Information flow advertising delivery and optimization methodology!

Information flow advertising delivery and optimization methodology!

Paid promotion ROI is the goal that every operation and sales person is pursuing. Learning the systematic advertising placement and optimization methodology is like a magical weapon that will help you navigate the world and increase your business skills several times. From then on, getting promoted and getting a raise, marrying a beautiful and wealthy woman, and becoming a CEO were no problem at all.

The following optimization methodology is a summary from the optimization manager of a leading media platform in an industry. I hope it will be helpful to everyone’s daily work!

01Determine the advertising optimization ideas

1. Confirm customer information (sales cooperates with AE to communicate and confirm)

  • Confirm target customer information (industry information, interest points, industry average data)
  • Confirm the conversion process (determine the conversion process of key nodes and the industry average conversion rate of key nodes)
  • Assessment nodes and assessment points (determine customer assessment nodes, current industry averages, etc.)
  • Customer data feedback content/feedback cycle
  • Customer budget and customer market strategy (sales support required)

2. Develop specific delivery strategies

  • Material direction
  • Targeting dimensions, including: delivery time, media, device value, etc.
  • Control the delivery rhythm according to the assessment points, budget, data feedback cycle, and expected cost ( landing page conversion).

3. Ad Optimization

  • Data analysis , key node data analysis
  • Locate key nodes and optimize key nodes
  • Key nodes: CTR /C VR / back-end conversion rate; corresponding issues such as: materials, landing pages, product connection, traffic sources, delivery time, etc.

4. About targeting dimensions and advertising materials (the importance of material optimization)

  • Directional dimension: traffic pool
  • Creative: User Filter
  • The targeting dimension determines how big the traffic pool is, and the advertising creative determines what types of users are screened out. The larger the scale of the material, the larger the user base will be screened out, and the less likely it is that the user quality will be guaranteed; the tighter the scale of the material, the more accurate the users will be, the better the user quality will be, but the user base will be smaller.
  • Note: The issue of material scale and user rights; the user rights of some ads are good enough, that is, there is no problem of large material scale

5. Requirements for Advertising Optimizers

  • Step 1: Understand optimization and master various optimization techniques (advertising optimization)
  • Step 2: Understand the industry, understand the industry operation, and obtain universal industry placement experience (advertising operation)
  • Step 3: Understand customers, understand their specific demands, and formulate different communication skills and delivery strategies based on their specific situations (advertising operations experts)

6. The ultimate goal of advertising optimization (ideal situation)

  • Ultimate goal: To maximize advertising consumption while balancing advertising effectiveness; that is, to balance quality and quantity
  • Key points:

1) Balance between platform revenue and advertiser effects, that is, the balance between AR PU value and ROK

2) Advertisers’ demands: balance between quality and quantity. Both quality without quantity and quantity without quality are not the most ideal conditions for advertisers/platforms, and it is difficult to become a top KA customer.

3) Customer relations and daily work communication to avoid advertisers making higher demands

02 About Data Analysis

[About the difficulty of data analysis] The data on industry awareness and industry transformation nodes are unclear; the core goals, key node confirmation, and how to achieve the target optimization plan are not clear.

1. Key data node analysis and optimization direction

  • The core of key data node analysis: making data analysis more layered and decomposing target link data in multiple dimensions
  • Analysis/decomposition ideas: Confirm industry characteristics - confirm industry conversion process and node data - confirm specific customer conversion process - confirm customer key assessment nodes - confirm key node conversion rate and conversion target - specifically analyze key node data - determine the optimization direction of key nodes
  • Determine the specific optimization direction (clarify the problem and goal)

[Specific abnormal situations and related suggestions are as follows]

2. Abnormal CTR data

  • This ad CTR
  • Industry average CTR confirmation
  • CTR of some media, mainly: Is the high CTR due to some individual media?
  • Specific analysis of CTR abnormality:

1) Compare the industry CTR with the advertising CTR, which is too high or too low;

2) Too high or too low, due to individual media or overall situation;

3) Issues with the material itself: Is the interest point prominent or overly prominent or exaggerated?

  • Solution:

1) Optimization of directional dimension;

2) Optimization of the material’s interest points and the degree of matching with the landing page;

3) If the CTR of the entire industry is low, you need to find a CTR solution for the industry (industry solution)

3. Media data anomalies

  • Consumption is concentrated in some media
  • The CTR of some media is abnormal, too high or too low
  • Solution: Analysis, confirmation and adjustment of the impact of fluctuations in some media data on costs

4. Abnormal customer data

  • Abnormalities in CVR data
  • Abnormal customer front-end conversion rate
  • Abnormal customer backend conversion rate
  • Solution logic:

1) Combine customer data feedback and advertising data to analyze actual problems and communicate with customers;

2) Communication logic: Where is the estimated problem now? Both parties work together to investigate whether the customer's products have been adjusted or whether there are any abnormalities in the traffic end.

3) Pay attention to the problem: cooperate with the investigation, not unilaterally; for KA customers, it is best to prioritize the customers to find the problem; when communication is difficult, sales are needed to cooperate with communication to solve the problem

【Location of abnormal customer data】(Media dimension)

[Client adjustment, impact on data] (Customer dimension)

Note: The problem here is more complicated. By inferring from the client side, the impact of the adjustment of the client side on the data of each link can be inferred

  • Adjustment of customer landing pages, affecting page reach/CVR/conversion rate and other related data
  • Adjustments to client activities on the site affect new backend conversions; activity thresholds and activity intensity will affect payment rates and paid ARPU accordingly
  • Adjustment of customer payment interface affects the back-end data such as new payment/payment rate, etc.
  • Adjustment of customer data interface affects the overall effective conversion rate (e.g. effective conversion of insurance from registration to free insurance)
  • Other comprehensive issues are located in combination with the key node processes of specific customers, such as credit conditions in the loan industry, changes in the definition of effective data settlement for collection list customers (such as insurance age restrictions, calls; connection/customer intentions, etc.), etc.

4. Data differences and analysis during the delivery period

  • Discrepancy issues: mainly for advertising plans with obvious differences in data delivery time
  • The conversion rate data for the current specific time period, whether the CVR data is positively correlated with the specific conversion rate.

If they are consistent, the conversion rate data for the time period can be determined based on the CVR of the time period;

If they are inconsistent, the customer needs to cooperate with the hourly conversion data to analyze the specific reasons for the impact: such as the media placement, etc.

5. CVR data reference and OCPC delivery

True conversion rate test formula: Success rate = customer data / effect UV (stable success rate means it is suitable for OCPC delivery, indicating stable delivery; if the success rate is unstable and fluctuates, it means the traffic quality is fluctuating)

Note: The authenticity of CVR data and the reference value of CVR data. The above formula and logic can be used to check and judge

6. Review and summary of advertising placement

  • Advertising/industry placement summary, customer/industry characteristics analysis, mainly customer/industry conversion process, industry gameplay, etc.

  • Summary of material packaging solutions
  • Summary of optimization techniques, such as media screening, targeting dimensions, etc.
  • Summary of the launch ideas

03 About communication skills

1. Grasp the principles of communication

  • Principle 1: Cooperation and win-win. We have a cooperative relationship with customers, and our ultimate goal is to maximize consumption and effects. (When encountering a client who is difficult to communicate with, emphasize that everyone’s goal is results and consumption, and everyone should cooperate well to get the project on track as soon as possible)
  • Principle 2: Communicate frequently (service awareness), grasp the key points of communication (professionalism); (You can synchronize the adjustments you make with the customers to let them know that you value and are doing things)

2. Some issues that need attention in communication

  • Customer jargon, such as: understand the cost, consumption and other data of competing products
  • Customer feedback is poor
  • Customer requested replenishment
  • How to get advertising budget

The above is today’s sharing content. Some professional terms are difficult to understand. You are welcome to leave a message to discuss and exchange.

Author: Huang Sir , authorized to be published by Qinggua Media .

Source: Xidongquan (ID: xianfaba)

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