As an e-commerce operator, how much do you know about the impact of average order value?

As one of the three axes of e-commerce operation , the average order value is often easily overlooked by operators. On the one hand, the average order value is one of the factors and variables of total sales; on the other hand, the average order value to some extent reflects many characteristics of your consumer groups and whether the profitability of your sales categories is healthy. Today I will give you a brief introduction to this unpopular little thing, and as an extension, I will also describe some related pricing knowledge.

Comprehensive net profit is one of the important indicators to measure an Internet company with the purpose of profit. Not to mention how much of the GMV transaction volume that is criticized by major e-commerce companies every year is fake orders and how much is real sales, I am afraid no one knows how deep the water is. The real way to test the comprehensive strength of a company is to rely on net profit. After all, the bigger the GP, the better the transaction volume will be. (Some companies with strategic loss capital can be ignored)

There are many ways to define the average order value. Baidu defines it as the average amount a customer spends on a product. On e-commerce websites, you can understand it as the average amount of a valid order. The formula is average order value = total amount of valid orders (transacted) / total number of consumers or total amount of valid orders (transacted) / total number of completed orders; for example, for some apps or public accounts, the average cost of acquiring a user during local promotion or promotion is sometimes also called average order value. The average order value always changes with time, so when calculating the average order value, we must consider "within a certain time frame"

Factors affecting average order value

1. Product Pricing

The first thing that is affected is the pricing of the products in one's own category. The high or low pricing basically determines the average order value. In theory, the average order value will only fluctuate within a certain range above or below the product pricing (under normal circumstances), which is somewhat similar to the law of value in market economics, with the exception of special categories such as snacks, fasteners, and other products that customers usually buy in multiples.

2. Promotional offers

During large-scale promotions, the average order value depends on the strength of the discount (coupons, discount flash sales, gift rebates). At the same time, the setting of the minimum consumption standard for free shipping based on the strength of the discount also has an important impact on the average order value. For example: During Double Eleven , a certain store set the minimum consumption standard for free shipping at 199, which means that shipping is free only if the purchase is over 199. This setting is a good way to encourage customers to choose to buy multiple items in a package when the promotion discount is large (of course, it still depends on the attractiveness of your product). At this time, the average customer spending will be much higher than usual.

3. Is the cross-recommendation reasonable?

This is an indirect influencing factor. The definition of cross-recommendation (different platforms have different views) is as follows. Taking Taobao as an example, generally on the product details page, the store will generally recommend purchasing a certain package and at the same time add picture links of other products on the product details page. This mutual link is the most original definition of cross-recommendation. In terms of traffic, we call it mutual diversion (don’t waste it). Now, based on big data algorithms (browsing history*category association*focus*others), there are ubiquitous recommendations for related products on the home page, middle page, search list page, details page, shopping cart page, and order details page. Amazon’s once awesome recommendation algorithm has now been slowly replaced by Alibaba (I was inspired by this when I was browsing the Taobao app recently).

4. Others

Category attributes (mentioned earlier) Different category attributes have different average order values. For example, when shopping on the Internet, would you rather buy just one piece of clothing or a bag of potato chips, excluding the impact of the minimum shipping fee? Here you are not asked to choose which one to buy but the quantity of items to buy. Normal consumers usually buy one or more pieces of clothing, rather than just one bag of potato chips (of course they will also check to see if there are other bags or buy multiple bags of potato chips). For low-priced goods, the time cost and operation cost of consumers' online consumption will certainly not be just to buy one low-priced goods (the normal market price of the goods). This is the same as when we go to the supermarket. In most cases, we don’t go to the supermarket just to buy a bottle of water. Therefore, the factors affecting average order value mentioned here are actually closely related to the number of items in the shopping cart. The trick to increasing average order value and sales is to increase the number of items in a single customer's shopping cart and the number of items in a single order. (The same applies to luxury goods). Of course, cultivating users’ consumption preferences is also a way to increase average order value. For example, this is what Xiaomi has been doing in recent years.

The above influencing factors are all based on the premise that the quality of the goods is guaranteed and the goods are not sold at a price significantly lower than the market price. Can be established.

Is it better to have a higher average order value or a lower average order value?

So the question is, is it better to have a higher average order value or a lower one? Let's look at the sales formula:

GMV = traffic volume * conversion rate * average order value

There is a close connection between these three.

1. If the traffic conversion rate remains unchanged and the average order value increases, then GMV will also increase with the increase in the average order value.

Based on this result, we can infer and analyze the cause: the growth of GMV is closely related to the number of products in the shopping cart of a single customer. When there is no obvious change in traffic conversion rate, influencing factors such as promotional discounts can be eliminated. The most direct possibility is that the product line has produced a hot-selling product with a higher average order value, or has produced multiple low-priced hot-selling products that buyers add to their shopping carts at the same time.

2. If traffic increases, conversion rate increases and average order value decreases.

This is definitely related to your promotional activities or marketing tools . The discount strength is an important factor in attracting traffic and forming conversions. I will not analyze the rest of the permutations and combinations one by one, you can use your brains to brainstorm. Now I will answer the question at the beginning of the paragraph: is it better if the average order value is higher or lower? The answer is the higher the better. Why do I say this? Because when we analyze the average order value, the sales results have actually come out. In other words, analyzing the average order value itself is a result of the analysis. The ultimate responsibility of operations is to continuously improve the values ​​of the three variables in the GMV calculation formula. Thereby driving up sales. Content, community, traffic, on-site page optimization, and products all revolve around this, without exception. (Please forgive me for giving such a sudden and direct reason)

Pricing Method

Product pricing is an important indicator and component that determines your average order value. Here are some pricing methods (personal preference)

1. Profit Control Pricing

This is also the simplest and most crude pricing method and the most commonly used. There is no technical content. Profit = pricing - cost. Some people will say that this formula, which even junior high school students know, is a lie. In fact, there is a variable here that you cannot control quickly, and that is profit. If you cannot determine the profit, there is no way to talk about pricing. Experience tells us that the quickest way to estimate profits is to directly use the average market price. When your product does not have a clear advantage, let alone a good marketing plan or promotion, the fastest way to enter the market is of course to have a low unit price and low profit. What needs to be done at this time is cost control and marketing promotion .

2. Strategic Target Pricing

Generally, large enterprises will formulate a full-year sales plan and tasks, which include GMV and GP. At this time, different category goals can be used to formulate different pricing strategies. GMV=cost+GP. First of all, the cost is fixed for a period of time, so what needs to be adjusted is the net profit GP. At this time, the formula is cost+GP=traffic*uv to effective order conversion rate*average order price=number of effective orders*average order price. At this time, we only need to know the average order price in the previous period of time and the estimated growth of traffic and conversion rate to determine the GP target value. Finally, (cost+GP target)/number of transactions = pricing. Of course, this algorithm can only be applied to category-level pricing ranges and single-site product pricing with relatively simple products.

3. Upper limit quantitative pricing method

• Let me give you an example here so you can understand this logic. For example, Qidian Academy wants to set a price for a paid teaching video. How should it be set? The method of upper limit quantitative pricing is as follows:
• First, determine the highest price you can give for a perfect paid tutorial video (refer to similar platforms)
• Second, the relevant characteristics of the video author are broken down and assigned weighted scores, such as: popularity, authority, video content quality, effective content video length, historical video ratings (if any), etc.

I assigned 20 points to each of these five dimensions, with a total score of 100. Someone may ask, how to measure popularity and authority? In fact, this can be broken down into quantifiable dimensions. Popularity can be measured by the number of fans, the number of people who watched previous videos, etc. Authority can be measured by the number of articles by the author and the rate of positive reviews. Attach the score and you will get a score in the range of 0-100. Multiply it by the percentage and you get the price of the video. Of course, the score of this dimension is adjusted dynamically. Based on the subsequent customer feedback, the pricing will be adjusted accordingly. This pricing method is to confirm a maximum price and then break it down into quantifiable dimensions and attach weights. It's very simple, right?