Editor's introduction: Now all walks of life have begun to use labels to build scenarios, and the essence of labels is to serve business scenarios. But for car companies, it is difficult to build a customer labeling system, let's take a look at the author's analysis.
Recently, the digital transformation of car companies is in full swing, and major car companies have first invested heavily in the construction of the CDP platform for customer data life cycle management. Open up from the delivery system, lead management platform, sales follow-up assistant, customer order system, after-sales service system, membership system, mall system and so on throughout the customer's entire life cycle of related systems. Then the data system structure based on global access (the essence of labeling is the dataization of the business). In theory, this approach is logical. However, from the perspective of the actual construction of car companies, it is full of difficulties:
First of all, these systems belong to multiple departments of car companies, involving sales department, after-sales department, marketing department, etc., cross-departmental communication costs are high, and the implementation cycle is long;
Secondly, the system construction of automobile enterprises has a large time span, involving a large amount of historical data, and the construction of unified data standards for each system is lacking. For a simple example, in addition to not having a customer OneID or customer master data system, even the model name is inconsistent in the sales system and the after-sales system name. It brings a huge workload to the data opening work.
Again, the construction of different suppliers of each system has great differences in the technology used, and the data synchronization ability of each system is also very different. Early construction of the system, in the data in the near real-time synchronization to achieve exceptionally difficult. The difference in technology has greatly increased the technical difficulty of opening up the system.
Although many car companies have implemented early data lake projects, now CDP projects. At present, the author does not see a car company can solve the customer data in each system to achieve real-time or quasi-real-time access. Even so, in fact, this does not mean that the labeling system cannot be built. The construction of the label system has two core key points: scenario-based and real-time.
The core of label construction is to use it as a scenario. The essence of labeling is to serve business scenarios. Business scenarios can form a small closed loop and label construction can be carried out. For the current situation of the use of labels by automobile sales consultants, the most used label scenario is that the consultant uses the personal WeChat "set notes and labels" function, of which the notes are used most frequently, such as "Mr. Zhang XX model", because the customer's WeChat name is difficult to identify the customer. Secondly, labels, phone numbers, and descriptions are used to supplement customer information. This is the scene of following up the customer in the personal WeChat scene.
We return to the essence of the label is the dataization of the business, in which this label needs to really help the consultant identify the customer, communicate with the customer, circle the customer, and the label that can be used for the business is the valuable label. Personal WeChat solves the basic function application of labels, as well as the convenience of label applications. However, there are also obvious deficiencies, these labels are manually labeled by consultants, and the channels for obtaining information are derived from the communication of the consultants themselves, which has great limitations.
Tencent's official enterprise WeChat automotive industry version, aiming at the pain points of the automotive industry business, the application of customer tag scenarios aims to be efficient and convenient. The industry scenarios have been upgraded in the native functions of personal WeChat and enterprise WeChat. Especially in the label, customer behavior label (dynamic radar) these two functions, sales consultants generally feedback these two functions are very practical.
The label section is divided into three parts, personal label, enterprise label and automatic label. Among them, the enterprise label, the background provides the overall configuration of the enterprise label, which can directly access the label system built in the construction of the CDP project of the main engine factory, and can be directly connected with the CDP label system to achieve CDP and enterprise WeChat data exchange. Let the underlying digital construction directly become the cannon fire of the front line of the business.
Secondly, if the main engine factory does not build a CDP label system, it will not affect the application of the consultant label system. The tag library function itself provides a complete set of tag templates, while OEMs, 4S, consultants, etc. can customize and manage the tag library to quickly build a set of label systems for practical applications.
Auto Tag provides Group Behavior Tags, Asset Interaction Tags, keyword tags. For these WeChat communication customer scenario process, the customer is automatically labeled to facilitate subsequent application.
Group behavior label: 4S stores in outdoor auto shows, storefront activities, closed store sales and other marketing activities, for scanning the code into the group of customers automatically labeled.
Material interactive tags: According to the behavior of sending different materials, customer clicks, forwarding, etc., the corresponding tags are marked for customers. For example, the test drive invitation letter, marked with "intended customer", "test drive" and other labels, send the corresponding materials to the customer, the customer clicks on the automatic trigger rule, automatically labeled. It can greatly reduce the workload of consultant labeling and improve the accuracy of labeling;
Keyword tags: Apply the enterprise WeChat session archiving function, the consultant in the process of conversation with the customer, automatically extract keywords, such as model entity name extraction, automatic label intention model; price negotiation session, automatic extraction of car purchase budget label; provide car purchase negotiation and car purchase plan reference.
The sales consultant follows up the customer experience from the actual battle, and the customer label itself has important value. For example, the reliability of information such as customer car purchase intentions, purchased models, car purchase time, and car purchase budget mastered by consultants through communication and interaction is high. These labels are well supplemented by CDP mining from the data itself, such as algorithm labels, even if it is speculated whether the customer is married, gender, etc. need a large amount of customer behavior data to support, and in the early stage of the clue, there is a large number of behavioral data missing, and the algorithm label is facing an embarrassing situation of technology but no data available.
The sales themselves are only fast and not broken, the sooner the portrait is clear, the more helpful it is for sales strategies, quotation schemes, and transaction customers. Therefore, the construction of our label system must take the "mass route", give full play to the advantages of the dealer system of many sales consultants, with the help of the consultant manual label label, on the one hand, enrich the data resources lacking in early customer information; on the other hand, the consultant label is the source of the training sample of the natural machine learning algorithm label, and at the same time lay the foundation for the subsequent large-scale automatic algorithm labeling.
From the perspective of the real-time nature of the label, the customer's own needs and cognition are constantly changing, so the customer's label nature is also constantly changing. For example, customers who have a bad impression of their own brands actually test-drive their own brand models one day, have a new experience for their own brand screens, technology configurations, etc., and suddenly turn to accept their own brands. I believe that many people will have this experience.
And if you span a lot of customer data from a long historical cycle, it is difficult to catch this change through the algorithm. We use historical data to calculate more of an average person, and the more historical data, the more it erases the mutation of customers and misses the sales opportunity.
Therefore, real-time behavior labels generated by real-time customer behavior data are important. For example, through the customer dynamic radar, for the consultant to send the customer trajectory materials in real time, test drive invitations, model introductions, circle of friends, etc., real-time capture of customer behavior dynamics, and through timely reminders to consultants, let the consultant seize the time node of communication is very valuable to enhance customer conversion.
For example: when the customer opens a certain model introduction sent by the consultant, or browses the promotions of consultant friends who have stayed for a long time, the consultant communicates with the customer in a timely manner "respect XX customers, whether I need me to further introduce XX models for you", "Respect XX customers, sincerely invite you to participate in promotional activities". Because at this time our customers are also paying attention to the same thing, the customer just needs it, the consultant just appears, and this communication becomes very warm, rather than dry and disturbing. This real-time behavior label, combined with real-time message reminders, forms a closed loop from the label to the business application, which plays the business value of the label well, and returns to the essence of the label" The label is the valuable label.
To sum up, we start from the scene where the sales consultant applies the label, and build around the use as the construction. For customers with different IT construction maturity levels such as OEMs, dealer groups, 4S stores, car dealers, and used car dealers, we provide solutions for label scenario applications.
For enterprises with complete CDP (customer data platform) construction: make full use of CDP's strong OneID connectivity capabilities and micro automotive industry version direct connection of 1 billion+ consumer capabilities, through the label front-end application scenario landing, in customer communication, customer circle selection and other customer cultivation / conversion links to give full play to the business value of labels.
For enterprises with imperfect CDP construction/ low DEGREE of IT construction: You can directly take the automotive industry version of Micro as a starting point, and with the help of Micro's platform integration capabilities, you can run through the design, construction, and application of closed loops in the application scenarios of Micro Micro customers in advance, and directly support the conversion of sales business. Looking at the needs of enterprise development in the later stage, synchronously building its own CDP platform, or directly applying the powerful front-end and background configuration functions of the micro-automotive industry version is also a solution with a very high input-output ratio.
Finally, it is emphasized that the construction of the labeling system must give play to the practical experience of the sales consultant group in sales practice, so that the consultant can effectively precipitate the label assets of the customer to the core assets of the enterprise.
This article was originally published by @wildyouth and everyone is a product manager. Reproduction without permission is prohibited.
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