When we talk about private domain transformation and marketing digitalization, it is inevitable that we don't expect all kinds of beautiful results. The yearning for private domain is the yearning for establishing and leading the marketing ecology of your own enterprise. The yearning for the digital transformation of marketing not only hopes that it can support private domain marketing, but also contains more content that will be brand-new after upgrading marketing. For example, multiple departments work together to integrate the separated links of initiation, undertaking, operation, membership and loyalty into an organic and flexible organization; For example, whether it is put into operation or consumption operation, a large amount of data can be applied to achieve the effect of thousands of people; For example, the application of various new technologies (CDP, MA, DAM) and so on. , creating more possibilities for marketing and greatly improving the efficiency of mass customization marketing; For example, efficient connection with external Internet platforms to maximize the resources provided by these Internet platforms; For example, self-built traffic ecology: doing DTC in private domain, building marketing logic that enterprises can stand on their own feet, hearing all kinds of wonderful cases and seeing the beautiful scenes described by service providers in various industries, it is difficult for enterprise managers to be indifferent. In addition, under the epidemic, business is not easy, and there is a sense of urgency and even suffocation. There is an urgent need to fall behind in the "modernization" of marketing and even stand at the forefront of the industry. The digital department and CRM department of the enterprise are suddenly on fire, and the wages and pressures are also rising. But as time went on, the bosses suddenly found that they had bought so many systems, invested so much manpower, spent so much time and booked so many projects, but made almost no progress. It's true: how grand the rhetoric and vision were at the beginning, and how slow the progress is today. So, what went wrong? A very, very huge problem is that enterprises pay too much attention to the introduction of technology, the innovation of models and the breakthrough of methods in the digital transformation, but their basic skills are terrible. For example, if we try to obtain consumer data for better insight and crowd selection, enterprises often feel that such a basic thing is not fresh or sexy at all, and even some marketing leaders are ashamed to mention consumer insight, preferring to use terms such as "360-degree portrait" instead. However, even if it is such a "basic" thing, how many brand enterprises can do it well? Simply put, even if the above example is so "basic", an enterprise needs "basic skills" in several fields. The first basic skill is "trying to get consumer data". Behind this, there are many subdivided professional fields: what channels are there to obtain data? What methods should be used to obtain data through various channels? What are the technical and commercial methods? Tell me about technical methods. Due to the difference of data sources, external data may need to access the API of external data sources (this is the most commonly used way at present) to obtain data. If the data source can't provide personal data or doesn't export data to the outside world, then the enterprise may need to build privacy calculation to match the data, so as to achieve the acquisition of non-personal data reports. For internal data, you need to use tools to install basic monitoring SDK (or basic monitoring code) on your contacts, and then add event monitoring (commonly known as "buried point") at the interaction points where various behaviors of users occur. Once we reach the burial point, there are two particularly bad problems that often appear: (1) waiting for the IT department to dispatch-such a simple thing, once dispatched, is equal to the road of no return. (2) The standardization and naming of buried points is another disaster. Without standardized buried points, the subsequent data will be difficult to apply. There is another kind of data, which is also very important. It is a kind of "internal and external" data, that is, data related to traffic. The most typical is what is the traffic source of each user. This kind of data requires the team to establish a label on the traffic source (pay attention to the standardization of label naming! ), and use simple technical means to capture these tags, so as to accurately identify the source of traffic. The company said, nothing. I will find an agent (agent) or a manufacturer (service provider) to help me with internal and external data! However, the pit in the middle is coming again. Agents or service providers may have skills (but they also lack staff to do these specific things) and may know how to do it. However, the realization of data acquisition involves a large number of business contexts, and it is difficult for these information external service providers to obtain in-depth knowledge. This is extra work for them. If the external team wants to do a good job, the internal team will probably spend as much effort as going out in person. Second, after obtaining the data, forming a so-called "portrait" or insight of consumers requires two important basic skills: the integration of data from different sources and the establishment of data labels after integration. Integrating data, that is, getting through the data of the same user, is divided into technical methods and business methods. There are simple and complex technical methods. The simple method (that is, the primary key method) can often solve most problems, which is not difficult for the technical team of the enterprise. However, this simple method is technically simple, but it is more demanding in business-it needs the cooperation of the business team: that is, the operation team needs to be able to obtain the mobile phone number of consumers on a large scale. But the problem is that many companies don't even have an "operation team". Therefore, although the word "integrated data" is catchy to read and its meaning is clear, it is not simple to realize it, but it also needs the joint efforts of technology and business. So some companies said: forget it, it's too complicated to do it yourself. Leave it to agents or manufacturers! Well, it was handed over to the outside team again. As mentioned above, giving it to an external service team will not make the company's own team become the shopkeeper of cutting, even more laborious than doing it yourself! Let's look at the establishment of labels, which is another basic skill that requires professional ability. Tags are divided into three categories: fact tags, rule tags and prediction tags (also known as model tags). Needless to say, the fact label is not difficult technically, but it needs to understand the business, forward-looking and careful planning, and a lot of tedious and meticulous naming work. In particular, it is necessary to establish a set of standards to ensure that there will be no problems in adding labels and customizing labels in the future. Rule tags and prediction tags come from fact tags and lower-level user behavior data. Rule labels need business colleagues to define rules, and forecast labels need business colleagues to put forward their needs and ideas, and then let colleagues who know data modeling establish forecast models. Because there may be many business departments that use labels, and there may be more departments or managers who create labels, the naming and definition of labels are easy to be confused, and labels themselves are easy to produce a lot of redundancy. I really haven't heard of any brand enterprise that can completely rely on an external team (agent or manufacturer) to do this well. Enterprises still need to have a leading organization that unifies the thinking and management of basic work such as data collection, and the corresponding person in charge of each corresponding department. They should make plans, have standards, supervise, manage and maintain them. But unfortunately, most enterprises think that it is enough to find an agent, and it is difficult to achieve better supervision, management and maintenance by relying on agents. Third, select people based on data and labels. Crowd selection depends largely on tools, but it still needs basic skills to do well, because crowd selection still depends on data acquisition and data integration. Enterprises also need to be discerning, able to identify the quality of circle selection tools. Therefore, the basic work of data acquisition, integration, labeling and insight seems sparse and common, but it is rarely done well. The more basic skills, the more difficult it is for enterprises. These examples are far from involving the further application of crowd data and doing some in-depth operations. After all, once the application is really involved, more basic skills are needed. Give an example of a common application: if the crowd selection mentioned above has been realized, I want to provide different pages (or interfaces) for different segments of people after crowd selection, so as to better "suit their interests". Such a simple application is difficult for most brands to achieve. First of all, after the crowd is selected, we should provide different interfaces for different segments of the population, and actually focus on the level of each individual user. In other words, a user, who belongs to the circle-selected crowd, should see a certain interface, but not the circle-selected crowd, and should see other interfaces. In other words, in the fact that the interface is presented to different people, it is actually necessary to judge what kind of person each visiting user belongs to. When the user appears on the digital interface of the brand, the brand needs tools to judge whether it belongs to a specific group in real time (by matching ID), and then show it different interfaces (or corresponding materials) in real time. A MA tool (marketing automation tool) can help realize "real-time", but it is not helpful in strategy. What kind of people need to match what kind of interface is a problem that enterprises need to solve, and it is also a basic skill that enterprises need to have. Behind this basic skill is a correct understanding of the expectations of different groups of people, which is closely related to the basic skill of insight mentioned above. Of course, many companies say that this is not a problem at all, and many opinions are not that complicated. There's no data. Who hasn't run through the market or done business? You can feel what kind of people will pay attention by your intuition! Well, even if insight doesn't need extra efforts, designing and making interfaces (or pages) for different people has become a very fatal thing. Don't you just make a few interfaces? Our company has a team of designers, an IT team and a development team. But what if we make five interfaces for five people? Moreover, these interfaces often change the digital transformation of marketing over time, not to mention "thousands of people". First realize "five people and five sides". But even if it is difficult to achieve "five people and five sides", it is too difficult: designers should schedule, front-end development should schedule, testing should schedule, and updates and changes should also be scheduled. It may take a long time to wait in a row, even longer than the responsible employees stay in this enterprise! Not only that, in the process of making these things, you can't be a shopkeeper, you have to sit with designers and front-end developers and constantly dig up the details of your ideas. Forget it, or find an agent to do it! An agent can save you a long time waiting for an appointment, but it is more important to sit together and dig up the details of your idea. When these interfaces are completed, we need to put the deployment of basic monitoring and embedded point (event monitoring) in it. We need a round of naming specifications for user behavior (fact labels), and we need to collect data accurately again, so that we can label users with more labels and make more accurate answers later. This is the most basic application after crowd selection! If it is complicated, such as making a customized customerjourney, applying the data of segmented people to the media platform, optimizing the conversion performance of private domain contacts based on the data, and so on. The basic skills needed are becoming more and more complicated. Therefore, the digital transformation of marketing is a systematic project in which countless seemingly insignificant basic skills and basic work are intertwined! Any so-called new game, new methodology, new model and new strategy is easy to say, but it needs to be clear about many details and do a lot of work when landing! Therefore, it is easier said than done, and it is vividly reflected in the digital transformation of marketing! This is why most brand enterprises clamor for digital transformation and data transformation every day, but turn around! Really don't pay attention to basic skills, really don't pay attention to basic work! No, it's not that I don't value it, but that I don't even know such a job exists! So how should enterprises get rid of barriers and stride forward? Talk less about doctrine and do more practical things! Building basic skills is an urgent task. Among them, the most urgent thing is that the management should also find out what the specific basic skills are of fundamental significance and what kind of abilities colleagues need to be competent. Find colleagues who are really willing to let themselves have these basic skills (it is more important to be willing to do than to be able to do, because you must learn, and the importance of responsibility is the first in this matter), and then let yourself master these basic skills quickly through systematic training. We can divide these basic skills into five quadrants. The first quadrant: the production quadrant, that is, the basic skills about production. That is to say, you can have strong ability to design and make user contacts, interfaces and interaction points. The second quadrant: the acquisition quadrant, about the basic skills of data acquisition. As mentioned above, this kind of basic skills can be divided into technical basic skills and business basic skills, both of which should be firmly grasped. The third quadrant: the organization quadrant, the basic skills of organizing data-labels, portraits, insights, etc. As mentioned above, it is not only a simple technical work, but also a systematic work involving planning, organization, management and maintenance. The fourth quadrant: the application quadrant, the basic skills of applying data in the primary scene. Don't think about those complicated applications. If you can do the most basic applications, such as targeted user reminders, recommendations, interactions, etc. , has been able to solve many operational problems. The fifth quadrant: the tool quadrant, the basic skills of using various tools. Access to the above data requires the use of appropriate tools; Data access, tagging, etc. , also need the right tools; Consumer insight and portraits, coupled with the application of data, also need appropriate tools. These tools, such as CDP, MA, SCRM, behavior analysis, DMP, and big data operation tools in the media, etc. The familiarity and use of these tools largely determines the maturity and practical level of enterprise marketing digitalization. The establishment of the basic skills of these quadrants can even completely ignore the direction of the future digital transformation of enterprise marketing-because no matter what kind of transformation, it is inseparable from the basic abilities of these five quadrants. We can move forward in this way: we can spend a lot of time studying and thinking about our future transformation strategy, and at the same time, we also spend time building the capabilities of the above five quadrants, which are parallel, without having to formulate the transformation strategy and then build the capabilities from scratch. Otherwise, the management will find that it is not easy to think clearly about the strategy, and it is impossible to build capacity during the term of office. In this way, the real digital marketing transformation of enterprises may never land. -Welcome to | Dark Horse Camp of Digital Marketing-