Course Background:
With the proposal of the national digital transformation strategy, enterprises must respond to the trend of digital transformation, and in the face of digital transformation of enterprises, whether employees at all levels have the ability to conduct digital analysis according to work business scenarios is an important condition to determine whether employees can adapt to new trends and complete personal digital transformation, and it is also the key to whether enterprises can successfully complete digital transformation.
Personal digital transformation is first faced with the transition from traditional relationship networks to personal digital networks, and on this basis, data analysis thinking is cultivated, so that digital methods and means can be applied to corresponding work scenarios. Therefore, this course starts with the establishment of personal digital networks, and on the basis of the establishment of personal digital networks, it cultivates data thinking, masters data analysis methods, and applies data analysis in various business scenarios, which is the core content of this course.
CoursesEarnings
Establish digital networks, especially personal digital networks, and master the methods and skills of digital network establishment;
Understand and master the methods of data thinking training, and master the methods of establishing a data thinking framework;
Understand business scenarios and grasp the core key points, master the methods and skills of data analysis in various business scenarios, and be able to draw inferences from one case and analyze new business scenarios.
Course Duration:2 days, 6 hours a day.
Course Target:Grass-roots management personnel in various industries (administrative, technical and other management personnel).
Course Method:Case + Interaction + Test + Thinking + Practice + Discussion + Tools.
Course outline
FirstSpeakUnderstand and utilizeIndividualsDigital Networks
1. The impact of digital networks on traditional personal relationship networks
1.Nodes and connections make up the digital network.
2.A starting point for analyzing the situation of the scene from the perspective of the network.
2. Build yourself into an irreplaceable digital network node
1.Construct a solid central underlying logic (grasp the three characteristics of node centrality).
2.Build a bridge of information relationships (occupy important gaps in the network).
3.Become a "traverser" to adapt to the development and change of the enterprise (exercise the ability to switch).
Case:How to tell who is the big V on WeChat
Discuss:How to use node theory to achieve life goals.
3. Make good use of digital network connections to build reliable relationships and build efficient collaborative teams
1.Establish homogeneous emotional connections and heterogeneous task connections to form teams.
2.Use strong and weak relationships to build team communication and leadership.
3.Understand the small world network and make connections to external resources within the team.
Case:The phenomenon of six degrees of separation.
Fourth, build your own effective digital network
1.Create priority connections to become high-impact super traffic nodes.
2.Maintain high-quality resources and maximize efficiency.
3.Build your own premium network.
4.Build a resilient network and establish a gardener-style leadership style.
5.Leverage informal networks in your organization to serve your goals.
6.In-depth analysis of multi-mode networks to foster on-demand digital networks.
7.Analyze the interaction patterns of circle design in digital networks.
Case 1:Dianping's information waterfall analysis.
Case 2:Impact diffusion model analysis.
Case 3:Alumni Network Analysis.
Case 4:Brace's Paradox.
SectionII. II. IISpeakDevelop a data mindset in digital networks
First, the essence of data-based work management
1.There are four main types of data.
2.Let the data speak for itself – understand the context in which the data is generated.
2. Establish a framework for data thinking
1.The origins of data thinking.
Dismantling the data mindset
1) The three realms of data thinking: no counting, having numbers, and controlling numbers.
The relationship between data thinking and big data thinking is distinguished in three aspects
Survivorship Bias Cases:Where armor should be added (taking care to prevent survivor deviations).
A** thinking.
b Mathematical logic.
c KPI thinking.
Four directions for data thinking training
Direction 1: Enhance the digital feel.
Direction 2: Establish the principle of mean regression.
Direction 3: Grasp the sense of data.
Direction 4: Establish a data model.
Five steps to data mindset development
Step 1: Ask—The business problem becomes a data problem.
Step 2: Splitting – Splitting the problem into the details.
Step 3: Solve – Use algorithmic thinking to solve problems.
Step 4: Seek the unity of data thinking and scientific and humanistic thinking.
Step 5: Present - Visualization is intuitive and efficient.
Case 1::**Doctor.
Case 2:: How to investigate car speeding.
Interaction 1: How does the dating platform serve customers?
Interaction 2: Did you lose money eating pizza like this?
Testing: What is your numerical quotient?
Think: The number of takeaways in a certain city in a year?
SectionThreeSpeakEnterprise-critical businessScene data analysis in practice
1. Analysis of business development strategy
1.Analyze ideas.
Application of relevant strategic analysis methods
1) External environment analysis tool - internal and external factor evaluation matrix.
2) Analytic Hierarchy Process Steps: Establish Hierarchy, Criterion Comparison, Calculate Criterion Priority, Scheme Determination, and Test.
3) McKinsey's strategic analysis tool - GE matrix.
4) Weight determination method - weight factor interpretation expert score.
5) Coefficient of variation method: mean score, standard deviation, coefficient of variation.
Case:How do I find someone I like?
2. Investment analysis
Analytical method
1) ** principle.
2) Qualitative methods.
3) Quantitative** Methods: Regression Hypothesis Testing.
Earnings analysis
1) Understand the cash flow statement.
2) Static economic evaluation.
3) Dynamic economic evaluation.
Risk analysis
1) Break-even analysis.
2) Sensitivity analysis.
3) Probabilistic analysis.
Case 1:**Sales of an electronic product.
Case 2:Determine if a project is feasible.
3. Analysis of new product research and development
Tools:User requirements analysis kano model.
There are four types of properties of the model
1) Necessary needs.
2) One-dimensional requirements.
3) The need for charisma.
4) Dispensable demand.
2.Model design.
3.Model application.
Case:Analysis of the development sequence of various features in the R&D department.
Fourth, marketing analysis
Analyze ideas.
1) User behavior analysis.
2) 4P Marketing Mix Method.
3) Marketing effect evaluation.
Cluster analysis
1) Cluster analysis ideas.
2) Hierarchical clustering.
3) Iterative clustering.
Case:Analysis of user consumption habits.
3.*Sensitivity test.
Tools:PSM model.
Case:Pricing analysis of a commodity.
Brand Perception Map Analysis
1) Brand perception map.
2) Problem setting.
3) Data analysis.
4) Steps.
Case:A brand perception map.
Calibration ratio hyper-analysis
1) Determine the benchmark.
2) Establish an evaluation index system.
3) Compare and propose measures.
Case:The operation of a product channel is improved.
6.Funnel analysis.
7. aidamodel
1) Where the ad will be placed.
2) Advertising content.
3) Advertising period.
Case:A product advertising plan.
5. Operation analysis - the application of circulation map
Circulation map 5 elements
1) Horizontal axis. 2) Circulation nodes.
3) Circulation line segments.
4) Circulation quantity.
5) Turnover rate.
2.There are three types of circulation maps: global, platform, and local.
How to apply the flow map
1) Evaluate efficacy.
2) Identify bottlenecks.
3) Blaze new trails.
Case:The overall operation of the e-commerce of a small program has been improved.
Draw a flow map
1) Select business objectives and identify key results.
2) Combined with the key results, the ** value behavior is reversed.
3) Quantify the page data and arrange the circulation nodes.
4) Apply the data model to complete the circulation data.
Practice:Draw a map of the flow of the enterprise.
5. Data analysis
Identify the problem, decompose the problem, evaluate the problem, and make a decision.
Case:The sales of a company's flagship products increased.