Marketing Analytics Fundamentals

• Understanding the fundamental concepts and techniques of marketing analytics is important
• Key metrics and KPIs used in marketing analytics are important to be able to define
• Customer segmentation strategies should be developed based on demographic, psychographic, and behavioral characteristics
• Marketing campaigns should be evaluated and optimized using A/B testing and experimentation
• Analytics impact on CRM, Digital marketing, Sales and customer engagement should be considered

Data

• Data in marketing is ubiquitous
• Data is collected at every purchase, flight taken, ad clicked, and social media post liked, making it more accessible to organizations
• Access to data requires employees who understand and know how to leverage it
• There is an increased demand for data-literate business professionals who can handle, analyze, and interpret data to drive decision-making
• Basic data literacy, like the ability to analyze, interpret, and question data is valuable
• Data can allow you to gain and act on customer insights, predict future financial and market trends, and enact systemic change for social good
• Big data has volume, variety, velocity, variability, veracity, value

Marketing Analytics

• Marketing Analytics uses Statistics and Machine Learning models and visualizations to provide data-driven inference for marketing decisions
• Marketing analytics increases return on investment (ROI) and create better customer experiences
• It impacts domains like CRM, advertising, and Digital marketing
• Types of marketing analytics tools can collect, analyze, and interpret your marketing data
• Common tools are web analytics, email analytics, social media analytics, mobile analytics, and lead generation analytics
• Some of the common models are descriptive, predictive, and prescriptive analytics
• Models can help you answer different questions and achieve different goals
• For example, you can use web analytics tools like Google Analytics to know how many people visited your website, how long they stayed, and what pages they viewed

How Data Helps

• Data science is building, cleaning, and structuring datasets to analyze and extract meaning
• Data analytics is analyzing data to answer questions, extract insights, and identify trends
• Customer habits: Data about customers can reveal their habits, demographics, preferences, and aspirations
• Data science can increase security; machine-learning algorithms can detect bank fraud with greater accuracy
• Data can inform internal finances
• An organization's financial team can utilize data science to create reports, generate forecasts, and analyze financial trends
• Data on accompany'scashflows,assets,anddebtsisconstantlygathered, which financial analysts use to manually or algorithmically detect trends in financial growth or decline
• Streamline manufacturing/Services :Manufacturing machines gather from production processes at high volumes.Incases where the volume of data collected is too large
• Collecting and analyzing data on a larger scale to identify emerging trends
• Staying up to date on the behaviors of your target market allows you to get ahead of the curve

Creating Target Groups

• Demographics describe audiences based on measurable and quantifiable factors such as: gender, age, income level, marital status, education, race, religion
• Psychographic segmentation divides the target market based on socio-economic class, personality, or lifestyle preferences examples are: attitudes, aspirations, habits including media & product consumption

Segmentation

• Segmenting is grouping sets of consumers into groups based on similar characteristics
• Segments can be based on demography, Psychographic, geography, category or brand related attitude and behaviors
• Subdividing a market into distinct subsets of customers that behave in the same way or have similar needs, allow each subset may conceivably be chosen as a market target to be reached with a different marketing stategy
• Uses in advertising: Help in defining Target groups
• Consumers segment by usage or income

Audience Measurement

• Audience measurement measures how many people are in an audience
• This is usually in relation to radio listenership and television viewership, newspaper and magazine readership and web traffic on websites
• Measurements are broken down by media market, which for the most part corresponds to metropolitan areas, both large and small
• IRS/TAM/RAM
• Diary method : researcher goes to the audience with a booklet and records TV audience

Digital / Web Analytics

• Techniques used to assess and improve the contribution of digital marketing to a business
• Traffic volume, clickstreams, online reach data, customer satisfaction surveys, leads, and sales are assessed
• Key tools are the following links:
  • https://www.smartinsights.com/
  • https://www.digitalanalyticsassociation.org/

Audience Measurement on Digital

• 1. Website Traffic
• 2. Demographics
• 3. Audience Interests/Behaviors
• 4. Search Keywords
• 5. Digital Media Consumption
• 6. Clicks
• 7. Conversions

Key Tools for Digital Advertising Measurement

• https://marketingplatform.google.com/about/analytics/
  • Google Analytics is the most-used tool for web analytics on the internet
• https://www.youtube.com/user/googleanalytics
• https://www.hubspot.com/
  • One in all solution that provides plenty of feedback and support for analysis and evaluation
• https://hootsuite.com/en-gb/
  • Manages multiple social platforms
• https://mixpanel.com/
  • Mixpanel is a tool for mobile, allowing you to measure the ways people are using app or site, whether they're coming back and using them again after their first visit

Analytics Approaches Explained

• Descriptive Business Intelligence answers the question of "what happened?", helping understanding

• Predictive Prediction engines answers the question "what will happen?", supporting a decision

• Prescriptive Decision automation answers the question "what should I do now?", and tells you what to do

• Strategic planning and initial product pricing are examples of descriptive role machine

• Demand planning and discount/promotion management are examples of predictive role machine, also CRM segment

• Inventory optimization and price optimization are examples of prescriptive role machine, also Markdown optimization

• Simplified manual approach is descriptive rationale

• Semi-automation is predictive rationale

• Full automation is prescriptive rationale

• Descriptive analysis involves checking variation in each variable sing the mean, variance, and covariance (or correlations) usually performed using EDA

• Correlation is a technique to examine how two variables move together

• Predictive analytics is a discipline focused on estimating the values of a metric Machine learning technologies play a critical role in predictive analytics

• Prescriptive analytics aims at suggesting a course of action and outcome associated with each action, like pricing change or increase in promotion spends in a channel

Overview of Marketing Analytics

• Marketing analytics is using statistical and machine learning models and visualizations to inform about key marketing decisions
• Marketing analytics possibilities start with availability and depth of data that can be from firm's internal sources or market research
• The marketing analytics use case begins with exploratory data analysis (EDA)
• EDA is a process used by data scientists to analyze and investigate data sets and summarize their main characteristics and involves:
  • Discovery
  • Spotting Anomalies
  • Test hypotheses
  • Checking assumptions

Digital Advertising Measurement Tools

• https://www.megalytic.com/ which allows data to be pulled in Google Analytics or Adwords to create integrated reports
• https://www.salesforce.com
• https://www.salesforce.com/products/einstein-analytics/overview/ features EinstienAnalytics for buildinga clear picture of how your marketing activity is impacting sales
• https://www.alexa.com/topsites/countries/IN is a tool to check ranking and page hits

Mean, Median, and Mode

• The mean is the most used measure of central tendency as it is the average of the given collection of data
• The mean can be calculated for both continuous and discrete data
• Mathematically, the mean is equal to the sum of all the values in the data set divided by the total number of values
• The median is the middle value of a data set when the values are arranged in ascending order
• It is the value that separates the lower half from the upper half of the data
• The mode is the data value that appears most frequently in the data set and represents the value with the highest frequency
• A data set can have one mode (unimodal), multiple modes (multimodal), or no mode if all values have equal frequency

Sources of Marketing Information:

• Users of Marketing Information can be:
  • Marketing Department People
  • Management People
  • Heads of other Departments
• Internal Data:
  • Order history
  • Sales and stock records
  • Internal Research findings
• External Data
  • Hiring MR Agency
  • Government Reports
  • Commercial Databases
  • Info from channel members
  • Competitors' data

Touchpoint and Profiling of the Customer

• Touch Point Idea: Every information you use you are adding information for the seller.
• Customer Information:
  • Bank account
  • Credit Card
  • Email account
  • Apps used
  • Institute data
  • Company data
  • Browser data
  • Social media accounts
  • Purchasing power
  • Education
  • Vehicle information
  • Professional memberships

Customer Engagement

• eMail
• Mobile Messaging including whatsapp
• Social Media -Facebook
• Mobile Apps
• Tele calling
• Make customer 'Subscribe'
• Ask for referrals and reward for references

Advanced Computing Terms

• Machine Learning: focuses on the development of methods that access data and infer patterns within the data, without the need of being explicitly programmed
• Blockchain: Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network
• Internet of Things (IoT): network of physical devices, vehicles, appliances, and other physical objects that are embedded with sensors, software, and network connectivity, allowing them to collect and share data

Advanced Computing Concepts

• Quantum Computing: It uses particles like electrons or photons to process complex information faster and more efficiently
• It uses particles like electrons or photons to process complex information faster and more efficiently
• Deep Learning: Machine learning based on artificial neural networks
• Large Language Model (LLM): A large language model is a type of artificial intelligence algorithm that applies neural network techniques with lots of parameters
• Neural Networks: computational models that mimic the complex functions of the human brain
• The neural networks consist of interconnected nodes or neurons that process and learn from data: This enables tasks such as pattern recognition and decision making in machine learning

Importance of marketing Analytics

• 1. Improves user experience by collecting and analyzing users' first-party data to reveal how they feel about their interactions with the product and website
• 2. Calculate the Return on Investment of Marketing Efforts
  • Calculating monetary gain attributed to specific marketing channels or campaigns.
• 4. Gain a better understanding of customers through actionable answers about customer base, their actions, and pain points for team improvements
• 5. Refine marketing strategy by refining it and using real time data
• 6. Predict the success of future marketing campaigns through data and predictive scoring

Calculating ROI

• For a specific marketing effort, use the following formula: ROI = (Net Profit / Cost of Investment) x 100 Net profit
• Ex. 10 Investment is 100 ROI will be ? 10/100x100 = 10%

Popular Marketing Analytics Software

• Hubspot: measures the performance of all marketing campaigns https://www.hubspot.com/products/marketing/analytics
• Sprout Social: manages, listens, and tracks social media engagement https://sproutsocial.com/
• SEMRush: tracks and measures content marketing efforts https://www.semrush.com/
• Brandwatch: finds trends, gathers consumer insights, and tracks marketing campaign performance https://www.brandwatch.com/
• Salesforce: marketing campaign performance across all channels https://www.salesforce.com/products/marketing-intelligence/

Marketing Analytics Process Steps

• Business Understanding
• Data understanding
• Data Preparation
• Modeling
• Evaluation
• Deployment

Explore the core concepts of marketing analytics. Understand its role, the importance of data literacy, and key aspects like data veracity. See how A/B testing, website tracking, and CRM are enhanced through marketing analytics.