On May 28, the second offline data business ecology summit forum of the 2019 Digital Expo was successfully held in Guiyang International Ecological Conference Center, hosted by the Organizing Committee of the China International Big Data Industry Expo and hosted by Zhongmeng Data, the guests at the forum shared the innovative achievements of offline data, discussed the new business way of offline data to empower the real economy, and jointly helped enterprises achieve intelligent business transformation and upgrading, so that more enterprises can catch the train of the era of intelligent business.
Jin Jianhua, founder and CEO of Love Analytics
Jin Jianhua, founder and CEO of Love Analytics, attended the forum and shared the keynote speech of "Industry Empowerment Practice and Trend of Offline Data Intelligence", and the core views are as follows:
1. After ten years of development, the big data industry has begun to enter a new era of data intelligence, and the value of data has been extended from monitoring and insight to the decision-making stage;
2. With the disappearance of online traffic dividends and the transformation of traditional industries to embrace "Internet +", the value of offline data intelligence has become increasingly prominent, and has become a key force in empowering industrial upgrading;
3. Technology-driven offline data infrastructure continues to improve, and future application scenarios increasingly become core barriers;
The following is the transcript of the speech of Jin Jianhua, founder and CEO of Love Analytics:
Good morning, everyone!
As a partner of Zhongmeng, on behalf of Ai Analytics, we are honored to share our research on the trend and application of offline data intelligence industry.
Haitao mentioned Internet anxiety, which was an anxiety disorder that had an impact on everyone in the offline industry two or three months ago. Haitao just mentioned a number, called the total retail sales of social consumer goods, so far, the proportion of online is only 24%, 76% is still offline real economy, so there is nothing to worry about. We can see that although there will be about 4 points of decline every year, after all, people live in the physical world and cannot be separated from the entity, so the real opportunities in the future must be offline.
There are two main points in today's talk: First, we put offline big data in the big track of data intelligence for research and judgment, including future trends and application scenarios. Second, offline data has its own distinctive characteristics, whether it is based on offline scenes or the application of offline big data, what value is created.
Love Analysis is a research institution focusing on scientific and technological innovation and industrial upgrading, the main areas we focus on are data intelligence, artificial intelligence, and new technologies such as blockchain and 5G.
We put offline data into the stage of big data development to judge, data itself is a new energy, we can compare, how the oil of that year was mined, transported, refined, processed into finished products and applications, how to enter daily life. The logic of data is the same as that of oil energy.
The data expo began in 2015, and in the first two years of 2015, the first stage of big data is collection, and there is no way to apply without data, so data collection is the first stage of data development and the most basic format of big data. After two years, it entered the data monitoring stage, and the core application was the large-screen application in the government affairs system or the application in the commercial field. After two years, we enter the data insight stage, mainly using data to do some better analysis, which we call big data analysis. In the big data insight stage, we see the gradual development of emerging formats.
In 2019, completely different from the past, we believe that after big data + artificial intelligence technology, it has become a data intelligence stage, which is a new stage. This stage is the stage where data directly drives decision-making. What is the concept of decision-making? For example, sometimes you will receive some intelligent customer service calls, that is, calls made through machines, and the voice synthesized by the machine is very similar to the human voice, and it is impossible to identify whether it is a human or a machine. Speech recognition is just a front-end application, it can call you directly through the monitoring of your user portrait and behavior, the process of calling is data-driven, is the process of the machine actively making decisions to call you, rather than making decisions through manual means and then calling you. This approach reduces the operating costs of many companies, which is the value of data directly driving decision-making.
After entering the decision-making stage, there will be a stage of reshaping in the future. Regarding the business reshaping of big data, you can simply imagine the future scenario, such as autonomous driving. l1-L3 is a scene of manual intervention, and l4-l5 basically no longer needs manual intervention. After data intelligence penetrates into the industrial chain, many of the original traditional industrial chain entities in automobile travel will gradually disappear, because the application of big data will reshape the entire industry, and the business model will change dramatically. In the process, we see that big data changes a lot every two years or so.
From this point of view, the change of big data to the business is divided into two core stages: one is business data, the other is business intelligence, 2019 has entered the stage of business intelligence, how to drive decision-making through business data.
How do we think about offline data, it is a depression in the field of big data, what opportunities will emerge? The application scenario of offline big data is the real economy, and the most direct scenario is that the consumers of the real economy have undergone tremendous changes. When the smart terminal is not popular, everyone takes the vegetable basket to the supermarket to shop. Now the post-80s are almost 40 years old, will deeply try the application of mobile terminals, resulting in offline entities of the customer base is not only young, but mobile or mobile intelligent, this group of people themselves have a strong habit of using mobile terminals.
This will lead to the problems that offline physical stores have to solve, one is how to understand the role and form of consumers, what kind of behavior, what kind of preferences they have, we boil down to the user portrait, that is, the problem of who the consumer is. The second is not only to understand him, but also to find a way to reach him, through online and offline channels, through lbs data to better reach him, after reaching the conversion. From the perspective of the real economy, it is better to reach consumers and recognize the core demands of consumers, so as to improve the ability to obtain customers and the efficiency of marketing investment.
From the perspective of suppliers, or from the perspective of data service providers, it is mainly new technologies and industrial integration, so the data middle office, technology middle office and business middle office are generated, and the better use of these data is equivalent to the refinery and processing link in the oil. With the development of big data, technology service providers have the ability to fuse data into solving enterprise customer problems.
The total retail sales of social consumer goods, in 2018, is 38 trillion, offline accounted for 76%, which is a large proportion, people are still willing to spend in the real economy.
How to make better business decisions through offline data? Its development logic is the same as the data intelligence logic or the big data development logic, and we believe that the value of offline data is huge from two aspects. The first is the demand for offline entities, the demand for 76% of the retail sales of social consumer goods; the second is the ability to collect and apply data.
For example, belles are known to sell shoes and there are thousands of retail stores. Some time ago, Hillhouse acquired it, but in fact, it acquired data assets. Retail terminals include offline physical stores, which use data and technology to improve overall sales capabilities and customer service capabilities. There is a particularly simple case, everyone will try on shoes when visiting the shoe store, in the process of trying on shoes, the shoes are equipped with a data collection sensor to collect customer try-on data. After that, the model is used to optimize and identify the user's personal preferences, including what kind of shoes he likes to wear, what type of shoes he likes, and how large the size is. After these data are integrated with online data, the final effect is that the conversion rate of the original interview wear in a physical store is 3%, and the shoe style is adjusted after the data is polished, and the trial conversion rate can reach 20%, and the efficiency of 6 times is improved. What is this concept? Spending 10 million in the past can bring 20 million in revenue; now spending 10 million can bring 120 million in revenue. This is the value of data to the retail industry, calculating output from your input. Because of the use of new offline intelligent technology, how much reward can it bring you in the end. Such things will happen in many offline physical formats.
From the perspective of the industry map, there are special scenes and technologies for offline collection, data obtained by wireless sensors and data from cameras, which is a more mainstream way. After obtaining data, not only the offline data itself can be better applied, the real value lies in the offline data acquisition, combined with the relatively rich and systematic data online, to better form a data middle platform, based on the business middle office and the data middle office to better achieve commercial value, which is the value of offline data.
From the perspective of offline big data acquisition, technology has changed the infrastructure of the real economy, from no data to the ability to collect data to finally be combined with online data, better feedback to specific offline scenarios, and achieve commercial value. It can be imagined that the commercialization of 5G that began in the second half of this year will have a huge impact on the offline real economy. You can see a lot of advertising about 5G commercialization, which is really shocking. 5G is what we are particularly looking forward to, how it can transform or improve the infrastructure of offline scenes, which is the tipping point of the future.
All data intelligence, whether online or offline, the ultimate competitive barrier must be based on the ability to apply the scene. Looking back at online data, online data after maturity is no longer a barrier to a technology service provider. Therefore, offline data will continue to be a very important barrier in the next 2-3 years, but in another 3-5 years, the barriers to offline data sources will gradually decrease. In the end, as an offline intelligent service provider, it is ultimately necessary to better realize the value of customers in customer application scenarios.
Therefore, we believe that from the perspective of the development trend of data intelligence, after the data middle office matures, combining the technical middle office and the business middle office and deepening into the business scenario is the most important point for establishing barriers. This point is not just that there is data, how to better support the technology of the product, this is all integrated. For example, in the data collection link, many places will be applied to computer vision technology and iot technology, and the application of knowledge graph can better distinguish between customer needs and customer portraits in different scenarios. Therefore, the fusion of technology and data must eventually go deep into the scene, which is the first point.
Second, the scene can be extended. Different industries, from retail to tourism, to travel, to finance further afield. Different application scenarios, because of your ability to understand the scene, you will have a unique data. From this point of view, from the perspective of the current role of offline data of Zhongmeng Data, the next 2-3 years can better establish barriers in the offline data industry.
Application scenarios are large and small, we consider from three perspectives: one is whether the industry technology is mature, the second is whether the business scenario is mature, and the third is how large the market size is. At present, the offline scenes of government affairs, retail, tourism and real estate, and education that can be seen should be a very important area of offline data intelligence. As a technology service provider, from retail to tourism, the ability to extend the application scenario depends entirely on the ability to obtain data and the ability of the application to serve offline after maturity.
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