The event is over, but your work has just begun!

In the Internet age, we are surrounded by various online activities, including sweepstakes (common both online and offline), interactive game activities (win prizes by passing levels), preferential subsidy activities (takeout and taxi coupons can be seen every day), topic interactive discussions... There are so many various activities that users are no longer surprised!

As operators , we treat every event as our own child, and we put a lot of effort into every so-called "small event": event planning, communication arrangements, development and testing, event warm-up, distribution channels, etc. before going online; various monitoring when going online, for fear of a bug; how to notify users and distribute prizes at the end of the event, etc. There are so many things to do, it's really busy! The event is over, but your job is not over. Your boss will immediately ask you: "How effective was this event? How many new users came? How many orders were converted..." In fact, when the event is over, your work has just begun, and that is the "event effectiveness analysis report."

The activity effect analysis report is actually mainly divided into: analysis and summary of the current activity, and plan and expectation of the next activity ;

Summary of this period's activities

Summary: It is nothing more than analyzing the data effect of this activity. If you have done similar activities before, you also need to understand the month-on-month situation. Let's talk about them one by one.

Note: Each website and each activity may focus on different data. I will simply start with a few common data for your reference only!

1. Visitor data: PV, UV

How to look at these data? Don’t be stupid and tell your leader the daily PV and UV during the event. It’s useless!

(1) Activity period vs. non-activity period

Only by comparing the two can we know how much more traffic the website has gained due to the event and reflect the value of the event! Only then can your hard work be reflected, 😊. I mainly look at the daily averages for the two periods, as follows.

(2) If the activity period is longer, you should also compare the weekly data.

For example, this is a comparison of data from two weeks of an event. From 10/19 to 10/25, both PV and UV increased (in this picture, the difference between the two-week data is not that big, we assume that the difference between the two weeks is big ), then think about the reason: are users becoming more and more interested in the event as time goes by (study the cyclical fluctuation rules of the event, for example, events for competition rankings generally have more users participating at the last minute, as everyone wants to take a gamble and win the prize in the end)? Or are there some new small features added to the event product (for example, in our previous activities, we added a sharing function in the later stage, and the data showed good feedback)? Or were there any new operations carried out during the week of 10/19-10/25 (such as SMS push, WeChat account push, etc.)?

(3) Month-on-month and year-on-year changes

If the activity is not the first time, then it must be compared with previous activities, that is, the month-on-month situation; the "year-on-year and month-on-month" indicator should be familiar to operation friends, and they should see it often. In this data tool , you can directly look for the month-on-month growth value or the month-on-month growth rate. Generally, you should look at the month-on-month growth rate , but it depends on your own needs. If this activity is an activity that takes place at a certain time every year (such as Double Eleven or fixed activities during the Chinese New Year), you can completely choose " Previous year's year-on-year value, Previous year's year-on-year rate " to immediately know whether this year's activity data is higher or lower than last year's data. Haha, if the data is high, it’s fine. If it’s low, my boss will probably have to treat you to coffee! !

2. Number of website registrations:

Or some websites can directly count the number of people who registered for the event.

(1) Activity period vs. non-activity period

Using the same analysis as above, we can find out how many new users can be brought in every day during the event? Does the daily registration change trend during the week follow the same trend as during non-active periods (e.g., higher on weekends than weekdays)?

(2) If the activity period is longer, you should also compare the weekly data.

Following the above analysis, let’s see if there are any regular patterns in the number of registrations each week.

(3) Year-on-year and month-on-month

The same analysis as above can be carried out.

(4) One important point to emphasize is whether all of them are valid users.

In other words, whether there are users who place false orders is very important and the problem is very serious. Previously, we conducted an activity. After the first phase ended, we immediately discovered from the data that there were a lot of users who placed fake orders, so we immediately implemented an anti-fake order mechanism in the second phase. Of course, most of the mechanisms for judging users who are placing fake orders should have been considered in advance in the risks of previous activity plans, but there may be some unexpected things during an activity. For example, in our previous activities, we did not consider certain risks, which was indeed a problem. However, if we can discover and solve them in time and use them as experience for subsequent activities, isn’t this progress?

3. Number of participants in the event and number of people logging into the event page

This data should be looked at for each activity, mainly to understand how many people participated in the activity and what the trend of participation was on a daily basis. For other data, please refer to the above dimensions for analysis! ! !

Here is an additional analysis dimension: the distribution of new and old users among the number of participants, to understand the value of your website's new and old users. Of course, for most websites, old users must be of great value! For old users, you can also learn about their activity enthusiasm (i.e., in addition to this time, what other activities have they participated in and how much they have contributed)

——This analysis dimension applies to the number of participants, conversion status, coupons, etc. . .

4. Activity conversion (number of conversions, conversion amount)

Conversion status mostly refers to: transactions ( e-commerce purchases, O2O orders, etc.), recharge status, etc.; of course, this data is not necessary for every activity, such as some interactive activities; other data can be analyzed by referring to the above dimensions! ! ! Remember to analyze the transaction conversion data: visits, orders, and actual purchase funnels (I have mentioned this before, please refer to my other article "As an operator, do you know how many of your users have "lost"?"); and analyze the average purchase amount per person, the average number of purchases per person, etc. (You should all understand these, so I won't say more)

5. Coupon data

Data such as coupon issuance and usage is very important. It is related to the return rate of the activity. In other words, it is related to whether the activity made a profit (how much) or a loss (how much) (of course, there are more indicators to evaluate losses, but this is very important, especially for those activities with huge subsidies). This must be analyzed carefully, otherwise the leader may not approve the next activity, la la la! For other data, please refer to the above dimensions for analysis, you know! !

6. Comparison of different gameplays

Each event may have different ways of playing, remember to compare which way of playing is more attractive!

7. Share your situation

This is relatively simple, and it mainly evaluates whether the sharing function is popular among everyone in the event and what the interactive effect is!

8. Comparison of various platforms and channels

There is not much to say about this. It is very important to analyze the effectiveness of each platform and channel and evaluate the quality of each platform and channel, especially for the channel delivery of your website. This data analysis dimension can include the above 7 points! Haha, that’s a lot of data…

I have only mentioned a few common data indicators. You can select some data dimensions to compare with your own activities, or add some data analysis dimensions according to your needs. It depends on your activities! Those data are not generated for easy viewing. We must remember to find problems and patterns from the data, summarize experience, and try to optimize and avoid them in the next activity. If you find something good, you should continue to do so...

There are also plans and expectations for the next activities. Ha, I’m tired of writing. Let’s stop here for today and have a happy weekend. Is there a next episode? Haha, I don’t know either. Maybe your expectations will give me more motivation!

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