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Data
Analytics
Advanced Digital Strategy MGMT X 466.05 UCLA
Lecturer: Valters Lauzums 2024
Overview & Concepts
Data analytics is a math-based discipline that seeks to find patterns in data to increase
actionable knowledge that can be used to improve business performance.
Quantify your
communication
Numbers are persuasive. Telling your CFO that customer
engagement has improved is less impactful than providing data
showing a 20% increase due to a specific campaign. Analytics
allows you to back up your claims with hard data.
Data analytics is a math-based discipline that seeks to
find patterns in data to increase actionable knowledge
that can be used to improve business performance. It
employs statistics, predictive modeling, and machine
learning to reveal insights and answer questions. Data
analytics is critical for understanding impact, predicting
trends, and optimizing strategies to drive growth.
Practical Data
Analytics
Turn Data into Information: Most businesses have access to data,
but the key is turning that data into useful information.
Compare and Contrast Data: Analytics enables you to compare
different data sets to understand which strategies are working.
Key Techniques
Uses historical data to forecast future events. Techniques include
regression analysis to find relationships between variables, time series
analysis to identify trends over time, and machine learning algorithms to
model and predict future behaviors based on past data.
Predictive Analytics
Offers recommendations to achieve desired outcomes. Techniques
include optimization models to find the best course of action within
constraints, simulation to test scenarios and predict outcomes, and
decision trees to map possible decisions and their consequences.
Prescriptive Analytics
Summarizing historical data to understand past events. Techniques
include data aggregation, which combines data from various sources
for a comprehensive view, data mining to extract patterns from large
datasets, and reporting using dashboards and visualizations.
Descriptive Analytics
Helps understand why certain events occurred. Techniques include root
cause analysis to identify underlying causes, drill-down analysis to break
down data for specific insights, and correlation analysis to examine
relationships between variables and identify causative factors.
Diagnostic Analytics
Data Types &
Structure
Each data type and the format of the data serves a unique purpose in
the analytics process and must be distinguished for proper analysis.
Structured Data
Quantitative Data: This includes numerical data that can be
measured and quantified. Examples in marketing include sales
figures, click-through rates, and conversion rates.
Qualitative Data: This encompasses descriptive data that is not
easily measured but provides insights into customer behaviors
and preferences. Examples include reviews and comments.
Types of Data
Unstructured Data is not organized in a pre-defined manner, while
structured data is highly organized and easily searchable. It fits neatly
into rows and columns, such as data found in spreadsheets .
Common Tools
Marketing Analytics Use Cases
Google Analytics
You should know how to use this tool.
Google Analytics is one of the most
widely used tools for tracking and
analyzing website traffic and provides
insights into user behavior, traffic
sources, and conversion rates.
Google Looker
Google Looker is a modern business
intelligence (BI) and data analytics
platform that allows organizations to
explore, analyze, and visualize their
data in real time. Formerly known as
“Google Data Studio”
Adobe Analytics
Adobe Analytics offers advanced
features for tracking and analyzing
customer data across various
channels. It provides robust data
visualization and reporting tools,
making it ideal for large enterprises.
Hubspot
HubSpot is an all-in-one inbound
marketing platform, which is very
similar to other CRM analytics
capabilities.. It helps marketers track
the performance of their content and
marketing campaigns.
Data Analysis Process
1. Data Goals 2. Data Collection
Ensure the data is accurate and consistent
by removing duplicates, correcting errors,
and filling in missing values.
3. Data Cleaning
4. Data Extrapolation 5. Data Modeling
Gather data from various sources such as
web analytics, social media, customer
surveys, and sales records.
What marketing questions do you want to
answer with the data? What decisions will
be made or influenced by the results?
Use statistical techniques to explore the
data and identify patterns or trends. This
includes summarizing data using math.
Apply analytical models to the data to
derive insights, e.g. regression analysis,
cluster analysis, and CLV models.
Interpret the results to make data-driven
decisions. This involves translating findings
into actionable marketing strategy.
6. Data Interpretation
Key steps for analyzing marketing data
Optimize Campaigns
Data on past campaign performance can help
marketers optimize future campaigns. For example,
A/B testing results can reveal which ad copy, visuals,
or calls-to-action resonate most with audiences. This
continuous optimization improves effectiveness.
Predicting Behavior
Predictive analytics uses historical data to forecast
future customer behavior. This can help in
anticipating market trends, customer needs, and
potential sales opportunities. Marketers can use this
to create new sales and marketing opportunities.
Define Audiences
Demographic data, purchase histories, and online
behaviors can be used identify and segment target
audiences more accurately. This will help tailor
marketing to the needs and preferences of each
segment and improve marketing effectiveness.
Practical Application
Solving Problems with Data
Heavy Duty
Analytics
Basic analytics techniques can be implemented
with digital tools that are used across all business
categories.
When using analysis techniques on a larger pool of
data at a business environment with a larger
budget (at a larger brand) you will be able to use
and implement more sophisticated techniques
based on team capabilities and resources.
Cohort Analysis
Groups individuals sharing common characteristics
over a specific period. This method focuses on
evaluating these cohorts over time to derive insights
into lifecycle patterns or behaviors.
Cluster Analysis
Used to group data points that are similar to each
other. By categorizing data into subsets, clustering
helps in revealing patterns, similarities, and areas of
concentration within a dataset.
Sentiment Analysis
Method of processing and analyzing text data to
determine the sentiment or emotional tone behind
that text. It’s widely used to gauge public opinion, and
monitor brand reputation.
Monte Carlo Simulation
Computational technique that employs random
sampling to estimate complex statistical problems.
The main objectives are to quantify uncertainty and
provide a range of possible outcomes.
Factor Analysis
Statistical method primarily used for data reduction
which explores how observed variables correlate
with one another, aiming to pinpoint underlying
factors that influence these correlations.
Regression Analysis
At its core, it estimates how one variable (the
dependent variable) is influenced by one or more
other variables (independent variables). The primary
goals of regression are to predict and explain.
Advanced Methods Data Analysis Techniques
Deciphering and transforming this raw information into actionable insights is the primary job for any analytics method. Simple
statistical techniques can be as impactful as complex machine learning algorithms.
Lecturer: Valters Lauzums 2024
Marketing Analytics
In the modern marketing landscape, data analytics plays a pivotal role in creating and delivering highly
targeted digital campaigns and tracking web properties.
1st party data is the most reliable and precise,. 2nd party
data provides additional value and. 3rd party data should
be used carefully due to lower accuracy and relevance.
Data Ownership
1st party data: the information a company collects directly from
its customers or audience. This includes data from their website
interactions, purchase history, and social media engagement.
2nd party data: someone else’s 1st party data that is shared
between trusted partners. Brands share customer data, benefiting
both parties with new audiences and data without intermediaries.
3rd party data: collected by entities that do not have a direct
relationship with the consumer. Aggregated from various sources
and sold to any company, anywhere, for any purpose.
Customer Data
The data broker market was valued at $245 billion in 2021 and is projected to grow at a
CAGR of 11.5% through 2028.
Data is collected from customer interactions, online behaviors, transaction
records, and publicly available information. This can be gathered through
websites, mobile apps, social media platforms, and physical stores.
Companies like Acxiom and Experian provide additional consumer data.
These brokers enhance collected data with information on purchasing
habits, lifestyle preferences, and demographic details.
Retailers use data to segment customers into categories like “frequent buyers,”
“bargain hunters,” and “luxury shoppers.”
Companies create ads with customized copy, visuals, and calls-to-action to align
with target segments’ preferences and behaviors.
2. Merge Data Sources
To create more detailed customer profiles,
companies often turn to data brokers like Acxiom or
Experian. These brokers provide additional consumer
data, such as purchasing habits and lifestyle.
3. Create Behavioral Profiles
Data brokers use advanced analytics techniques to
develop behavioral profiles. These profiles include
inferred data points, such as likely interests, spending
patterns, and potential future behaviors.
1. Data Collection
Start by collecting data from sources including
customer interactions, online behaviors, transaction
records, and publicly available information. This data
is only limited by legalities and budget..
Targeted Advertising
Customer Data Implementation
5. Targeting Criteria
Select specific criteria for targeting ads. This cab
include any demographic information (age, gender,
income level), location, interests, online behaviors,
purchase history, or any new data combinations.
6. Ad Deployment
The final step involves delivering the targeted ads.
Customize the ad copy, visuals, and calls-to-action
within platform limitations to align with the
preferences and behaviors of the target group.
4. Audience Segmentation
Analyze these enriched profiles to segment their
customer base into distinct categories like “frequent
buyers” or “luxury shoppers” to tailor their messages
and offers to each group more effectively.
Customer journey mapping involves collecting and
analyzing data from various touchpoints to visualize the
entire customer experience from initial contact to final
purchase and beyond. This process includes tracking
interactions across multiple channels such as websites,
social media, email, and in-store visits.
Analytics for the
Customer Journey
Businesses can identify key moments of engagement, understand
customer behaviors and preferences, and pinpoint issues.
Analysis techniques like cohort analysis, funnel analysis, and path
analysis provide insights into how different segments of customers
move through the sales funnel and other opportunities.
Data analysis methods are tools that help us unlock the value hidden
within the data, but we need to be comfortable with the platforms and
understand the data to effectively know what the value is wortht. Start
using analytics tools without expectations so you can become
comfortable with the data source before you start unraveling the data.
Understand the Data
Master the tools and platforms which require technical skills to identify
patterns, trends, and correlations that may otherwise remain unnoticed for
lack of technical skills. Knowing how to use software and platforms is
necessary for analysis.
Tools & Software
Beginning Data
Analytics
Web Analytics
Mastering website analytics is a smart approach for
beginners. Website analytics is a crucial aspect of digital
marketing that involves the collection, measurement, and
analysis of web data to understand and optimize web usage.
By leveraging website analytics, businesses can gain
valuable insights into user behavior, track performance
metrics, and make informed marketing.
Google Analytics Academy provides comprehensive courses on
Google Analytics, including tracking social media traffic and
campaign performance. Users can earn a Google Analytics
Individual Qualification (GAIQ).
Social Analytics
Mastering the built-in analytics tools through Meta business
manager, linkedin, or any other platform is highly recommended.
Social media analytics is essential for understanding audience
behavior, optimizing content performance, and enhancing
campaign effectiveness. As social media continues to evolve, the
role of robust analytics will remain critical in navigating this
dynamic landscape.
Meta Blueprint: Meta offers various certifications including Marketing
Science Professional and Media Planning.
TikTok Ads Academy: training on TikTok Ads and analytics through TikTok
for Business, offering badges upon completion of their courses.
Linkedin Marketing Labs: LinkedIn offers courses and certifications on
LinkedIn Ads, analytics, and marketing strategies.
Visualization
Communicating data effectively involves presenting complex
information in a clear, concise, and engaging manner to ensure it is
easily understood by the intended audience. This can be achieved
through the use of data visualizations such as charts, graphs, and
infographics, which help to highlight key insights and trends.
Storytelling
Contextual storytelling that explains the significance of the data and its
implications can make the information more relatable and actionable.
Effective communication of data ensures that insights drive informed
decision-making. Working within the audience's level of expertise and
focusing on the most relevant points further enhances comprehension.
Experience and
Benchmarking
Familiarity and experience makes analytics more useful. Historical context and
performance standards against which current data can be evaluated are critical for
improving performance relative to competing businesses. Experienced analysts can
leverage past familiarity to make deeper insights with more impact.
Thank you
Advanced Digital Strategy MGMT X 466.05 2024

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Data Analytics for Digital Marketing Lecture for Advanced Digital & Social Media Strategy at UCLAx (MGMTX 466.05)

  • 2. Lecturer: Valters Lauzums 2024 Overview & Concepts Data analytics is a math-based discipline that seeks to find patterns in data to increase actionable knowledge that can be used to improve business performance.
  • 3. Quantify your communication Numbers are persuasive. Telling your CFO that customer engagement has improved is less impactful than providing data showing a 20% increase due to a specific campaign. Analytics allows you to back up your claims with hard data.
  • 4. Data analytics is a math-based discipline that seeks to find patterns in data to increase actionable knowledge that can be used to improve business performance. It employs statistics, predictive modeling, and machine learning to reveal insights and answer questions. Data analytics is critical for understanding impact, predicting trends, and optimizing strategies to drive growth. Practical Data Analytics Turn Data into Information: Most businesses have access to data, but the key is turning that data into useful information. Compare and Contrast Data: Analytics enables you to compare different data sets to understand which strategies are working.
  • 5. Key Techniques Uses historical data to forecast future events. Techniques include regression analysis to find relationships between variables, time series analysis to identify trends over time, and machine learning algorithms to model and predict future behaviors based on past data. Predictive Analytics Offers recommendations to achieve desired outcomes. Techniques include optimization models to find the best course of action within constraints, simulation to test scenarios and predict outcomes, and decision trees to map possible decisions and their consequences. Prescriptive Analytics Summarizing historical data to understand past events. Techniques include data aggregation, which combines data from various sources for a comprehensive view, data mining to extract patterns from large datasets, and reporting using dashboards and visualizations. Descriptive Analytics Helps understand why certain events occurred. Techniques include root cause analysis to identify underlying causes, drill-down analysis to break down data for specific insights, and correlation analysis to examine relationships between variables and identify causative factors. Diagnostic Analytics
  • 6. Data Types & Structure Each data type and the format of the data serves a unique purpose in the analytics process and must be distinguished for proper analysis. Structured Data Quantitative Data: This includes numerical data that can be measured and quantified. Examples in marketing include sales figures, click-through rates, and conversion rates. Qualitative Data: This encompasses descriptive data that is not easily measured but provides insights into customer behaviors and preferences. Examples include reviews and comments. Types of Data Unstructured Data is not organized in a pre-defined manner, while structured data is highly organized and easily searchable. It fits neatly into rows and columns, such as data found in spreadsheets .
  • 7. Common Tools Marketing Analytics Use Cases Google Analytics You should know how to use this tool. Google Analytics is one of the most widely used tools for tracking and analyzing website traffic and provides insights into user behavior, traffic sources, and conversion rates. Google Looker Google Looker is a modern business intelligence (BI) and data analytics platform that allows organizations to explore, analyze, and visualize their data in real time. Formerly known as “Google Data Studio” Adobe Analytics Adobe Analytics offers advanced features for tracking and analyzing customer data across various channels. It provides robust data visualization and reporting tools, making it ideal for large enterprises. Hubspot HubSpot is an all-in-one inbound marketing platform, which is very similar to other CRM analytics capabilities.. It helps marketers track the performance of their content and marketing campaigns.
  • 8. Data Analysis Process 1. Data Goals 2. Data Collection Ensure the data is accurate and consistent by removing duplicates, correcting errors, and filling in missing values. 3. Data Cleaning 4. Data Extrapolation 5. Data Modeling Gather data from various sources such as web analytics, social media, customer surveys, and sales records. What marketing questions do you want to answer with the data? What decisions will be made or influenced by the results? Use statistical techniques to explore the data and identify patterns or trends. This includes summarizing data using math. Apply analytical models to the data to derive insights, e.g. regression analysis, cluster analysis, and CLV models. Interpret the results to make data-driven decisions. This involves translating findings into actionable marketing strategy. 6. Data Interpretation Key steps for analyzing marketing data
  • 9. Optimize Campaigns Data on past campaign performance can help marketers optimize future campaigns. For example, A/B testing results can reveal which ad copy, visuals, or calls-to-action resonate most with audiences. This continuous optimization improves effectiveness. Predicting Behavior Predictive analytics uses historical data to forecast future customer behavior. This can help in anticipating market trends, customer needs, and potential sales opportunities. Marketers can use this to create new sales and marketing opportunities. Define Audiences Demographic data, purchase histories, and online behaviors can be used identify and segment target audiences more accurately. This will help tailor marketing to the needs and preferences of each segment and improve marketing effectiveness. Practical Application Solving Problems with Data
  • 10. Heavy Duty Analytics Basic analytics techniques can be implemented with digital tools that are used across all business categories. When using analysis techniques on a larger pool of data at a business environment with a larger budget (at a larger brand) you will be able to use and implement more sophisticated techniques based on team capabilities and resources.
  • 11. Cohort Analysis Groups individuals sharing common characteristics over a specific period. This method focuses on evaluating these cohorts over time to derive insights into lifecycle patterns or behaviors. Cluster Analysis Used to group data points that are similar to each other. By categorizing data into subsets, clustering helps in revealing patterns, similarities, and areas of concentration within a dataset. Sentiment Analysis Method of processing and analyzing text data to determine the sentiment or emotional tone behind that text. It’s widely used to gauge public opinion, and monitor brand reputation. Monte Carlo Simulation Computational technique that employs random sampling to estimate complex statistical problems. The main objectives are to quantify uncertainty and provide a range of possible outcomes. Factor Analysis Statistical method primarily used for data reduction which explores how observed variables correlate with one another, aiming to pinpoint underlying factors that influence these correlations. Regression Analysis At its core, it estimates how one variable (the dependent variable) is influenced by one or more other variables (independent variables). The primary goals of regression are to predict and explain. Advanced Methods Data Analysis Techniques Deciphering and transforming this raw information into actionable insights is the primary job for any analytics method. Simple statistical techniques can be as impactful as complex machine learning algorithms.
  • 12. Lecturer: Valters Lauzums 2024 Marketing Analytics In the modern marketing landscape, data analytics plays a pivotal role in creating and delivering highly targeted digital campaigns and tracking web properties.
  • 13. 1st party data is the most reliable and precise,. 2nd party data provides additional value and. 3rd party data should be used carefully due to lower accuracy and relevance. Data Ownership 1st party data: the information a company collects directly from its customers or audience. This includes data from their website interactions, purchase history, and social media engagement. 2nd party data: someone else’s 1st party data that is shared between trusted partners. Brands share customer data, benefiting both parties with new audiences and data without intermediaries. 3rd party data: collected by entities that do not have a direct relationship with the consumer. Aggregated from various sources and sold to any company, anywhere, for any purpose.
  • 14. Customer Data The data broker market was valued at $245 billion in 2021 and is projected to grow at a CAGR of 11.5% through 2028. Data is collected from customer interactions, online behaviors, transaction records, and publicly available information. This can be gathered through websites, mobile apps, social media platforms, and physical stores. Companies like Acxiom and Experian provide additional consumer data. These brokers enhance collected data with information on purchasing habits, lifestyle preferences, and demographic details. Retailers use data to segment customers into categories like “frequent buyers,” “bargain hunters,” and “luxury shoppers.” Companies create ads with customized copy, visuals, and calls-to-action to align with target segments’ preferences and behaviors.
  • 15. 2. Merge Data Sources To create more detailed customer profiles, companies often turn to data brokers like Acxiom or Experian. These brokers provide additional consumer data, such as purchasing habits and lifestyle. 3. Create Behavioral Profiles Data brokers use advanced analytics techniques to develop behavioral profiles. These profiles include inferred data points, such as likely interests, spending patterns, and potential future behaviors. 1. Data Collection Start by collecting data from sources including customer interactions, online behaviors, transaction records, and publicly available information. This data is only limited by legalities and budget.. Targeted Advertising Customer Data Implementation 5. Targeting Criteria Select specific criteria for targeting ads. This cab include any demographic information (age, gender, income level), location, interests, online behaviors, purchase history, or any new data combinations. 6. Ad Deployment The final step involves delivering the targeted ads. Customize the ad copy, visuals, and calls-to-action within platform limitations to align with the preferences and behaviors of the target group. 4. Audience Segmentation Analyze these enriched profiles to segment their customer base into distinct categories like “frequent buyers” or “luxury shoppers” to tailor their messages and offers to each group more effectively.
  • 16. Customer journey mapping involves collecting and analyzing data from various touchpoints to visualize the entire customer experience from initial contact to final purchase and beyond. This process includes tracking interactions across multiple channels such as websites, social media, email, and in-store visits. Analytics for the Customer Journey Businesses can identify key moments of engagement, understand customer behaviors and preferences, and pinpoint issues. Analysis techniques like cohort analysis, funnel analysis, and path analysis provide insights into how different segments of customers move through the sales funnel and other opportunities.
  • 17. Data analysis methods are tools that help us unlock the value hidden within the data, but we need to be comfortable with the platforms and understand the data to effectively know what the value is wortht. Start using analytics tools without expectations so you can become comfortable with the data source before you start unraveling the data. Understand the Data Master the tools and platforms which require technical skills to identify patterns, trends, and correlations that may otherwise remain unnoticed for lack of technical skills. Knowing how to use software and platforms is necessary for analysis. Tools & Software Beginning Data Analytics
  • 18. Web Analytics Mastering website analytics is a smart approach for beginners. Website analytics is a crucial aspect of digital marketing that involves the collection, measurement, and analysis of web data to understand and optimize web usage. By leveraging website analytics, businesses can gain valuable insights into user behavior, track performance metrics, and make informed marketing. Google Analytics Academy provides comprehensive courses on Google Analytics, including tracking social media traffic and campaign performance. Users can earn a Google Analytics Individual Qualification (GAIQ).
  • 19. Social Analytics Mastering the built-in analytics tools through Meta business manager, linkedin, or any other platform is highly recommended. Social media analytics is essential for understanding audience behavior, optimizing content performance, and enhancing campaign effectiveness. As social media continues to evolve, the role of robust analytics will remain critical in navigating this dynamic landscape. Meta Blueprint: Meta offers various certifications including Marketing Science Professional and Media Planning. TikTok Ads Academy: training on TikTok Ads and analytics through TikTok for Business, offering badges upon completion of their courses. Linkedin Marketing Labs: LinkedIn offers courses and certifications on LinkedIn Ads, analytics, and marketing strategies.
  • 20. Visualization Communicating data effectively involves presenting complex information in a clear, concise, and engaging manner to ensure it is easily understood by the intended audience. This can be achieved through the use of data visualizations such as charts, graphs, and infographics, which help to highlight key insights and trends. Storytelling Contextual storytelling that explains the significance of the data and its implications can make the information more relatable and actionable. Effective communication of data ensures that insights drive informed decision-making. Working within the audience's level of expertise and focusing on the most relevant points further enhances comprehension.
  • 21. Experience and Benchmarking Familiarity and experience makes analytics more useful. Historical context and performance standards against which current data can be evaluated are critical for improving performance relative to competing businesses. Experienced analysts can leverage past familiarity to make deeper insights with more impact.
  • 22. Thank you Advanced Digital Strategy MGMT X 466.05 2024