Last Updated on November 16, 2022 by admin
Data analysis is the backbone of the modern enterprise product management suite. Every aspect of modern business is now influenced by various data analysis processes that are found to be making an immense impact on the outcome of the product strategy and vision, influenced by the heightened scope of automation and AI. If you are to unlock your true potential as a Product Manager, learning data analysis skills is a must. Many companies are now hiring skilled data analysts and business intelligence managers with an MBA in product management. This crossover from data analysis to full-scale product management is only possible if you are able to make the fullest use of data for your product strategy and product vision during your stint as a Product Manager.
In this article, we have explained how to make this crossover to successful product management career leveraging data analytics. Let’s begin.
Table of Contents
Understanding the Basics of Data Analysis
There are many different facets and nuances of data analysis as applied to the core product management journeys. Product Managers have to deal with different types of data and each type leaves its own footprint on the overall outcome of the final product journey. Thanks to the large scale availability of Business Intelligence dashboards and self-service data analysis platforms, PMs no longer have to shell out additional time from their busy schedule to particularly learn any data analysis technique, but in the Product Management MBA course, a quick overview of data types and their analyses is part of the curriculum nonetheless.
The basic types of data used in product management are as follows:
Also referred to as user data, customer data is the “personal information” shared by an existing user or a former user who has engaged with the product or its previous versions in some way or the other. For example, when a new version of a mobile application is launched, existing users are notified that new updates and features are available for download. Some updates are tested on beta platforms. The data that is extracted in this stage is what we call as user data. User data can be further segmented into subfamilies for better analysis. These are mostly sub-categorized as Personal Information, Behavioural Data, Intent Data, Engagement Data, and Attitudinal Data. Each data type has its own importance and the analysis largely varies on the interaction between product, Marketing, and Sales teams. Overall, an MBA in product management would help aspiring PMs to make a scientific assessment of how customers engage and prefer to use a product. AI, predictive intelligence, and sales forecasting are all parts of the results achieved by building a customer data centric product management journey.
A product can speak for itself these days. Don’t believe us? Well, we recommend that you get a first hand understanding of product data specifications and how smartly these are crafted for product designers, managers, and marketers. Product data is the self-explanatory set of information pertaining to universal features and specifications that can be associated with a product’s unique attributes, its correlation to pricing, and sustainability. Whether the product is a designer handbag, a CRM software, an online e-ticket booking platform, or your voice recognizer, each has its own set of product data that can be used to ascertain the properties and attributes of that product.
Product data analysis involves:
- Understanding of the product schema
- Deciding the source of origin or location of the product development lab
- Product version and last iterations
- Age of product
- Next updates available
- Pricing and recommendations
- Number of vendors available in the marketplace
- Certification from government and quality checkers
- Keyword ranking, web page ranking, etc
- The volume of downloads from application stores
- Images, videos, and training manuals
- Product reviews, best features and marketing events, etc.
Product managers could be using either Qualitative Data, Quantitative Data, or both, to build a successful product. We never get tired of using data driven approaches in product management, and thanks to the advent of some really cool programming techniques pertaining to Low Code and No Code and Self-service (Drag and Drop features), PMs are taking a keen interest in data analysis at all stages of product development. PMs use data analysis in analyzing NPS, intent, and the emotional attachment/sentiment associated with a large part of the product suite. From breaking down customer conversations with pre-sales and post-sales executives to analyzing digital metrics, PMs use data analysis in innovative ways.
It can prove to be a rabbit hole when it comes to distinguishing various types of product data, but smart Product Managers refer to advanced data analysis tools like Heat Maps, Website Analytics (Google Analytics, Semrush), Intent Data, Product Metadata, Knowledge graphs, and data visualization techniques to outsmart the competition in the product management marketplace.