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Problem scenario:
A certain video website charges fees in the form of monthly membership, and now I understand that the peers are preparing to increase prices and plan to increase together. After the price increase, a data analyst is required to evaluate the effect of the price increase. You are a data analyst for the company, how would you evaluate it?
Thinking
test
Yi
Fen
Zhong
1. The most basic price increase model
Income = total number of users purchase rate per capita amount. Everyone knows this formula. The question then is: what impact will the price increase have? Answer: The purchase rate may decrease if the price increases, and the per capita amount will increase. As for whether the total income is more or less after the price increase, it depends on the ratio of changes between the two. This is the most basic and most basic price change evaluation model (as shown below)
The price sensitivity can be tested in advance. In advance, you can use coupons as leverage to test the user's purchase rate in the form of a lottery, so as to infer how much price increase/decrease is appropriate. However, this method is more suitable for measuring price cuts. If the price increases, the user's instinctive aversion will be stronger, so it is not suitable.
So, does it end here?
What else is missing?
2. Consider commodity attributes
The drop in user purchase rate will be affected by the following factors:
l Price anchoring: the more obscure the anchoring, the less the decline
l Degree of rigid demand: the higher the degree of rigid demand, the less the decline
l Monopoly: the higher the monopoly, the less the decline
l Price level: the lower the price, the less the decline
l Awareness: The lower the awareness, the less the decline
Of these five elements, the first four are easy to understand intuitively, and the fifth one is a little explanation: the so-called awareness is how much the user cares about. Many tariffs in our lives are silently deducted, such as water, electricity, and gas bills. Unless there is a sudden increase in a certain month, or the merchant actively promotes marketing activities, these tickets may have flowed from people's fingertips.
So the question is: how many of the video members’ products meet the above?
Almost all (as shown below)
It is estimated that this is why the operation has the confidence to raise prices. Under the influence of the epidemic, people's online entertainment has increased significantly. This can be easily observed from data such as DAU, online duration, and continuous playback rate. Since rigid demand is increasing, awareness is inherently low, and the price is not expensive, then the increase is a steady profit.
So, considering this layer, is it enough?
What else is missing?
Three, consider the details of price increases
The biggest difference between the price of video members and rice white noodles is that the price anchoring of this thing is entirely artificial. The marginal cost of providing an additional user service is almost zero, so operations can squeeze prices arbitrarily, create new anchor points, and obscure users' judgments.
For example, originally there was only one member who paid 25 yuan a month to become a member, and now it has launched a 20-month continuous monthly subscription service. At first glance, it is 5 yuan cheaper, and users are likely to subscribe. Considering the actual usage rate changes (for example, Wokai members want to chase a hit drama, and rarely watch it after chasing it), it is very likely that in the following months, the user forgets to cancel the payment, and the automatic payment will deduct the extra Money. This is the strategy of rising and falling.
Note that there is a problem with this strategy, that is, income will decline in the short term. Therefore, you can also think in the opposite direction and set a strategy of rising and falling. By sacrificing the ARPU value of the following months, you can quickly increase your income in the short term and reap a lump sum (as shown below).
Of course, you can also directly create a new package through alliance packaging to further blur the price anchor. For example, pull up the takeaway platform to send members together, package pricing. Don't take out your phone, now ask your Meituan or Hungry members how much a month it costs! More than 80% of the people couldn't answer, but they felt: It cost only 40 or 50 yuan to get two members. It's a good deal. Anyway, I want to order takeaway. In short, the more obscure the price anchor is, the more likely users are to bear the price increase.
Therefore, this question should not be asked from the beginning. If in a real work environment, the first thing a data analyst must do is to figure out:
1. How did it rise?
2. Which specific combinations of membership packages are increasing in price?
3. Is it a hard price increase or a soft price increase for new packages?
4. Is it bright and dark up or down?
Knowing this, can we predict the business trend and know what is expected and what is unexpected. Otherwise, it is very likely that you will be busy for a long time and only end up with "I knew it".
However, there is still a problem here, which is the wishful thinking of the business. Do consumers really pay the bill?
Four, consider user behavior
Note that each of the above strategies has premises, such as:
Bright and dark promotion strategy: sufficient proportion of
insensible users /low cancellation rate Bright promotion and dark decline strategy: users have sufficient payment rate for quarterly/annual packages
Anchor fuzzy strategy: joint products must have sufficient user base
If these premises are not established, the minute-by-minute strategy will play badly, or it may not attract enough users, or it may be left behind. Therefore, the user’s purchase conversion rate and repurchase rate will directly affect the price increase effect.
Let us further ask: What does the user purchase conversion rate and repurchase rate have to do with? You may be able to say casually: if you watch a hit show, other people’s prices will increase more drastically, and new users have no idea about charges. But note: these reasons cannot be quantified by data. Therefore, it is necessary to find something that can be verified with data, such as: reduction in online frequency, reduction in single online duration, reduction in continuous playback, etc., to distinguish between new and old users (as shown below). Only in this way can we find a deeper reason, instead of stopping at: since the price increase, the 20 yuan package has been sold less, at the level of the repeater that beeps the chart again.
So, considering this layer, is it enough?
What else is missing?
Five, consider business actions
Are all price increases
- Wait for the opponent to adjust the price first VS I will raise the price first
- Put the newly packaged package on the front VS straight hook and change the price list
- All the streets shouted: I want to increase the price! VS secretly rubbed the price list
These methods have been determined to adjust the price by changing the propaganda language, propaganda rhythm, and timing to achieve more different effects. Especially for virtual products, when the price anchor is vague, it is easier to give consumers the illusion, and then produce stronger/weaker effects.
As a data analysis, it is necessary to understand these specific details in order to fully evaluate the time range of the impact of price increases, rather than the most basic model, starting from the moment of price adjustment.
Six, summary
In summary, a seemingly simple topic, seemingly simple business logic, can be combined with specific industry characteristics, product attributes, user habits, and business actions to create various possibilities.
Therefore, if you want to make a comprehensive assessment, you must have an in-depth understanding of the business details and sort out the business assumptions in advance. Only in this way can we clearly define when the influence cycle starts, which user behaviors are natural evolution, and which are driven by promotions. Otherwise, don’t think deeply, just put out the daily payment data in a naive way. Not only will you not be able to see the meaning behind the data, but you will also be defeated in business attacks:
"Have you considered the influence of publicity!"
"Did you eliminate external factors!"
"Did you consider long-term effects!"
"You used the old product system to simulate a fart!"
"Conversion rate is low, so what?"
"We want Deep analysis!"
None of them can be answered.
Author: Chen grounded gas, micro-channel public number: down to earth school. A data analyst with ten years of experience has launched a series of data analysis courses and has more than 20,000 students.