The job search environment in 2024 can be summarized by keywords such as "read and can't go back", "sinking into the sea", "job downsizing" and other keywords, so if you are lucky enough to have an interview opportunity with the target company, you need to spend more effort to prepare, understand the purpose of the interview and inspection of the data product manager, and know each other and know yourself.
1. The selection and employment dimensions of data product managers and their response plans
Data products are a vertical direction for product managers in the data field, and the ability requirements for candidates must include product thinking skills, and it is best to have a certain data thinking.
1. Willingness to apply for a job
The willingness to work in data products is a very important criterion, if it is dispensable, then even if you get an offer, you can do a good internship or learn data product capabilities in a down-to-earth manner.
2. Product thinking
There is no standard definition of product thinking, but it can be summed up as a deeper way to think about and understand user problems, give as comprehensive solutions as possible, and drive the product to achieve the desired results. For example, during the interview, I asked that in the second half of the Internet, data-driven operation is the pursuit of almost every enterprise, if you are asked to make an intelligent marketing product, what are you going to do? For this kind of scenario hypothetical questions, you may not even know what intelligent marketing is, so don't rush to answer, you can understand the business scenario, the main user groups, what the current solution is, what are the pain points, and review the problem clearly. The next step is the problem-solving process.
The first step is the background goal. Through demand communication research, we learned that there are many hierarchical and refined operation needs of users in the current products, operations and other businesses, but the current method is mainly to provide data retrieval for the data team, which has a long process and low efficiency, which affects the marketing frequency. The appeal of the business is to carry out precision marketing more efficiently. Therefore, the positioning of the product is determined to provide more efficient and intelligent refined operation tools for business colleagues such as operations, products, and marketing.
The second step is product planning. Combined with the results of user research and the research of competing products, the core functions that an intelligent marketing platform needs to have include:
- Label management, the production and maintenance of labels, such as label system and metadata information management, label query.
- Crowd selection: The business operation scenario is split into tag conditions, and the target group is selected by using the tag circle.
- Marketing reach: How do different groups of people use to reach out, SMS, Push or red envelope coupons, and how to achieve automatic docking with the reach channel.
- Effect feedback: Analyze the operation effect, adjust the conditions in time, and improve the conversion. Form a closed loop of operations.
- System management: Label permission, crowd permission management, data security issues should always be paid attention to, and permission design is an important part of data products.
The above are the core functions of the intelligent marketing platform, with the wide range of business applications, the product functions will be gradually improved, such as crowd analysis function, intelligent expansion function, etc.
The third step is scheme design and review. After transforming the demand into product planning and planning and designing into a product functional plan, it is necessary to review and confirm with the user again, and communicate with the developer to confirm the feasibility of the plan.
Step 4: Development Schedule. Through the project kick-off meeting, development review meeting and project management methods, follow up the implementation of the product plan.
Step 5: Online operation, after the product is launched, find seed users for trial, and focus on following up on users when collecting needs. Let the product be put into production from point to surface, collect new needs and feedback from users, and continue to iterate and optimize. The above steps are an example of ideas that can reflect the thinking ability of the product when you get a problem. When it comes to actually answering, you may not have that much knowledge to expand, but it's just your thoughts, not everything. You see, isn't product thinking a process-based way of thinking that can train abstract problem-solving?
3. Product capability
The competencies required by product managers include: demand analysis, product planning, product design, communication and coordination, project management, and product operation capabilities.
Requirements analysis
In the case of the hypothetical scenario above, what do you ask more when you get the question? The main thing is to examine how you analyze and deal with a new demand after receiving it, whether to do it directly, or to dig out the root problem and dig out the real demand.
Product planning capabilities
It is mainly the product framework thinking, starting from the goal, to the product function module, from the logic of the total to the score, rather than thinking of a point and a point.
Product design
Generally, it is the specific scheme design effect, such as the ability to use prototype tools and aesthetic ability, which is not easy to investigate through communication, usually sketching on the spot, or sending past demo cases afterwards. For tools, there are mainly common product design tools such as Axure and Mind. The basic functions of Axure are very simple, and if you know how to use PPT, you will definitely use Axure.
Communication and coordination
During the interview process, you can introduce yourself and answer the questions in a language that can reflect the logical thinking of the expression. Sometimes you will also be given a conflict scenario depending on how you deal with it, for example, what will you do when your product design is questioned by many people during the development review.
project management
In order to complete the product on time and with quality and quantity, what project management methods do you have? You can summarize the school projects or internship projects you have done in the past in advance, and integrate them into stakeholder management, demand management, schedule management, communication management and other methods to reflect the awareness of project management.
Product operation capabilities
What are the ways to make data products more useful and better used?For example, product launch publicity and training, core user group operation, user demand feedback channel construction, questionnaire research, etc
4. Data thinking
Data products must have a direct or indirect relationship with data, and have higher requirements for data thinking than C-end product managers. It mainly refers to the thinking of data analysis and the thinking of using data analysis methods to solve problems. For example, let's estimate how many gas stations there are in Beijing. This problem is to look at the analysis idea rather than the accurate result, and it is useless for Baidu to have an accurate number. First of all, clarify the problem: find the number of gas stations in the Beijing area. Then establish a dismantling formula, the gas station is the supply side, the car is the demand side, and we can estimate the number of gas stations from the demand side. The number of gas stations = the number of vehicles that need to be refueled every day / the number of vehicles that can be refueled per day at each gas station, and then it can be further split, the number of cars that need to be refueled every day = the number of cars in Beijing / the average refueling cycle per vehicle. Let's say Beijing has a population of 20 million, and each family has an average of four people, and each family has a car. By analogy, it is sufficient to derive the estimation results by means of indicator dismantling and numerical estimation assumptions.
5. Job knowledge of data products
- The understanding of data products, even if many students have experience in data products, their understanding of data products is limited to a single module for which they are responsible, such as data visualization report products.
- An understanding of the data product workflow, the process that a requirement goes through from generation to end, and the responsibilities of the product manager in it.
- The understanding of data product positions, the ability requirements, and the differences between them and C-end product managers
- Application scenarios and cases that combine data and business cannot simply be a tool and a platform
- Data analysis methods, user behavior analysis methods, such as event analysis, funnel analysis, retention analysis, path analysis, etc.
- Data-based operation methodology, indicator system construction model, such as Polaris indicator selection, and indicator system construction method based on OSM, UJM, and AARRR models. User portrait tag system construction method, RFM model, etc.
- Knowledge of data technology, such as SQL fundamentals. Data collection and burying scheme, big data technology foundation. The big data component is mainly related to the lower level of development tools products have higher requirements, the upper level of data analysis, data application products are relatively lower requirements, and it is less difficult to find a job at this level, but the competition is also large.
3. What methods and ways to better improve relevant skills?
- "Work Notes for Data Product Newcomers", as a data product manager's introductory science is still very good, you can have a comprehensive understanding of the work content of data products, and you can also take a look at the actual combat advancement of data product managers.
- "The Road to Big Data", a very classic summary of Alibaba's big data practice, involves a wide range of knowledge, from data collection, data technology, and data products. After reading this book, the data flow process of big data ecology is very clear.
- "SQL must know must know", you can master the chapters related to data query, and if you are interested, you can understand the principle in depth. A PDF version of some of the data has been shared with Knowledge Planet users for free.
Fourth, about the logic of the interview answers
GET THE PURPOSE OF THE QUESTION FIRST, AND THEN ORGANIZE THE LANGUAGE RESPONSE. Interview questions are generally asked to verify a certain ability, and the questions asked follow the Star Rule. The so-called STAR principle, i.e. Situation, Task, Action and Result, is a very important theory in structured interviewing. Answering questions according to this theory will also make it easier to get to the point of the interviewer.
S: In what context did the project, requirement, or product take place?
T: What are the tasks you will be working on?
A: What do you do when you receive the task?How long did it take you to acquire the necessary knowledge to complete the task?------ in-depth understanding of the learning ability of employees, and when you encountered difficulties in the process?----- understand tenacity, and the flexibility to deal with incidents
R: How did you get to the end of the mission?
5. Summary
For students who want to find a job as a data product manager, they must first be very clear about the data products, the ability requirements of the position, and the evaluation dimension of the interview. Rome was not built in a day, and it is necessary to continuously learn and supplement the requirements of various capabilities, and over time, the capabilities of product thinking, data thinking, and data products will gradually solidify. Finally, I wish every student who is interested in engaging in data products can successfully get their favorite offer. The general environment is not good, you need to be prepared for a protracted battle, and cherish every opportunity to interview.