Marketing Analytics: Forecasting Models with Excel
- Description
- Curriculum
- FAQ
- Reviews
You’re looking for a complete course on understanding Forecasting models and forecasting analytics to drive business decisions involving production schedules, inventory management, manpower planning, demand forecasting, and many other parts of the business., right?
You’ve found the right Marketing Analytics: Forecasting Models with Excel! This course teaches you everything you need to know about different forecasting models and how to implement these models for devising forecasting analytics in Excel using advanced excel tool.
After completing this course you will be able to:
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Implement forecasting analytics and forecasting models such as simple linear, simple multiple regression, Ratio to Moving Average, Winter’s method for exponential smoothing with trend and seasonality, famous Bass diffusion model and many more.
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Increase revenue/profit of your firm by implementing accurate forecasting analytics using Excel solver Add-in
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Confidently practice, discuss and understand different Forecasting analytics strategies and forecasting models used by organizations
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Creating demand forecasting strategies using forecasting analytics techniques and various forecasting models.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Marketing Analytics: Forecasting Models with Excel course.
If you are a business manager or an executive, or a student who wants to learn and apply forecasting analytics and forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it for effective demand forecasting and for devising forecasting analytics techniques.
Why should you choose this course?
We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts on forecasting analytics, demand forecasting, forecasting models through how-to examples. Each section has the following components:
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Theoretical concepts and use cases of different forecasting models and forecasting analytics techniques
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Step-by-step instructions on implement forecasting models and forecasting analytics techniques in excel for demand forecasting
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Downloadable Excel file containing data and solutions used in each lecture on forecasting models and forecasting analytics
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Class notes and assignments to revise and practice the concepts on demand forecasting, forecasting models and forecasting analytics techniques
The practical classes where we create the model for each of these strategies is something which differentiates this course from any other course available online.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course
We are also the creators of some of the most popular online courses – with over 170,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
What is covered in this course?
Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will explore how one can use forecasting models to
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See patterns in time series data
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Make forecasts based on models
Let me give you a brief overview of the course
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Section 1 – Introduction
In this section we will learn about the course structure
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Section 2 – Basics of Forecasting
In this section, we will discuss about the basic of forecasting and we will also learn the easiest way to create simple linear regression model in Excel
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Section 3 – Getting Data Ready for Regression Model
In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.
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Section 4 – Forecasting using Regression Model
This section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.
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Section 5 – Handling Special events like Holiday sales
In this section we will learn how to incorporate effects of Day of Week Effect, Month Effect or any special event such Holidays, pay day etc.
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Section 6 – Identifying Seasonality & Trend for Forecasting
In this section we will learn about trends and seasonality and how to use the Solver to develop an additive or multiplicative model to estimate trends and seasonality. We will also learn how to use moving averages to eliminate seasonality to easily see trends in sales.
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Section 7 – Handling Changing Trend & Seasonality over time
In this section we will learn about Winter’s Method that changes trend and seasonal index estimates during each period has a better chance of keeping up with changes than other methods.
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Section 8 – Forecasting models for New Products
In this section we will learn techniques to forecast new product sales. It is difficult to forecast when we have little or no historical data. The S curve can be used when we have little data and the famous bass diffusion model can be used to predict product sales even before the product is launched in the market.
Some of the examples in this course are from the book Marketing Analytics: Data-Driven Techniques with Microsoft Excel [Winston, Wayne L.]. We suggest this book as reading material for anyone aspiring to be a marketing analyst.
I am pretty confident that the course will give you the necessary knowledge and skills related to forecasting analytics, forecasting models and demand forecasting strategies; to immediately see practical benefits in your work place.
Go ahead and click the enroll button, and I’ll see you in lesson 1 of this course on forecasting analytics and forecasting models!
Cheers
Start-Tech Academy
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2Basics of ForecastingVideo lesson
In Lecture 2 of Section 2: Basics of Forecasting in the course Marketing Analytics: Forecasting Models with Excel, we will be covering the fundamental concepts and techniques used in forecasting. We will begin by discussing the importance of forecasting in marketing and how it can help businesses make informed decisions about future strategies. We will also delve into the different types of forecasting models and how they can be applied to various marketing scenarios.
Additionally, we will explore the basic principles of time series analysis, including trend analysis, seasonality, and cyclical patterns. By the end of this lecture, students will have a solid understanding of the key components of forecasting and how to effectively utilize Excel to create and analyze forecasting models. This foundational knowledge will set the stage for more advanced forecasting techniques to be covered in subsequent lectures. -
3Course ResourcesText lesson
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4This is a milestone!Video lesson
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5Creating Linear Model with TrendlinesVideo lesson
In Lecture 5 of Section 2 of the Marketing Analytics course, we will be diving into the basics of forecasting using Excel. Specifically, we will be focusing on creating linear models with trendlines. We will discuss how trendlines can help us identify patterns and trends in data, which can then be used to make predictions for future sales, customer behavior, or market trends.
Through this lecture, students will learn how to use Excel to create linear models with trendlines, which can be a powerful tool for predicting future outcomes based on historical data. We will cover the process of adding a trendline to a scatter plot, interpreting the slope and intercept of the trendline, and using the trendline equation to forecast future values. By mastering these techniques, students will be equipped with the skills needed to analyze data and make informed marketing decisions based on data-driven insights. -
6QuizQuiz
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7Gathering Business KnowledgeVideo lesson
In Lecture 6 of Section 3 of the Marketing Analytics course, we will discuss the importance of gathering business knowledge before working on regression models. Understanding the business context in which you are analyzing data is crucial for ensuring that your forecasts and predictions are relevant and actionable. We will explore how to identify key business questions and goals, as well as how to work closely with stakeholders to ensure that your analysis addresses their needs.
Additionally, we will cover the importance of preparing the data for regression modeling. This includes data cleaning, transforming variables, handling missing data, and dealing with outliers. By following best practices for data preparation, you can ensure that your regression models are accurate and reliable. Overall, this lecture will provide you with the skills and knowledge necessary to effectively gather business insights and prepare data for regression modeling in Excel. -
8Data ExplorationVideo lesson
In Lecture 7 of Section 3 of the Marketing Analytics course, we will be focusing on getting data ready for regression models. We will cover the importance of data exploration in the context of marketing analytics, as well as the steps involved in preparing the data for regression analysis. This includes dealing with missing values, handling outliers, and transforming variables to ensure they meet the assumptions of regression models.
Additionally, we will discuss the different techniques and tools available for data exploration, such as histograms, scatter plots, and correlation matrices. We will also delve into the concept of feature engineering and how selecting the right variables can improve the predictive power of regression models. By the end of this lecture, students will have a better understanding of the importance of data preparation in marketing analytics and how it can impact the accuracy and reliability of forecasting models. -
9The Data and the Data DictionaryVideo lesson
In Lecture 8 of Section 3 of the Marketing Analytics course, we will be focusing on preparing the data for regression modeling. We will discuss the importance of cleaning and preprocessing data before building forecasting models with Excel. We will cover techniques for handling missing values, outliers, and categorical variables to ensure that our data is of high quality and ready for analysis.
Furthermore, in this lecture, we will delve into the concept of a data dictionary and why it is essential for any regression analysis. We will learn how to create a data dictionary that describes the variables in our dataset, including their names, descriptions, data types, and unique values. By establishing a clear understanding of our data through a data dictionary, we can effectively interpret and analyze our regression model results with confidence. -
10Univariate analysis and EDDVideo lesson
In Lecture 9 of the Marketing Analytics course, we will be diving into the topic of univariate analysis and exploratory data analysis (EDD) as we prepare our data for regression modeling. We will explore the importance of understanding the distribution of our data and how to identify outliers, missing values, and other data issues that could impact the accuracy of our regression model. Through visualizations and statistical measures, we will learn how to assess the quality and validity of our data before moving forward with our analysis.
Additionally, we will cover techniques for data preprocessing and transformation to ensure that our data is suitable for regression modeling. This includes handling categorical variables, scaling numerical features, and addressing multicollinearity among predictors. By the end of this lecture, students will have a solid foundation in data preparation for regression analysis, setting the stage for more advanced forecasting models using Excel in the upcoming sections of the course. -
11Discriptive Data Analytics in ExcelVideo lesson
In Lecture 10 of Section 3 of the Marketing Analytics course, we will be diving into the world of descriptive data analytics in Excel. We will explore how to prepare data for regression models by understanding and analyzing the data in Excel. Through this lecture, we will learn how to identify and clean data, handle missing values, and transform data to make it suitable for regression analysis.
Additionally, we will cover techniques for visualizing data using Excel graphs and charts to gain insights into patterns and trends within the data. By the end of this lecture, students will have a solid understanding of how to perform descriptive data analytics in Excel, which will serve as a strong foundation for building and interpreting regression models in future lectures. -
12Outlier TreatmentVideo lesson
In this lecture, we will be focusing on the important step of getting data ready for regression modeling by addressing outliers. Outliers are data points that deviate significantly from the rest of the data, and can have a major impact on the accuracy and reliability of our forecast models. We will discuss different methods for identifying outliers in our dataset, including visualization techniques and statistical tests.
Once we have identified outliers in our data, we will explore various approaches for treating them to ensure our regression models are robust and accurate. This may include removing outliers, transforming the data, or applying robust regression techniques. By understanding how to properly handle outliers, we can improve the quality of our forecasting models and make more informed marketing decisions. -
13Identifying and Treating Outliers in ExcelVideo lesson
In Lecture 12 of the Marketing Analytics course, we will focus on identifying and treating outliers in Excel. Outliers are data points that significantly differ from the rest of the data set, and can greatly impact the accuracy of regression models. We will learn how to identify outliers through visual inspection using scatter plots, box plots, and histograms, as well as through statistical methods such as Z-scores and IQR.
Once outliers have been identified, we will explore various techniques for treating them in Excel. This may include removing the outlier data points, transforming the data using mathematical techniques such as log or square root transformations, or using robust regression techniques that are less affected by outliers. By the end of this lecture, you will have a thorough understanding of how to effectively deal with outliers in your regression models to ensure more accurate forecasting results. -
14Missing Value ImputationVideo lesson
In this lecture, we will discuss the important topic of Missing Value Imputation in the context of preparing our data for regression models. Missing values can significantly impact the accuracy and reliability of our forecasting models, so it is crucial to address them appropriately. We will explore various methods for imputing missing values, such as mean imputation, mode imputation, and regression imputation, and discuss the advantages and limitations of each approach.
Additionally, we will cover techniques for identifying and handling outliers in our dataset, as they can also have a substantial impact on the performance of our regression models. We will explore different methods for detecting outliers, such as z-score analysis and box plots, and discuss how to decide whether to remove or transform outliers in our data. By the end of this lecture, students will have a comprehensive understanding of how to effectively manage missing values and outliers when preparing their data for regression analysis. -
15Identifying and Treating missing values in ExcelVideo lesson
In Lecture 14 of Marketing Analytics: Forecasting Models with Excel, we will be focusing on identifying and treating missing values in our dataset. We will discuss the importance of handling missing values before building a regression model, as they can significantly impact the accuracy and reliability of our analysis. We will learn various methods to identify missing values in Excel, including using filters, conditional formatting, and built-in functions like ISBLANK and COUNTBLANK.
Additionally, we will explore different strategies for treating missing values in our dataset. This includes techniques such as imputation, which involves replacing missing values with estimated values based on statistical methods or averages. We will also discuss the implications of different treatment methods on the results of our regression model, and how to choose the best approach based on the characteristics of our dataset. By the end of this lecture, you will have the skills and knowledge to effectively handle missing values in Excel and ensure the accuracy of your forecasting models. -
16Variable Transformation in ExcelVideo lesson
In Lecture 15 of Section 3 of the Marketing Analytics course, we will be focusing on variable transformation in Excel. This lecture will cover the importance of transforming variables before fitting them into regression models to ensure accurate results. We will learn about different techniques such as log transformation, square root transformation, and inverse transformation.
Additionally, we will discuss the process of standardizing variables to make them comparable and easier to interpret in regression analysis. By the end of this lecture, students will have a clear understanding of how to transform variables in Excel and prepare them for regression modeling. This knowledge will be valuable in making data-driven marketing decisions and creating effective forecasting models. -
17Dummy variable creation: Handling qualitative dataVideo lesson
In this lecture, we will delve into the importance of dummy variable creation when handling qualitative data in marketing analytics. We will discuss how to convert categorical variables into numerical ones through the creation of dummy variables. By doing this, we can effectively include qualitative data in regression models, ensuring that we capture all relevant information without losing the meaning behind the different categories.
Additionally, we will cover the process of interpreting the results of regression models that include dummy variables. Understanding how to interpret the coefficients of dummy variables is crucial for making informed decisions based on the analysis of qualitative data. We will also explore how to avoid common pitfalls when working with dummy variables, such as multicollinearity and the dummy variable trap. By the end of this lecture, you will have the knowledge and skills to effectively handle qualitative data in marketing analytics using dummy variable creation. -
18Dummy Variable Creation in ExcelVideo lesson
In Lecture 17 of Section 3 of the Marketing Analytics course, we will cover the topic of dummy variable creation in Excel. Dummy variables are essential in regression analysis as they allow us to include categorical variables in our models. We will learn how to create dummy variables for categorical variables with multiple levels in Excel, which is a crucial step in preparing our data for regression analysis. By the end of this lecture, you will be able to confidently create and interpret dummy variables in Excel for your forecasting models.
Additionally, we will discuss the importance of dummy variable encoding and how it can impact the accuracy and performance of our regression models. We will explore different encoding techniques such as one-hot encoding and dummy trap, and when to use each method based on the nature of our data. Understanding how to properly create and use dummy variables in Excel will be a valuable skill that will help you build more accurate and reliable forecasting models in your marketing analytics projects. -
19Correlation AnalysisVideo lesson
In Lecture 18 of Section 3 on Correlation Analysis, we will delve into the importance of preparing data for regression models in marketing analytics. We will discuss various techniques and strategies to clean and transform data, ensuring that it is in the right format for analysis. Understanding the correlation between different variables is crucial for building effective forecasting models, as it helps us identify patterns and relationships that can drive meaningful insights for marketing strategies.
During this lecture, we will explore how to calculate and interpret correlation coefficients using Excel. We will cover different types of correlation measures, such as Pearson, Spearman, and Kendall correlations, and discuss when each one is most appropriate to use. By the end of this session, students will have a thorough understanding of how to perform correlation analysis and how to leverage this information to build accurate regression models for predicting future consumer behavior and market trends. -
20Creating Correlation Matrix in ExcelVideo lesson
In Lecture 19 of the Marketing Analytics course, we will focus on creating a correlation matrix in Excel, a crucial step in preparing data for regression models. We will discuss the importance of understanding the relationships between variables through correlation analysis, and how it can help marketers make informed decisions based on data insights. By the end of this lecture, students will be able to effectively use Excel to calculate and interpret correlation coefficients for their marketing analytics projects.
During this lecture, we will walk through the step-by-step process of creating a correlation matrix in Excel, including how to input data, calculate correlations, and format the matrix for better visualization. We will also cover the interpretation of correlation coefficients and discuss how to identify strong, weak, or no correlations between variables. By the end of the lecture, students will have a clear understanding of how to use correlation matrices to analyze data and prepare it for regression modeling, making them better equipped to forecast trends and make data-driven marketing decisions. -
21QuizQuiz
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22The Problem StatementVideo lesson
In this lecture, we will be focusing on using regression models for forecasting in marketing analytics. We will start by discussing the importance of defining a clear problem statement when working on forecasting projects. A well-defined problem statement helps to set clear objectives, identify key variables, and determine the appropriate regression model to use for forecasting.
Next, we will dive into the steps involved in formulating an effective problem statement for forecasting using regression models. This will include identifying the target variable, selecting relevant predictor variables, and defining the time period for the forecast. By the end of this lecture, you will have a solid understanding of how to define a problem statement that will guide your regression modeling approach for accurate and reliable marketing analytics forecasting. -
23Basic Equations and Ordinary Least Squares (OLS) methodVideo lesson
In Lecture 21 of our Marketing Analytics course, we will delve into the basics of forecasting using regression models. We will begin by discussing the basic equations involved in regression analysis, including how to calculate the slope and intercept of a regression line. Understanding these fundamental concepts is crucial for building accurate forecasting models using Excel.
Next, we will explore the Ordinary Least Squares (OLS) method, which is a widely used technique for estimating the parameters of a linear regression model. OLS minimizes the sum of the squared differences between the observed and predicted values, providing us with the best-fitting line for our data. By mastering OLS, you will be able to create robust forecasting models that can help guide marketing strategies and business decisions. -
24Assessing accuracy of predicted coefficientsVideo lesson
In Lecture 22 of Marketing Analytics: Forecasting Models with Excel, we will focus on assessing the accuracy of predicted coefficients in regression models. We will explore different methods and tools that can be used to evaluate how well the coefficients in our regression model are able to predict the outcome variable. We will discuss the importance of understanding the reliability and precision of these coefficients in order to make sound business decisions based on the model's predictions.
Throughout this lecture, we will delve into various statistical techniques such as hypothesis testing, confidence intervals, and goodness-of-fit measures to assess the accuracy of predicted coefficients. By understanding these concepts, we will be able to determine the level of confidence we can have in the coefficients estimated by our regression model and make informed decisions regarding the impact of different variables on the outcome variable. Ultimately, this lecture aims to equip students with the knowledge and skills necessary to assess the accuracy of predicted coefficients in regression models and leverage this information to enhance their forecasting capabilities in marketing analytics. -
25Assessing Model Accuracy: RSE and R squaredVideo lesson
In Lecture 23 of the Marketing Analytics course, we will be focusing on assessing the accuracy of forecasting models using regression techniques. Specifically, we will discuss two important metrics: Root Mean Squared Error (RSE) and R squared. RSE measures the average difference between the observed values and the predicted values, providing insight into the overall accuracy of the model. Meanwhile, R squared indicates the proportion of variance in the dependent variable that is explained by the independent variables in the model, with higher values indicating a better fit.
Throughout this lecture, we will demonstrate how to calculate RSE and R squared using Excel, providing hands-on examples to help students understand how to interpret these metrics in the context of marketing analytics. By the end of the session, students will have a solid grasp of how to evaluate the accuracy of regression models and make informed decisions based on the results. This knowledge will be invaluable for marketing professionals looking to improve their forecasting abilities and optimize their decision-making processes. -
26Creating Simple Linear Regression modelVideo lesson
In Lecture 24 of Marketing Analytics: Forecasting Models with Excel, we will be diving into the topic of creating Simple Linear Regression models. This lecture will cover the basics of regression analysis and how it can be used to forecast future trends in marketing data. We will discuss how to calculate the regression equation and interpret the coefficients, as well as how to evaluate the predictive power of the model using measures such as R-squared and the standard error.
Furthermore, we will explore practical examples and case studies to demonstrate the application of Simple Linear Regression in marketing analytics. By the end of this lecture, you will have a solid understanding of how to build and interpret regression models using Excel, and how to use these models to make informed marketing decisions based on data-driven insights. So be prepared to sharpen your forecasting skills and enhance your marketing analytics toolkit in Section 4 of this course. -
27Multiple Linear RegressionVideo lesson
In Lecture 25 of Marketing Analytics: Forecasting Models with Excel, we will delve into the topic of multiple linear regression. This section will focus on how to use regression models to forecast sales, customer demand, and other key marketing metrics. We will discuss how to build and interpret regression models in Excel, taking into account multiple independent variables to make more accurate forecasts.
Specifically, we will cover the concept of multiple linear regression, which involves predicting a dependent variable based on two or more independent variables. We will explore how to identify the strength of the relationships between the variables, assess the significance of the coefficients, and test the overall fitness of the regression model. By the end of this lecture, you will have a deeper understanding of how to use regression analysis to make informed marketing decisions and improve forecasting accuracy. -
28The F - statisticVideo lesson
In Lecture 26 of the Marketing Analytics course, we will be diving into the F-statistic and its use in forecasting using regression models. The F-statistic is a statistical measure that helps us determine the overall significance of our regression model. By calculating the F-statistic, we can assess whether our model is a good fit for the data and if the relationship between our independent variables and the dependent variable is statistically significant. In this lecture, we will learn how to calculate the F-statistic and interpret its results in the context of marketing analytics.
Additionally, we will explore how the F-statistic can be used to compare the fit of different regression models. By comparing the F-statistic values of different models, we can determine which model is the best fit for our data and which variables are the most significant predictors of our dependent variable. Understanding the F-statistic is crucial for making informed decisions when building forecasting models using regression analysis, and this lecture will provide valuable insights into how to leverage this statistic effectively in the field of marketing analytics. -
29Interpreting results of Categorical variablesVideo lesson
In Lecture 27 of Marketing Analytics: Forecasting Models with Excel, we will be focusing on interpreting the results of categorical variables in regression models. Categorical variables are important in marketing analytics as they allow us to analyze the impact of different categories on our forecasted results. We will discuss how to interpret the coefficients, significance levels, and overall goodness of fit of these variables in our regression models using Excel.
Additionally, we will explore how to create dummy variables for categorical variables and incorporate them into our regression analysis. By understanding how to interpret and use categorical variables effectively in forecasting models, we can make more informed decisions in our marketing strategies. Join us in Lecture 27 as we delve into the world of categorical variables and learn how to effectively interpret their results in regression modeling. -
30Creating Multiple Linear Regression modelVideo lesson
In this lecture, we will delve into the world of marketing analytics by exploring forecasting using regression models, specifically focusing on creating a multiple linear regression model. We will discuss the advantages of using regression models in marketing analytics, and how they can help companies make informed decisions based on past data and trends. By the end of this lecture, you will have a solid understanding of how to leverage regression models to forecast future sales, customer behavior, and other important metrics in the marketing realm.
We will cover the steps involved in creating a multiple linear regression model using Excel, including data cleaning, variable selection, model building, and interpretation of results. We will also discuss how to evaluate the performance of the model and make adjustments as needed. By the end of this lecture, you will be equipped with the knowledge and skills to confidently apply multiple linear regression models in your marketing analytics projects, and make data-driven decisions to drive business growth. -
31Assignment 1: Regression based ForecastingText lesson
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32Forecasting in presence of special eventsVideo lesson
In Lecture 29 of the Marketing Analytics course, we will be focusing on forecasting in the presence of special events, specifically holiday sales. We will discuss the challenges that special events such as holidays pose for forecasting models and how to effectively handle them using Excel. We will explore various techniques and strategies for adjusting our forecasting models to account for the impact of special events on sales data.
Additionally, we will cover best practices for incorporating holiday sales data into our forecasts and how to accurately predict the potential impact of these special events on future sales. By the end of this lecture, students will have a better understanding of how to create more accurate and reliable forecasts in the presence of special events like holiday sales, ultimately improving their ability to make informed marketing decisions based on data-driven insights. -
33Excel: Running Linear Regression using SolverVideo lesson
In this lecture, we will dive into how to incorporate special events like holiday sales into our forecasting models using Excel. We will discuss why it is important to account for these special events and how they can impact sales and consumer behavior. By understanding how to handle special events in our forecasting models, we can more accurately predict future sales and develop effective marketing strategies to capitalize on these events.
Next, we will learn how to use Excel to run linear regression models using Solver. Linear regression is a powerful tool for identifying trends and relationships between variables, allowing us to make informed decisions based on data. By applying Solver in Excel, we can automate the process of fitting a linear regression model to our data, making it easier to analyze large datasets and make accurate predictions for future sales. Overall, understanding how to run linear regression using Solver in Excel will help us make more data-driven decisions and improve the effectiveness of our marketing campaigns. -
34Excel: Including the impact of Special EventsVideo lesson
In this lecture, we will delve into the topic of handling special events like holiday sales in marketing analytics. We will discuss the importance of factoring in these special events when creating forecasting models in Excel. Special events such as Black Friday, Cyber Monday, and other holidays can have a significant impact on sales and consumer behavior, so it is crucial to understand how to accurately incorporate them into our analysis.
We will learn how to identify and track special events using Excel, and how to adjust our forecasting models to account for their effects. We will explore different methods for including the impact of special events in our analysis, such as creating dummy variables or using specific formulas in Excel. By the end of this lecture, you will have a better understanding of how to effectively handle special events in your marketing analytics, and be able to make more accurate sales forecasts.
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35Models to identify Trend & SeasonalityVideo lesson
In Lecture 32 of our Marketing Analytics course, we will be focusing on identifying trend and seasonality in forecasting models using Excel. We will cover the importance of understanding seasonality and trend in marketing analytics to accurately forecast future trends and make informed business decisions. Students will learn how to use various models to identify trends and seasonal patterns in their data, allowing them to create more accurate forecasts and optimize their marketing strategies.
During this lecture, we will discuss different methods and techniques for identifying seasonality and trend in marketing data, including moving averages, exponential smoothing, and regression analysis. By the end of the lecture, students will have a comprehensive understanding of how to apply these models in Excel to analyze and interpret trends and seasonality in their marketing data. This knowledge will enable them to make data-driven decisions and develop effective forecasting models to drive business growth and success. -
36QuizQuiz
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37Excel: Additive model to identify Trend & SeasonalityVideo lesson
In Lecture 33 of the Marketing Analytics course on Forecasting Models with Excel, we will be focusing on using an additive model to identify both trend and seasonality in our data. By understanding the relationship between these two components, we can create more accurate forecasts to help guide our marketing strategies. We will walk through the process step-by-step, using Excel to analyze our data and identify patterns that can be used to make predictions about future trends.
Throughout this lecture, we will explore the role of trend and seasonality in forecasting, and how they can impact the accuracy of our predictions. By utilizing the additive model in Excel, we will be able to separate these two components from our data and better understand how they influence our sales or marketing performance. By the end of this session, students will have a solid grasp of how to leverage Excel to identify and analyze trend and seasonality, and use this knowledge to make informed decisions in their marketing campaigns. -
38Excel: Multiplicative model to identify Trend & SeasonalityVideo lesson
In this lecture, we will be focusing on using Excel to identify trend and seasonality in marketing analytics for forecasting models. We will explore the concept of the multiplicative model, which can help us better understand the relationship between seasonal patterns and overall trends in our data. By applying this model in Excel, we will be able to identify and quantify the seasonal variations and trends that may impact our forecasting accuracy.
Additionally, we will walk through the steps to build a multiplicative model in Excel and how to interpret the results for forecasting purposes. Understanding the seasonality and trend in our data is crucial for making accurate predictions and planning marketing strategies. By the end of this lecture, you will have a solid grasp on how to use Excel to identify and analyze seasonality and trend in your marketing analytics data for more informed forecasting decisions. -
39Moving Average MethodVideo lesson
In Lecture 35 of Marketing Analytics: Forecasting Models with Excel, we will focus on the Moving Average Method as a tool to identify seasonality and trend for forecasting purposes. We will begin by defining what the Moving Average Method entails and how it can be calculated using Excel. We will discuss how this method can be utilized to smooth out fluctuations in data and provide a clearer picture of the underlying patterns in a time series.
Furthermore, we will explore how the Moving Average Method can be applied to identify both short-term fluctuations and long-term trends in sales data, allowing marketers to make more accurate predictions and optimize their strategies accordingly. By the end of the lecture, students will have a solid grasp of how to implement the Moving Average Method in Excel to improve their forecasting capabilities and make informed marketing decisions based on seasonality and trend analysis. -
40QuizQuiz
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41Excel: Moving Average MethodVideo lesson
In Lecture 36 of the Marketing Analytics course, we will be diving into the Moving Average Method in Excel for identifying seasonality and trend in forecasting models. We will discuss how this method can help forecasters understand and predict future trends by smoothing out fluctuations in data, making it easier to identify patterns and make informed decisions. By learning how to calculate moving averages in Excel, students will be equipped with a valuable tool for making more accurate and reliable predictions in their marketing analytics work.
Throughout this lecture, we will walk through practical examples of how to apply the Moving Average Method in Excel to different marketing scenarios. This will include step-by-step guidance on setting up moving averages, determining the appropriate time periods for analysis, and interpreting the results to make informed forecasting decisions. By the end of this session, students will have a solid understanding of how to identify seasonality and trend using the Moving Average Method in Excel, and be able to incorporate these techniques into their own marketing analytics projects. -
42Assignment 2: Identifying Trend and SeasonalityText lesson
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43Winter's Method to accomodate changing Trend & SeasonalityVideo lesson
In Lecture 37 of the Marketing Analytics course, we will delve into the Winter's Method, a powerful forecasting model that can effectively handle changing trends and seasonality over time. This method incorporates both additive and multiplicative adjustments, allowing analysts to accurately predict future trends even when faced with fluctuating patterns. By understanding how to apply Winter's Method in Excel, students will gain the necessary skills to generate insightful forecasts for their marketing campaigns.
Throughout this lecture, we will explore the intricacies of implementing Winter's Method in Excel, including how to adjust for changing trends and seasonality within a dataset. By learning how to interpret the results and make informed decisions based on the forecasted data, students will be better equipped to optimize their marketing strategies and drive business growth. Overall, mastering Winter's Method in forecasting models is essential for marketers looking to stay ahead of the competition and make data-driven decisions that yield impactful results. -
44Excel: Winter's methodVideo lesson
In this lecture, we will be covering Winter's method in Excel for handling changing trend and seasonality over time in marketing analytics forecasting models. Winter's method is a popular technique used in time series analysis to account for both trend and seasonality in data. By applying this method in Excel, we will learn how to accurately forecast future trends and make informed decisions based on historical data.
We will dive into the mechanics of Winter's method, including how to calculate and apply seasonal indices, trend values, and seasonal forecasts in Excel. By the end of this lecture, students will have a solid understanding of how to use Winter's method to effectively forecast trends and seasonality in their marketing analytics projects, enabling them to make data-driven decisions and optimize their strategies for success. -
45QuizQuiz
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46S-curve for New productsVideo lesson
In this lecture, we will be discussing the use of the S-curve for forecasting new products in marketing analytics. The S-curve is a mathematical model that can help businesses predict the growth pattern of new products over time. By understanding how new products typically follow an S-shaped growth curve, businesses can better plan their marketing strategies and allocate resources effectively to maximize profit and minimize risk.
We will delve into the key components of the S-curve, such as the stages of introduction, growth, maturity, and decline. By analyzing data on past product launches and sales trends, businesses can use the S-curve model to forecast the sales potential of new products and make informed decisions on pricing, distribution, and promotion strategies. Understanding the S-curve can give businesses a competitive edge in the market by enabling them to anticipate market fluctuations and adjust their marketing plans accordingly to stay ahead of the curve. -
47Excel: Using Logistic curve to model S-curveVideo lesson
In this lecture, we will delve into the exciting world of forecasting models for new products using Excel. Specifically, we will focus on using the logistic curve to model the S-curve, which is a common technique used in marketing analytics to predict the growth and adoption of new products in the market. By understanding this curve and how to apply it in Excel, you will be able to make informed decisions about product launches and create more accurate forecasts for the future success of your products.
We will cover the key concepts behind the logistic curve and how it represents the natural growth pattern of new products as they are introduced into the market. Through practical examples and step-by-step instructions in Excel, you will learn how to create and analyze S-curve models for new product forecasting. By the end of this lecture, you will have the skills and knowledge needed to confidently use the logistic curve in Excel to forecast the potential success of new products and optimize your marketing strategies for maximum impact. -
48Excel: Using Gompertz curve to model S-curveVideo lesson
In this lecture, we will be focusing on how to use the Gompertz curve in Excel to model S-curve for forecasting new products. The Gompertz curve is a sigmoid function that is commonly used in growth models to predict the saturation point of a new product in the market. By understanding how to apply the Gompertz curve in Excel, you will be able to accurately forecast the growth trajectory of your new product and make informed decisions about marketing strategies.
We will cover the step-by-step process of using Excel to create a Gompertz curve and inputting the necessary variables to create a reliable forecasting model for new products. By the end of this lecture, you will have a thorough understanding of how to leverage the power of the Gompertz curve in Excel to predict the sales and market penetration of your new product. This will enable you to make data-driven decisions and effectively allocate resources for the successful launch and promotion of your new product in the market. -
49Bass Diffusion Model for New ProductsVideo lesson
In Lecture 42, we will be diving into the Bass Diffusion Model, a popular forecasting model used for new products in marketing analytics. We will explore how this model can be applied to predict the adoption rate of a new product in the market, taking into consideration factors such as the innovators and imitators in the market, as well as the influence of word-of-mouth and marketing efforts on the product's diffusion.
Additionally, we will examine case studies and real-world examples where the Bass Diffusion Model has been successfully used to forecast the adoption of new products. By understanding the mechanics of this model and how to implement it using Excel, students will gain valuable insights into how to effectively forecast the success of new products in the competitive marketplace. -
50Excel: Implementing Bass Diffusion ModelVideo lesson
In this lecture, we will be focusing on implementing the Bass Diffusion Model using Excel for forecasting new products. The Bass Diffusion Model is a popular tool used in marketing analytics to predict the rate of new product adoption among consumers. We will discuss the key components of the Bass Diffusion Model, including the coefficients for innovation and imitation, as well as the implications of these coefficients on forecasting new product sales.
Additionally, we will walk through a step-by-step demonstration of how to input the necessary data into Excel to calculate the adoption curve for a new product using the Bass Diffusion Model. By the end of this lecture, you will have a thorough understanding of how to apply this forecasting model in Excel to make informed decisions about the launch and marketing strategy for new products in your business. -
51Final Course QuizQuiz
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52Assignment 3: Bass ModelText lesson
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53Important Excel Functions - Sum, Average, Concatenate, TrimVideo lesson
In this lecture, we will be covering some important Excel functions that are commonly used in marketing analytics. These functions include Sum, which allows you to quickly add up numbers in a range of cells, Average, which calculates the average value of a range of cells, Concatenate, which is used to combine text from different cells into one, and Trim, which removes any extra spaces in a cell. Understanding and mastering these functions will be crucial in effectively analyzing and interpreting data in Excel for marketing forecasting models.
By the end of this lecture, you will have a solid understanding of how to use the Sum function to quickly total up values, the Average function to calculate the average of a set of numbers, the Concatenate function to merge text from multiple cells, and the Trim function to clean up any unnecessary spaces in your data. These functions will help you streamline your data analysis process and make more accurate predictions for your marketing strategies. Make sure to practice using these functions in Excel to become more proficient in applying them to your marketing analytics projects. -
54Important Excel Functions- Vlookup, If, Count If, Sum ifVideo lesson
In this lecture, we will cover important Excel functions that are commonly used in marketing analytics forecasting models. The focus will be on Vlookup, If, Count If, and Sum If functions, which are essential tools for data analysis and manipulation. We will discuss how these functions can be applied to extract and summarize data, perform conditional calculations, and create dynamic reports in Excel.
Additionally, we will provide a crash course on Excel, specifically focusing on these functions to ensure that all students are comfortable using them in their marketing analytics projects. We will walk through step-by-step examples and practical exercises to demonstrate how to utilize Vlookup, If, Count If, and Sum If functions effectively. By the end of this lecture, students will have a solid understanding of these essential Excel functions and how they can be leveraged to improve their forecasting models and marketing analytics strategies. -
55Sorting, Filtering and Data ValidationVideo lesson
In this lecture, we will delve into the importance of sorting, filtering, and data validation in Excel when it comes to marketing analytics. These functions are crucial for organizing and manipulating large datasets in order to make informed decisions based on the data. We will learn how to effectively sort data in ascending or descending order, filter out specific data points to focus on key metrics, and ensure data accuracy through validation techniques.
Additionally, we will explore advanced techniques for sorting and filtering, including custom sorting options and complex filter criteria. By mastering these tools, marketers can streamline their data analysis process and uncover insights that will drive strategic marketing decisions. We will also cover how to set up data validation rules to ensure data integrity and accuracy, preventing errors and inconsistencies in our datasets. -
56Text-to-columns and remove duplicatesVideo lesson
In Lecture 47 of this Marketing Analytics course, we will delve into the topic of Text-to-columns and remove duplicates in Excel. We will discuss how to use the Text-to-columns feature in Excel to split data from one column into multiple columns based on a delimiter. This can be particularly useful when dealing with text data that needs to be separated into different categories for analysis. Additionally, we will explore the process of removing duplicates from a dataset to ensure data integrity and accuracy in our forecasting models.
In this lecture, we will provide a step-by-step guide on how to use the Text-to-columns tool in Excel and demonstrate its practical application in marketing analytics. We will also cover various scenarios where removing duplicates from a dataset can be beneficial, such as in identifying and eliminating redundant information to improve the quality of our forecasts. By the end of this lecture, students will have a better understanding of how to leverage these Excel features to enhance their data analysis skills and make more informed marketing decisions. -
57Advanced Filter optionVideo lesson
In Lecture 48 of the Marketing Analytics course, we will be diving into the advanced filter option in Excel. This tool allows us to narrow down our data set and extract only the information that meets certain criteria. We will learn how to use this feature to effectively analyze our marketing data and make informed decisions based on the filtered results. By the end of this lecture, you will have a thorough understanding of how to leverage the advanced filter option in Excel to enhance your forecasting models and drive successful marketing strategies.
Additionally, in Section 9 of the course, we will cover an Appendix to provide a crash course on Excel for those who may be less familiar with the software. This supplementary material will help ensure that all students are on the same page when it comes to using Excel for marketing analytics. By mastering the basics of Excel, you will be better equipped to understand and implement the forecasting models that we will explore in this course. Join us for Lecture 48 as we unlock the potential of the advanced filter option and gain essential Excel skills to excel in the world of marketing analytics. -
58QuizQuiz
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59Pivot tablesVideo lesson
In this lecture, we will be diving into the world of pivot tables within Excel. Pivot tables are an incredibly powerful tool for analyzing and organizing data in a clear and concise manner. We will cover how to create pivot tables, manipulate them to display the data that is relevant to your analysis, and how to customize the layout and appearance of the tables. Pivot tables can help you quickly summarize and visualize important information, making them a valuable asset in the world of marketing analytics.
Additionally, we will discuss how pivot tables can be used to forecast and predict future trends in your data. By utilizing pivot tables in conjunction with forecasting models, you can make informed decisions about your marketing strategies based on historical data. Understanding how to effectively use pivot tables will not only streamline your data analysis process but also help you make strategic decisions that can impact the success of your marketing campaigns. -
60Popular Excel chartsText lesson
In this lecture, we will be covering popular Excel charts that are commonly used in marketing analytics. Understanding how to create and interpret these charts is essential for effectively communicating data and insights to stakeholders. We will be discussing various types of charts such as line charts, bar charts, pie charts, and scatter plots, and how to choose the appropriate chart based on the data and the message you want to convey.
Additionally, we will explore advanced chart features in Excel, such as adding trendlines, error bars, and data labels to enhance the visual representation of your data. By the end of this lecture, you will have a deep understanding of how to create visually appealing and informative charts in Excel that can help you effectively analyze and present marketing data. -
61NEW! Analyze Data option in Excel - only for Microsoft 365 usersVideo lesson
In this lecture, we will be covering a new feature in Microsoft Excel that is only available to Microsoft 365 users. The Analyze Data option allows users to quickly and easily analyze their data using built-in forecasting models. We will walk through how to access this feature and how to use it to make predictions and create visualizations based on your data.
Additionally, we will provide a crash course in Excel for those who may be new to the software or need a refresher. We will cover basic functions, shortcuts, and tips for efficiently working with data in Excel. By the end of this lecture, you will have a better understanding of how to utilize Excel for marketing analytics and forecasting models. -
62Comprehensive Interview Preparation QuestionsText lesson
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