An Enterprise-level Data Empowerment System That Features a Closed Loop, Accumulation, and Sustainability

Developing the Enterprise-level Data Empowerment System

Four Key Steps

Purpose of Developing This System

The Umeng DataBank

U-SDC

  • Tracking management: Tracking in the big data field is difficult, and few want to get their hands dirty doing it. Often, it’s the case that, when a product is launched and you need to have usage data, the tracking is all too rushed. Also, many companies lack people who understand how data tracking actually works. They do not know what types of tracking are appropriate, when they are appropriate, and which are defective. This is the case for many companies. And this kind of problem can have a significant impact on company operations. If a company does not understand its own tracking problems and continues to operate with incorrect data for a long time, the impact will only be amplified over time.
  • Intelligent tracking solution recommendations: Assume the following, a company in the video industry has two teams, each responsible for different live broadcast channel businesses, and both teams define some tracking specifications for the company. However, the data specifications of both teams are inconsistent. When video playback starts, team A defines the global tracking parameter as Play, which indicates the playback start event, while team B defines it as Start. In addition, both teams are not aware that their definitions are inconsistent. This problem may not seem serious, but it means that the company’s data will be unsustainable. This problem cannot be solved with any tools. A company’s data management must be based on a deep understanding of the industry, which will allow the company to define consistent standards and specifications for business scenarios. Umeng+ solves user problems through more standardized scenarios, including providing standard tracking solution recommendations for different industries. Umeng+ aggregates the practices of many excellent enterprises to tell users how to track data and what scenario problems can be solved after tracking. At the same time, Umeng+ provides various intelligent recommendations for tracking and collects the knowledge maps of the company’s scenario-based tracking solution to assist the relevant technical teams.
  • Intelligent tracking and verification: Developers use SDK code to perform tacking, report data, and print logs from the backend. However, this does not mean that data reporting marks the end of tracking. If you directly place a startup log on the logon page, you may find that, one day, the number of logons is nearly twice the number of page visits. This is because the tracking point is also placed in the loading process of the logout page. In this case, you have accidentally tracked a single event at two locations. Umeng+ hopes to provide various intelligent verification tools. For example, a service will be provided to developers that does the following. If a tracking point is named “start”, a series of intelligent checks are performed to verify that the pages from which the tracking point is reported are correct for the business scenario. Intelligent tracking and verification testing are very important. Umeng+ verifies the correctness of tracking through visual screenshot computing, greatly reducing work costs and the pressure on the technical team.
  • One-click checks of tracking health: When all tracking points are completed, the company needs to verify the tracking health, check whether the tracking points comply with their specifications, and detect anomalies. The degree of tracking health determines the accuracy of a company’s data collection. Data teams and client development teams often encounter conflicts due to tracking problems. When the data team believes that the data is problematic, they generally blame it on a tracking problem, but the development team sees it as a failure of cooperation on the part of the data team. The KPIs of tracking call for tracking be visualized first, so we can see who placed the tracking points, whether there is a problem with operations, and whether tracking complies with relevant specifications. This can tell us whether the team should be held responsible when tracking does not fully comply with the specifications. Therefore, from the management, organization, and product capability perspectives, it is necessary to solve the core problems that impact company data tracking and collection.

U-CDP

  • Clear understanding of user assets: U-CDP allows multi-source data to be connected to platforms with one click. This data can come from mobile clients or other clients, servers, and other sources. U-CDP ensures trust identification and multi-terminal normalization. It helps users normalize and purify data, filter junk data, and defend against frauds through global data identification. After identifying and interconnecting data, we can achieve user asset visualization. This clarifies the company touchpoint data sources and shows us the accumulation of private domain users. After clarifying the preceding issues, we can determine the touchpoints that need to be added or enhanced. In the end, this allows us to accumulate our own private domain data assets. One premise of accumulated private domain user assets is that they must be operational. If they are unoperational and invisible, the data is useless.
  • For a user tag management library, configuration means production: Business teams are always dissatisfied with technical teams. When the operation team wants to hold an activity, they need to prepare materials according to the business scenario and prepare the activity page. In addition, they must select a group of target users based on rules and then perform operations for them. To meet these needs, they must first work with the project manager to propose requirements. Then, the product manager communicates with the algorithm and technical teams and writes a product requirements document (PRD). Next, the operation team waits a few days for the activity to be developed and launched. The process often takes a long time and cannot meet the needs of the operation team for quick iteration, quick trial and error, and quick customer operations. The requirements of the operation team are usually not very complicated. For example, the operation team may only want to see the users who have accessed the app and mini program in the last 30 days and the people hit by advertisements over the past 2 days. However, many enterprises face difficult technical schedules.
  • Preset analysis models and custom report structures: The operation team only needs to use preset analysis models and various union, intersection, and difference combinations to obtain various insights. After formulating insights, they can generate and save their own user group package and reuse it for the operations and activities of each business. After the tracking for custom private domain user segments is completed, the MaxCompute data warehouse solution can be implemented in Umeng Cloud to automatically aggregate the behavior of individual users who use multiple terminals each day, automatically generate user archive sequences, and complete automatic configuration. As long as the tracking is correct, the operation team can immediately complete private domain group segmentation. Umeng+ hopes to apply the preceding lightweight solution to solve various support problems in actual production.
  • Multiple combination modes for filtering target users: Assume the following scenario. A building material company has a website and initially contacted customers through the website and a messaging account. Later, the company developed an app and mini program. In this case, a single customer may appear in all three areas at the same time. When this problem occurs, the data is not interoperable and the organization must operate these areas separately. In fact, the essential problem is whether mini program customers can be quickly found through the app and whether the client can deliver ads, perform operations, and return data. Umeng+ combines multiple modes to help the operation team find the right users without being tied to a schedule.
  • Multi-channel outreach and interaction effect tracking: U-CDP supports multiple channels, including SMS, EDM, and app messages. All operation effects are visible in real time. In essence, U-CDP works with technical teams to empower business teams by solving problems of efficiency and enhancing their operational capabilities, and accumulating user data assets.

U-DOP

  • One-click data packet subscriptions and return: As shown below, Umeng Cloud collection helps customers quickly collect data from different platforms, such as mobile clients or other clients, and servers. If customers perform this process by themselves, it will take a long time to complete it and the ultimate effect cannot be guaranteed. Based on UMID connection capabilities, multiple accounts and multiple terminals are normalized, supporting data interconnection between different terminals. Umeng+ helps customers process and generate different data packets. As long as customers use SDKs, data packets are automatically generated and data is automatically transferred to MaxCompute. Then, DataWorks, DataV, and QuickBI can be used to integrate this data with customer data, significantly reducing costs. In this way, the customer no longer uses raw data, but data that has been processed by Umeng+. Then, instead of concerning themselves with raw data, customers can focus on business product development, business scenario empowerment, and business innovation.
  • Quick BI intelligent data analysis and presentation: The following figure shows a customer’s smart data analysis presentation when Umeng+ and Quick BI are used. After data integration and return, business personnel can use MaxCompute and Quick BI to perform self-service analysis, including drag-and-drop self-service analysis and online table analysis. This greatly improves the efficiency of the analyst team as they no longer need to do the time-consuming work of merging different data.

Summary

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