Application of AI Interactive Recommendation System in the Maternal and Infant E-commerce Industry

E-commerce in the maternal and infant industry

Staff allocation, data analysis, user profile, and life cycle management are all common problems in e-commerce. To this day, the dividends of consumer traffic in e-commerce have basically disappeared. Businesses want to continue to grow, they must carry out fine management, and increase the repurchase rate through accurate and personalized services, which is to dig deep into the long tail of users’ consumption behavior.

E-commerce in the maternal and infant industry has a large number of consumers — strong purchasing power and interaction demand lead to data sensitivity and high complexity. In addition to dialogue and recommendation systems, knowledge graphs are also one of the thresholds in this industry as they require not only technical support but also business experience and long-time engagement.

E-commerce pre-sales guide in the maternal and infant industry

Many online retailers are very clear about their problems, but they are not particularly aware of the common problems in the industry. “AI Application Consultants” should understand the situation of each company, conduct in-depth research and analysis of common problems in the industry, and then provide suggested solutions according to the different situations.

Some phenomena are learned through data and models, such as 10–15% of customer inquiries from the early morning(0 am) to 9 am. This group doesn’t get contacted in time as the customer service usually goes to work at 9 in the morning and leaves at 12 in the evening. Today, when the consumer traffic dividend has disappeared, even 5% is not a small amount for e-commerce. And customers who come to consult in the middle of the night tend to have a strong buying intent.

For this situation, there are generally two solutions. One is to reschedule customer service at 6:30 in advance. But it is unrealistic. Moreover, it will increase the cost. The other one is to use AI to do the job.

AI interactive recommendation system

This system uses multiple rounds of human-computer interaction to understand the user’s intentions and then make accurate and effective recommendations. It can also be simply understood as an “enhanced robot”. Currently, the robot system on the market adopts the “QA robot + search” technical implementation method. This method is characterized by single-round dialogue and passive question and answer, which can only solve part of the problems from consumers. And a large number of problems still require help from human customer service.

Understanding consumers’ intentions and recommending products to consumers based on this is of greater significance, as making money is always more important than saving money. On one hand, AI companies use technology to find a better solution from the history data; on the other hand, the mechanisms such as active guidance designed by humans are added, so that robots are no longer as rigid as keyword responses.

The characteristic of consumers is that as long as they can talk to real people, they will not talk to machines. What AI companies have to do is to make this gap closer, even if we are chatting with a robot, we don’t feel the difference. But it does not mean that robots are able to solve all problems. We hope that robots can better collaborate with people.

Customized dataset

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

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Source: https://zhuanlan.zhihu.com/p/30865781