Resources > Articles

Staying Ahead of the Competition with Predictive Analytics

Post Author
predictive analytics

predictive analytics

 

Most product professionals are competitive, well-researched, and typically grounded in their thinking and expectations. They’re adept at influencing without having direct power. It’s the things outside of their control that keep them awake at night, and the primary “thing” that’s out of their control is competitive disruption.

Changes in customer behavior, the industry, and competitors’ offerings are why products routinely go out of favor—particularly in the digital space. For example, a digital enterprise product that was well-received when it launched in 2015 may be on the “reinvent or die” list this year after a series of startups enter the market with highly competitive offerings.

But this is the typical tale of many software solutions that failed to read changes in both customer behaviors and the market. Companies that see these changes early in their innovation journey have an edge, while the rest find out the hard way. The trick is to use your business data to figure out how to reach and keep that competitive edge.

 

Leveraging Predictive Insights

Today, most applications, such as apps or dashboards you use in your daily job, tend to talk about what happened in the recent past. This is useful for the business to understand the health of the company, but it doesn’t help shape the future. With a gold mine of business data, you have an opportunity to get an edge by predicting what is likely to happen as well as recommendations for the best action to take.

old applications vs new applications

Predictive analytics allows us to uncover what will happen—and subsequently incorporate those insights into current and future digital products.  The following are examples of refactoring current applications to add predictive insights for significant differentiation from the competition.

 

predictive analysis

 

Adding Predictive Insights into Your Existing Application

Following are four simple baseline areas product professionals can explore to create the right frame of mind for adding predictive insights. Each includes example questions to put your thinking on the right path. And remember: Every question you are trying to answer should have a good ROI.

Predicting outcomes

    1. Will this customer churn?
    2. Will this customer default?

Forecasting metrics

    1. Number of orders?
    2. Number of calls to call center?

Identifying anomalies

    1. Is this a fraudulent transaction?
    2. Is this a fraudulent claim?

Creating segments

    1. Segmenting customers by demographics/sales patterns
    2. Segmenting patients by demographics, health history, and medications taken

 

Predictive insights provide an opportunity to innovate and add new capabilities to existing products or help create new product offerings. For the digital products you’re currently managing, look at your existing product and ask what future outcomes can be predicted that will create an opportunity for you to add significant value for your customers. It can be as simple as taking your current key operational metrics, using them to predict their future value and deciding whether that prediction, along with some recommended actions, will help positively affect your business.

 

Getting a Handle on the Technical Side

To truly understand how you’re able to acquire and leverage the predictive insights you need to make your products successful, it’s important to understand the behind-the-scenes work it takes to get the analytics you want and the information you’ll need to get it done.

Traditional programming involves someone (a programmer) coding a program (building rules) that uses available data to produce an output. To keep the rules updated, the programmer writes more rules:

traditional programming process

In machine learning, the data that is input and output are fed to an algorithm that automatically creates the rules. In turn, this program can predict on future data:

machine learning process

For example, to build a customer churn application, you would feed the machine-learning algorithm with data (e.g., demographics, product use, transactions) that includes samples of customers who have churned as well as those who have not. The algorithm mines this data and formulates the program to predict whether someone will churn in the future:

predictive analysis process

This enables the creation of higher-order complex rules that can be updated with changing customer behaviors—something that is impossible if manually written by programmers. It can also identify multi-factor rules that can be hard for humans to identify.

This automated process provides you with an opportunity to leverage business data as a financial asset and add new predictive features to your products.

 

Putting Analytics into Action

After identifying a key predictive insight, it’s time to test and check in with your customer base. It’s important to constantly experiment and engage customers with new ideas—that’s what leads to innovation and the ability to remain relevant in the competitive market. This work leads to enhancements of the existing product and possibly results in a new product for a cross-sell/upsell opportunity.

 

A Call to Action

Identifying the predictive problem you’re trying to solve is your starting point. Define the benefits this solution will have for both customers and the business. And don’t be afraid to experiment to get early customer feedback. Capture enough data to engage with stakeholders and, ultimately, navigate your way in the right direction.

Author

Author:

Tags: Metrics

Other Resources in this Series

Most Recent

The image features the term use scenario being revealed underneath a ripped piece of paper
Article

What is a Use Scenario [ +7 Examples]

The purpose of drafting use scenarios is to help your development and design teams to start thinking about solutions. Context is the foundation of innovation, and you’ll be providing a tool that will be the starting point for collaborative and productive meetings.
Article

[Comprehensive Guide] Product Owner vs Product Manager

Learn how to separate the roles of product owner and product manager on Agile teams and uncover some common challenges with confusing these roles. Including a short primer on the Agile revolution.
Article

Use Scenarios are Stories That Provide Context

The problem with today’s user stories is that they aren’t interesting. And they aren’t stories. The solution is use scenarios. It’s a narrative. It explains the problem in the form of a real-life story.
Article

Benefits of Bundle Pricing

Bundle pricing is simply a strategy where services or products are packaged together for one (often reduced) price rather than priced separately. This article covers some benefits of bundle pricing followed by a system for getting started.
Article

A Quick Guide to Value-Based Pricing

Value-based pricing begins with knowing the customer’s willingness to pay based on the perceived value of your product. You can charge less than a customer’s willingness to pay, and they feel like they’ve received an

OTHER ArticleS

The image features the term use scenario being revealed underneath a ripped piece of paper
Article

What is a Use Scenario [ +7 Examples]

The purpose of drafting use scenarios is to help your development and design teams to start thinking about solutions. Context is the foundation of innovation, and you’ll be providing a tool that will be the starting point for collaborative and productive meetings.
Article

[Comprehensive Guide] Product Owner vs Product Manager

Learn how to separate the roles of product owner and product manager on Agile teams and uncover some common challenges with confusing these roles. Including a short primer on the Agile revolution.

Sign up to stay up to date on the latest industry best practices.

Sign up to received invites to upcoming webinars, updates on our recent podcast episodes and the latest on industry best practices.

Subscribe

Subscribe

Training on Your Schedule

Fill out the form today and our sales team will help you schedule your private Pragmatic training today.

First Name*
Last Name*
Email Address*
Phone
Company
Job Title
Location
How can we help you?
Preferred method of contact
Privacy Policy*
Map Your Message to Its Audience with the Communication Compass
Map Your Message to Its Audience with the Communication Compass
Ensure your message hits the mark. This eBook helps you visually map communication styles so you can tailor your design story to a stakeholder or business partner.

Download Ebook

Demystifying Data Projects: A Guide for Business Leaders
While data science is a competitive advantage, data isn’t magic. Learn how to make magic happen by partnering more effectively with data professionals. This eBook delves into types of data projects, sample questions, tools and methods, key points and cautions—so stakeholders like you can initiate data projects with real business impact.

Download Ebook

Define Ebook Thumbnail
What’s the difference between a successful data analysis project and one that falls flat? 

Before you begin working with the data, you need to understand what you’re solving for. Gathering context and aligning around goals with your stakeholders from the outset will help you avoid disconnects and deliver actionable insights. Discover the most vital questions to ask before embarking on a data analysis project in our in-depth guide, “Define: Laying the Foundation for Successful Data Analysis.”

Download Ebook

Download Now