More than a hundred years ago, the concept of analytics might not have meant much in the minds of business owners and managers. Product developers and managers probably didn’t even know what the word “analytics” meant. But things have changed since then, and now analytics is seen as something crucial for the improvement and ultimate success of product management.

And it is not just in product management, either, because analytics is now everywhere. Practically any part of an organization or business that makes use of, and generates, data will find itself dealing with analytics.

Product Management: The Role of Analytics

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In this article, we will explore 1) analytics definitions, 2) key concepts of analytics, 3) main uses of analytics in product management, and 4) top analytics tools for product managers.


But what, exactly, is analytics?

Analytics was derived from the Greek word analytika, which translates to “science of analysis”. In the context of business, it is the analysis of often large sets of business data, through statistics, mathematics and, nowadays, computer software and applications. Some even go so far as to say that it is a word coined from the combination of the phrases “analysis of data” and “statistics”. When they say “analytics”, it is all about numbers (and crunching them).

To strictly stick to the translation “science of analysis” would seem unfair to analytics, however, since, more than a science, it is now seen as a process, which involves the use of skills, technology, applications, and business practices.

The general purpose of business analytics is to study past performance in order to gain valuable insights into the current state of the company, and make decisions based on historical data. In product management, the goal is basically the same, although the application of analytics is more precise.

Analytics used in product management measures the current state of the product, and how the customers or users are doing with it. For it to properly qualify as analytics, it must involve a series of measurements, since analysis cannot be done on a single measurement alone.

Let us say that the business has a wealth of data generated. However, it has poor analytic tools. This means that all that data will be rendered useless since the management cannot act on the wealth of information that it has. Therefore, it is not enough to have the data on hand; what you do with the data afterwards is the real game-changer.

There is one major reason why analytics is deemed to be an important aspect of product management: improvement. Without the measurements, there is no way for a product team to know whether the product actually meets the needs of the users or not. The product team will also be unable to make informed decisions on whether they should make changes on the product or keep it as it is. In the event that they did make a change, having zero analytics would also mean that they will never know whether the change they implemented was effective or not.

In short, much of the success or failure of a product rides on analytics, and on how it is used.


Analytics also goes by other names, such as “metrics” and, simply, “data”. For purposes of discussion, let us use “analytics” and “metrics” to refer to the same thing.

There are four key concepts in analytics: data points, segmentation, funnels and cohorts.

Data points refer to the collected individual data that also happen to be measurements of particular items. Basically, data points are the measurements, supported by the dates and times that the measurements were made. Data points are important since they facilitate sorting or classification of measurements later on. For example, the product team can opt to analyze trends by plotting the individual measurements chronologically, or per a specific category.

Segmentation takes place when people that share a common trait or characteristic are grouped, and subsequently assessing the usage of the product depending on the groups. Examples of the most common groupings are according to demographics (e.g. age groups, gender, location, region, country etc.) and technical or utilities (e.g. type of devices used, operating systems utilized). The usual method would be getting all the measurements and taking the average. That’s not how it goes with analytics, however, since the latter allows trends and patterns to be developed by taking the measurements together. In this way, it would be easier for the product team to focus on the groups of users that have the most impact or make the most difference. The primary consideration that one should not forget when it comes to segmentation is that the trait or characteristic must be quantifiable; otherwise, there would be no way to accurately measure it.

Funnels are used to measure or quantify the flow or journey that users take in relation to your product. You can follow the flow of their actions, from the time they make their choice of products to when they add the product to their cart up until they check it out. Funnels will show analysts where there is a “leakage”, or where there is a hold-up in the flow. They can then take a closer look on the reasons why there are such leakages. For example, in the checkout process, many users discontinue and do not make the final checkout because the modes of payment are limited. The product team can then look into how this can be rectified or improved, such as finding ways to offer more modes of payment.

Cohorts play a vital role in cohort analysis where grouping is conducted using two determinants: a specific point in time and a certain characteristic of the users. This allows the product team to perform analysis on how the behavior of users change or evolve over time. This is especially useful when it comes to long-term analysis, or when you want to see the value of the user or customer in the long run.


Nowadays, product management is becoming more data-driven, whether product managers and product teams like it or not. They may not be too keen on the idea of wading through huge amounts of data, but it is a reality that, without data, effective product management is just not possible.

Let us take, for example, an online store for women’s apparel. Through analytics, the team found out that 80% of the users who visited the website’s homepage are more likely to create an account. Out of those who created accounts, 75% are much more likely to make a first purchase, and half of that group is likely to become long-term customers.

So what does this mean for the product management team? They will focus all their efforts to improving the website and the product offerings to convince more visitors to create an account, purchase, and keep coming back as a long-term customer. These decisions would not have been arrived at without analytics playing a crucial role.

What role does analytics play in the product management? We take a look at what analytics can do.

1. Analytics is a useful aid in understanding user and customer behavior.

It is not just the marketing team of a business that ought to be in the know when it comes to their users’ and customers’ behavior. Product teams should also be aware and, most importantly, understand why their users and customers are buying their product, and how they are using the products that they have purchased. It is not enough to know what the customers are saying about the product; what they actually do with the product is actually more important.

2. Analytics is a tool that is used for the measurement of product progress.

Product teams find it more comfortable to rely on quantifiable data in order to make decisions on moving forward with products. Guesswork may be employed, yes, but a strong product team knows that data that is verifiable holds more water and, thus, a more persuasive driver. Their confidence in making decisions is definitely more solid, since they know that they are on the right track.

Analytics is very useful when tracking the performance of the product team, particularly with respect to the products they are developing and managing. They are clear on what features are working and which ones aren’t; which are already fully operational and which ones need more work. In the event that adjustments have to be made, such as tweaking some features a bit or adding a few functions, analytics will inform them if the changes they have applied are actually solving the problem.

You can say that analytics will help you create a better product roadmap. The combination of data and feedback that you will get through analytics tools will enable you to create a roadmap that is more well-rounded and detailed. You will know where your product currently is, what you want it to be, and how you can get there.

3. Analytics is used in order to prove product ideas’ viability.

One of the huge challenges constantly faced by product teams is proving that their ideas actually do work. It is easy to state or declare that they have a brilliant idea; proving that it is truly brilliant (meaning “sellable” and “profitable”) is another thing.

Thanks to various analytics tools, coming up with plausible explanations and proofs that an idea is truly brilliant becomes easier. Adding a new feature to a product, for instance, becomes justified (or not) once analytics has entered the picture.

Testing, such as split testing and using live-data prototypes, is something that product teams find themselves conducting on a regular basis, and there are also many analytics tools that can be used for this purpose. When testing, the team cannot afford to spend too much time. What they want is to test the product, learn, adjust, and pivot, if necessary. The decisions must be made fast. With analytics, this is possible.

4. Analytics enables making informed product decisions.

It used to be that most product decisions were based on opinions which are, at their core, subjective. There was a time when many business decisions, even product-related ones, were intuitive by nature. This is a bit tricky in an organizational setup since there is a hierarchy involved, despite the fact that there is a product team in place. Everyone – from top management to each individual member of the product team – has an opinion. With so many differing opinions, everything becomes convoluted, and the decision-making gets affected, often negatively.

Thanks to analytics, making decisions is easier and more objective, since there is data to be relied on, instead of subjective opinions. There are many ways to gather these metrics, including running tests and conducting surveys. Data collected from these activities will then be used to influence opinions, adding more than a little degree of objectivity to them. The result would be more informed and, therefore, accurate, data-based decisions on product management.

This does not mean that product managers should ignore their intuition altogether. Some product managers are gifted with that “sense”, and there is nothing wrong with putting stock on it. However, it would still be a good idea to proceed with caution, and backing up your intuition with data is definitely a wise idea.

5. Analytics also provides further inspiration for product work.

Product teams are responsible not only for product management but also product development. Thus, they are always on the lookout for product ideas and other opportunities to come up with new products, or new innovations to their already existing products.

Usually, it would seem that the common method used to come up with new product ideas and product opportunities is through observation. However, analytics changed that perception a bit by allowing product teams to make use of data to become inspired with new or better product ideas.

All the data gathered using analytics tools are potential sources of the next “brilliant product idea”. Product opportunities, just like any other type of opportunity, often springs up where we do not expect them, and there is very high likelihood that you will find these opportunities with the help of analytics.

Now here is a very important point that every product team must always remember: analytics provides the numbers, but not the reasons. It answers the question, “what is happening?” but not “why is it happening?” Product managers are told what their customers are doing, but not really why they are doing it.

On its own, analytics is a very powerful tool for product management. However, product teams should never overlook the importance of delving deeper into the qualitative aspect, instead of solely focusing on the quantitative data provided by analytics. Applying analytics alongside the many qualitative techniques available will ensure that the product team has a strong hold on product management.


One of the reasons why some product teams are not yet fully embracing the concept of analytics is that it looks somewhat complicated. It involves data, numbers, statistics… does that mean the product managers have to know their math, as well?

At a glance, it does look that way. However, things are changing, and for the better. There are now loads of analytics tools that business can choose from, and they just keep getting better and better. Don’t know math? That’s fine; the tools will do the math for you.

It appears that developers of these applications and software are designing them from the point of view of the users – the product managers – so they are becoming more and more user-friendly. Some tools are straightforward and very easy to install, easily squashing away any doubts and apprehensions that product managers may have about using them.

Let us take a brief look at some of the analytics tools that are highly recommended for product management teams.

  • Google Analytics: The internet giant has been releasing many useful tools (and is still doing so), and Google Analytics is arguably one of their best.
  • Geckoboard: One good thing about Geckoboard is that it is not just designed for use by the product team. It can also be adapted for use by sales, marketing, and support. It creates performance reports that can be shared to everyone involved, in all channels, and across devices.
  • Segment: What happens when the team is using multiple analytics tools simultaneously? Monitoring all of them becomes a pain. This is where this analytics tool, aptly called “Segment”, comes in. It displays all the data in one dashboard, so there is no need to toggle between and among tools, and be confused.
  • CrazyEgg: CrazyEgg offers a heatmaps platform that is extremely useful when you want to know why users that visit your webpage leave at a specific time. This tool is clearly made to allow product managers to optimize their product management strategies.
  • Mixpanel: Mixpanel is a great tool since it offers funnels and segmentations. Product teams can build database engines, customized in accordance with their preferences.
  • Qualaroo: Conducting surveys is one of the oldest tricks in the data-gathering book, so to speak, but creating surveys with the right questions is not as easy as it seems. Collating the results of the surveys later on and arranging them in an understandable order is just as challenging to product managers. Qualaroo simplifies this by helping product managers create the best surveys, with custom options that allow them to target their users better.

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