We need to
the year of the rooster, WenJiQiWu, need more in the new year and always stand head and shoulders above others. Product operations naturally need this, but what skills and practices do we need? And that’s what we need to plan.
first, product operations, Wang needs to know how to build content
The biggest difference between
product operators and market operators may be that product operations have been responsible for pulling up user active values, while market operators need to keep high user value. It is clear that user activity is the primary goal of product operators. But when it comes to the product, the main thing for the user to show is the content of the product, so the content of the product construction has been the top priority of the product.
content construction is not only a few copies, a few pictures of things, but also related to all the information displayed to the user. Content construction involves many positions, such as copy planning, UI, product design and so on. A good user boot might be as good as you can expand ten user channels. Everyone has a halo effect, so it is very important for the user’s first impression. Product operations need to practice writing, Wang art and the functions of product design. The books recommended here are "user experience elements" and "copies that make copywriting desperate.".
second, data analysis, so that the product data to drive your decision
data is still a big concept, it involves day-to-day operations of the product KPI, user’s DAU, user portrait, and so on. As an operator, you can not every kind of psychological like a psychologist saw users, so this time we will need the help of our own data to guide our work in the next step of the plan.
once there was a case where we and our team developed a content based product. After a hard time getting a certain amount of users, the ad sponsor wanted to add ads to the launch page of the product. But because of the limitations of the product’s own style, we have finally discussed two alternatives, one is button type, one is pure text guided, and the two team has been arguing over the two. Finally, we choose a way to test A/B, which is tested by 50% of users. Finally, we found that using button styles is much better than plain text. At the same time, we started the ad itself to join the data points, and it was a good way to improve the design and location of the button, which is the power of data driven, recommended here book "big data analysis.".
third, product design, so that product operations to product managers change
first contact with a lot of product related work.