Four key factors we should focus on in an analytics project
There are plenty of critical elements in an analytics project. This article does not cover the data analysis process but raises the level to a project leader's point of view and explains four factors that matter the most in a project.
Target oriented, which correlates to expectation management.
Quite a standard, right. But have you ever thought that setting the right target strongly correlates to high-level expectation management? For example, when your boss is willing to investigate an ML project to identify an audience who may be interested in your product/service. The target setting is essential. 1. To generate 2M customer list, or 2. to generate 1M customer list(as what the company did before) but with a lower budget. They are different. And it connects to what your boss or what the C level expects on ROI. Another example is that media exposure and conversion are different targets, and we should use other KPIs to evaluate them. So always align the targets with your team and manage stakeholders' expectations.
Hypothesis and verification
Nowadays, it is challenging to find an SOP or standard way to achieve your goal/target. The efficient way is to set a hypothesis, collect data, execute experiments, and verify the idea. We often put the assumption of many possible reasons or methods for the project. Still, we quickly remove those impossible ways when we demonstrate the possibility one by one through A/B tests, experiments, and data validation. And many times, you will figure out that anything is possible when you get your final answer to achieve your goal. (which may be the one you don't put much emphasis on at the very beginning)
How long can the final answer you get from hypothesis and verification last? Well, it depends. But typically, we need to revisit it quarterly because the customer behavior, business environment, and competitor improvement change rapidly. Sometimes our methodology works well in the Q2, but it is just off in the Q3, so revisit it. Lead your team and focus on environmental change and iterate your solution. The most critical thing here is to align with your boss or high-level about your goal. From historical experience, the goal changes due to business competition, and how you try to achieve your goal also changed.
We did an excellent job. But many times, we didn't set a comprehensive handover plan. No matter whether you are relocating to another position in the same workplace or having a better opportunity outside. A handover plan helps the company and yourself maintain the suitable method you imported/developed/ executed, which leaves a good reputation for you. As a leader, one of the responsibilities is to incubate successors and help others grow. Your good experience plays a vital role in the replacement process.
Start with the goal and end with the handover. I believe these four factors are more than crucial for analytics projects and all tasks in our lives. Stay tuned.