When it comes to why product analysis matters so much to a product manager (PM), we have to begin with “Product Value” and where it comes from.
The initial intention of developing a product is to provide users with a product or solution that is easy to use, able to solve problems and continue to deliver its value. As such, a so-called good product has to meet the criteria of “continuing to be used by users” in order to deliver its rightful value. The job of a product manager is to present products that truly meet the needs of users and fit the usage scenarios. Subsequently, his/her most important task is to understand customer needs, explore usage scenarios, and keep pursuing the 2Ws and 1H about our users: “Why would users want to use the product?”, “Why do users like the product?”, “How to increase product/customer stickiness?”, And this exploration process is what we call Product Analysis.
For a product manager, how to create a better product based on the information provided by user behaviors is the key reason for the central purpose of conducting “Product Analysis”.
Why product analysis matters to a product manager?
In fact, product analysis is not only to help a product manager clarify the core value of a product but also to grasp how users interact with a product, including interaction frequency, duration period and the retention rate.
Data reflects behaviors, and behaviors react to value. Regarding a digital product, its value is to solve the problems of: “how it can help us act more effectively?” and “how it can make life easier?” So, a product manager’s duty is to dig, to explore what data represents its key value on a customer journeyand what data represents its unidentified, unresolved problems. Such exploration and excavation will help optimize products and improve user retention.
Different product types work with different product analysis modules. Based on business models, products can usually be divided into the following monetization methods to quantify their visible profits. For instance,
- Transaction-based: Pay per purchase of the product
- Subscription-based: Pay per use of the product
- Permit-based: Pay for a permit before using or selling the product
- Free: Get a basic product for free. Pay for advanced features
Each monetization mechanism corresponds to different user behaviors. Take Netflix for instance. For a subscription-based service provider of video content with recurring payment, the most important product metric is how to maintain its subscription renewal rate by continuously satisfying users’ viewing demands and providing users with an insatiable streaming experience. As for software products that require single payment for a single transaction, for instance, iPad’s Notability App, the focus is more on the number of user logins and what functions users access after purchase, and whether they are willing to carry on paying for advanced products when the product continues to roll out add-on services.
This goes to show that for products with different business models, the data module for its product analysis would be different too. This is why a product manager needs to identify the product metric befitting the growth of its business model, based on the product type.
What is the first step in product analysis? Begin with finding the “Product Metric”!
Many people would ask: what is the first step in product analysis?
To answer this question, we can look at the duties of a product manager. A product manager’s primary task is to push the product to success. To this end, it needs to be complemented with a large number of “metrics” as the basis for product adjustment and optimization.
In hence, when we talk about the first step in product analysis, we need to first locate the effective “Product metric” based on the product type, business model, and user behavior model. Locating such metrics is akin to running an overall checkup on the product to gauge the user tendency. The metrics differ according to product type, payment method, and so on. Presently the familiar product metrics are as follows:
- Business outcome metric: revenue and revenue growth rate, cost and gross margin;
- Product outcome metric: number of logins, usage duration/frequency, membership acquisition rate, activity level, retention rate, number of recommendations;
- User satisfaction metric: user Net Promoter Score (NPS), Customer Satisfaction Score (CSAT);
- Product operation process metric: Operating cycle, automated process
As metrics all have a different focus, trying to locate the core data is like looking for a needle in a haystack. So you will often see that product managers make the most common mistake of giving all metrics the same treatment and focus, while all product metrics should receive its due attention in the order of priority.
Mixpanel, a leader in the product analysis field, classifies product metrics into three levels: “Focus Metric”, “Level 1 Metric” and “Level 2 Metric”. The so-called Focus Metric is the very center of all metrics, i.e. the key metrics that product managers and their teams should place the most focus on. They will impact the value and direction of product growth to the greatest extent. Level 1 and Level 2 are complementary to the Focus Metric for the enhancement effect. Here is an example of how a “subscription-based AV streaming media” defines its product metrics:
How to conduct product analysis through a “data-driven process”?
How to find timely, applicable data to assist with a product manager’s decision-making is a critical aspect of the product optimization process. A truly outstanding product manager will look into the following four questions when assessing which metrics to follow:
- Will the data effectively reflect product changes?
- Can the data better gauge user benefits when using the product?
- Can the data link to the company’s ultimate business goals?
- Will the data float easily or fluctuate excessively?
All in all, when a product manager conducts product analysis, first and foremost, clarify “What product metrics to observe” and “The direct relationship between these metrics and the company goals, product growth objectives.” Meanwhile, affirm the stability of such data and whether it provides continuous and effective information for user behavior analysis, and whether such information will continue to serve as a basis for product optimization.
Lastly, when formulating “Product Metrics” and “Product Analysis”, do not forget to associate with performance evaluation in order to help product managers better assess the achievement rate. Meanwhile, this will help the team avoid communication ambiguity and build a group consensus so as to make decisions more effectively based on the metrics.
About Master Concept
As Mixpanel gold partner, Master Concept will focus on providing a full range of services for enterprises, including needs assessment, metric architecture configuration, account setting, tracking code arrangement, visualization report establishment and technical support. For the special needs of non-product development and data departments, Master Concept also provides training services.
In recent years, the market’s demand for product analysis has continued to rise, making behavioral data the core of product development and precision marketing. As an influential cloud technology consulting company in the Asia-Pacific region, Master Concept looks forward to expanding its territory by working with Mixpanel to provide customers with real-time user data, practice higher ROI, and unlock value for product development teams.