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Module 1-Introduction to Marketing Analytics.pptx
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- 2. DATA ANALYSIS VERSUSDATA ANALYTICS Aspect Data Analysis Data Analytics Focus Understanding current and past events (Current, Past), Predicting future events and prescribing actions Nature Descriptive (Descriptive), Predictive and Prescriptive Techniques Basic statistical analysis, data visualization (Basic),Advanced statistical methods, machine learning, AI Data Historical Data Historical and real time data Outcome Insights into past performance, identification of trends Insights to drive future decisions, optimize processes Select Tools Excel, SQL, basic BI tools (Excel, SQL, BI tools), R,
- 4. MARKETING ANALYTICS • MarketingAnalytics • Involves the use of data and analytical techniques to understand and optimize marketing strategies and tactics. • Encompasses a wide range of activities, including data collection, data analysis, and the application of statistical and mathematical models to solve marketing problems • Helps businesses make data-driven decisions to improve marketing performance and return on investment (ROI)
- 5. Resource Allocation Managers mustunderstand their marketing efforts as precisely as possible to determine how much to spend on each marketing channel Resource allocation is the endgame of analytics for any company Resource allocation provides a strategic and unifying framework for the wide ranging purposes of marketing analytics within an organization You can view analytics as the engine that provides a forward-looking perspective for marketing dashboards. Using marketing analytics properly, any firm should be able to determine the optimal level of spending it should make on each of its marketing channels to maximize success Cutting-Edge Marketing Analytics - Real World Cases and Data Sets for Hands On Learning (2015) , Rajkumar Venkatesan, Paul Farris , Ronald T. Wilcox , Pearson Education
- 6. Resource Allocation Frameworkfor Marketing Analytics Determine the Objective Function •Conversion rates to sales •Incremental margins and profits •Customer Lifetime Value •Near term sales life •New buyers •Repeat Sales •Market Share •Retention Rates •Cross-sell rates •Future growth potential •Balance Sheet Equity •Business Valuation Connect to Marketing inputs •Determine relationships •Accounting relationships oAttributes that contribute to profits are gross profit and marketing costs •Empirical relationships oAnalyse historical data to connect marketing costs to sales Estimate best weights for empirical relatonships •Econometric (regression) model •Identify marketing inputs of interests (e.g. price, advertising, sales calls) •Dependent variables (market share, profits, CLV (Customer Lifetime Value) •Predict best shape of objective function Identify the Optimal Value •Optimal value of marketing inputs that maximise the objective function Cutting-Edge Marketing Analytics - Real World Cases and Data Sets for Hands On Learning (2015) , Rajkumar Venkatesan, Paul Farris , Ronald T. Wilcox , Pearson Education
- 7. Components of MarketingAnalytics – Broad level
- 8. Components of MarketingAnalytics • Data Collection • Description: The process of gathering data from various sources to analyze customer behavior, market trends, and campaign performance • Examples: Customer surveys, web analytics, transaction records, social media data • Data Management • Description: Organizing, storing, and maintaining data to ensure it is accurate, consistent, and accessible for analysis • Examples: Data warehouses, ETL processes, data governance policies.
- 9. Components of MarketingAnalytics • Descriptive Analytics • Description: Analyzing historical data to understand past performance and identify trends and patterns • Examples: Sales reports, market segmentation, performance dashboards. • Diagnostic Analytics • Description: Examining data to determine the causes of past outcomes and understand why certain events occurred • Examples: Root cause analysis, correlation analysis, anomaly detection.
- 10. Components of MarketingAnalytics • Predictive Analytics • Description: Using statistical models and machine learning algorithms to forecast future events and trends based on historical data • Examples: Sales forecasting, customer churn prediction, demand forecasting. • Prescriptive Analytics • Description: Providing recommendations for actions to achieve desired outcomes, often through optimization and simulation techniques • Examples: Marketing mix optimization, personalized marketing strategies, budget allocation..
- 11. Components of MarketingAnalytics • Description: Identifying and assigning credit to the various marketing channels and touchpoints that contribute to conversions and sales • Examples: Multi-touch attribution models, first-click attribution, last-click attribution. Marketing Attribution • Description: Dividing a market into distinct groups of customers with similar characteristics to tailor marketing strategies more effectively • Examples: Demographic segmentation, behavioral segmentation, psychographic segmentation.. Customer Segmentation
- 12. Components of MarketingAnalytics Campaign Analysis • Description: Evaluating the effectiveness of marketing campaigns to determine their impact and optimize future efforts • Examples: A/B testing, lift analysis, ROI analysis. Data Visualization • Description: Presenting data in graphical or visual formats to make insights more accessible and actionable • Examples: Charts, graphs, dashboards, interactive visualizations.
- 13. Components of MarketingAnalytics • Description: Creating and distributing reports that summarize key metrics and insights for stakeholders to inform decision-making • Examples: Monthly performance reports, executive dashboards, KPI scorecards. Reporting • Description: Combining data from different sources to create a unified view for more comprehensive analysis • Examples: Integrating CRM data with web analytics, merging sales data with social media insights. Data Integration
- 14. Applications of Marketing Analytics Customer Segmentation •Description: Dividing a market into distinct groups of customers based on characteristics such as demographics, behavior, and preferences to tailor marketing efforts more effectively. • The goal of segmenting customers is to decide how to relate to customers in each segment in order to maximize the value of each customer to the business. • Example: Using clustering algorithms to segment customers into different groups and then targeting each segment with personalized marketing messages • Tools: R, Python, SQL, Tableau.
- 15. Applications of Marketing Analytics Customer LifetimeValue (CLV) Analysis • Description: Calculating the total value a customer is expected to bring to a business over their entire relationship, helping prioritize marketing efforts and resource allocation • CLV Analysis plays a crucial role in business strategy and planning. It is used to determine how much a company should invest in acquiring new customers and retaining existing ones. Businesses use this information to segment their customers and develop targeted marketing strategies. • Example: Predicting CLV using historical purchase data and customer behavior patterns to identify high-value customers and focus retention efforts on them • Tools: Excel, R, Python, SQL..
- 16. Applications of Marketing Analytics Predictive Modeling •Description: Using statistical models and machine learning algorithms to predict future customer behavior, sales, and market trends • Example: Developing a predictive model to forecast sales based on historical data, seasonality, and market conditions. • Tools: R, Python, SAS, SPSS.
- 17. Applications of Marketing Analytics • MarketingMix Modeling • Description: Analyzing the effectiveness of different marketing channels and strategies to optimize the allocation of marketing resources and budget • Example: Evaluating the impact of advertising, promotions, and pricing strategies on sales and determining the optimal marketing mix • Tools: R, Python, SAS. *OOH-Out Of Home- like billboards, posters… TPR-Temporary Price Deductions DTC-Direct To Consumer marketing
- 18. Applications of Marketing Analytics • CampaignAnalysis and Optimization • Description: Assessing the performance of marketing campaigns to understand what works and what doesn't, and optimizing future campaigns based on these insights • Example: Analyzing the results of an email marketing campaign to measure open rates, click-through rates, and conversion rates, and using A/B testing to optimize future campaigns • Tools: Google Analytics, HubSpot, Marketo, Tableau.
- 19. Applications of Marketing Analytics • ChurnAnalysis and Retention Strategies • Description: Identifying customers who are likely to leave (churn) and developing strategies to retain them. • Customer Retention Analytics is determining how many of your customers are loyal customers that are likely to keep purchasing your services, and the reasons behind it. Different aspects of user behavior such as customer lifetime, customer satisfaction, and churn cohort are affected by and affect customer retention, therefore, analyzing it can help you grow in every aspect possible. • Example: Using predictive analytics to identify at-risk customers and implementing personalized retention campaigns to reduce churn rates • Tools: R, Python, SAS, SQL.
- 20. Applications of Marketing Analytics Sentiment Analysis •Description: Analyzing customer feedback and social media data to understand customer sentiments and opinions about products, services, and brands • Example: Using natural language processing (NLP) to analyze social media posts and customer reviews to gauge sentiment and address negative feedback promptly • Tools: R, Python, SAS, SQL.
- 21. Applications of Marketing Analytics Web andSocial Media Analytics • Description: Analyzing web traffic and social media interactions to understand user behavior, engagement, and the effectiveness of online marketing efforts • Example: Using Google Analytics to track website visits, user behavior, and conversion rates, and using social media analytics tools to measure engagement and reach • Tools: Google Analytics, Facebook Insights, Twitter Analytics, Tableau.
- 22. Applications of Marketing Analytics Pricing Optimization •Description: Determining the optimal pricing strategy for products and services based on market conditions, competitor pricing, and customer demand • Example: Using regression analysis and A/B testing to identify the price point that maximizes revenue and profitability • Tools: R, Python, Excel, SAS. *A/B testing—also called split testing or bucket testing—compares the performance of two versions of content to see which one appeals more to visitors/viewers. It tests a control (A) version against a variant (B) version to measure which one is most successful based on your key metrics.
- 23. Applications of Marketing Analytics Attribution Modeling •Description: Identifying and assigning credit to the various marketing channels and touchpoints that contribute to conversions and sales • Example: Using multi-touch attribution models to understand the customer journey and the relative impact of each marketing channel on conversion • Tools: Google Analytics, Adobe Analytics, R, Python.
- 24. Applications of Marketing Analytics Personalization • Description:Tailoring marketing messages, offers, and content to individual customers based on their preferences, behavior, and purchase history • Example: Implementing personalized email marketing campaigns that recommend products based on past purchases and browsing history • Tools: HubSpot, Marketo, Salesforce Marketing Cloud.
- 25. Applications of Marketing Analytics Sales Forecasting •Description: Predicting future sales based on historical data, market trends, and other relevant factors to inform business planning and inventory management • Example: Using time series analysis and machine learning models to forecast monthly sales and adjust inventory levels accordingly • Tools: R, Python, Excel, SAS
- 26. TYPES OF MARKETINGANALYTICS DESCRIPTIVE ANALYTICS Definition: Descriptive analytics involves analyzing historical data to understand what has happened in the past. It focuses on summarizing and interpreting data to provide insights into past performance Key Characteristics • Purpose: To provide a clear picture of past events and performance • Methods: Data aggregation, data mining, and data visualization • Outputs: Reports, dashboards, and visualizations that display historical trends and patterns. Examples • Sales Reports: Summarizing monthly sales data to identify trends and patterns. • Website Analytics: Analyzing web traffic data to understand user behavior and engagement • Customer Segmentation: Grouping customers based on demographics, purchase history, or other criteria. Tools: Excel, Google Analytics, Tableau, Power BI.
- 27. TYPES OF MARKETINGANALYTICS DESCRIPTIVE ANALYTICS
- 28. TYPES OF MARKETINGANALYTICS PREDICTIVE ANALYTICS Definition: Predictive analytics uses statistical models and machine learning techniques to forecast future events based on historical data. It aims to identify patterns and predict future outcomes. It focuses on summarizing and interpreting data to provide insights into past performance Key Characteristics Purpose: To predict future trends and behaviors Methods: Regression analysis, time series analysis, classification algorithms, and machine learning. Outputs: Forecasts, predictions, and risk assessments. Examples Sales Forecasting: Predicting future sales based on historical sales data and market trends Customer Churn Prediction: Identifying customers who are likely to leave based on their past behavior and interactions Demand Forecasting: Estimating future product demand to optimize inventory and supply chain management. Tools: R, Python, SAS, SPSS, Microsoft Azure Machine Learning.
- 29. TYPES OF MARKETINGANALYTICS PREDICTIVE ANALYTICS
- 30. TYPES OF MARKETINGANALYTICS PRESCRIPTIVE ANALYTICS Definition: Prescriptive analytics goes beyond predicting future outcomes by recommending actions to achieve desired results. It uses optimization and simulation techniques to suggest the best course of action. It focuses on summarizing and interpreting data to provide insights into past performance Key Characteristics Purpose: To prescribe actions that can lead to optimal outcomes Methods: Optimization algorithms, decision analysis, and scenario simulations Outputs: Actionable recommendations, optimization models, and decision support systems.. Examples Marketing Mix Optimization: Determining the optimal allocation of marketing budget across different channels to maximize ROI Personalized Marketing: Recommending personalized offers and content to individual customers based on their preferences and behavior Resource Allocation: Optimizing the allocation of resources such as sales personnel and inventory to improve efficiency and performance.. Tools: BM ILOG CPLEX, Gurobi, MATLAB, AnyLogic.
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- 32. Data for MarketingAnalytics Customer Relationship Management (CRM) Systems CRM systems store detailed information about customer interactions, purchase history, preferences, and behaviors. Example Providers: Salesforce, HubSpot, Zoho CRM. Web Analytics Tools These tools track website activity, including page views, user sessions, bounce rates, and conversion rates. Example Providers: Google Analytics, Adobe Analytics, Matomo. Social Media Platforms Social media data includes metrics such as likes, shares, comments, followers, and engagement rates Example Providers: Facebook Insights, Twitter Analytics, LinkedIn Analytics Email Marketing Platforms Email marketing platforms track email campaign performance, including open rates, click-through rates, and conversion rates. Example Providers: Mailchimp, Constant Contact, SendinBlue
- 33. Data for MarketingAnalytics Point of Sale (POS) Systems POS systems capture in-store transaction data, including product details, prices, quantities, and payment methods Example Providers: Square, Clover, Lightspeed E-commerce Platforms E-commerce platforms provide data on online sales, customer behavior, product performance, and marketing campaign effectiveness. Example Providers: Shopify, WooCommerce, Magento. Advertising Platforms Advertising platforms provide data on ad performance, including impressions, clicks, conversions, and ROI. Example Providers: Google Ads, Facebook Ads, LinkedIn Ads Surveys and Feedback Tools These tools collect customer feedback, satisfaction scores, and market research data Example Providers: SurveyMonkey, Qualtrics, Typeform.
- 34. Data for MarketingAnalytics Market Research Firms Market research firms provide syndicated data, industry reports, and custom research insights Example Providers: Nielsen, Gartner, Forrester Customer Data Platforms (CDPs) CDPs aggregate and unify customer data from various sources to create a single customer view Example Providers: Segment, Treasure Data, BlueConic Data Aggregators Data aggregators compile data from multiple sources, including public records, social media, and third-party data providers Example Providers: Acxiom, Experian, Oracle Data Cloud. Mobile Analytics Tools Mobile analytics tools track app usage, user engagement, and in-app purchases Example Providers: Firebase, Flurry, Mixpanel
- 35. Data for MarketingAnalytics Loyalty Programs Loyalty programs capture data on customer purchases, rewards, and redemption patterns Example Providers: Punchh, LoyaltyLion, Smile.io. IoT Devices Internet of Things (IoT) devices provide data on product usage, location, and environmental conditions Example Providers: AWS IoT, Cisco IoT, Microsoft Azure IoT Data available with the firm may not be enough and not always readily useful for inference
- 36. BIG DATA inMarketing Analytics Customer Data Description: Information about customers' demographics, preferences, purchase history, and behavior Examples: CRM systems, loyalty programs, transaction records Social Media Data Description: Data from social media platforms that reveal customer sentiment, engagement, and interactions Examples: Tweets, Facebook posts, Instagram comments, LinkedIn interactions.
- 37. BIG DATA inMarketing Analytics •Web Data • Description: Data generated from website interactions, such as page views, clicks, and conversion paths • Examples: Google Analytics, web server logs, heatmaps •Mobile Data • Description: Information from mobile apps and devices, including app usage, location data, and mobile transactions • Examples: In-app behavior, GPS data, mobile payment records..
- 38. BIG DATA inMarketing Analytics Transactional Data Description: Records of transactions that provide insights into sales, purchase patterns, and customer spending Examples: Point-of-sale (POS) data, e- commerce transactions, subscription data Sensor Data Description: Data from IoT devices and sensors that track customer interactions with physical products and environments Examples: Smart home devices, wearable technology, in-store sensors.
- 39. BIG DATA in MarketingAnalytics •Third-Party Data • Description: Data acquired from external sources to supplement and enhance internal data • Examples: Market research reports, industry benchmarks, data brokers.
- 40. Statistical Foundations ofMarketing SLICING AND DICING MARKETING DATA WITH PIVOT TABLES USING EXCEL CHARTS TO SUMMARISE MARKETING DATA
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- 42. Exercise on t-testand ANOVA Using Excel Functions to Summarise Marketing Data Perform t-test and ANOVA Load the Analysis ToolPak in Excel https://support.microsoft.com/en-us/office/load-the-analysis-toolpak-in-excel-6a63e598- cd6d-42e3-9317-6b40ba1a66b4
- 43. HYPOTHESIS TESTING using ttest (two sample t test) • Two-sample t-tests compare the means of precisely two groups. • We perform this test to determine whether two population means are different. For example, • Hypothesis-Do students who learn using Method A have a different mean score than those who learn using Method B? • Null: The two population means are equal. • Alternative: The two population means are not equal. • If the p-value is less than your significance level (e.g., 0.05), you can reject the null hypothesis. The difference between the two means is statistically significant. Your sample provides strong enough evidence to conclude that the two population means are different.
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- 46. Interpretation of Result •The output indicates that mean for Method A is 71.50362 and for Method B it is 84.74241. • One-tailed t-tests can detect differences between means in only one direction. For example, a one-tailed test might determine only whether Method B is greater than Method A. • Two-tailed tests can detect differences in either direction—greater than or less than. • For our results, we’ll use P(T<=t) two-tail, which is the p-value for the two- tailed form of the t-test. Because our p-value (0.000336) is less than the standard significance level of 0.05, we can reject the null hypothesis.
- 47. HYPOTHESIS TESTING using Pairedt-Tests • Paired t-tests assess paired observations, which are often two measurements on the same person or item. • These are called dependent samples. Eg. -Suppose we gather a random sample of people and give them all a pre-test, administer a treatment, and then perform a post-test. Each subject has a pretest and posttest score. • In such cases we use a paired t-test to determine whether the difference between the means of the two sets of scores is statistically significant.
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- 50. Interpretation of Results •Null: The difference between population means is significant. • Alternative: The difference between population means is not significant. • The output indicates that mean for the Pretest is 97.06223 and for the Posttest it is 107.8346. • If the p-value is less than your significance level, the difference between means is statistically significant. • For our results, we’ll use P(T<=t) two-tail, which is the p-value for the two-tailed form of the t-test. Because our p-value (0.002221) is less than the standard significance level of 0.05, we can reject the null hypothesis. So, The difference between population means of pretest and posttest is not significant.
- 51. HYPOTHESIS TESTING using ANOVA 1.Analysisof Variance (ANOVA) is a statistical formula used to compare variances across the means (or average) of different groups. A range of scenarios use it to determine if there is any difference between the means of different groups. 2.You should have at least one categorical variable for using ANOVA. The categorical variable signifies the independent variable (factor). Independent variables are those that may or may not have a significant effect on the dependent variable. In our example, levels of education (undergraduate, postgraduate, and doctorate) is the categorical variable. 3.The data should also contain the values of their corresponding continuous dependent variable. This is the variable that you suspect might be affected by the independent variables. In this example, salary is the dependent variable.
- 52. Hypotheses • Null Hypothesis(H0):Salary doesn’t vary based on the level of education (𝛍1= 𝛍2= 𝛍3) • Alternate Hypothesis (H1): Salary varies based on the level of education.
- 54. • In thisexample, since the F (6.02) > Fcritical (3.4), we can reject the null hypothesis and safely conclude that the employees’ salary varies based on their level of education
- 55. Two Factor ANOVA •You should have at least two categorical variables for using the two factor ANOVA Excel tool. The categorical variables signify the independent variables (factors). In this example, levels of education (undergraduate, postgraduate, and doctorate) and age group (junior, middle, senior) are the two categorical variables and salary is the dependent variable.
- 56. • Hypothesis 1(H1): Salary doesn’t vary based on the level of education. (𝛍1= 𝛍2= 𝛍3) • Hypothesis 2 (H2): Salary doesn’t vary based on the age group. (𝛍1= 𝛍2= 𝛍3) • Hypothesis 3 (H3): There is no interaction between level of education and age group.
- 58. • In thisexample, since the F (9.75) > Fcritical (3.55) for factor 1 (level of education) we can reject hypothesis H1 and conclude that the employees’ salary varies based on their level of education. • Similarly, since the F (29.13) > Fcritical (3.55) for factor 2 (age group), we can reject hypothesis H2 and conclude that the employees’ salary also varies based on their age. • However, since the F (0.337) < Fcritical (2.92) for the interaction effect, we can accept hypothesis H3 and conclude that there is no significant interaction between the factors ( education and age).
- 59. Exercise on Regression Analysis •Using Multiple Regression to Forecast Sales
- 60. • Regression analysisdescribes the relationships between a set of independent variables and the dependent variable. It produces an equation where the coefficients represent the relationship between each independent variable and the dependent variable. •Temperature (o C): Dependent variable •Pressure: Independent variable •Fuel Rate: Independent variable
- 63. • The R-squaredvalue of ~0.858 indicates that our model accounts for about 85.8% of the dependent variable’s variance. Usually, higher R-squared values are better. • The coefficient for Pressure is approximately 4.79. The positive sign indicates that as pressure increases, temperature also tends to increase. There is a positive association between these two variables. For every one-unit increase in pressure, temperature increases by an average of 4.79 degrees. • The coefficient for Fuel Rate is -24.21. The negative sign indicates that as the fuel rate increases, temperature tends to decrease. There is a negative association between these two variables. For every one-unit increase in fuel rate, temperature decreases by an average of 24.21 degrees. • The p-values for the coefficients indicate whether the dependent variable is statistically significant. When the p-value is less than your significance level, you can reject the null hypothesis that the coefficient equals zero. Zero indicates no relationship. In our example, Pressure and Fuel Rate are both statistically significant.
- 64. Optimisation Problems Optimization …the action of making the best or most effective use of a situation or resource. Optimization means Finding the best alternative with the highest achievable performance under the given constraints/limitations, by maximizing desired factors and minimizing undesired ones. Optimization problem is the problem of finding the best solution from all feasible solutions. LPP (Linear programming problem) is an optimization problem. Use Excel solver add-in
- 66. • Max z=1600x1 +1300x2 +600x3 Sub to: 2x1 +1.5x2 +x3 <=20 2x1 + x2 +1.5x3 <=24 X1 + 2x2 +0.5x3 <=20 https://www.youtube.com/watch?v=AUhFvjqOU1U
- 67. PROBLEM 3 (FORMULATION) •A COY HAS THREE DEPT:WEAVING, PROCESSING & PACKING • 3 TYPES OF CLOTHES NAMELY SUITINGS, SHIRTING AND WOOLEN ARE PRODUCED YIELDING PROFIT OF Rs 2, Rs 4 & Rs 3 PER METRE RESPECTIVELY. • ONE METRE OF SUITING REQUIRES 3 MIN IN WEAVING, 2 MIN IN PROCESSING & 1 MIN IN PACKING. • ONE METRE OF SHIRTING REQUIRES 4 MIN IN WEAVING, 1 MIN IN PROCESSING & 3 MIN IN PACKING. • ONE METRE OF WOOLEN REQUIRES 3 MIN IN EACH DEP • TOTAL RUN TIME OF EACH DEPT IS 60,40 & 80 HRS PER WEEK FOR WEAVING, PROCESSING AND PACKING • FORMULATE THIS AS LPP TO MAX PROFIT
- 68. • Max Z=2x+4y+3z • Sub to: Weaving Dept: 3x+4y+3z <=60*60 Processing Dept: 2x+1y+3z <=40*60 Packing Dept: 1x+3y+3z <=80*60 x,y,z >=0
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