Excel for Market Research Strategies

Explore how demand shapes marketing decisions and how to forecast it using Excel, covering factors like price, buying power, and consumer tastes, with methods from market research and historical data.

Analyze census housing data to forecast satellite internet demand by households, using Excel to identify top and fastest-growing states and guide market strategy.

Perform an automatic regression to estimate 2024 housing units from 2020–2023 data for satellite internet demand, then use filters to rank states by housing demand, revealing California, Texas, and Florida.

Calculate growth rate in Excel by comparing new and old houses from 2023–24, identify the top ten fastest growing states, and visualize results to guide market entry.

Focus on net increases instead of growth rates to reveal market changes, using Excel to calculate net increases (new minus old) and identify top states like Texas, Florida, and California.

Sort data by region, apply the subtotal function to sum regional demand, and collapse or expand regions to reveal growth and net increase across the United States.

Learn to calculate market size and market share in Excel by aggregating regional data, computing annual revenue potential from a $49.99 monthly fee, and deriving regional shares as percentages.

Explore how historical sales data reveals the demand curve's price–quantity relationship, enabling profit optimization and cost considerations through economy of scale.

Create a price–demand chart in Excel from a 24-month data set to reveal the inverse relationship between price and units sold, then adjust the axis for clarity.

Explore three forecasting methods—naive, moving average, and regression—and when to use them. See how regression uses dependent and independent variables, and why large fluctuations limit these methods.

Explore forecasting quarterly sales for a new coffee machine using naive forecast and three period moving averages in Excel, compute absolute percentage error, compare accuracy, and visualize results with graphs.

Use a three period moving average to forecast quarterly sales, starting from quarter four. Measure error with the average absolute error divided by actual sales.

Learn to run a regression in Excel with time as x and sales as y using the Analysis Toolpak, then interpret r-squared, p-values, coefficients, and compare with moving averages.

The lecture demonstrates creating Excel charts to compare forecasting methods—regression, naive, and moving averages—by selecting data and adjusting axes.

Transform categorical variables into numeric 0/1 indicators, run a regression in Excel to forecast demand, and interpret coefficients, p-values, and R square for price, campaigns, and seasonality.

Build a multiple regression model in Excel to forecast demand using price and a binary sales campaign variable, interpreting coefficients and the intercept while learning formula steps.

Navigate a large airline satisfaction dataset in Excel by backing up data and creating ID. Use filters, sorts, and date functions to extract month and day of week.

Calculate total and per-class airport spending using sumif, countif, and average to estimate revenue. The lesson demonstrates class breakdowns (business, economy, economy plus) and per-person spending patterns.

Learn to create and customize pivot tables in Excel to analyze large data sets quickly; drag fields, set sums and averages, and filter by gender and class.

Learn to use Excel's analyze data AI features to create pivot tables and charts, switch to averages, and uncover insights such as eating and drinking by class and gender.

Uncover seasonality in airport traffic and use countif and averageif in Excel to compute daily customers, average spend, and totals, then chart trends to guide staffing and inventory planning.

Learn to build a macroeconomic data set in excel using the fred add-in to forecast tire production and inventory for next year, using gdp, cpi, and unemployment data.

Forecast next year's US auto and tire sales by pulling data from FRED, integrating, unifying, and preparing monthly data on employment, CPI, and oil prices for analysis.

Extract and organize FRED data in an excel worksheet, identify anomalies using the bell curve, and compute mean, median, standard deviation, and variance to assess outliers for regression and forecasting.

Conduct an advanced regression in Excel with FRED data and forecast, adding year and month as predictors alongside job claims, CPI, and oil price to forecast auto sales.

Build a regression model using real FRED macroeconomic data and Excel's sum product function to compute forecasts with coefficients, then run a stress test with scenario analysis.