Marketing Analytics Notes PDF

# MARKETING ANALYTICS * Sensitive Marketing * General Marketing ## Data Analytics: * Collection of the data * Analysis of the data * Marketing means the act of spraying your product. (प्रचार करना) * Advertisement is an act of marketing. * Market strategy: Celebrity enrolments ### Types of Data...

# MARKETING ANALYTICS * Sensitive Marketing * General Marketing ## Data Analytics: * Collection of the data * Analysis of the data * Marketing means the act of spraying your product. (प्रचार करना) * Advertisement is an act of marketing. * Market strategy: Celebrity enrolments ### Types of Data Analytics: 1. Descriptive Analysis 2. Statistical Analysis 3. Data Visualization 4. Predictive Analysis # Marketing Analytics * Marketing involves analysing data to evaluate effectiveness of marketing strategies and campaigns. * Key components include: * **Data Collection:** Gathering data from various sources such as social media, website, email campaigns, and sales data. * **Data Analysis:** Using statistical method and software tools to interpret the data. This includes segmentation, trend analysis, predictive modeling. * **Performance Matrix:** Tracking key performance indicator (KPI) like conversion rates, customer acquisition cost (CAC), *[unclear text]* customer lifetime value (CLV) and return on investment (ROI). * **Customer Insights:** Understanding customer behaviour, preferences and trends to tailor marketing efforts more effectively. * **Campaign Optimization:** Using insights from data analysis to improve marketing strategies such as adjusting ad spend, targeting different audience segments or modifying messaging. * **Reporting and Visualization:** Presenting data in an understable and actionable format through dashboards and visualizations. * **Technology and Tools:** Utilizing software and tools like Google analytics, Adobe analytics, Tableau, and CRM system to collect and analyze data. * **Data Privacy and Ethics:** Ensuring compliance with data protection regulations and maintaining ethical stands in data usage. ## Need for Data Driven Marketing Approach: A data-driven marketing approach in marketing analytics is essential for several reasons: * **Personalization:** With access to customer data, businesses can create personalized marketing campaigns that cater to individual preferences and behaviors, leading to higher engagement and conversion rates. * **Better Decision Making:** Data-driven insights help marketers make informed decisions, reducing the reliance on gut feeling or intuition. This leads to more strategic planning and allocation of resources. * **Optimize Marketing Spend:** By analyzing data, marketers can identify which channels and campaign delivers the best ROI, enabling them to optimize their marketing budgets and invest in most effective strategies. * **Performance Tracking:** Data analytics allows for continuous monitoring and evaluation of marketing campaigns, making it easier to track performance, identify issues and adjust tactics in real life. * **Customer Insights:** Understanding customer behavior, preferences and trends through data, helps in developing products and services that better meet customer needs, ultimately driving customer satisfaction and loyalty. * **Competitive Advantages:** Companies that leverage data effectively can gain a competitive edge by anticipating market trends, staying ahead of competitors and quickly adapting to changes in the market. * **Measurable Results:** A data-driven approach provides clear matrix and KPI that can be tracked overtime, demonstrating the impact of marketing efforts and justifying marketing spend to stakeholders. * **Enhanced Customer Experience:** By using data to understand and predict customer needs, companies can improve the overall customer experience, resulting in increased customer retention and advocacy. ## Marketing Engineering In Summary, a data driven marketing approach is crucial for creating effective, efficient and impactful marketing strategies that drive business growth and enhance customer relationships. Marketing Engineering is the practice of applying quantitative methods and analytical tools to solve marketing problems. It combines marking concepts with data analysis modeling, and technology to help marketers make informed decisions. The term reflects the growing importance of data driven strategies in marketing, where traditional intuition *[unclear text]* based approaches are supplemented or even replaced by scientific and analytical methods. ### Key aspects of Marketing Engineering: * **Data Driven Decision:** It involves the use of data analytics to understand customer behaviour, segment markets and predict outcomes. This might include analyzing big data leveraging machine learning or using statistical methods. * **Modeling and Optimization:** Marketing engineers often create mathematical models to simulate market scenarios, optimize pricing, forecast demand or allocate resources efficiently. * **Software and Tools:** Various software tools are used in Marketing engineering, such as CRM Systems, Marketing automation platforms and advanced analytics tools like R, Python, or specialized marketing software like SAS, SPSS or Google Analytics. * **Customer Relationship Management (CRM):** Marketing engineering often involves using CRM system to mange and analyze customer interactions and data throughout the customer lifecycle. * **Automation:** Implementing automated marketing processes such as email campaign, *[unclear text]* targeting, and customer engagement strategies is a common practice within marketing engineering. ## Application of Marketing Engineering: * **Market Segmentation:** Identifying distinct groups within a market to target more effectively. * **Pricing Strategy:** Using models determine optimal pricing that maximizes profit while remaining competitive. * **Customer Lifetime Value Analysis:** Estimating the total revenue a business can expect from a customer over the duration of the relationship. * **Sales Forecasting:** Predicting future sales based on historical data, market conditions, and other variables. * **Resource Allocation:** Optimizing the distribution of marketing resources such as budget or time to maximize returns. ### Benefits of Marketing Engineering 1. **Increase Efficiency:** By using data and models in marketing engineering helps reduce waste and improve the effectiveness of marketing efforts. 2. **Better Customer Insights:** Allows for a deeper understanding, customer needs, behavior and preferences. 3. **Scalability:** Data driven approaches can be scaled more easily than traditional methods, making it possible to adapt to larger markets or more complex marketing environments. ## Model Building in Marketing Engineering Model building in marketing engineering refers to the process of creating mathematical, statistical, computational models to analyze, predict, and optimize marketing decisions. This model helps marketers understand customer behavior, forecast sales, segment markets, optimize pricing strategies, allocate budgets and more. ### Key Steps in Model Building in Marketing Engineering: 1. **Problem Analysis:** Clearly define the marketing problem or decisions that need to be addressed. This could involve understanding customer segmentation, predicting customer churn, optimizing pricing, etc. 2. **Data Collection:** Gather relevant data that can be used to build the model. This data could come from various sources such as customer transaction data, market research, social media, or other external data resources. 3. **Data Preprocessing:** Clean and prepare the data for analysis. This step may involve handling missing values, normalizing data, removing outliers and transforming data into a suitable format for modeling. 4. **Model Selection:** Choose the appropriate type of model based on the problem at hand. Common models in marketing engineering include: * **Regression Models:** For predicting continuous outcomes (eg: Sales Forecasting). * **Classification Models:** For predicting categorical outcomes (eg: Customer churn prediction). * **Cluster Analysis:** Cluster analysis is used for marketing segmentation. * **Time Series Analysis:** For forecasting trends over time. * **Decision Trees/Random Forest:** For classification and regression problems. * **Machine Learning Models:** For more complex pattern recognition and predicting tasks. 5. **Model Building:** Develop a model using the selected methodology. This involves defining the model structure, selecting and estimating model parameters. 6. **Model Validation:** Validate the model using a separate data set or through cross-validation technique to ensure it generalizes well to unseen data. This step is crucial to avoid overfitting. 7. **Model Implementation:** Implement the model in a real-world marketing context. This could involve integrating the model into a decision support system, using it to inform marketing strategies, or conducting simulations. 8. **Model Evaluation and Monitoring:** Continuously evaluate the models performance and update it as needed. Marketing environments are dynamic, so models may need adjustments over time to stay relevant. ## Application of Marketing Engineering Models: * **Customer Segmentation:** Identifying distinct customer groups based on purchasing behavior, demographics and *[unclear text]*. * **Market Response Model:** Analyzing how change in marketing variables impacts sales or market share. * **Sales Forecasting:** Predicting future sales based on historical data. * **Marketing Mix Optimization:** Allocating marketing resources (eg: Budget) across different channels to maximize ROI (return on investment). * **Price Optimization:** Determining the optimal price points for products/services to maximize profit or market share. ### Tools and Techniques: * **Statistical Software:** R, SAS, SPSS * **Machine Learning Platform:** Python (Scikit-learn, TensorFlow), Rapid Miner. * **Data Visualization:** Tableau, Power BI * **Optimization Tool:** Excel, Solver etc. **Marketing Engineering Models are crucial for data driven decision making, allowing marketers to move from intuition-based strategies to those based on empirical evidence and analysis** ## Model building in Regression Analysis: Model building *[unclear text]* regression analysis involves selecting and fitting a model that explains the relationship between a dependent variable on one or more independent variables. Below is an example of the steps involved in building a simple linear regression model. **Problem Statement:** Suppose you want to predict the salary (Y) of an employee based on their years of experience (X). **Step-1 → Data Collection:** You gather *[unclear text]* with the following columns: - Years of Experience *[unclear text]* - Salary (Y) **For example:** | Years of Experience (X) | Salary (Y) | | ----------------------- | ------------- | | 1 | 30,000 | | 2 | 35,000 | | 3 | 40,000 | | 4 | 45,000 | | 5 | 50,000 | **Step-2→ Data Exploration** * **Scattered Plot:** The relationship between years of experience and salary is visualized. This helps to check if a linear relationship seems like a good fit. * **Summary Statistics:** Calculate mean, variance, correlation, etc to understand the distribution. **Step-3 → Model Specification:** Specify a linear regression model: $Y = β_0 + β_1x + ε ———— (ⅰ)$ Where: * $Y$ = dependent variable (Salary) * $X$ = independent variable (Years of experience) * $β_0$ = Intercept (The salary when x = 0) * $β_1$ = Slope (The change in salary for each additional year of experience) * $ε$ = Error term **Step-4 → Estimation of Parameters:** Use ordinary least square (OLS) to estimate the parameters $β_0$ and $β_1$. The goal is to minimize the sum of square errors. **Basic principles of Marketing Analytics to real-life problems:** Marketing analytics involves the use of data to access the performance of marketing strategies and campaigns, helping businesses make informed decisions. It applies statistical models, machine learning, and data visualization techniques to gain insights into consumer behavior, marketing efforts, and major ROI. Below are the basic principles of marketing analytics and their applications to real-life problems: **1. Customer Segmentation:** * **Principle:** Grouping customers based on shared characteristics (Demographics, Behavior, Preferences) to better target and personalize marketing strategies. * **Application:** * **Problem:** A retail company wants to increase customer engagement and sales. * **Solution:** By using customer segmentation, the company can identify high-value customers (eg Frequent buyers) and personalized offers or promotions, improving engagement and driving sales. **2. Attribution Modeling:** * **Principle:** Determining which marketing channels or touchpoints contribute to conversion and assigning credits to these touchpoints. * **Application:** * **Problem:** A company invests in multiple marketing channels (social media, email, paid search) but it is unsure which channel drives the most conversion. * **Solution:** Attribution modeling helps the company determine the role of each channel in customer conversions, allowing them to allocate their marketing budget more effectively. **3. Customer Lifetime Value Analysis:** * **Principle:** Estimating the total value a customer will bring to a business over their lifetime, considering factors like repeated purchases and retention rates. * **Application:** * **Problem:** A subscription-based service wants to retain customers and maximize profits. * **Solution:** By analyzing CLV, the company can identify profitable customer segments, focus retention efforts on high-value customers, and tailor retention campaigns to reduce churn and increase loyalty. **4. Market Mix Modeling (MMM):** * **Principle:** Analyzing how different marketing channels (TV, print, digital, etc.) contribute to overall sales and optimizing the mix of channels for maximum impact. * **Application:** * **Problem:** A brand wants to know if its TV ads, digital marketing, or in-store promotions are driving more sales. * **Solution:** Through MMM, the brand can determine the effectiveness of each channel and relocate its marketing spend to the most efficient platforms, improving overall campaign ROI. **5. A/B Testing:** * **Principle:** Experimenting with two versions of marketing elements (eg email, subject lines, landing pages) to see which one performs better in terms of conversions or engagement. * **Application:** * **Problem:** An e-commerce business is unsure which website design results in more completed purchases. * **Solution:** By running an A/B test with two different designs, the business can determine which layout leads to higher sales and implement that design across its platform. **6. Predictive Analysis:** * **Principle:** Using historical data and statistical algorithms to predict future outcomes such as customer behaviour, sales trends, or market demand. * **Application:** * **Problem:** A company wants to forecast the demand for its products during a specific season. * **Solution:** Predictive analytics can help the company anticipate future demands based on past sales trends, allowing for more accurate inventory planning and avoiding stockouts or overstock situations. **7. Track the Right Matrices:** * **Principle:** Track the right matrix that aligns with business goals. * **Application:** A subscription service company focuses on CLV and customer acquisition cost (CAC) as KPI... They monitor how marketing campaigns influence the balance between CSE and CLV to ensure profitability. **8. Test & Optimize:** * **Principle:** Regularly test different marketing strategies, campaigns, or elements to find what works best. * **Application:** A fashion retailer conducts A/B testing on email subject lines and call-to-action buttons to see which variation leads to higher click-through rates. They optimize future email campaigns based on the successful elements. **9. Continuous Monitoring & Reporting:** * **Principle:** Regularly monitor and report on performance metrics to stay aligned with business goals. * **Application:** A financial services firm uses real-time dashboards to monitor the performance of its lead generation campaigns. Marketing teams review weekly reports to make quick adjustments to campaigns that are not meeting their goals. **10. Social Media and Sentimental Analysis:** * **Principle:** To understand how customers feel about a brand or product in real-time, allowing businesses to adapt their marketing strategies accordingly. * **Application:** Businesses use social media to listen to what customers are saying about their products, services, competitors. It helps to quantify feedback into measurable data, determining whether the overall perception is positive, negative, or neutral. ## Clustering Clustering means grouping objects (variables or items). Searching the data for a structure of "natural" groupings is an important exploratory technique, which is quite helpful in understanding the complex nature of multivariate relationships. Groupings can provide an informal means for assessing dimensionality, identifying outliers, and suggesting interesting hypothesis concerning relationships. Grouping or clustering is distinct from the methods of classification. Classification pertains to a known number of groups, and the operational objective is to assign new observations to one of these groups. Cluster analysis *[unclear text]* a more primitive technique in that no assumptions are made concerning the number of groups or the group structure. Grouping is done on the basis of "similarities" or "distances" (dissimilarities). The inputs required are similarity measures or data from which similarities can be computed. Let us consider sorting the 16 face cards in an ordinary deck of playing cards into clusters, of similar objects. [Diagram of playing cards is present here, with the image missing] Let us consider sorting the 16 face cards in an ordinary deck of playing cards into clusters, of similar objects. There is often a great deal of subjectivity involved in the choice of a similarity measure. Important considerations include the nature of the variables (discrete, continuous, binary); scales of measurement (nominal, ordinal, interval, ratio), and subject matter knowledge. When items (units or cases) are clustered, proximity is usually indicated by some sort of distance. On the other hand, variables are usually grouped on the basis of correlation coefficients or like measures of association. **Distance & Similarity Coefficients for pairs of items** ## Advantages of the Bayesian Decision Rule: * **Flexibility:** In cooperation, prior information can handle various loss functions. * **Customizable:** Adapts to different decision-making contexts by defining different loss functions. * **Optimality:** Provides decisions that minimize the expected cost or risk. ## Limitations: * **Computational Complexity:** Calculating the posterior distributions and expected loss functions can be computationally complex. * **Subjectivity:** The choice of priors and loss functions can introduce subjectivity into the decision-making process. **Example:** Imagine a scenario where you want to estimate a parameter θ. 1. **Actions (a)** represent possible estimates for θ. 2. **The loss function L(θ, a)** is (θ - a)^2 (Squared Error). 3. **The expected loss for a particular action** is R(a) = ∫(θ - a)^2. P(θ|(Given) data) dθ. To find the optimal action (a*) a* = argminR(a) In this case, the solution is posterior mean because a* = E[Given data], the expected *[unclear text]* minimizes the expected squared error. ∑(x_i - A)^2 is a least when A = x. (x_i - A) = (x_i - x + x - A) (x_i - A)^2 = [(x_i - x) + (x - A)]^2 ⇒ (x_i - A)^2 = [(x_i - x)^2 + 2(x_i - x)(x - A) + (x - A)^2] ∴ ∑(x_i - A)^2 = ∑(x_i - x)^2 + 0 + ∑(x - A)^2 ≥ 0 = ∑(x_i - x)^2 + n(x - A)^2 ≥ 0 n(x - A)^2 ≥ 0; x = A for posterior mean of *[unclear text]* {∑(x_i - x)^2 = 0}