Product Operation: How to use data to create a good MVP?

The implementation of MVP is inseparable from data analysis, and even more so from the concept and practice of "user-centricity". Enterprises need to fully tap the value of data and, under the guidance of data, create products that users love, grow rapidly, and win user reputation and market opportunities.

The "MVP" referred to in this article is the acronym for "Minimum Viable Product", which means "minimum viable product". The MVP concept was first proposed by Eric Ries.

In his book "The Lean Startup", Eric Ries gave a systematic explanation of MVP: MVP refers to a product version that is developed by an enterprise at the lowest cost and is usable and can express the core concept, so that its functions are extremely simple but can help the enterprise quickly verify the concept of the product, so that the enterprise can continue to iterate and optimize the product after obtaining user feedback and constantly adapt to the market environment. The MVP concept has been recognized by many Silicon Valley entrepreneurs and has provided useful guidance for many Silicon Valley companies in product innovation.

With the vigorous development of China's Internet, more and more practitioners are applying MVP theory to product design practice.

Many companies will design minimum viable products through the "Build-Measure-Learn" operational process to verify product ideas.

1. How to build MVP?

First, the company will build an MVP through the following 5 steps, which is the prerequisite for verifying product hypotheses and realizing the value of MVP.

MVP construction flow chart

Step 1: Clarify product goals.

The team’s primary goal is to determine what pain points the product solves for users? What kind of value does it bring to users? How did users meet their needs before this? It is best to summarize it in one sentence.

Step 2: Define the user behavior path of the product.

Sort out the user's behavioral process and break down the user's specific operation steps for the product.

Step 3: List product features based on user paths.

The team brainstorms and lists the features that the product needs to have to help users achieve their goals. During this process, the team can want to develop many and rich functions.

Step 4: Prioritize the features listed in the previous step.

Through early brainstorming, the team listed a variety of functions on the whiteboard. They prioritized the functions based on R&D resources, design resources, user experience and other dimensions, and reduced and converged the function list.

Step 5: Build the final feature set.

After clarifying the final set of functions, you can draw a product prototype, have in-depth communication with some target users, determine the final draft of the prototype, and enter the development phase.

Instagrame’s product design and innovation process can be seen as a perfect MVP implementation case.

Filters are a core feature of Instagram. It was thanks to filters that Instagram stood out among mobile social applications that mainly presented information in text at the time. In the early version of Instagram (the MVP version), users could only select filters but could not adjust the strength of the filters. With continuous iterations, Instagram gradually added functions such as filter templates and brightness, allowing users to always produce high-quality pictures.

2. How to verify (Measure) MVP?

As the MVP goes online, companies also need to pay attention to a series of data indicators to measure whether the original product demand assumptions are correct.

For example, for a tool-type product, you need to pay attention to the number of new users and user retention, including daily active users, next-day retention, seven-day retention, and next-week retention; for a content-type product, you need to pay attention to data indicators such as the number of content releases, the number of interactions, and the number of publishers.

We can quickly obtain these data indicators through data operations or application statistics platforms.

If the team is in the early stages of a project or is a startup team, resources are tight and data is scarce. The product manager or operations staff may consider using a third-party application statistics platform to obtain this data.

As shown in the figure, the data operation platform can clearly present the status of users at various stages, such as new additions, activity, and retention, helping product managers and operations personnel to fully understand the product's data performance through data dashboards and analysis reports.

(Note: only demo data)

During this stage, the company should also focus on the core users' feedback on product usage, understand whether the product meets the users' expectations and real needs, and verify whether the assumptions when establishing the MVP are correct.

How to verify it? In addition to point-to-point communication with core users, product managers or operations staff can also use mature user behavior analysis tools to understand the target users' behavior paths within the product and analyze how they use product features.

An effective and practical method is to conduct point-of-care analysis on users’ behavior paths through the event configuration module of the data operation platform.

For example, an e-commerce app is preparing to hold a hot sale event. There are multiple links between entering the hot sale main venue of the e-commerce app and finally placing an order. If you set up tracking points for each link, including entering the hot sale main venue - viewing product details - adding to the shopping cart - clicking to buy - placing an order and paying, you can calculate the conversion rate between each link, and based on the size of the conversion rate, you can determine which links need to be specifically optimized.

(Note: only demo data)

To sum up, we can see that data is of paramount importance in the process of MVP verification. Although each product type is different and the data indicators that need to be observed are also different, core indicators such as new user growth, user retention and user usage behavior are the key indicators that every product manager and operator must focus on.

3. Enter the continuous iteration (Learning) process

Through data analysis and user feedback, product managers can quickly verify MVP.

For non-essential features that users are not interested in, product managers should consider subtraction or iteration; for features that users like, product managers should consider addition and continuous optimization.

For example, for a tool product, if the second-week retention is not good, then the product manager should reflect on whether he has a good grasp of the user pain points and whether the core function design meets user needs? Then refer to the MVP's operating procedures, redesign and polish the product, and enter the MVP iteration (learning) phase.

IV. Conclusion

Creating MVP at the lowest cost to verify ideas is increasingly becoming the first choice for many companies. The implementation of MVP is inseparable from data analysis, and even more so from the concept and practice of "user-centricity".

Enterprises need to fully tap the value of data and, under the guidance of data, create products that users love, grow rapidly, and win user reputation and market opportunities.

Author: Personal recommendation

Source: Personal recommendation