In recent years, several home appliance manufacturers are fighting price wars. Following last year's "Double 1 1 Promotion" and "June 18 Carnival", large-scale promotion activities with price as the main appeal in e-commerce have emerged one after another, and almost all festivals that can be used to create momentum have been used. As the weak in this game, consumers are constantly teased and guided by these true and false price wars. However, in today's shopping malls, there is another kind of enterprise that won the business war not through a simple and rude price war, but through the full use and mining of data.

The most typical example is Amazon (Amazon.com), the founder of global e-commerce. Since 1995 first sold books online, Amazon has completely subverted the market rules and competitive relations of many industries starting from the book industry with lightning speed. In 10 years, many century-old shops like Borders and Barnes and Noble were forced to go bankrupt or on the verge of bankruptcy. The fundamental reason for Amazon's success in the less profitable book industry competition lies in its strategic understanding and application of data. When people still don't quite understand what e-commerce is, Amazon has obtained unprecedented rich user behavior information that traditional stores can't match through the Internet, and conducted in-depth analysis and mining.

What is "user behavior information"? Simply put, it refers to all the behaviors of users on the website, such as searching, browsing, rating, commenting, adding shopping baskets, taking out shopping baskets, adding wish lists, purchasing, using discount coupons, and returning goods. Even related behaviors on third-party websites, such as comparing prices, watching related reviews, participating in discussions, communicating on social media, interacting with friends, etc.

Compared with the information related to the final transaction that stores can usually collect, such as purchases, returns, discounts, coupons, etc. The outstanding feature of e-commerce is that it can collect a lot of customer behavior information before buying, rather than the transaction information collected by stores.

In the field of e-commerce, the amount of user behavior information is unimaginable. According to the incomplete statistics of companies focusing on user behavior analysis in e-commerce industry, a user has to browse 5 websites and 36 pages on average before choosing a product, and has dozens of interactions on social media and search engines. If all the collected data are integrated and deduced, a user's purchase may be affected by thousands of behavioral dimensions. For a medium-sized e-commerce company with a daily average of nearly one million PU, this represents the active data of nearly 1tb a day. From the perspective of the whole China e-commerce, it means thousands of TB of active data every day.

It is these pre-purchase behavior information that can profoundly reflect the purchase psychology and purchase intention of potential customers. For example, customer A browsed five TV sets in succession, including four domestic brands S and 1 foreign brands T; 4 models are LED technology, and 1 model is LCD technology; The five prices are 4,599 yuan, 5 199 yuan, 5,499 yuan, 5,999 yuan and 7,999 yuan respectively; These behaviors reflect the brand recognition and tendency of customer A to some extent, such as favoring domestic brands and medium-priced LED TVs. Customer B browsed 6 TV sets continuously, including 2 foreign brands T, 2 foreign brands V and 2 domestic brands S; 4 models are LED technology and 2 models are LCD technology; The six prices are 5999 yuan, 7999 yuan, 8300 yuan, 9200 yuan, 9999 yuan, 1 1050 yuan respectively; Similarly, these behaviors also reflect the brand recognition and tendency of customer B to some extent, such as favoring imported brands and high-priced LED TVs.

Through the analysis and understanding of these behavioral information, Amazon makes intimate service and personalized recommendation to customers. For example, when a customer browses a variety of TV sets without making a purchase, within a certain period of time, he actively sends the customer promotional information of another TV set suitable for the customer's brand, price and model by e-mail; For another example, when customers go back to the website to browse the refrigerator again, they can recommend domestic medium-priced refrigerators to customer A and imported high-priced goods to customer B.

Such personalized recommendation service often has a very good effect, which can not only improve the customer's purchase intention, shorten the purchase path and time, but also capture the customer's best purchase impulse at a more appropriate time, reduce the unreasonable harassment of customers by traditional marketing methods, and enhance the user experience. It is a good means to achieve multiple goals.

Looking at the successful e-commerce companies at home and abroad, they have invested a lot of money in the analysis and utilization of user behavior information. Their high understanding and application of data strategy is worth learning and learning from domestic e-commerce.

The embarrassment of group buying

I believe that many people, like me, are filled with such emails every day, but they never open them.

Many group buying companies devoutly send all kinds of promotional information to subscribers every day. Frankly speaking, many of them are really favorable and attractive, but these group buying companies ignore one thing-user experience.

Let's look at one or two group buying emails and analyze the problems inside:

1, food recommendation is not geographical: an email, from Wudaokou to Wangfujing, from Suzhou Bridge to Guijie, someone may be interested in a group purchase with a discount of less than 30%, but will it really open from the East Fifth Ring Road to the West Fourth Ring Road?

2, the recommendation of entertainment has similar problems, without considering the user's geographical location, price and other factors.

What's more, regardless of the gender of the recipient, have you considered the possible embarrassment?

In fact, these phenomena show that the current group buying websites are basically rough in EDM thinking, treating all customers as one person, completely giving up the advantages of e-commerce and returning to the stage of traditional retail stores and mail sales models. Not only that, in fact, in the e-commerce environment, such EDM is sometimes worse than nothing, because they just give these people who may become their customers a very negative user experience, occupying users' mailboxes with long-term irrelevant emails and occupying a lot of space. In the long run, they are only one step away from "hate" ... and their punishment may be simple and cruel. With a click of the mouse, their e-mail addresses will be sent to the junk e-mail box, so that they can no longer.

If you look at your users' spam, what do you think when you see that you have worked hard to design and promote direct mail and report it regularly and completely in spam? According to the analysis of professionals, for an e-commerce company with 5 million members, every 0.5% unsubscribe or put in spam mail means that the marketing expenses of nearly 6,543.8+0,000 yuan hit Shui Piao hard.

So, what should the group buying website do? Personalization is the most basic and effective method:

1, multi-dimensional analysis of customers: establish an analysis dimension based on human attributes such as user's region, gender and age, and filter all group purchase information, so that the relevance of simple EDM can be greatly improved. At least, the emails received by users are basically near their homes and workplaces, which is related to their general attributes, and there may be some interesting goods.

2. Quantify the customer's past clicks, purchases, the value of purchased products, the frequency of purchases, and the time of the last purchase, so as to generate customer value scores, classify customers into different values, and evaluate the difficulty of accepting recommendations. According to these scores, we can decide the frequency of EDM operation for customers and the segmentation of recommended products, thus improving the feedback rate.

3. Analyze the purchase records of customers who have purchased and clicked on the goods, and score, count and classify the discount ratio, original price, discount amount, group purchase time, whether it can be refunded, whether it is used by a single person, taste (catering) and style. , so as to "predict" customers' possible points of interest. This is a relatively advanced and complicated process, but if used well,

4. Consider adding a "don't like" button next to all recommended products. Collecting what customers don't like is almost as important as the products they like for personalized recommendation. Suppose a customer tells you that he doesn't like lunch for two in a western restaurant in 49 yuan, which may give you more information than he clicks or even buys a daily meal for two in 99 yuan.

For example, I observed a successful case: a travel website tracked customers' previous browsing and searching behaviors and made effective recommendations. Note: This proposal reflects customers' needs in many aspects, such as destination, price and travel demand. According to these methods, companies specializing in EDM optimization in the industry can increase the click-through rate of EDM from about 1% in the traditional sense to nearly 10%, and 5000 edms generate 370 people with 5 10 clicks, and finally * * * generates 800 orders.

Therefore, no matter from what point of view, e-commerce and group buying still have a lot of room for optimization. I believe that personalized marketing with big data as the core is a sword to help e-commerce win the battle in this Red Sea War.

The above is what Bian Xiao shared for you about the application of big data "user behavior analysis" in group buying websites. For more information, you can pay attention to Global Ivy and share more dry goods.