Demand Forecasting

Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase.

I’ll Be Back – DVD Sales Forecasting demonstrates how a Monte Carlo simulation is used to assess the number of DVDs the client must purchase in order to begin renting to its customer database.

Monte Carlo Product Demand Forecast combining not only survey research results, but incorporates sales and marketing data from the client company as well.

Pricing and Revenue Monte Carlo Forecast Model reveals the ‘price point’ winner, not only of product demand at test price levels, but taking into account the estimated market area and fixed costs.

Market Size Forecasting Using the Monte Carlo Method estimates the potential sales for a tire company of its new product.

Magazine Checkout Sales Forecasting Model helps to determine the forecast of sales of four magazines with different magazines that have different sales cycles.

Loyalty ROI – Maximizing Loyalty Points examines ‘Loyalty’ programs and discusses a forecast method to maximize point-value.

Donor Predictive CHAID Tree uses a classification tree method to box in most-likely donors to a political party.

Revenue Forecasting – Is there Money in Mouth Pain? answers the question, ‘How much money, potentially, can a new over-the-counter mouth pain relief medicine make.’

Tacos in Tel Aviv, published in Quirk’s Marketing Research Review, demonstrates how a Monte Carlo simulation analyzed the primary survey research in a market forecast feasibility study about bringing a chain of upscale Mexican restaurants to Israel’s Silicon Valley.

Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. It involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets.

Multivariate Solutions uses demand forecasting in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.

While it is a relatively straightforward matter to develop confidence intervals for each of the market sizes taken alone, what is really at issue is the confidence interval of all the market sizes taken together to get a wide ranging guestimate of the actual size potential markets, the penetration of a product, or using the Monte Carlo method of demand forecasting to determine the optimal price for a product, or value for loyalty points, or or the potential market for a new product.