Sales Forecasting

Secrets of Sales Forecasting with Professional Techniques

Marketing Analytics 4.6(57)4h 1m26 lectures2 sections

What you'll learn

Understand the importance of data analysis in sales forecasting
Analyze historical sales data in Excel
Tell the difference between quantitative and qualitative methods
Forecast with the Exponential Smoothing method
Forecast with the Trend Projection method
Use Excel functions in forecasting
Combine quantitative and qualitative methods
Use artificial intelligence in sales forecasting
Deal with uncertainty and risk

About this course

This course teaches sales forecasting, meaning predicting future sales volume using data and sound methods to make more accurate decisions. It starts with an introduction and the importance of data analysis in sales forecasting, with a practical application in Excel, then the sources of historical sales data and the steps to analyze them correctly, with real-life cases. Next you learn the difference between quantitative and qualitative methods and when to use each one, and you learn forecasting with Exponential Smoothing and Trend Projection, the main qualitative methods and the factors that affect choosing the most suitable one. Then you learn how to combine quantitative and qualitative methods and forecast using Excel functions. In the last part you learn about artificial intelligence and its link to forecasting, deal with uncertainty and risk, and carry out the graduation project, with the workbook and the Excel functions file. It suits people working in sales, marketing and management, and business owners. Afterwards, you will be able to build sales forecasts supported by data.

Expected outcomes

Master data analysis and sales forecasting skills with high accuracy.
Ability to apply advanced forecasting methods for successful decision-making.
Proficient in integrating quantitative and qualitative methods and using AI.
Enhance strategic planning efficiency and achieve sales goals.

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Course content

Sales Forecasting

24 lectures

Course IntroductionIntroduces the sales forecasting course and its goals with hands-on Excel practice, from analyzing historical data and quantitative and qualitative methods to combining them, AI, uncertainty, and a graduation project.5:03
The Importance of Data Analysis in Sales ForecastingExplains why analyzing historical sales data matters for understanding trends, seasons and customer buying patterns, planning inventory and setting realistic targets, and the risks of forecasting without analysis.10:39
Practical Application of Data Analysis Using ExcelA hands-on Excel exercise: entering months and sales in a table and turning it into a chart to read the trend and peak and slump periods, and drawing observations to base plans on.4:49
Historical Sales Data Sources and Their ImportanceExplains sources of historical sales data such as point-of-sale systems and Excel sheets, quality requirements like covering at least twelve months and being accurate, and identifying key indicators and seasonality.12:46
Live Practical Application for Data Extraction and Analysis in ExcelA hands-on Excel exercise on extracting and analyzing data: a table of month, sales value, invoice count and units, computing average invoice and units per invoice to make decisions that raise sales.12:38
Steps for Properly Analyzing Historical Sales DataExplains the steps for analyzing historical sales data: cleaning it of duplicates, blank rows and entry errors and standardizing formats, then visualizing it with line, column and pie charts to spot recurring patterns.12:25
Practical and Applied Cases from Real LifeA practical Excel case: entering four months of sales and drawing a line chart to read rises and drops, then looking for causes of the changes such as seasons and campaigns, and the importance of accurate data.6:39
The Difference Between Quantitative and Qualitative Methods for Sales ForecastingExplains the difference between quantitative forecasting methods based on numbers and statistical models, and qualitative ones based on expert opinion and personal judgment, with the strengths, weaknesses, tools and costs of each.14:09
When to Use Quantitative Methods and When to Use Qualitative Methods?Explains when to use qualitative methods, such as a new product or volatile market, and when to use quantitative ones with organized historical data and a stable market, with examples like a new phone, an online store and chronic-disease drugs.7:48
Practical Examples from RealityPractical examples showing the difference between the two approaches: a clothing company forecasting quantitatively from summer average sales, and a startup with a new product and no data relying on a sales-team poll and a market survey.5:51
Forecasting Using Exponential Smoothing MethodExplains exponential smoothing as a quantitative forecasting method: an equation adding the smoothing factor times actual sales to its complement times the previous forecast, and how the factor sets the weight of recent data.15:42
Forecasting Using Trend Projection MethodExplains trend projection: finding the overall sales trend by drawing a scatter chart and adding a trendline in Excel, then using the line equation to forecast any coming month, along with its limits such as ignoring seasonality.11:10
The Most Important Qualitative Methods in Sales ForecastingExplains the main qualitative sales forecasting methods that rely on experience rather than data: sales-team polling, market surveys, expert panels, and the Delphi method with repeated rounds to bring estimates closer.14:38
Factors Influencing the Choice of the Most Suitable Method for Sales ForecastingExplains the factors that affect choosing the right forecasting method, such as availability of historical data, market stability, product seasonality and novelty, and forecast purpose, comparing quantitative, qualitative and AI methods.14:51
Why Should We Combine Quantitative and Qualitative Methods in Forecasting?Explains why to combine quantitative and qualitative methods: each offsets the other's weaknesses, with numbers limiting personal bias and experience capturing non-numeric factors like competitors, giving more flexibility in changing markets and new products.7:27
The Most Famous Techniques for Merging Quantitative and Qualitative Methods in ForecastingExplains the most common ways to combine quantitative and qualitative methods: the hybrid approach, experts revising estimates after seeing quantitative data, weighting by relative importance, and manually adjusting a quantitative result to reflect an ad campaign.9:52
Practical Activity for Merging Quantitative and Qualitative Methods in ForecastingA hands-on exercise combining two methods for a men's suit company: the six-month average as the quantitative forecast, the sales team's higher forecast due to campaigns, an exhibition and Ramadan, then weighted averaging to reach a final forecast.5:10
Forecasting Using Excel FunctionsExplains forecasting with Excel functions after preparing the historical sales table, including TREND, GROWTH and FORECAST.ETS which accounts for seasonality, and when each function suits depending on how sales grow.13:35
What is Artificial Intelligence and What is its Relation to Sales Forecasting?Explains what AI is in the forecasting context: simulating human intelligence to analyze large data and learn patterns, with examples such as Amazon's recommendation system, fashion inventory management and Netflix customer segmentation.7:24
Sales Forecasting Using Artificial Intelligence (Part One)Explains the first steps of AI forecasting: collecting data from sources like APIs, SQL and Web Scraping, then cleaning it and handling outliers and missing values, then choosing the model that fits the forecast goal and data type.13:41
Sales Forecasting Using Artificial Intelligence (Part Two)Continues the steps of AI forecasting: common models such as linear regression and ARIMA, then evaluating a model by comparing its forecast with actual sales and the deviation rate, and improving it continually as the market changes.7:44
Understanding Sources of Uncertainty and Risks in Sales ForecastingExplains the difference between risks, whose likelihood can be estimated and calculated, and uncertainty, which cannot be calculated like the COVID pandemic, and the sources of each in sales forecasting such as competitors, technical failures, data quality and material prices.9:10
Tools and Techniques for Dealing with Uncertainty in ForecastingExplains tools for handling uncertainty in sales forecasting: planning with three scenarios, sensitivity analysis in Excel to measure how price and customer count affect revenue, and probabilistic forecasting using the normal distribution.14:50
Graduation ProjectGraduation project: build a sales forecasting model for an online electronics store in Excel, covering descriptive analysis, forecast functions, optimistic, pessimistic and realistic scenarios, conversion-rate sensitivity analysis, probability estimates, and final recommendations.3:47