, so you must comprehensively understand it before launching any new features or products. You need to know what your rivals are still doing, how you may differentiate yourself from competitors, and what unmet demands persist.

Market research types:

  • Competitor analysis
  • Consumer insights
  • Segmentation of users
  • Brand positioning analysis

3. Customer Information

User research is the most crucial form of study that any organization can do. How can you serve them and create a great product if you don't understand your customers? You cannot, to put it simply. Customer comprehension dies when assumptions are made. You may learn how your consumers think and their habits and begin speculating about their future demands using interviews, user testing, card shuffling, and A/B testing. Utilizing user data keeps you user-focused and guarantees you're creating the ideal offering at the moment.

In what ways might data analytics enhance product marketing strategy?

Analyzing data collections to conclude the data in analytics is the process of data analytics. To be a successful Product Manager, you must use data, but this doesn't require you to be a full-fledged Data Scientist. Every Product expert has various abilities that may or may not be related to data and reasoning. If you're more of a creative thinker with a good eye for product design and marketing, you can still employ data to produce the best possible product. Using data analytics techniques, it is possible to take unstructured data and identify patterns to draw insightful conclusions.

You require as much consumer feedback as you can gather. One of the greatest (and most effective) methods for gathering user data is product analytics. When you understand how people interact with your product, you can make better judgments about how to serve them. Jobs to be done must be understood properly so our work can be eased.

Analytics for products are useful for assessing product objectives. You may set benchmarks, compare data across time, and find product gaps that you need to fill by monitoring user interaction. While corporations utilize data analytics to make decisions, analysts, data scientists, and researchers use it to do studies. Data analytics for firms has several significant advantages, including:

Effectiveness: It increases the effectiveness of your marketing since you will know which of your marketing campaigns or advertisements are most well-liked by the target market and produce the finest outcomes. Additionally, it aids in assessing whether or not advertising initiatives are effective. It also tells you when to alter your adverts.

Improved customer service: Data analytics provides a comprehensive understanding of your clients, enabling you to customize your offerings to meet their requirements and preferences. You can give them more individualized services and goods and improve customer interaction.

Better decision-making: Businesses can use the data analytics information they receive to make informed choices that produce better results included in jobs to be done. The proper content, the best marketing plan, which products to create, and many other decisions can all be made without making numerous educated assumptions, thanks to data analytics. A 360-degree perspective of your consumer is provided by all-inclusive data analytics, which significantly reduces the amount of work you must do by hand.

Data management: It is crucial to have a mechanism that can control how data enters and leaves the system while also keeping it structured before data analysis. But you should always be certain that the information you gather is stored on a centralized platform for data management that can be accessed whenever you need it and by anybody with the proper permission. Understanding the jobs to be done can easily help in data management.

When to spend money on product analytics tools?

Because it is your primary tool for gathering, interpreting, and visualizing all of your product data, it's a smart idea to invest in product analytics anytime you have a viable product. The sooner you engage in product analytics, the more it will benefit you as your company expands, from informing your very first product launch to assisting you in retaining crucial clients.

Every product analytics solution you use should be used with the same overarching goal: to enhance both the product and the customer experience. This calls for considering your project holistically and focusing on the data that offers the most insightful information. This way of thinking results in producing a product that consumers adore.


When employing analytics for product management, a lot would depend on the accuracy of the data. The process involves the jobs to be done-Creating new offers, improving a current product, or pursuing new markets, helps motivate wiser judgments. Data-driven decision-making may assist in maximizing profits and improving deliveries. Product managers may harness the strength of data-driven insights to assist teams in gaining visibility into user behavior patterns. This enables items to be customized to meet client wants and succeed in certain market niches. Product managers use data to comprehend consumer patterns and preferences, improve the performance of their products, and find new business prospects.

Another aspect is that data may be utilized to refine product designs and features to improve the consumer experience. Data analytics may be used to evaluate the performance of product launch campaigns and monitor consumer response patterns. Additionally, product managers strive to seize fresh possibilities fast before the market is overrun with alternatives, maintaining the product relevant in the face of escalating competition. Jobs to be done can ease our work.

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