Process of measuring, analyzing, and interpreting data related to marketing performance to improve decision-making and optimize marketing strategies.
Data collection: Collecting relevant data about customer behavior, preferences, and purchase history is a key factor in marketing analytics.
Data cleaning and preparation: Data collected needs to be processed before it can be analyzed to ensure it is accurate, consistent, and complete.
Segmentation: Grouping customers into segments based on their common characteristics or behaviors, enables marketers to target their efforts effectively.
Predictive modeling: Identifying trends and patterns in historical data to anticipate future trends and make better marketing decisions.
ROI analysis: Measuring the success and profitability of marketing campaigns to determine whether they are achieving business objectives.
Digital analytics: Understanding the customer journey on digital channels, such as website visits, social media, and email interactions.
Behavioral economics: The study of psychological and emotional factors that influence consumer behavior helps marketers better understand and predict customer actions.
Machine learning: Utilizing algorithms and statistical models to learn from data, adapt, and improve performance over time.
Customer lifetime value (CLV): Calculating the future value of a customer based on their predicted lifetime purchases and the cost of acquiring and retaining them.
Attribution modeling: Determining the impact of different marketing channels on the customer's journey and the role each marketing touchpoint plays in a customer's decision-making process.
Customer Analytics: Customer Analytics deals with understanding customers, their behavior, what they like, and dislike about a product or service.
Social Media Analytics: Social Media Analytics focuses on analyzing social media data to know how the brand is performing on different social platforms.
Web Analytics: Web Analytics deals with measuring website performance in terms of user behavior, key metrics, page views, time spent on a page, and conversion rates.
Digital Analytics: Digital Analytics involves analyzing all digital marketing channels and campaigns such as email marketing, display advertising, PPC, and SEO.
Predictive Analytics: Predictive Analytics uses statistical algorithms to predict future outcomes of marketing campaigns, such as predicting churn rates, customer lifetime value, and lead scoring.
Advertising Analytics: Advertising Analytics is about measuring the performance of advertising campaigns, measuring success rates of TV, radio, print, and online ads.
Email Marketing Analytics: Email Analytics helps you in tracking the success rate of email campaigns, such as open rates, click-through rates, and conversion rates.
Content Analytics: Content Analytics deals with understanding how users are interacting with website content, such as types of content, pages viewed, and time spent on pages.
Mobile Analytics: Mobile Analytics focuses on users' interaction with mobile apps, including user behavior, mobile app performance, and mobile engagement rate.
Segmentation Analytics: Segmentation Analytics divides the target market into smaller groups to understand user behavior, demographics, and targeted marketing.