How Product Managers Should Use Analytics to Improve Their Business
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How Product Managers Should Use Analytics to Improve Their Business

Peter Modica, Senior Director of Products at iCIMS, a Talent Acquisition Solution
Peter Modica, Senior Director of Products at iCIMS, a Talent Acquisition Solution

Peter Modica, Senior Director of Products at iCIMS, a Talent Acquisition Solution

Software product managers wear many hats. They spend their day understanding their market, speaking with customers and balancing input from executives and cross-departmental stakeholders, all while delivering solutions to keep them competitive. On top of this, they are accountable for the success of the products they bring to market.

But how can you quantify the success of a product outside of how much it’s selling? For instance, are customers happy with the functionality and output? Can the product be improved without having to tap clients for feedback? Is the solution uncovering trends that can make a bigger impact on an industry?

The answer is yes; analytics uncover a swatch of useful information that quantifies results and helps organizations make better informed decisions. SaaS companies sit on enormous amounts of data, but often times might not know how to translate it into something actionable.

Here are five ways product managers should be using analytics:

1. Use analytics to prove if an idea is viable

Before putting something into play, product managers are challenged across the business on whether a concept is needed or will work. In this era of digital information, an abundance of data is available at the hand of organizations that can readily be synthesized into insight, enabling one to test specific ideas and whether or not they provide any actionable path forward. This is an essential first step in envisioning many aspects of enterprise growth, which spans from product development, marketing, features, pricing to sale, revenue and growth.

Avenues like Kaggle, a platform for predictive modeling and analytics competitions, exist to help organizations translate data. It’s based on the process of building a model, testing the feasibility of an idea, the scope and transforming into a pattern or insight. Product managers should take advantage of these types of platforms when vetting an idea.

 Knowing what customers want to solve with a product is more important than what customers are saying about the product 

2. Use data to uncover customer behavior

Knowing what customers want to solve with a product is more important than what customers are saying about the product. The true power of analytics lies in its ability to correctly capture customer behavior and turn it into a business opportunity.

Take Amazon, for example. Thirty five percent of Amazon’s sales are generated from correctly predicting customer’s purchasing behavior with its “customers who bought this item also bought these items” suggestion. With Netflix, 75% of viewer activity is driven by its recommendations, which reduces churn and improves their recommendation algorithms.

According to research cited by McKinsey, organizations that leverage customer behavior data to generate behavioral insights outperform peers by 85% in sales growth and more than 25% in gross margin.

3. Use artificial intelligence to get predictive recommendations

Given the immense amount of data that exists today, product managers can apply AI/deep learning and machine learning methods to develop recommendation engines. Machine learning can provide guidance on trends product teams may have missed. Once data is cleansed, normalized and analyzed, product teams can more easily predict what their next move should be.

Our own proof of concept for resume and job recommendations predicts which candidates are most likely to be hired using AI and ML, so recruitment teams can fill roles faster in a time when jobs outnumber talent. Other features like candidate search, job search for candidates, content and salary recommendations are initial ideas that we’re working on feasible solutions for using AI and ML.

4. Use data to make better informed decisions

Before analytics were in the picture, product decisions were based on gut notions. Instead, use data to drive more informed decision-making.

Wal-Mart uses data to its advantage by browsing behavior data to constantly learn and improve the understanding of its shoppers, their habits, and their needs. This information is used to ensure the business maximizes profits. Vanguard and Fidelity investment firms use customer data to recommend them investment opportunities, retirement planning and more, which in turn, boosts the company’s own profits.

5. Incorporate analytics to inspire your roadmap and end-to-end strategy

Product development is a critical part of being a product manager. Roadmaps must be driven by a strategy that has built-in analytic components with ample feedback from the above steps, and should contain a checks and balances approach that’s scalable, easily susceptible to automation with replicable dependencies (nos and-pile approach). Once you have digested the data, you should have the tools to build an entirely unified approach integrating all pillars of the business (sales, marketing, technology, etc.). It’s a significant effort where different parts of the business contribute.

For instance, the results from our work with Google for Jobs is worth mentioning here as it is guiding our roadmap strategy to create better candidate sourcing products for our customers. Our partnership with Google is driving more relevant talent directly to our client’s career sites (a 134% increase in candidate traffic) instead of third-party job boards.

Product managers that best utilize insights derived from past data will have the most success now and in the future. From initial concept to product deployment, if you’re not taking advantage of the insights your data offers, your business — and your customers—could be missing out.

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