Operational case analysis: How to formulate an operational strategy?

In the first few years of operations , everyone was in the role of executor. Every time a task is assigned by the leader, they start racking their brains to think of a hit copy, drawing demos all night long, and constantly reviewing logic to avoid mistakes... After a period of tempering, you will find that some operations begin to look for breakthroughs from the executor level, jump out of the executor's framework, and try to solve the problem from a higher perspective. Then, from the perspective of operational strategy , they think about how to formulate user growth strategies, how to manage the user life cycle system, how to build a productized activity system... You will find that both are looking for ways to achieve their goals, but the former finds a systematic and replicable combination of gameplay, while the former is a one-way scattered operational method.

So how do we learn to formulate operational strategies? Let’s discuss this through a case study . The case study content is “How to establish an effective strategy for automated reach of new users on the Internet financial platform.”

First, clarify the basic process of formulating strategy:

1. Clarify the goals (clarify the problems to be solved and the results to be achieved)

2. Basic research (user profile/basic data/platform tools and current status, etc.)

3. Group testing (more professional is AB test)

4. Data feedback ( data analysis feedback and summary)

5. Iterative optimization (achieving a stable strategy system through continuous debugging)

Let's analyze this case. The business we are dealing with is financial management, and the user group is new financial management users. The goal is to use automated contact methods to increase the conversion rate of new users. If you have not developed strategies for related sectors before, then you must first sort out the new user conversion path of this financial management platform, that is, the life cycle nodes of new users. The simple way is to try to register and log in with a new account to complete the entire process. It is best to experience the processes of several competing products.

Based on the previous basic experience, we can sort out the new user life cycle process into the following steps.

Next, use the current platform data reports or existing conclusions to clarify the current means of contact and the current status of conversion rate data. These are your reference systems for formulating strategies. We found that the conversion rate of real-name card binding in each conversion node is close to 90%, and the biggest difficulty lies in registration/card binding/first recharge/re-investment. Therefore, by combining the nodes that need to be converted and the basic means of reaching out, we can compile version V1.0.

OK, when this version of the solution comes out, we can push the foundation of "cluster testing-data feedback" into place first, build a framework for automatic contact with new users, and then focus on optimization and iteration.

Comparing the data from two weeks, we found that using push alone can increase the overall account opening conversion rate from the day of registration to T+5 by 6%, and increase the account opening conversion rate from T+1 to T+5 after registration by 1%. We then continued to improve the coverage of the population, the channels reached, and the effect review, so we iterated version V2.0 based on the resource position.

Combined with AB data comparison, this version can increase business conversion rate by nearly 7%.

But is this the end of the operational strategy? During the actual operation, there are still many variables in this system that can be continuously optimized, including reward methods, copy content, touch nodes, conversion efficiency, time nodes, combination forms, label system, configuration background, data reports, etc. The departments involved include data, products, technology, design, project management, etc. In fact, this real strategy has gone through more than 10 versions and is still ongoing.

The above-mentioned automated operation strategy is close to a product strategy plan. The capabilities and perspectives of a high-level operation are by no means limited to the operation itself, but require consideration of multiple aspects such as users, products, and businesses. At the same time, the formulation and implementation of operational strategies are never a one-man show, but rather are deployed and logical, with the ultimate goal of saving manpower and improving efficiency.

Finally, let’s borrow the view on strategy once mentioned by Liu Ying, former product director of Didi Chuxing, to further understand, “ Based on your own experience, disassembly and verification, further implement your refined operation plan into certain rules that can help you achieve optimal efficiency without relying on a large amount of manpower investment, and deliver these rules to machines; give the rules the possibility of ‘self-evolution’ .” When you learn to use systematic and replicable combination gameplay and formulate operation strategies, it is also the time for you to advance in your operation.

Author: Mrs. Mingli

Source: Mrs. Mingli