The document outlines the significance of marketing analytics in understanding and optimizing marketing strategies through data collection, analysis, and statistical modeling. It details various components of marketing analytics, including descriptive, predictive, and prescriptive techniques, as well as their applications in customer segmentation, campaign analysis, and resource allocation. Furthermore, it emphasizes the role of data from multiple sources in enhancing decision-making processes and achieving optimal marketing outcomes.

1. MARKETING ANALYTICS
MODULE I

2. DATA ANALYSIS VERSUS DATA 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
• Marketing Analytics
• 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 must understand 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 Framework for 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
•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 Marketing Analytics –
Broad level

8. Components of Marketing Analytics
• 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 Marketing Analytics
• 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 Marketing Analytics
• 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 Marketing Analytics
• 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 Marketing Analytics
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. A/B Testing

14. Lift Analysis

15. Components of Marketing Analytics
• 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

16. 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
• Example: Using clustering algorithms to segment
customers into different groups and then
targeting each segment with personalized
marketing messages
• Tools: R, Python, SQL, Tableau.

17. Applications of
Marketing
Analytics
• Customer Lifetime Value (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
• 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..

18. 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.

19. Applications of
Marketing
Analytics
• Marketing Mix 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.

20. Applications of
Marketing
Analytics
• Campaign Analysis 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.

21. Applications of
Marketing
Analytics
• Churn Analysis and Retention Strategies
• Description: Identifying customers who are
likely to leave (churn) and developing
strategies to retain them
• Example: Using predictive analytics to
identify at-risk customers and implementing
personalized retention campaigns to reduce
churn rates
• Tools: R, Python, SAS, SQL.

22. 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.

23. Applications
of
Marketing
Analytics • Web and Social 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.

24. 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.

25. 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.

26. 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.

27. 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

28. TYPES OF MARKETING ANALYTICS
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.

29. TYPES OF MARKETING ANALYTICS
DESCRIPTIVE ANALYTICS

30. TYPES OF MARKETING ANALYTICS
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.

31. TYPES OF MARKETING ANALYTICS
PREDICTIVE ANALYTICS

32. TYPES OF MARKETING ANALYTICS
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.

33. Data for Marketing
Analytics

34. Data for Marketing Analytics
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

35. Data for Marketing Analytics
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.

36. Data for Marketing Analytics
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

37. Data for Marketing Analytics
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

38. BIG DATA in Marketing 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.

39. BIG DATA in Marketing
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..

40. BIG DATA in Marketing 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.

41. BIG DATA in
Marketing Analytics
• Third-Party Data
• Description: Data acquired from
external sources to supplement and
enhance internal data
• Examples: Market research reports,
industry benchmarks, data brokers.

42. Statistical Foundations of Marketing
SLICING AND DICING
MARKETING DATA WITH PIVOT
TABLES
USING EXCEL CHARTS TO
SUMMARISE MARKETING DATA

43. General Linear Models

44. Exercise on t-test and ANOVA
Using Excel Functions to
Summarise Marketing Data
Perform t-test and ANOVA

45. Exercise on
Regression Analysis
• Using Multiple Regression to Forecast
Sales

46. Optimisation Problems

47. Exercise on Optimisation Problems