Marketing Analytics and Strategy finished study guide.pdf

Marketing Analytics and Strategy Section 1: Types of Marketing Analytics Descriptive Analytics Examines past data for insights. Common forerunner to predictive or prescriptive analysis. Key Questions: Alerts: "What actions are needed?" Query/Drill Down: "What exactly...

Marketing Analytics and Strategy Section 1: Types of Marketing Analytics Descriptive Analytics Examines past data for insights. Common forerunner to predictive or prescriptive analysis. Key Questions: Alerts: "What actions are needed?" Query/Drill Down: "What exactly is the problem?" Ad-hoc: "How many, how often, where?" Standard Report: "What happened?" Business Metrics Evaluate performance against goals (e.g., efficiency, revenue) Data Analysis Process ​ 1) Data Required: Extracted from reports or databases. ​ 2) Data Collection: Ensures accuracy and cleanliness. ​ 3) Data Analysis: Utilizes statistical techniques for pattern identification. ​ 4) Data Presentation: Communicates findings through charts and graphs. Predictive Analytics Forecasts future brand trajectory and potential client lifespan. Key Questions: Optimization: "What is the best that can happen?" Predictive: "What will happen next?" Randomized: "What if we try this?" Statistical Analysis: "Why is this happening?" Section 2: Marketing Plan Objectives 5 C's: Customer analysis. Company assessment. Competitor research. Collaborator evaluation. Context understanding. Strategy Segmentation, Targeting, Positioning. SWOT and PEST Analysis: SWOT: Internal analysis. PEST: External analysis (Political, Economic, Social, Technological) Tactics 4 P’s: Product: Understanding product quality, brand lifecycle. Price: Considering costs, competitor prices, and customer willingness to pay. Place: Deciding where and how to distribute and display products. Promotion: Communicating the product's value to target customers. Section 3: Brand Personality and Architecture Brand Personality Characteristics attributed to a brand. Consistency across messaging, images, and campaigns. Emotional connection with the target audience. Brand Architecture (Five Categories) ​ 1) Brand Core  ​ 2) Brand Personality ​ 3) Emotional Benefits ​ 4) Product Benefits ​ 5) Product Attributes Section 4: Customer Lifetime Value (CLV) Customer Lifetime Value (CLV) is a crucial metric in predictive analysis for estimating the total value a customer will bring to a business over their entire relationship. There are various ways to calculate CLV. What CLV can be used for in Marketing: Customer Segmentation and Targeting: CLV helps identify high-value customers. By understanding the value each customer brings over their lifetime, marketers can segment customers based on their potential value. This allows for targeted marketing strategies, ensuring resources are focused on retaining and acquiring the most valuable customers. Customer Relationship Management (CRM): CLV is instrumental in shaping CRM strategies. By understanding the value of each customer, businesses can tailor their interactions, services, and loyalty programs to enhance the overall customer experience, thus improving retention and lifetime value. Product Development and Service Enhancement: Understanding CLV can influence product development and service enhancement. High CLV customers can provide insights into what products or services are most profitable and what features or improvements might attract or retain these valuable customers. Forecasting and Long-Term Planning: CLV aids in long-term forecasting and business planning. By estimating the future value of customers, businesses can make strategic decisions regarding growth, expansion, and product development Formulas for CLV Section 5: Break Even Analysis Formula: Break even = Total Fixed Costs / Contribution Margin per Unit. Total Fixed Costs: These are the costs that do not vary with the level of sales or the scope of the marketing effort. For instance, costs of advertising, campaign setup, salaries, etc. Contribution Margin per Unit: This represents the amount of revenue from each sale that contributes to covering fixed costs and profit. It's calculated as the difference between the sale price per unit and the variable costs per unit.  Significance in Marketing: Risk assessment, decision-making, performance evaluation. Section 6: Regression Analysis Common statistical method to understand relationships between variables. Basic Formula: Y = Bo + B1(X). Understanding Relationships: Regression analysis allows marketers to explore and understand relationships between variables. For instance, it could be used to assess the impact of advertising spending on sales, price sensitivity of customers, or the effect of different marketing channels on customer acquisition. Predictive Modeling: By analyzing historical data, regression models can predict future outcomes. For instance, by examining past sales data and marketing expenditures, a regression model could predict potential sales based on various marketing budget allocations. Market Segmentation: Regression analysis can also assist in identifying different customer segments and their behaviors. It helps in understanding what factors or strategies work best for different groups of customers, allowing for targeted and personalized marketing approaches. Section 7: Marketing Mix Models Analytical technique to measure the impact of marketing campaigns. Focus on the 4 P’s: Product, Price, Place, Promotion. Emphasizes statistical significance. Section 8: Performance Goal Planning ROAS (Return on Ad Spending): Measures short-term performance of a campaign. Steps for Goal Planning: Confirm business and marketing goals. Conduct market research. Define campaign goals. Define cost-related performance goals. Cost-Related Performance Goals: Include smart bidding, manual bidding, and metrics like CPA (Cost per Acquisition), CPC (Cost per Click), and A/B testing. A/B Testing: Method for comparing different versions to improve results List of Common Formulas for Marketing Analysis Section: 9 Charts Marketing professionals use a variety of charts and visualizations, especially in the context of clustering analysis, to understand patterns, relationships, and segments within their target audiences. Here's how some of the mentioned charts are commonly used in clustering Graphs/Scatter Plots: ○ Usage: Graphs and scatter plots are used to visualize the relationship between two variables. In clustering, they can help identify natural groupings or clusters based on the proximity of data points. ○ Example: A scatter plot could show the distribution of customers based on two key features, and clusters might emerge based on similarities in their purchasing behavior. Bar Charts: ○ Usage: Bar charts are useful for comparing the frequency or distribution of categories within a dataset. In clustering, they can help highlight differences or similarities in certain attributes across clusters. ○ Example: A bar chart might display the distribution of customer segments based on demographic characteristics like age or income. Pie Charts: ○ Usage: Pie charts represent parts of a whole and can be useful for illustrating the proportion of each cluster within the total population. ○ Example: A pie chart might visually represent the percentage of customers in each identified cluster within the overall customer base. Frequency Tables: ○ Usage: Frequency tables provide a tabular representation of the distribution of values within a dataset. They are helpful for understanding the occurrence of different values or clusters. ○ Example: A frequency table could display how often certain product categories are purchased by different customer segments. Contingency Tables: ○ Usage: Contingency tables are used to show the relationship between two categorical variables. In clustering, they might be employed to explore how certain attributes co-occur within identified clusters. ○ Example: A contingency table could display how product preferences vary among different demographic segments. Perceptual Charts: ○ Usage: Perceptual charts, such as perceptual maps, are often used to visualize how customers perceive a brand or product in relation to others.  They can help identify clusters of products or brands based on customer perceptions. ○ Example: A perceptual map might show how different customer segments perceive the attributes of products or brands, allowing marketers to identify clusters with similar perceptions. These visualizations help marketers make sense of complex data, identify patterns, and make informed decisions about targeting specific customer segments or tailoring marketing strategies based on the characteristics of different clusters. Notes broken down using bing AI: Let’s organize your notes on Marketing Analytics and Strategy into a more visually appealing and study-friendly format. Here’s a structured outline with key points highlighted and formulas presented clearly: Section 1: Types of Marketing Analytics Descriptive Analytics Insights from Past Data: Common precursor to predictive or prescriptive analysis. Key Questions: ○ Alerts: What actions are needed? ○ Query/Drill Down: What exactly is the problem? ○ Ad-hoc: How many, how often, where? ○ Standard Report: What happened? Business Metrics Evaluate Performance: Against goals like efficiency and revenue. Data Analysis Process 1. Data Required: Extracted from reports or databases. 2. Data Collection: Ensures accuracy and cleanliness. 3. Data Analysis: Utilizes statistical techniques for pattern identification.  4. Data Presentation: Communicates findings through charts and graphs. Predictive Analytics Forecasts: Future brand trajectory and potential client lifespan. Key Questions: ○ Optimization: What is the best that can happen? ○ Predictive: What will happen next? ○ Randomized: What if we try this? ○ Statistical Analysis: Why is this happening? Section 2: Marketing Plan Objectives 5 C’s: Customer analysis, Company assessment, Competitor research, Collaborator evaluation, Context understanding. Strategy Segmentation, Targeting, Positioning. SWOT and PEST Analysis: ○ SWOT: Internal analysis. ○ PEST: External analysis (Political, Economic, Social, Technological). Tactics 4 P’s: Product, Price, Place, Promotion. Section 3: Brand Personality and Architecture Brand Personality Characteristics: Attributed to a brand. Consistency: Across messaging, images, and campaigns. Emotional Connection: With the target audience. Brand Architecture 1. Brand Core 2. Brand Personality 3. Emotional Benefits 4. Product Benefits 5. Product Attributes Section 4: Customer Lifetime Value (CLV)  CLV: A metric in predictive analysis for estimating the total value a customer brings over their relationship. Uses of CLV in Marketing Customer Segmentation and Targeting: Identifies high-value customers for targeted marketing strategies. Customer Relationship Management (CRM): Shapes CRM strategies to enhance customer experience and retention. Product Development and Service Enhancement: Influences product development based on insights from high CLV customers. Forecasting and Long-Term Planning: Aids in strategic business decisions regarding growth and expansion. Section 5: Break Even Analysis Formula: Break even=Total Fixed CostsContribution Margin per Unit Break even=Contribution Margin per UnitTotal Fixed Costs​. Total Fixed Costs: Costs that do not vary with sales level or marketing scope. Contribution Margin per Unit: Revenue from each sale contributing to covering fixed costs and profit. Section 6: Regression Analysis Basic Formula: Y=B0+B1(X) Y=B0​+B1​(X). Understanding Relationships: Explores relationships between variables for marketing insights. Predictive Modeling: Predicts future outcomes based on historical data analysis. Market Segmentation: Assists in identifying customer segments and behaviors. Section 7: Marketing Mix Models Analytical Technique: Measures the impact of marketing campaigns focusing on the 4 P’s. Section 8: Performance Goal Planning  ROAS: Measures the short-term performance of a campaign. Steps for Goal Planning: Confirm goals, conduct research, define campaign and cost-related performance goals. Section 9: Charts Graphs/Scatter Plots Usage: Visualize relationships between two variables to identify clusters. Bar Charts Usage: Compare frequency or distribution of categories within a dataset. Pie Charts Usage: Illustrate the proportion of each cluster within the total population. Frequency Tables Usage: Tabular representation of the distribution of values within a dataset. Contingency Tables Usage: Show the relationship between two categorical variables. Perceptual Charts Usage: Visualize customer perceptions of brands or products to identify clusters. This structured format should make your notes easier to review and study. If you have specific 📚✨ formulas or additional content you’d like to include, feel free to let me know, and I can help format those as well. Happy studying!