Data-Driven Decisions: Customer Segmentation

• Data-driven decisions rely on the skill of extracting meaningful data.
• Collecting and analyzing data is paramount for businesses to identify patterns and trends.
• Companies must regularly monitor and update data sources to ensure relevance.

Customer Segmentation

• One way to unlock meaning from data involves customer segmentation.
• Customer data, including demographics, purchase history, and browsing behavior, can be collected.
• Analysis of the collated customer data helps identify distinct customer segments like frequent buyers or bargain shoppers.
• Tailored marketing strategies can then be developed to cater to each segment's unique characteristics.

Product Optimization

• Another area to unlock data in business is product optimization.
• Information on product sales, customer feedback, and product features can be collected.
• Analyzing data help identify popular product and in-demand features.
• Decisions can then be made to optimize product development and discontinue underperforming products.

Collecting Data from Different Sources

• Businesses must identify methods of collecting data from different places to make data-driven decisions.
• Organizations can use methods such as surveys, interviews, focus groups, and web scraping to collect data.
• Organizations should consider the type of data needed, sample population size, and level of detail needed.
• These considerations impact the quality and usefulness of data analysis.

Customer Satisfaction Surveys

• Customer satisfaction surveys is one type of data collection in business.
• Companies can use the data for improvement, developing new products, and improve satisfaction.

Market Research

• Market research is the next type of data collection used in businesses.
• Gathering feedback informs product development, marketing strategies, and pricing decisions.

Web Scraping

• In addition to case studies, experiments, and observations, businesses are allowed to collect data from secondary sources.
• Scraping extracts data from websites using automated software tools.
• Efficient research objectives, budget, and timeline must be considered for compliant data collection.
• For example, customer segmentation requires accurate and updated customer data.
• Poor data quality leads to inaccurate results.
• Poor data quality undermines analysis credibility and requires more resources to fix.

Questions About Data Sources and Quality

• Investigating data sources and quality are vital for data analytics, because misleadng and incorrect data will result if they are not.
• Data sources range from internal sales to external market research.
• Incomplete data can lead to mistakes.
• Businesses must enforce data governance via data cleaning and validation.
• Remove duplicates and ensure consistent data entry.
• Marketing agencies must confirm data source credibility for relevant information.
• Accurate data leads to success.

customer segmentation

• divides customers into different groups based on similar characteristics such as demographics, behavior, and preferences
• it allows businesses to tailor their marketing strategies and offerings to different customer groups, leading to more effective and efficient use of resources

directed data mining

• used when the historical data contains examples of what is being looked for, a target variable

exploratory data mining

• produces insights or answers questions, rather than producing models used for scoring

market research

• collects and analyzes information about a market, including its size, trends, competitors, and customer preferences
• helps businesses make informed decisions about their products and services, pricing, promotion, and distribution strategies

undirected data mining

• does not use a target variable

Data Analytics Techniques

• Businesses should understand the unique strengths and weaknesses of data analytic techniques.
• Techniques include regression analysis, decision trees, clustering, association rules, and machine learning.
• A marketing agency uses regression analysis to analyze marketing spend and sales revenue.
• Regression analysis identifies the relationship between independent and dependent variables.
• A retail company uses decision trees to analyze customer behavior.
• Decision trees help companies develop targeted marketing campaigns and improve customer experience.
• Clustering helps segment customers based on shared characteristics and develop personalized marketing.
• Association rules identify patterns and relationships between products or services.
• Machine learning extracts insights from large datasets.
• Data analysis summarization leads to machine learning algorithms to build predictive models.
• Machine learning can automate data cleaning, feature selection, and model selection.

Time series

• Historical data is used to forecast trends.
• Time series analysis identifies patterns in data, informing investment strategies..

Market Basket Analysis

• Market basket analysis identifies patterns in customer purchasing behavior.
• Businesses gain insights into customer behavior and make decisions based on it.
• Data in a transactional database is stored as a list of purchased items.
• This analysis reveals frequent item sets and association rules.
• For example, customers who purchase bread are also likely to purchase butter and jam.

Process Mining

• Process mining analyzes business processes.
• Data extraction leads to visualization to identify inefficiencies.
• This technique improves business processes and optimize costs.

T-test

• A t-test compares two independent samples to determine differences.
• Independent samples and paired samples are two common t-tests.

Correlation Analysis

• Correlation analysis measures the relationship between variables.
• The correlation coefficient indicates the relationship's direction and strength,
• This type of analysis helps understand patterns and predict them based on observations.

Text Mining

• Text mining identifies patterns via unstructured data.

Neural Networks

• Neural networks recognize patterns and relationships in data.
• This models the human brain using interconnected nodes to process information.
• For example, social media platforms can use neural networks to filter content.
• Neural networks require large data amounts for accurate models.
• With machine learning algorithms, organizations can drive growth and success in various industries.

Impacts of Data Analytics Techniques

• Data analytic strategies improve decision-making
• An effective data analytics strategy will identify opportunities for growth, enhance efficiency, and reduce costs.

Regression Analysis

• A retail company uses regression analysis to identify influential factors driving sales.
• Companies can improve company performance by maximizing impact.
• Healthcare can reduce readmission rates with the use of machine learning and proactive support.
• Clustering identifies customer groups for targeted marketing strategies, improving customer experience.
• Investigation of the impacts of data analytics techniques should drive successes.

The Data Analytics Process

• Data analysts must choose the correct method to answer specific research questions.
• The selection depends on data type, research objectives, and questions.
• The method should identify data as continuous or categorical.
• Choose a correlation or regression tests to test the relationship between two variables.
• Use a t-test to compare the means of two groups.
• Measurement levels allow for identification of statistical methods.

Visualization

• Effective visualizations communicate insights and drive action.

Three Major components

• Audience affects visual elements in design.
• Message helps communicate findings.
• Insights highlight patterns from data.
• Communication leads to engaging visualizations.
• Visuals highlight insights that other representations may miss.

Other Visuals

• A financial services firm can use a line chart to show the trend of stock prices over time to inform investment decisions.
• A retail company uses a scatterplot to analyze the relationship between sales and customer satisfaction.
• A heat map visualizes customer behavior on a website to optimize user experience.
• A bar chart compares product performance to facilitate informed decisions.

Metrics

• Measuring and evaluating success depends on identifying the correct metrics based on initiative and goals.
• Tracking and reporting frequency impacts insights.
• Too frequent data causes noise and makes it difficult to see trends.
• Infrequent tracking leads to missed opportunities.
• Metrics quantify and measure data across periods, groups, or categories.

Commonly Used Metrics

• Conversion rate measures the percentage of website visitors who complete a desired action.
• Click-through rate measures the percentage of people who click on a link.
• Customer lifetime value measures the value of a customer throughout their relationship.
• Churn rate measures the percentage of customers who stop doing business with a company.
• Customer acquisition cost measures the cost of acquiring a new customer.
• Return on investment measures an investment's profitability.
• Bounce rate measures the percentage of website visitors who leave after viewing only one page.
• Time on site measures a user’s time on a website.
• Engagement rate measures the level of engagement with content.
• Revenue growth measures the increase in revenue.

machine learning

• a branch of artificial intelligence focused on developing algorithms and models that allow computers to learn and make predictions or decisions based on data
• in the context of a data analytics course, machine learning is a set of techniques and tools used to analyze and derive insights from large and complex datasets

optimization

• the process of finding the best solution to a problem or maximizing or minimizing an objective function, subject to constraints

conversion rate

• metric that measures the percentage of website visitors who complete a desired action, such as purchasing or filling out a form

click-through rate (CTR)

• metric that measures the percentage of people who click on a link or advertisement

customer lifetime value (CLV)

• metric that measures the total value of a customer to a business throughout their relationship

churn rate

• metric that measures the percentage of customers who stop doing business with a company over a certain period

customer acquisition cost (CAC)

• metric that measures the cost of acquiring a new customer

return on investment (ROI)

• metric that measures an investment's profitability

bounce rate

• metric that measures the percentage of website visitors who leave after viewing only one page

time on site

• metric that measures a user’s time on a website

engagement rate

• metric that measures the level of engagement with content or advertisements, such as likes, comments, and shares on social media

revenue growth

• metric that measures the increase in revenue over a specific period

predictive analytics

• uses statistical algorithms and machine learning techniques to analyze historical data and predict future events or outcomes
• for example, a healthcare provider might use predictive analytics to identify patients who are at high risk for certain diseases based on their medical history and lifestyle factors

clustering

• technique used to group similar data points based on their characteristics or attributes
• for example, a marketing team might use clustering techniques to group customers with similar purchasing behaviors to create targeted marketing campaigns

regression analysis

• statistical method for examining the relationship between a dependent variable and one or more independent variables
• for example, a business might use regression analysis to determine how much changes in advertising spending affect sales

data visualization

• uses graphical representations to communicate complex data and insights to stakeholders
• for example, a business might use data visualization techniques to create interactive dashboards that display key performance indicators and allow executives to monitor the business's health in real time

neural networks

• class of machine learning algorithms used in data analytics that are inspired by the structure and function of the human brain
• they are a type of artificial neural network that can learn and make predictions on complex data patterns