Data analysis improves e-commerce conversion rate

The average time spent on online shopping was reduced from 20 minutes to 17 minutes compared with June of last year's June. On the other hand, when customers stay on the site less time, the conversion rate of most of the electricity quotient is only about 0.5%.

Today, with more and more distractions, 99.5% of customers are losing, and how the electric business is going to understand the characteristics of this group of customers ' shopping behavior and turn it into an order quantity.

Dilemma: Customer stay time is decreasing

Time is a scarce resource

For the electrical business, the time per capita browsing the web is becoming scarce competitive resources.

&http://www.aliyun.com/zixun/aggregation/37954.html ">nbsp;

Figure II shows that the number of people covered daily, shopping sites (including Taobao) traffic growth is 68%, but the per capita online browsing time (in the electricity quotient side) reduced by 16%. The time for online shopping, compared to June of the year and June of this year, was reduced from 20 minutes to 17 minutes.

We looked at each website in detail (see figure III) and found the same thing: Jing-dong, excellent, when, every guest, Dream Bazaar, these several representative of the customer, we found that most of the traffic is increasing, but if we look at these sites per capita on the day browsing time, Beijing East last year is about 10 minutes, This year is only about 8 minutes. So, this is because the site is more efficient to find things, so browsing the site less time, or other reasons?

In fact, we can use other data mining, in the end is the effectiveness of the site is small, or the total time is less? I think one of the things that really matters is what each site does in 8 minutes after getting a customer in.

The electric business's eyeball economy is only 17 minutes, which is the average, which means that every netizen will stay for 17 minutes on E-commerce site. Taobao Mall, Jingdong Mall, if we really take their time to browse away, you will find other sites to get the traffic will be very small.

While users stay on the site of effective shopping time to reduce, while the conversion rate of the electricity quotient is generally not very high.

From access to the shopping cart, on average, 100 people come in, only 4.5 people put things in the shopping cart, 96 people do not put things into the shopping cart, then 96 people do?

In addition, we can see that Jingdong Mall under the single to online payment of the percentage is 29.4%, where the customer prudential goods is 29%, a shop is 8.3%.

Chasing the loss of customer shopping behavior characteristics

Let's look at the data in Figure five first.

Figure five, the blue portion of this data shows 35%, is that only 35% of people came today, bought today, 65% of the people came and bought today. Here 65% is about new customers, not old customers, new customers came to this site today, bought today. From the bottom up to the second red, is yesterday, bought today's customers, Green is 2-6 days to come, buy today's customers, the highest orange is 21 days ago, bought today's customers. Of course, this data, each industry is different, not exactly the same.

From the data we can find that the customer from the access page to the final payment, the time spent is not the same. Some users are the first World single, separated one weeks before payment. In particular, some non-standard, no brand products, consumer parity is widespread, resulting from access to the next purchase time longer. (I interviewed some of the domestic electricity dealers, the data is basically consistent but the percentages are different.) )

So, electric business people will find that the day came to the site can not fully use the funnel (Figure VI) to see, because he did not want to buy your things before, he just came to see if the product is cheap or expensive. In the face of such customers, you need to know what they did after they got to the site.

First of all, the Web site can ask the customer before the order to browse which pages and products, his browsing history is very important.

Second, be aware of the customers who are browsing the site, which are explicitly to buy things, which are just casual, and what entrance they enter;

Third, did not buy the user, in the end saw how many product pages, how many put into the shopping cart did not pay, how many is a product page did not look;

Four, how many customers put the product into the shopping cart from the genius payment.

In addition, it is very important that customers login to the homepage of the site, in addition to a 40% pop-up rate, the remaining 60% of users from the search, sorting shopping and guide shopping channels into, as the electrical business, should understand them from which channel into the product page, three channels after the proportion of payment is how much, Find out where the problem lies.

This idea and the overall structure of the site related to the current domestic concern is relatively small, but first can try to use this idea to see the existing problems.

Finally, the most wanted to tell the reader is that, with these simple methods, you can know that the consumers do not pay the shopping behavior, only understand their shopping behavior characteristics, can let this slip 99.5% of the user to generate payment, thereby enhancing the site conversion rate.