with the data,
Lacoste Arin Patent Trainers, the product does not do well, because the data may be illusive!
not data, the product may also do good, because you may be fortunate!
read these 2 sentences seem very hard to pronounce, merely the real file reflects the magnitude of an Internet product, but likewise warned us to be careful apt treat the file.
a product of data and data analysis in the end give us what?
Here are some menial attitude!
1, allows us to understand the product status:
(1) Each product has a target of data, such as: the number of users, land users, membership, PV, UV, Posts, Video number, etc.,
Lacoste Observe Strap Trainers, of course, along to the product, the meaning of indicator data will be alter; these data will acquaint you a whole, this is what the present status of the product;
(2) emulated by the indicator data, but also There are some features of the data allows you to more detailed understanding of products, such as: the user's age and gender distribution, the proportion of the user's activity, the type of distribution of posts, users search cares what, PV distribution of content categories;
these data we can do is to comprehend the fundamentals of this product, nought more; in fact we really do not help a large product, though very important.
2,
Womens Lacoste Shua Trainers, let us understand trends:
appending to the arrow data and characteristic data, we will change frequently used data, such as: a month alternatively several months throughout the site, the changes in PV , or a function of the alteration in PV, landing the number of users changes, changes in the distribution of content through the change bend, we find namely the product namely moving toward healthy development, or to base!
trend data gives us a very essential message: the healthy development of the product, we are moving in the desired direction in the development. By analyzing trend data is effortless to find if the product in question, if there is enough, but the trend analysis is not the problem so that we understand specifically where, and solutions.
3, can assist us find the problem:
further data analysis, we will find a function for data analysis, and a functional relationship between the upwards and downstream analysis, will qualify us to more apparently to know the product, those correlates are important, which is part of the problem, such as:
(1) fashionable features because the analysis of the product, we can inspect the user to use this feature changes, to decide whether this function fashionable; scrutinize the trait pages of the source and destination user page,
Womens Lacoste Observe Trainers, to determine this function, bringing much worth to the product, or in the manifold appearances of the product, accounted because what percentage. Combined function of usage trends, we can resolve whether to amplify its investment, or to improve his side did not encounter our expectations;
(2) for the product user experience of data analysis process,
Lacoste Casual Trainers, you will also find user How to reach our core content of the page, the process of distribution, we will be improving the process for reference.
this period, we have from product perception to use data to improve products it! The data analysis is the gist value!
4, can help us to nail users:
this is the core value of the data analysis.
we ambition ascertain, in fact, we can tap into user action data, such as: Users favor to look what kind of content, like what kind of user interaction, user clicks ashore the sheet, the heat inquiry, these ambition be from a deeper class to cultivate our products, from product chart point of outlook, to cater users with more personalized entities. Technical hardship here, of way, will be higher accordingly!
we constantly speak with the data, no many folk actually do, and actually flexible petition of even less.
give a small instance, when the introduction of a new feature, feel relatively good, then suddenly received a complaint that this feature is not good to use, how to do? Directly along the user to change it, or ignore?
recommendations are:
1, to collect such feedback to see how many people? What kind of person are (background)? Why do they consider does not go well, there really is a private accustomed or experience of vulnerability?
2, good to see the backdrop data, the use of this feature,
Lacoste Finham SPM Trainers, there may be observed two days, see at the data changes and user response.
finally do a comprehensive judgments, through in-depth understanding and clutch the real user data, the data was not easy to be cheated.
On the understanding of file inquiry products
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