Validating the value of interaction design by analyzing and designing methods

Adventure starting in the Kunlun User Experience Team Blog, reproduced please specify.

Why do I need data validation?

"It's very busy to see you every day, but how do you measure how much the user experience of the game has improved?" ”

Yes, the senior level is more focused on the strategic direction and it is impossible to understand all the work details of each employee. In the final http://www.aliyun.com/zixun/aggregation/18698.html "> Gaming experience, you can see the beautiful visual, interesting play, but only do not feel the interaction more wonderful." In fact, the pursuit of interactive design is "so that users do not feel the existence of the interface", only to feel the existence of the interface, in order to immerse in the game, feel the immersive pleasure, feel the fun of play.

For most companies, the senior level is basically very concerned about input and output, the game is the same, we do a game, through the ads to attract the number of users to play, to play in the user, probably how many users can be retained, in the retained users, and how many users pay. Ultimately, the company is concerned with how much it costs and how many players pay.

Second, we have generally worked out a method of data validation through analysis and design.

Considering the higher level of input-output related data, we decided to calculate and "quality, customer retention, customer service pressure" of the 3 related data. The quality of the relationship between the brand influence, the reputation of the product, the user retained relationship with the company's income, reduce customer service pressure can reduce costs for the company.

Here's a virtual example to illustrate the validation process.
1. In the system designed, sample 7 or so, each prototype analysis. Here is a statistical procedure for the procedure. (If you want to pursue objectivity, you can find a few more dimensions.) ）
Example of this step: the teaming process is indicative (one of the examples in the sample)

When there is no team, by clicking the leading flight ' team –> to create a successful process:

Comparison table before and after optimization

Column chart Comparison

Comparison of operational procedures and information cognition

From this we analyzed that the team process was reduced by 40% in operational steps.

2. Aggregate the sampled data into a single table

(The data in the table is virtual for subsequent calculations.)

3. Calculate the company level of concern according to the basic data of step 2

Improve product quality

Averages 7 values.

(40%+40%+54.5%+30%+60%+45%+50%)/7=45.64%

Conservative estimate, product optimization 45.64%

(Note: Data extraction, we can according to their own needs for extraction, as far as possible to reflect the average production level.) ）

Impact on user Retention

User retention factors, conservative assumptions: Play 50%, Style 30%,ue20%

45.64%x20%=9.128%

In the increase in user retention, the design factor accounted for 9.128%

(Note: User retention factors can also be adjusted according to the actual data they hold.) ）

Reduce customer pressure estimates

Customer service pressure factor, assuming: bug20%, operation Problem 50%, other 30% (this part of the assumption is best to listen to the Customer service department's opinion)

45.64%x50%=22.82%

In the reduction of customer service pressure factors, design factors accounted for 22.82%

(Note: Customer service factors can also be based on their own actual data adjustments accounted for the proportion.) ）

Summary: There are many methods of validation, this is mainly in the design after the completion of the analysis of the way to complete the preliminary verification. The ultimate true effect also needs to use objective quantitative method to verify, because not this point, not to repeat.