Why Analytics is Important for Product Managers

PIN Analytics-Product-Managers-rafael-ferraz-@rzarref

Data scientists can bring tons of useful information to the Product Manager, and the Product Manager needs to know how to use that information to benefit the product. Our job is making products for customers, and therefore listening to them, and gathering information about them is how we can satisfy their needs the best.

Also, understanding analytics will help you work better with the data scientists on your team, and help you communicate better with them.

If for one thing, the product managers don’t need to be deeply technical, in another hand, understanding analytics will help you work better with the data scientists on your team, helping you communicate with them.

Before moving on this topic, let’s try to stay on the same page about data science in the product management context. Data science means “taking sets of information from your customer and product channels online and offline and organizing it in a way that gives insight into the business.”

Data is about people, not numbers. Data is the voice of your customer.

Why is analytics important?

Analytics are essential for one reason: “What you don’t measure, you can’t improve.”

Without knowing what the state of the system is, it is very hard, if not impossible, to do much to change or affect the system. You can, of course, make changes blind, but without analytics, you will never know whether the system was changed or whether nothing happened. It allows you to see what is currently happening, make a change, and see what effect the change has.

Google Analytics per example only tells us what is going on and not why it is going on, without analytics is down to random chance whether your product will fail.

Analytics Concepts

In my opinion, within analytics, there are a seven key set of concepts that product managers need to understand to get the best value from both analytics and metrics:

. Descriptive Analytics
. Predictive Analytics
. Prescriptive Analytics
. Data points
. Segmentation
. Funnels
. Cohort

1. Descriptive Analytics

A predictive model builds on a descriptive model to predict future behavior. However, unlike a descriptive model that only profiles the population, a predictive model focuses on predicting single customer behavior.

Tools used to run predictive models vary by the nature of the model’s complexity; however, some of the commonly used tools are RapidMiner, R, Python, SAS, Matlab, Dataiku DSS, amongst many others.

As an example, if you watched the movie Moneyball (made famous by Brad Pitt), Billy Bean used predictive analytics to dramatically improve his low-performing Major League Baseball Team, the Oakland A’s, despite their low budget.

You can use predictive analytics to identify customers that are likely to abandon a service or product, send marketing campaigns to customers who are most likely to buy or improve customer service, for example.

Helpful resources:

. Predictive model building on Coursera: https://www.coursera.org/learn/predictive-analytics

. Online free course on R for beginners:
https://www.coursera.org/learn/r-programming

. Python for beginners: https://wiki.python.org/moin/BeginnersGuide/Programmers

Descriptive models use basic statistical and mathematical techniques to derive key performance indicators that highlight historical trends. The primary purpose of the model is not to estimate a value, but gain insight on the underlying behavior. Standard tools used for running descriptive analysis include MS Excel, SPSS, and STATA.

The objective of descriptive models is to analyze historical trends and figure out relevant patterns to gain insights on population behavior. Descriptive analytics involves finding answers to ‘what has happened?’. It is the most commonly used form of analytics by organizations for their day to day functioning and is generally the least complex.

For example, descriptive analytics examines historical electricity usage data to help plan power needs and allow electric companies to set optimal prices or to categorize customers by their product preferences and life stage.

You can use descriptive modeling tools can be utilized to develop further models that can simulate a large number of individualized agents and make predictions.

Helpful resources:

. Fundamentals of Descriptive Analytics: https://www.dataversity.net/fundamentals-descriptive-analytics/#

. Online free course for learning basic descriptive statistics: https://www.coursera.org/learn/descriptive-statistics-statistical-distributions-business-application

. Interesting video on running descriptive statistics: https://www.youtube.com/watch?v=QoQbR4lVLrs

2. Predictive Analytics

A predictive model builds on a descriptive model to predict future behavior. However, unlike a descriptive model that only profiles the population, a predictive model focuses on predicting single customer behavior.

Tools used to run predictive models vary by the nature of the model’s complexity; however, some of the commonly used tools are RapidMiner, R, Python, SAS, Matlab, Dataiku DSS, amongst many others.

As an example, if you watched the movie Moneyball (made famous by Brad Pitt), Billy Bean used predictive analytics to dramatically improve his low-performing Major League Baseball Team, the Oakland A’s, despite their low budget.

You can use predictive analytics to identify customers that are likely to abandon a service or product, send marketing campaigns to customers who are most likely to buy or improve customer service, for example.

Helpful resources:

. Predictive model building on Coursera: https://www.coursera.org/learn/predictive-analytics

. Online free course on R for beginners:
https://www.coursera.org/learn/r-programming

. Python for beginners: https://wiki.python.org/moin/BeginnersGuide/Programmers

3. Prescriptive Analytics

Prescriptive analytics involves finding answers to ‘What should be done?’. It is the sophisticated type of analytics that uses stochastic optimization and simulation to explore a set of possible options and recommend the best possible action for a given situation.

Prescriptive models go beyond descriptive models that only address what is going on, and beyond predictive models that can only tell what will happen, as they go on to advise what actually should be done in the predicted future. They quantify the effect of future actions on key business metrics and suggest the most optimal action.

Prescriptive models synthesize big data and business rules using complex algorithms to compare the likely outcomes of many actions and choose the most optimum action to drive business objectives. Most advanced prescriptive models follow a simulation process where the model continuously and automatically learns from the current data to improve its intelligence.

A common example of the prescriptive model approach is used in the airline ticket pricing systems to optimize the price of air tickets based off travel factors, demand levels, purchasing timing, etc. to maximize profit margins, but also at the same time not discourage sales.

Helpful resources:

. How to build a Simple Recommendation in Python: https://towardsdatascience.com/how-to-build-a-simple-recommender-system-in-python-375093c3fb7d

. Random Forest Simple Explanation: https://medium.com/@williamkoehrsen/random-forest-simple-explanation-377895a60d2d

. Machine Learning Foundations:
https://www.coursera.org/learn/ml-foundations

4. Data points

The data point is one bit of information or a collection of data points with a collection of information that can be graphed and that are measurements of particular items within a context.

Analytics is based on right collecting of these data points. Along with the individual measurement, a data point also includes the date and time the measurement was made. This allows you to plot trends and separate unique measurements from one another.

5. Segmentation

Segmentation is an effective method to increase the focus on market segments, grouping together people by a common characteristic and seeing what the usage patterns of the product are as a group.

These usage patterns include – but are not limited to:

. Demographics (e.g., language, country, etc.)
. Behavioral (e.g., new or returning)
. Technical (e.g., Browser, iOS, Android, mobile, etc.)

6. Funnels

A funnel is made up of the measurement of the key event at each step of the flow or user journey. This is important because users don’t just do something in isolation. Instead, they perform a series of actions to accomplish a task, be it registering or checking out a cart. That’s why it is essential to analyze the flows or user journeys measuring using funnels.

Funnel analysis is a powerful analytics method that every product should take advantage of. It shows the conversion between the most critical steps of the user journey. A funnel is made up of the measurement of the key event at each step of the flow or user journey. This is important because users don’t just do something in isolation. Instead, they perform a series of actions to accomplish a task, be it registering or checking out a cart, for example.

Funnel analysis is typically useful if you want to map out a linear user journey and it can be a simpler thing like the steps to filling out a registration form or more complex like an integrated process on an app.

You can use the predictive analytics model as I showed above in this article to optimize your funnel analysis.

7. Cohorts

Cohort analysis is a type of behavioral analytics in which we group our users based on their shared traits to better track and understand their actions. In summary, a cohort is a group of users who share a common characteristic over time.

Cohort analysis allows you to ask more specific, targeted questions, and make informed product decisions that will reduce churn and drastically increase revenue. Some author also calls it customer churn analysis.

We can only do this by segmenting the users into groups — or cohorts — based on a particular trait. The two most common cohort types are:

Acquisition cohorts: Is a group divided based on when they signed up for your product.
Behavioral cohorts: Is a group divided based on their behaviors and actions in your product.

We can use analytics and data techniques, to find out why the users stop using the app, analyzing and answer the three W’s of user retention:

Who is engaging with your app — and who isn’t?
When do they churn?
Why do they lose interest?

Analytics are critical as they tell you what is going on in your product. Before launching and implementing a platform to measure data, we should plan what needs to be measured.

There are many toolboxes, techniques, and platforms available to help us to measure the data and for free.

Do you think data solve everything? Let me know your opinion! It can be a subject for the next article.

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Also published on Medium.